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Optimization for Green Container Shipping: A Review and Future Research Directions

Year 2023, Volume: 12 Issue: 3, 282 - 311, 28.09.2023
https://doi.org/10.33714/masteb.1224099

Abstract

Maritime freight transportation is one of the least emissions-producing transportation alternatives in terms of transported tonnage per distance. However, it produces a high amount of emissions as around 80% of international freight transportation is conducted through seas and 20% of maritime transportation is conducted through container shipping. This makes it crucial to reduce emissions in container shipping. In this regard, this study reviewed previous studies on the environmental optimization of container shipping and identified various future research directions. The results showed that in the sea segment of environmental optimization of container shipping, decisions which require further attention include resource allocation, emission reduction technology choice, disruption recovery, freight rate optimization, and shipment scheduling. The decisions that require future research in the port segment are related to internal transportation and handing operations in container terminals (i.e., yard crane deployment, yard truck deployment, yard truck scheduling, yard container stack allocation, yard container retrieval), renewable energy source installation, and emission reduction technology choice. Vessel scheduling and speed optimization decisions are the most frequently studied decisions in the sea segment, but they are rarely considered for inland shipping of containers. In the sea-port combined segment of container shipping, future studies are required in quay crane scheduling, vessel scheduling, container route allocation, ship route allocation vessel deployment, and emission reduction technology choice. The least studied decision in the door-to-door segment of container shipping includes hub location-allocation, empty container relocation, ship route allocation, vessel deployment, environmental taxation and subsidy scheme, emissions reduction technology choice, and speed optimization. It was also demonstrated that modeling of future studies should more frequently consider uncertainties and social sustainability parameters.

References

  • Abdelmagid, A. M., Gheith, M. S., & Eltawil, A. B. (2022). A comprehensive review of the truck appointment scheduling models and directions for future research. Transport Reviews, 42(1), 102–126. https://doi.org/10.1080/01441647.2021.1955034
  • Abioye, O. F., Dulebenets, M. A., Pasha, J., & Kavoosi, M. (2019). A Vessel Schedule Recovery Problem at the Liner Shipping Route with Emission Control Areas. Energies, 12(12), 2380. https://doi.org/10.3390/en12122380
  • Abu Aisha, T., Ouhimmou, M., & Paquet, M. (2020). Optimization of Container Terminal Layouts in the Seaport—Case of Port of Montreal. Sustainability, 12(3), 1165–1165. https://doi.org/10.3390/su12031165
  • Alharbi, A., Wang, S., & Davy, P. (2015). Schedule design for sustainable container supply chain networks with port time windows. Advanced Engineering Informatics, 29(3), 322–331. https://doi.org/10.1016/j.aei.2014.12.001
  • Alvarez, J. F., Longva, T., & Engebrethsen, E. S. (2010). A methodology to assess vessel berthing and speed optimization policies. Maritime Economics & Logistics, 12(4), 327–346. https://doi.org/10.1057/mel.2010.11
  • Ambrosino, D., & Sciomachen, A. (2021). Impact of Externalities on the Design and Management of Multimodal Logistic Networks. Sustainability, 13(9). https://doi.org/10.3390/su13095080
  • Aydin, N., Lee, H., & Mansouri, S. A. (2017). Speed optimization and bunkering in liner shipping in the presence of uncertain service times and time windows at ports. European Journal of Operational Research, 259(1), 143–154. https://doi.org/10.1016/j.ejor.2016.10.002
  • Caballini, C., Gracia, M. D., Mar-Ortiz, J., & Sacone, S. (2020). A combined data mining – optimization approach to manage trucks operations in container terminals with the use of a TAS: Application to an Italian and a Mexican port. Transportation Research Part E: Logistics and Transportation Review, 142, 102054–102054. https://doi.org/10.1016/j.tre.2020.102054
  • Cariou, P., Cheaitou, A., Larbi, R., & Hamdan, S. (2018). Liner shipping network design with emission control areas: A genetic algorithm-based approach. Transportation Research Part D: Transport and Environment, 63, 604–621. https://doi.org/10.1016/j.trd.2018.06.020
  • Caris, A., Macharis, C., & Janssens, G. K. (2008). Planning problems in intermodal freight transport: accomplishments and prospects. Transportation Planning and Technology, 31(3), 277–302. https://doi.org/10.1080/03081060802086397
  • Chang, Y.-T., Lee, P. T.-W., Kim, H.-J., & Shin, S.-H. (2010). Optimization model for transportation of container cargoes considering short sea shipping and external cost. Transportation Research Record: Journal of the Transportation Research Board, 2166(1), 99–108. https://doi.org/10.3141/2166-12
  • Cheaitou, A., & Cariou, P. (2019). Greening of maritime transportation: A multi-objective optimization approach. Annals of Operations Research, 273(1–2), 501–525. https://doi.org/10.1007/s10479-018-2786-2
  • Chen, G., Govindan, K., & Golias, M. M. (2013). Reducing truck emissions at container terminals in a low carbon economy: Proposal of a queueing-based bi-objective model for optimizing truck arrival pattern. Transportation Research Part E: Logistics and Transportation Review, 55, 3–22. https://doi.org/10.1016/j.tre.2013.03.008
  • Chen, J., Ye, J., Liu, A., Fei, Y., Wan, Z., & Huang, X. (2022). Robust optimization of liner shipping alliance fleet scheduling with consideration of sulfur emission restrictions and slot exchange. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04590-x
  • Chen, J., Ye, J., Zhuang, C., Qin, Q., & Shu, Y. (2022). Liner shipping alliance management: Overview and future research directions. Ocean & Coastal Management, 219, 106039–106039. https://doi.org/10.1016/j.ocecoaman.2022.106039
  • Chen, K., Xin, X., Niu, X., & Zeng, Q. (2020). Coastal transportation system joint taxation-subsidy emission reduction policy optimization problem. Journal of Cleaner Production, 247, 119096–119096. https://doi.org/10.1016/j.jclepro.2019.119096
  • Chen, L., Yip, T. L., & Mou, J. (2018). Provision of Emission Control Area and the impact on shipping route choice and ship emissions. Transportation Research Part D: Transport and Environment, 58, 280–291. https://doi.org/10.1016/j.trd.2017.07.003
  • Chen, R., Meng, Q., & Jia, P. (2022). Container port drayage operations and management: Past and future. Transportation Research Part E: Logistics and Transportation Review, 159, 102633–102633. https://doi.org/10.1016/j.tre.2022.102633
  • Chen, S., & Zeng, Q. (2021). Carbon-efficient scheduling problem of electric rubber-tyred gantry cranes in a container terminal. Engineering Optimization, 1–19. https://doi.org/10.1080/0305215X.2021.1972293
  • Chen, Y., Guo, D., Chen, Z., Fan, Y., & Li, X. (2018). Using a multi-objective programming model to validate feasibility of an underground freight transportation system for the Yangshan port in Shanghai. Tunnelling and Underground Space Technology, 81, 463–471. https://doi.org/10.1016/j.tust.2018.07.012
  • Christiansen, M., Hellsten, E., Pisinger, D., Sacramento, D., & Vilhelmsen, C. (2020). Liner shipping network design. European Journal of Operational Research, 286(1), 1–20. https://doi.org/10.1016/j.ejor.2019.09.057
  • Dai, Q., & Yang, J. (2020). A distributionally robust chance-constrained approach for modeling demand uncertainty in green port-hinterland transportation network optimization. Symmetry, 12(9). https://doi.org/10.3390/sym12091492
  • De, A., Wang, J., & Tiwari, M. K. (2021). Fuel bunker management strategies within sustainable container shipping operation considering disruption and recovery policies. IEEE Transactions on Engineering Management, 68(4), 1089–1111. https://doi.org/10.1109/TEM.2019.2923342
  • Digiesi, S., Facchini, F., & Mummolo, G. (2019). Dry port as a lean and green strategy in a container terminal hub: A mathematical programming model. Management and Production Engineering Review, 10(1), 14–28. https://doi.org/10.24425/mper.2019.128240
  • Do, N. A. D., Nielsen, I. E., Chen, G., & Nielsen, P. (2016). A simulation-based genetic algorithm approach for reducing emissions from import container pick-up operation at container terminal. Annals of Operations Research, 242(2), 285–301. https://doi.org/10.1007/s10479-014-1636-0
  • Dong, G., & Tae-Woo Lee, P. (2020). Environmental effects of emission control areas and reduced speed zones on container ship operation. Journal of Cleaner Production, 274, 122582–122582. https://doi.org/10.1016/j.jclepro.2020.122582
  • Du, Y., Chen, Q., Quan, X., Long, L., & Fung, R. Y. K. (2011). Berth allocation considering fuel consumption and vessel emissions. Transportation Research Part E: Logistics and Transportation Review, 47(6), 1021–1037. https://doi.org/10.1016/j.tre.2011.05.011
  • Du, Y., Meng, Q., & Wang, Y. (2015). Budgeting fuel consumption of container ship over round-trip voyage through robust optimization. Transportation Research Record: Journal of the Transportation Research Board, 2477(1), 68–75. https://doi.org/10.3141/2477-08
  • Duan, J., Li, L., Zhang, Q., Qin, J., & Zhou, Y. (2023). Integrated scheduling of automatic guided vehicles and automatic stacking cranes in automated container terminals considering landside buffer zone. Transportation Research Record. In Press. https://doi.org/10.1177/03611981231168862
  • Duan, J., Liu, Y., Zhang, Q., & Qin, J. (2021). Combined Configuration of Container Terminal Berth and Quay Crane considering Carbon Cost. Mathematical Problems in Engineering, 2021, 1–16. https://doi.org/10.1155/2021/6043846
  • Dulebenets, M. A. (2016). Advantages and disadvantages from enforcing emission restrictions within emission control areas. Maritime Business Review, 1(2), 107–132. https://doi.org/10.1108/MABR-05-2016-0011
  • Dulebenets, M. A. (2018a). A comprehensive multi-objective optimization model for the vessel scheduling problem in liner shipping. International Journal of Production Economics, 196, 293–318. https://doi.org/10.1016/j.ijpe.2017.10.027
  • Dulebenets, M. A. (2018b). The green vessel scheduling problem with transit time requirements in a liner shipping route with Emission Control Areas. Alexandria Engineering Journal, 57(1), 331–342. https://doi.org/10.1016/j.aej.2016.11.008
  • Dulebenets, M. A. (2022). Multi-objective collaborative agreements amongst shipping lines and marine terminal operators for sustainable and environmental-friendly ship schedule design. Journal of Cleaner Production, 342, 130897. https://doi.org/10.1016/j.jclepro.2022.130897
  • Dulebenets, M. A., Golias, M. M., & Mishra, S. (2017). The green vessel schedule design problem: Consideration of emissions constraints. Energy Systems, 8(4), 761–783. https://doi.org/10.1007/s12667-015-0183-3
  • Dulebenets, M. A., Moses, R., Ozguven, E. E., & Vanli, A. (2017). Minimizing carbon dioxide emissions due to container handling at marine container terminals via hybrid evolutionary algorithms. IEEE Access, 5, 8131–8147. https://doi.org/10.1109/ACCESS.2017.2693030
  • Dulebenets, M. A., & Ozguven, E. E. (2017). Vessel scheduling in liner shipping: Modeling transport of perishable assets. International Journal of Production Economics, 184, 141–156. https://doi.org/10.1016/j.ijpe.2016.11.011
  • Dulebenets, M. A., Pasha, J., Abioye, O. F., & Kavoosi, M. (2021). Vessel scheduling in liner shipping: A critical literature review and future research needs. 33(1), 106. https://doi.org/10.1007/s10696-019-09367-2
  • Duran, C., Derpich, I., & Carrasco, R. (2022). Optimization of port layout to determine greenhouse gas emission gaps. Sustainability, 14(20), 13517. https://doi.org/10.3390/su142013517
  • Fan, Ren, Guo, & Li. (2019). Truck scheduling problem considering carbon emissions under truck appointment system. Sustainability, 11(22), 6256–6256. https://doi.org/10.3390/su11226256
  • Fazili, M., Venkatadri, U., Cyrus, P., & Tajbakhsh, M. (2017). Physical Internet, conventional and hybrid logistic systems: A routing optimisation-based comparison using the Eastern Canada road network case study. International Journal of Production Research, 55(9), 2703–2730. https://doi.org/10.1080/00207543.2017.1285075
  • Feng, Y., Song, D.-P., Li, D., & Zeng, Q. (2020). The stochastic container relocation problem with flexible service policies. Transportation Research Part B: Methodological, 141, 116–163. https://doi.org/10.1016/j.trb.2020.09.006
  • Gao, C.-F., & Hu, Z.-H. (2021). Speed optimization for container ship fleet deployment considering fuel consumption. Sustainability, 13(9), 5242–5242. https://doi.org/10.3390/su13095242
  • Giovannini, M., & Psaraftis, H. N. (2019). The profit maximizing liner shipping problem with flexible frequencies: Logistical and environmental considerations. Flexible Services and Manufacturing Journal, 31(3), 567–597. https://doi.org/10.1007/s10696-018-9308-z
  • Golias, M. M., Saharidis, G. K., Boile, M., Theofanis, S., & Ierapetritou, M. G. (2009). The berth allocation problem: Optimizing vessel arrival time. Maritime Economics & Logistics, 11(4), 358–377. https://doi.org/10.1057/mel.2009.12
  • He, J. (2016). Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving. Advanced Engineering Informatics, 30(3), 390–405. https://doi.org/10.1016/j.aei.2016.04.006
  • He, J., Huang, Y., & Yan, W. (2015). Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption. Advanced Engineering Informatics, 29(1), 59–75. https://doi.org/10.1016/j.aei.2014.09.003
  • He, J., Huang, Y., Yan, W., & Wang, S. (2015). Integrated internal truck, yard crane and quay crane scheduling in a container terminal considering energy consumption. Expert Systems with Applications, 42(5), 2464–2487. https://doi.org/10.1016/j.eswa.2014.11.016
  • He, W., Jin, Z., Huang, Y., & Xu, S. (2021). The inland container transportation problem with separation mode considering carbon dioxide emissions. Sustainability, 13(3), 1573–1573. https://doi.org/10.3390/su13031573
  • Heilig, L., Lalla-Ruiz, E., & Voß, S. (2017). Multi-objective inter-terminal truck routing. Transportation Research Part E: Logistics and Transportation Review, 106, 178–202. https://doi.org/10.1016/j.tre.2017.07.008
  • Hu, Q., Gu, W., & Wang, S. (2022). Optimal subsidy scheme design for promoting intermodal freight transport. Transportation Research Part E: Logistics and Transportation Review, 157, 102561–102561. https://doi.org/10.1016/j.tre.2021.102561
  • Hu, Q.-M., Hu, Z.-H., & Du, Y. (2014). Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels. Computers & Industrial Engineering, 70, 1–10. https://doi.org/10.1016/j.cie.2014.01.003
  • Hu, Z.-H. (2020). Low-emission berth allocation by optimizing sailing speed and mooring time. Transport, 35(5), 486–499. https://doi.org/10.3846/transport.2020.14080
  • Irannezhad, E., Prato, C. G., & Hickman, M. (2018). The effect of cooperation among shipping lines on transport costs and pollutant emissions. Transportation Research Part D: Transport and Environment, 65(September), 312–323. https://doi.org/10.1016/j.trd.2018.09.008
  • Iris, Ç., & Lam, J. S. L. (2019). A review of energy efficiency in ports: Operational strategies, technologies and energy management systems. Renewable and Sustainable Energy Reviews, 112, 170–182. https://doi.org/10.1016/j.rser.2019.04.069
  • Jiang, X., Mao, H., Wang, Y., & Zhang, H. (2020). Liner shipping schedule design for near-sea routes considering big customers’ preferences on ship arrival time. Sustainability, 12(18), 7828–7828. https://doi.org/10.3390/su12187828
  • Kanellos, F. D. (2019). Multiagent-system-based operation scheduling of large ports’ power systems with emissions limitation. IEEE Systems Journal, 13(2), 1831–1840. https://doi.org/10.1109/JSYST.2018.2850970
  • Karakas, S., Kirmizi, M., & Kocaoglu, B. (2021). Yard block assignment, internal truck operations, and berth allocation in container terminals: Introducing carbon-footprint minimisation objectives. Maritime Economics & Logistics, 23(4), 750–771. https://doi.org/10.1057/s41278-021-00186-7
  • Kim, S., Park, M., & Lee, C. (2013). Multimodal freight transportation network design problem for reduction of greenhouse gas emissions. Transportation Research Record: Journal of the Transportation Research Board, 2340(1), 74–83. https://doi.org/10.3141/2340-09
  • Kurtuluş, E. (2022). Optimizing inland container logistics and dry port location-allocation from an environmental perspective. Research in Transportation Business & Management, 100839. https://doi.org/10.1016/j.rtbm.2022.100839
  • Lagemann, B., Lindstad, E., Fagerholt, K., Rialland, A., & Ove Erikstad, S. (2022). Optimal ship lifetime fuel and power system selection. Transportation Research Part D: Transport and Environment, 102, 103145. https://doi.org/10.1016/j.trd.2021.103145
  • Lam, J. S. L., & Gu, Y. (2013). Port hinterland intermodal container flow optimisation with green concerns: A literature review and research agenda. International Journal of Shipping and Transport Logistics, 5(3), 257–257. https://doi.org/10.1504/IJSTL.2013.054190
  • Lam, J. S. L., & Gu, Y. (2016). A market-oriented approach for intermodal network optimisation meeting cost, time and environmental requirements. International Journal of Production Economics, 171, 266–274. https://doi.org/10.1016/j.ijpe.2015.09.024
  • Lan, X., Tao, Q., & Wu, X. (2023). Liner-shipping network design with emission control areas: A real case study. Sustainability, 15(4), 3734. https://doi.org/10.3390/su15043734
  • Lan, X., Zuo, X., & Tao, Q. (2023). Container shipping optimization under different carbon emission policies: A case study. Sustainability, 15(10), 8388. https://doi.org/10.3390/su15108388
  • Lee, H., Aydin, N., Choi, Y., Lekhavat, S., & Irani, Z. (2018). A decision support system for vessel speed decision in maritime logistics using weather archive big data. Computers & Operations Research, 98, 330–342. https://doi.org/10.1016/j.cor.2017.06.005
  • Li, C., Qi, X., & Lee, C.-Y. (2015). Disruption recovery for a vessel in liner shipping. Transportation Science, 49(4), 900–921. https://doi.org/10.1287/trsc.2015.0589
  • Li, H., & Li, X. (2022). A branch-and-bound algorithm for the bi-objective quay crane scheduling problem based on efficiency and energy. Mathematics, 10(24), 4705. https://doi.org/10.3390/math10244705
  • Li, L., Zhu, J., Ye, G., & Feng, X. (2018). Development of green ports with the consideration of coastal wave energy. Sustainability, 10(11), 4270. https://doi.org/10.3390/su10114270
  • Li, M., & Sun, X. (2022). Path optimization of low-carbon container multimodal transport under uncertain conditions. Sustainability, 14(21), 14098. https://doi.org/10.3390/su142114098
  • Li, S., Tang, L., Liu, J., Zhao, T., & Xiong, X. (2023). Vessel schedule recovery strategy in liner shipping considering expected disruption. Ocean & Coastal Management, 237, 106514. https://doi.org/10.1016/j.ocecoaman.2023.106514
  • Li, S., Wu, W., Ma, X., Zhong, M., & Safdar, M. (2023). Modelling medium- and long-term purchasing plans for environment-orientated container trucks: A case study of Yangtze River port. Transportation Safety and Environment, 5(1), tdac043. https://doi.org/10.1093/tse/tdac043
  • Li, X., Kuang, H., & Hu, Y. (2019). Carbon mitigation strategies of port selection and multimodal transport operations—A case study of northeast China. Sustainability, 11(18), 4877. https://doi.org/10.3390/su11184877
  • Li, X., Peng, Y., Wang, W., Huang, J., Liu, H., Song, X., & Bing, X. (2019). A method for optimizing installation capacity and operation strategy of a hybrid renewable energy system with offshore wind energy for a green container terminal. Ocean Engineering, 186, 106125. https://doi.org/10.1016/j.oceaneng.2019.106125
  • Li, X., Sun, B., Guo, C., Du, W., & Li, Y. (2020). Speed optimization of a container ship on a given route considering voluntary speed loss and emissions. Applied Ocean Research, 94, 101995. https://doi.org/10.1016/j.apor.2019.101995
  • Li, X., Sun, B., Jin, J., & Ding, J. (2022). Speed optimization of container ship considering route segmentation and weather data loading: Turning point-time segmentation method. Journal of Marine Science and Engineering, 10(12), 1835. https://doi.org/10.3390/jmse10121835
  • Lin, D., & Leong, P. (2022). A stochastic sailing speed optimization and vessel deployment problem in liner shipping. Journal of Marine Science and Technology-Taiwan, 30(3), 249–259. https://doi.org/10.51400/2709-6998.2580
  • Liu, D., & Ge, Y.-E. (2018). Modeling assignment of quay cranes using queueing theory for minimizing CO2 emission at a container terminal. Transportation Research Part D: Transport and Environment, 61, 140–151. https://doi.org/10.1016/j.trd.2017.06.006
  • Liu, M., Liu, R., Zhang, E., & Chu, C. (2022). Eco-friendly container transshipment route scheduling problem with repacking operations. Journal of Combinatorial Optimization, 43(5), 1010–1035. https://doi.org/10.1007/s10878-020-00619-8
  • Liu, M., Liu, X., Chu, F., Zhu, M., & Zheng, F. (2020). Liner ship bunkering and sailing speed planning with uncertain demand. Computational and Applied Mathematics, 39(1), 22–22. https://doi.org/10.1007/s40314-019-0994-2
  • Liu, S. (2023). Multimodal transportation route optimization of cold chain container in time-varying network considering carbon emissions. Sustainability, 15(5), 4435. https://doi.org/10.3390/su15054435
  • Liu, Y., Xin, X., Yang, Z., Chen, K., & Li, C. (2021). Liner shipping network—Transaction mechanism joint design model considering carbon tax and liner alliance. Ocean & Coastal Management, 212, 105817. https://doi.org/10.1016/j.ocecoaman.2021.105817
  • Liu, Y., Zhao, X., & Huang, R. (2022). Research on comprehensive recovery of liner schedule and container flow with hard time windows constraints. Ocean & Coastal Management, 224, 106171. https://doi.org/10.1016/j.ocecoaman.2022.106171
  • Lu, J., Wu, X., & Wu, Y. (2023). The construction and application of dual-objective optimal speed model of liners in a changing climate: Taking Yang Ming route as an example. Journal of Marine Science and Engineering, 11(1), 157. https://doi.org/10.3390/jmse11010157
  • Ma, J., Wang, X., Yang, K., & Jiang, L. (2023). Uncertain programming model for the cross-border multimodal container transport system based on inland ports. Axioms, 12(2), 132. https://doi.org/10.3390/axioms12020132
  • Ma, M., Fan, H., Jiang, X., & Guo, Z. (2019). Truck arrivals scheduling with vessel dependent time windows to reduce carbon emissions. Sustainability, 11(22), 6410. https://doi.org/10.3390/su11226410
  • Ma, Q., Wang, W., Peng, Y., & Song, X. (2018). An optimization approach to the intermodal transportation network in fruit cold chain, considering cost, quality degradation and carbon dioxide footprint. Polish Maritime Research, 25(1), 61–69. https://doi.org/10.2478/pomr-2018-0007
  • Ma, W., Hao, S., Ma, D., Wang, D., Jin, S., & Qu, F. (2021). Scheduling decision model of liner shipping considering emission control areas regulations. Applied Ocean Research, 106, 102416. https://doi.org/10.1016/j.apor.2020.102416
  • Ma, W., Zhang, J., Han, Y., Zheng, H., Ma, D., & Chen, M. (2022). A chaos-coupled multi-objective scheduling decision method for liner shipping based on the NSGA-III algorithm. Computers & Industrial Engineering, 174, 108732. https://doi.org/10.1016/j.cie.2022.108732
  • Maia, L. C., & Couto, A. (2013). Strategic rail network optimization model for freight transportation. Transportation Research Record: Journal of the Transportation Research Board, 2378(1), 1–12. https://doi.org/10.3141/2378-01
  • Mansouri, S. A., Lee, H., & Aluko, O. (2015). Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions. Transportation Research Part E: Logistics and Transportation Review, 78, 3–18. https://doi.org/10.1016/j.tre.2015.01.012
  • Martínez-López, A. (2021). A multi-objective mathematical model to select fleets and maritime routes in short sea shipping: A case study in Chile. Journal of Marine Science and Technology, 26(3), 673–692. https://doi.org/10.1007/s00773-020-00757-y
  • Martínez-López, A., Caamaño Sobrino, P., Chica González, M., & Trujillo, L. (2018). Optimization of a container vessel fleet and its propulsion plant to articulate sustainable intermodal chains versus road transport. Transportation Research Part D: Transport and Environment, 59, 134–147. https://doi.org/10.1016/j.trd.2017.12.021
  • Martínez-López, A., Caamaño Sobrino, P., Chica González, M., & Trujillo, L. (2019). Choice of propulsion plants for container vessels operating under Short Sea Shipping conditions in the European Union: An assessment focused on the environmental impact on the intermodal chains. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 233(2), 653–669. https://doi.org/10.1177/1475090218797179
  • Martínez-López, A., & Chica, M. (2020). Joint optimization of routes and container fleets to design sustainable intermodal chains in Chile. Sustainability, 12(6), 2221. https://doi.org/10.3390/su12062221
  • Martínez-López, A., Sobrino, P. C., & González, M. M. (2016). Influence of external costs on the optimisation of container fleets by operating under motorways of the sea conditions. International Journal of Shipping and Transport Logistics, 8(6), 653–686. https://doi.org/10.1504/IJSTL.2016.079293
  • Matsukura, H., Udommahuntisuk, M., Yamato, H., & Dinariyana, A. A. B. (2010). Estimation of CO2 reduction for Japanese domestic container transportation based on mathematical models. Journal of Marine Science and Technology, 15(1), 34–43. https://doi.org/10.1007/s00773-009-0069-y
  • Meng, Q., Wang, S., Andersson, H., & Thun, K. (2014). Containership routing and scheduling in liner shipping: overview and future research directions. Transportation Science, 48(2), 265–280. https://doi.org/10.1287/trsc.2013.0461
  • Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 339, b2535. https://doi.org/10.1136/bmj.b2535
  • Nadi, A., Nugteren, A., Snelder, M., Van Lint, J. W. C., & Rezaei, J. (2022). Advisory-based time slot management system to mitigate waiting time at container terminal gates. Transportation Research Record: Journal of the Transportation Research Board, 2676(10), 036119812210909. https://doi.org/10.1177/03611981221090940
  • Niu, Y., Yu, F., Yao, H., & Yang, Y. (2022). Multi-equipment coordinated scheduling strategy of U-shaped automated container terminal considering energy consumption. Computers & Industrial Engineering, 174, 108804. https://doi.org/10.1016/j.cie.2022.108804
  • Omran, M., Ghousi, R., & Kadkhodaei, A. (2023). Sustainable model of port-hinterland freight distribution network considering uncertainty: A case study of Iran. Scientia Iranica, 30(2), 784–802. https://doi.org/10.24200/sci.2021.55884.4447
  • Palacio, A., Adenso-Díaz, B., & Lozano, S. (2015). A decision-making model to design a sustainable container depot logistic network: The case of the port of Valencia. Transport, 33(1), 119–130. https://doi.org/10.3846/16484142.2015.1107621
  • Palacio, A., Adenso-Díaz, B., Lozano, S., & Furió, S. (2016). Bicriteria optimization model for locating maritime container depots: Application to the Port of Valencia. Networks and Spatial Economics, 16(1), 331–348. https://doi.org/10.1007/s11067-013-9205-7
  • Pasha, J., Dulebenets, M. A., Fathollahi-Fard, A. M., Tian, G., Lau, Y., Singh, P., & Liang, B. (2021). An integrated optimization method for tactical-level planning in liner shipping with heterogeneous ship fleet and environmental considerations. Advanced Engineering Informatics, 48, 101299. https://doi.org/10.1016/j.aei.2021.101299
  • Peng, Y., Li, X., Wang, W., Wei, Z., Bing, X., & Song, X. (2019). A method for determining the allocation strategy of on-shore power supply from a green container terminal perspective. Ocean & Coastal Management, 167, 158–175. https://doi.org/10.1016/j.ocecoaman.2018.10.007
  • Peng, Y., Wang, W., Song, X., & Zhang, Q. (2016). Optimal allocation of resources for yard crane network management to minimize carbon dioxide emissions. Journal of Cleaner Production, 131, 649–658. https://doi.org/10.1016/j.jclepro.2016.04.120
  • Pian, F., Shi, Q., Yao, X., Zhu, H., & Luan, W. (2021). Joint optimization of a dry port with multilevel location and container transportation: The case of northeast China. Complexity, 2021, 5584600. https://doi.org/10.1155/2021/5584600
  • Pourmohammad-Zia, N., Schulte, F., Gonzalez-Ramirez, R., Voss, S., & Negenborn, R. (2023). A robust optimization approach for platooning of automated ground vehicles in port hinterland corridors. Computers & Industrial Engineering, 117, 109046. https://doi.org/10.1016/j.cie.2023.109046
  • Psaraftis, H. N., & Kontovas, C. A. (2013). Speed models for energy-efficient maritime transportation: A taxonomy and survey. Transportation Research Part C: Emerging Technologies, 26, 331–351. https://doi.org/10.1016/j.trc.2012.09.012
  • Qi, J., & Wang, S. (2023). LNG bunkering station deployment problem-A case study of a Chinese container shipping network. Mathematics, 11(4), 813. https://doi.org/10.3390/math11040813
  • Qi, X., & Song, D.-P. (2012). Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times. Transportation Research Part E: Logistics and Transportation Review, 48(4), 863–880. https://doi.org/10.1016/j.tre.2012.02.001
  • Rajkovic, R., Zrnic, N., Kirin, S., & Dragovic, B. (2016). A review of multi-objective optimization of container flow using sea and land legs together. FME Transaction, 44(2), 204–211. https://doi.org/10.5937/fmet1602204R
  • Reinhardt, L. B., Pisinger, D., Sigurd, M. M., & Ahmt, J. (2020). Speed optimizations for liner networks with business constraints. European Journal of Operational Research, 285(3), 1127–1140. https://doi.org/10.1016/j.ejor.2020.02.043
  • Sáinz Bernat, N., Schulte, F., Voß, S., & Böse, J. (2016). Empty container management at ports considering pollution, repair options, and street-turns. Mathematical Problems in Engineering, 2016, 1–13. https://doi.org/10.1155/2016/3847163
  • Schmidt, J., Meyer-Barlag, C., Eisel, M., Kolbe, L. M., & Appelrath, H.-J. (2015). Using battery-electric AGVs in container terminals—Assessing the potential and optimizing the economic viability. Research in Transportation Business & Management, 17, 99–111. https://doi.org/10.1016/j.rtbm.2015.09.002
  • Schulte, F., Lalla-Ruiz, E., González-Ramírez, R. G., & Voß, S. (2017). Reducing port-related empty truck emissions: A mathematical approach for truck appointments with collaboration. Transportation Research Part E: Logistics and Transportation Review, 105, 195–212. https://doi.org/10.1016/j.tre.2017.03.008
  • Shi, H., Xu, P., & Yang, Z. (2016). Optimization of transport network in the Basin of Yangtze River with minimization of environmental emission and transport/investment costs. Advances in Mechanical Engineering, 8(8), 168781401666092. https://doi.org/10.1177/1687814016660923
  • Shiri, S., & Huynh, N. (2018). Assessment of U.S. chassis supply models on drayage productivity and air emissions. Transportation Research Part D: Transport and Environment, 61, 174–203. https://doi.org/10.1016/j.trd.2017.04.024
  • Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
  • Song, D.-P., Li, D., & Drake, P. (2015). Multi-objective optimization for planning liner shipping service with uncertain port times. Transportation Research Part E: Logistics and Transportation Review, 84, 1–22. https://doi.org/10.1016/j.tre.2015.10.001
  • Sun, Y. (2020). Green and reliable freight routing problem in the road-rail intermodal transportation network with uncertain parameters: A fuzzy goal programming approach. Journal of Advanced Transportation, 2020, 1–21. https://doi.org/10.1155/2020/7570686
  • Sun, Y., Hrušovský, M., Zhang, C., & Lang, M. (2018). A time-dependent fuzzy programming approach for the green multimodal routing problem with rail service capacity uncertainty and road traffic congestion. Complexity, 2018, 1–22. https://doi.org/10.1155/2018/8645793
  • Sun, Y., & Lang, M. (2015). Modeling the multicommodity multimodal routing problem with schedule-based services and carbon dioxide emission costs. Mathematical Problems in Engineering, 2015, 1–21. https://doi.org/10.1155/2015/406218
  • Sun, Y., Zheng, J., Han, J., Liu, H., & Zhao, Z. (2022). Allocation and reallocation of ship emission permits for liner shipping. Ocean Engineering, 266, 112976. https://doi.org/10.1016/j.oceaneng.2022.112976
  • Tan, R., Duru, O., & Thepsithar, P. (2020). Assessment of relative fuel cost for dual fuel marine engines along major Asian container shipping routes. Transportation Research Part E: Logistics and Transportation Review, 140, 102004. https://doi.org/10.1016/j.tre.2020.102004
  • Tan, R., Psaraftis, H., & Wang, D. (2022). The speed limit debate: Optimal speed concepts revisited under a multi-fuel regime. Transportation Research Part D-Transport and Environment, 111, 103445. https://doi.org/10.1016/j.trd.2022.103445
  • Tan, Z., Wang, Y., Meng, Q., & Liu, Z. (2018). Joint ship schedule design and sailing speed optimization for a single inland shipping service with uncertain dam transit time. Transportation Science, 52(6), 1570–1588. https://doi.org/10.1287/trsc.2017.0808
  • Tan, Z., Zeng, X., Shao, S., Chen, J., & Wang, H. (2022). Scrubber installation and green fuel for inland river ships with non-identical streamflow. Transportation Research Part E: Logistics and Transportation Review, 161, 102677. https://doi.org/10.1016/j.tre.2022.102677
  • Tao, Y., Zhang, S., Lin, C., & Lai, X. (2023). A bi-objective optimization for integrated truck operation and storage allocation considering traffic congestion in container terminals. Ocean & Coastal Management, 232, 106417. https://doi.org/10.1016/j.ocecoaman.2022.106417
  • Tran, N. K., Haasis, H.-D., & Buer, T. (2017). Container shipping route design incorporating the costs of shipping, inland/feeder transport, inventory and CO2 emission. Maritime Economics & Logistics, 19(4), 667–694. https://doi.org/10.1057/mel.2016.11
  • Trapp, A. C., Harris, I., Sanchez Rodrigues, V., & Sarkis, J. (2020). Maritime container shipping: Does coopetition improve cost and environmental efficiencies? Transportation Research Part D: Transport and Environment, 87, 102507. https://doi.org/10.1016/j.trd.2020.102507
  • Tsao, Y., & Linh, V. (2018). Seaport-dry port network design considering multimodal transport and carbon emissions. Journal of Cleaner Production, 199, 481–492. https://doi.org/10.1016/j.jclepro.2018.07.137
  • Tsao, Y., & Thanh, V. (2019). A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment. Transportation Research Part E: Logistics and Transportation Review, 124, 13–39. https://doi.org/10.1016/j.tre.2019.02.006
  • UNCTAD. (2021). Review of Maritime Transport.
  • Venturini, G., Iris, Ç., Kontovas, C. A., & Larsen, A. (2017). The multi-port berth allocation problem with speed optimization and emission considerations. Transportation Research Part D: Transport and Environment, 54, 142–159. https://doi.org/10.1016/j.trd.2017.05.002
  • Wang, C., & Chen, J. (2017). Strategies of refueling, sailing speed and ship deployment of containerships in the low-carbon background. Computers & Industrial Engineering, 114, 142–150. https://doi.org/10.1016/j.cie.2017.10.012
  • Wang, C., Yu, S., & Xu, L. (2022). Decisions on sailing frequency and ship type in liner shipping with the consideration of carbon dioxide emissions. Regional Studies in Marine Science, 52, 102371–102371. https://doi.org/10.1016/j.rsma.2022.102371
  • Wang, S. (2016). Fundamental properties and pseudo-polynomial-time algorithm for network containership sailing speed optimization. European Journal of Operational Research, 250(1), 46–55. https://doi.org/10.1016/j.ejor.2015.10.052
  • Wang, S., Alharbi, A., & Davy, P. (2014). Liner ship route schedule design with port time windows. Transportation Research Part C: Emerging Technologies, 41, 1–17. https://doi.org/10.1016/j.trc.2014.01.012
  • Wang, S., & Meng, Q. (2012). Sailing speed optimization for container ships in a liner shipping network. Transportation Research Part E: Logistics and Transportation Review, 48(3), 701–714. https://doi.org/10.1016/j.tre.2011.12.003
  • Wang, S., & Meng, Q. (2015). Robust bunker management for liner shipping networks. European Journal of Operational Research, 243(3), 789–797. https://doi.org/10.1016/j.ejor.2014.12.049
  • Wang, S., & Meng, Q. (2017). Container liner fleet deployment: A systematic overview. Transportation Research Part C: Emerging Technologies, 77, 389–404. https://doi.org/10.1016/j.trc.2017.02.010
  • Wang, S., Meng, Q., & Liu, Z. (2013). Containership scheduling with transit-time-sensitive container shipment demand. Transportation Research Part B: Methodological, 54, 68–83. https://doi.org/10.1016/j.trb.2013.04.003
  • Wang, S., Qu, X., & Yang, Y. (2015). Estimation of the perceived value of transit time for containerized cargoes. Transportation Research Part A: Policy and Practice, 78, 298–308. https://doi.org/10.1016/j.tra.2015.04.014
  • Wang, S., & Wang, X. (2016). A polynomial-time algorithm for sailing speed optimization with containership resource sharing. Transportation Research Part B: Methodological, 93, 394–405. https://doi.org/10.1016/j.trb.2016.08.003
  • Wang, S., Zhuge, D., Zhen, L., & Lee, C.-Y. (2021). Liner shipping service planning under sulfur emission regulations. Transportation Science, 55(2), 491–509. https://doi.org/10.1287/trsc.2020.1010
  • Wang, T., Wang, X., & Meng, Q. (2018). Joint berth allocation and quay crane assignment under different carbon taxation policies. Transportation Research Part B: Methodological, 117, 18–36. https://doi.org/10.1016/j.trb.2018.08.012
  • Wang, W., Peng, Y., Li, X., Qi, Q., Feng, P., & Zhang, Y. (2019). A two-stage framework for the optimal design of a hybrid renewable energy system for port application. Ocean Engineering, 191, 106555. https://doi.org/10.1016/j.oceaneng.2019.106555
  • Wang, Y., Meng, Q., & Kuang, H. (2018). Jointly optimizing ship sailing speed and bunker purchase in liner shipping with distribution-free stochastic bunker prices. Transportation Research Part C: Emerging Technologies, 89, 35–52. https://doi.org/10.1016/j.trc.2018.01.020
  • Wen, X., Ge, Y.-E., Yin, Y., & Zhong, M. (2022). Dynamic recovery actions in multi-objective liner shipping service with buffer times. Proceedings of the Institution of Civil Engineers - Maritime Engineering, 175(2), 46–62. https://doi.org/10.1680/jmaen.2021.005
  • Wong, E. Y. C., Tai, A. H., Lau, H. Y. K., & Raman, M. (2015). An utility-based decision support sustainability model in slow steaming maritime operations. Transportation Research Part E: Logistics and Transportation Review, 78, 57–69. https://doi.org/10.1016/j.tre.2015.01.013
  • Wong, E. Y. C., Tai, A. H., & So, S. (2020). Container drayage modelling with graph theory-based road connectivity assessment for sustainable freight transportation in new development area. Computers & Industrial Engineering, 149, 106810. https://doi.org/10.1016/j.cie.2020.106810
  • Wu, Y., Huang, Y., Wang, H., & Zhen, L. (2022a). Joint planning of fleet deployment, ship refueling, and speed optimization for dual-fuel ships considering methane slip. Journal of Marine Science and Engineering, 10(11), 1690. https://doi.org/10.3390/jmse10111690
  • Wu, Y., Huang, Y., Wang, H., & Zhen, L. (2022b). Nonlinear programming for fleet deployment, voyage planning and speed optimization in sustainable liner shipping. Electronic Research Archive, 31(1), 147–168. https://doi.org/10.3934/era.2023008
  • Wu, Y., Huang, Y., Wang, H., Zhen, L., & Shao, W. (2023). Green technology adoption and fleet deployment for new and aged ships considering maritime decarbonization. Journal of Marine Science and Engineering, 11(1), 36. https://doi.org/10.3390/jmse11010036
  • Xing, Y., Yang, H., Ma, X., & Zhang, Y. (2019). Optimization of ship speed and fleet deployment under carbon emissions policies for container shipping. Transport, 34(3), 260–274. https://doi.org/10.3846/transport.2019.9317
  • Xu, B., Liu, X., Li, J., Yang, Y., Wu, J., Shen, Y., & Zhou, Y. (2022). Dynamic appointment rescheduling of trucks under uncertainty of arrival time. Journal of Marine Science and Engineering, 10(5), 695. https://doi.org/10.3390/jmse10050695
  • Yang, Y., Zhu, X., & Haghani, A. (2019). Multiple equipment integrated scheduling and storage space allocation in rail–water intermodal container terminals considering energy efficiency. Transportation Research Record: Journal of the Transportation Research Board, 2673(3), 199–209. https://doi.org/10.1177/0361198118825474
  • Yang, Z., Xin, X., Chen, K., & Yang, A. (2021). Coastal container multimodal transportation system shipping network design—Toll policy joint optimization model. Journal of Cleaner Production, 279, 123340. https://doi.org/10.1016/j.jclepro.2020.123340
  • Yu, D., Li, D., Sha, M., & Zhang, D. (2019). Carbon-efficient deployment of electric rubber-tyred gantry cranes in container terminals with workload uncertainty. European Journal of Operational Research, 275(2), 552–569. https://doi.org/10.1016/j.ejor.2018.12.003
  • Yu, H., Deng, Y., Zhang, L., Xiao, X., & Tan, C. (2022). Yard operations and management in automated container terminals: A review. Sustainability, 14(6), 3419. https://doi.org/10.3390/su14063419
  • Yu, H., Fang, Z., Fu, X., Liu, J., & Chen, J. (2021). Literature review on emission control-based ship voyage optimization. Transportation Research Part D: Transport and Environment, 93, 102768. https://doi.org/10.1016/j.trd.2021.102768
  • Yu, H., Ge, Y.-E., Chen, J., Luo, L., Tan, C., & Liu, D. (2017). CO2 emission evaluation of yard tractors during loading at container terminals. Transportation Research Part D: Transport and Environment, 53, 17–36. https://doi.org/10.1016/j.trd.2017.03.014
  • Yu, H., Huang, M., Zhang, L., & Tan, C. (2022). Yard template generation for automated container terminal based on bay sharing strategy. Annals of Operations Research, In Press. https://doi.org/10.1007/s10479-022-04657-9
  • Yu, J., Voß, S., & Song, X. (2022). Multi-objective optimization of daily use of shore side electricity integrated with quayside operation. Journal of Cleaner Production, 351, 131406. https://doi.org/10.1016/j.jclepro.2022.131406
  • Yu, J., Voß, S., & Tang, G. (2019). Strategy development for retrofitting ships for implementing shore side electricity. Transportation Research Part D: Transport and Environment, 74, 201–213. https://doi.org/10.1016/j.trd.2019.08.004
  • Yu, M.-M., & Chen, L.-H. (2016). Centralized resource allocation with emission resistance in a two-stage production system: Evidence from a Taiwan’s container shipping company. Transportation Research Part A: Policy and Practice, 94, 650–671. https://doi.org/10.1016/j.tra.2016.10.003
  • Yu, Y., Tu, J., Shi, K., Liu, M., & Chen, J. (2021). Flexible Optimization of International Shipping Routes considering Carbon Emission Cost. Mathematical Problems in Engineering, 2021, 6678473. https://doi.org/10.1155/2021/6678473
  • Zacharioudakis, P. G., Iordanis, S., Lyridis, D. V., & Psaraftis, H. N. (2011). Liner shipping cycle cost modelling, fleet deployment optimization and what-if analysis. Maritime Economics & Logistics, 13(3), 278–297. https://doi.org/10.1057/mel.2011.11
  • Zhang, M., Wiegmans, B., & Tavasszy, L. (2013). Optimization of multimodal networks including environmental costs: A model and findings for transport policy. Computers in Industry, 64(2), 136–145. https://doi.org/10.1016/j.compind.2012.11.008
  • Zhang, Q., Wang, S., & Zhen, L. (2022). Yard truck retrofitting and deployment for hazardous material transportation in green ports. Annals of Operations Research, In Press. https://doi.org/10.1007/s10479-021-04507-0
  • Zhang, X., Lam, J. S. L., & Iris, Ç. (2020). Cold chain shipping mode choice with environmental and financial perspectives. Transportation Research Part D: Transport and Environment, 87, 102537. https://doi.org/10.1016/j.trd.2020.102537
  • Zhang, Y., Atasoy, B., & Negenborn, R. R. (2022). Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search. Transportation Research Record: Journal of the Transportation Research Board, 2676(3), 71–87. https://doi.org/10.1177/03611981211049148
  • Zhang, Y., Liang, C., Shi, J., Lim, G., & Wu, Y. (2022). Optimal port microgrid scheduling incorporating onshore power supply and berth allocation under uncertainty. Applied Energy, 313, 118856. https://doi.org/10.1016/j.apenergy.2022.118856
  • Zhao, S., Duan, J., Li, D., & Yang, H. (2022). Vessel scheduling and bunker management with speed deviations for liner shipping in the presence of collaborative agreements. IEEE Access, 10, 107669–107684. https://doi.org/10.1109/ACCESS.2022.3211311
  • Zhao, W., Wang, Y., Zhang, Z., & Wang, H. (2021). Multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm. Journal of Marine Science and Engineering, 9(4), 357. https://doi.org/10.3390/jmse9040357
  • Zhao, Y., Chen, Y., Fagerholt, K., Lindstad, E., & Zhou, J. (2023). Pathways towards carbon reduction through technology transition in liner shipping. Maritime Policy & Management, In Press. https://doi.org/10.1080/03088839.2023.2224813
  • Zhao, Y., Ye, J., & Zhou, J. (2021). Container fleet renewal considering multiple sulfur reduction technologies and uncertain markets amidst COVID-19. Journal of Cleaner Production, 317, 128361. https://doi.org/10.1016/j.jclepro.2021.128361
  • Zhao, Y., Zhou, J., Fan, Y., & Kuang, H. (2020). An expected utility-based optimization of slow steaming in sulphur emission control areas by applying big data analytics. IEEE Access, 8, 3646–3655. https://doi.org/10.1109/ACCESS.2019.2962210
  • Zhen, L., Hu, Z., Yan, R., Zhuge, D., & Wang, S. (2020). Route and speed optimization for liner ships under emission control policies. Transportation Research Part C: Emerging Technologies, 110, 330–345. https://doi.org/10.1016/j.trc.2019.11.004
  • Zhen, L., Jin, Y., Wu, Y., Yuan, Y., & Tan, Z. (2022). Benders decomposition for internal truck renewal decision in green ports. Maritime Policy & Management, 1–23. https://doi.org/10.1080/03088839.2021.2021596
  • Zhen, L., Lin, S., & Zhou, C. (2022). Green port oriented resilience improvement for traffic-power coupled networks. Reliability Engineering & System Safety, 225, 108569. https://doi.org/10.1016/j.ress.2022.108569
  • Zhen, L., Sun, Q., Zhang, W., Wang, K., & Yi, W. (2021). Column generation for low carbon berth allocation under uncertainty. Journal of the Operational Research Society, 72(10), 2225–2240. https://doi.org/10.1080/01605682.2020.1776168
  • Zhen, L., Wang, S., & Wang, K. (2016). Terminal allocation problem in a transshipment hub considering bunker consumption. Naval Research Logistics (NRL), 63(7), 529–548. https://doi.org/10.1002/nav.21717
  • Zhen, L., Wang, S., & Zhuge, D. (2017). Dynamic programming for optimal ship refueling decision. Transportation Research Part E: Logistics and Transportation Review, 100, 63–74. https://doi.org/10.1016/j.tre.2016.12.013
  • Zhen, L., Wu, Y., Wang, S., & Laporte, G. (2020). Green technology adoption for fleet deployment in a shipping network. Transportation Research Part B: Methodological, 139, 388–410. https://doi.org/10.1016/j.trb.2020.06.004
  • Zheng, Y., Xu, M., Wang, Z., & Xiao, Y. (2023). A genetic algorithm for integrated scheduling of container handing systems at container terminals from a low-carbon operations perspective. Sustainability, 15(7), 6035. https://doi.org/10.3390/su15076035
  • Zhong, M., Yang, Y., Zhou, Y., & Postolache, O. (2020). Application of hybrid GA-PSO based on intelligent control fuzzy system in the integrated scheduling in automated container terminal. Journal of Intelligent & Fuzzy Systems, 39(2), 1525–1538. https://doi.org/10.3233/JIFS-179926
  • Zhu, M., Chen, M., & Kristal, M. (2018). Modelling the impacts of uncertain carbon tax policy on maritime fleet mix strategy and carbon mitigation. Transport, 33(3), 707–717. https://doi.org/10.3846/transport.2018.1579
  • Zhu, S., Gao, J., He, X., Zhang, S., Jin, Y., & Tan, Z. (2021). Green logistics oriented tug scheduling for inland waterway logistics. Advanced Engineering Informatics, 49, 101323. https://doi.org/10.1016/j.aei.2021.101323
  • Zhuge, D., Wang, S., & Wang, D. Z. W. (2021). A joint liner ship path, speed and deployment problem under emission reduction measures. Transportation Research Part B: Methodological, 144, 155–173. https://doi.org/10.1016/j.trb.2020.12.006
Year 2023, Volume: 12 Issue: 3, 282 - 311, 28.09.2023
https://doi.org/10.33714/masteb.1224099

Abstract

References

  • Abdelmagid, A. M., Gheith, M. S., & Eltawil, A. B. (2022). A comprehensive review of the truck appointment scheduling models and directions for future research. Transport Reviews, 42(1), 102–126. https://doi.org/10.1080/01441647.2021.1955034
  • Abioye, O. F., Dulebenets, M. A., Pasha, J., & Kavoosi, M. (2019). A Vessel Schedule Recovery Problem at the Liner Shipping Route with Emission Control Areas. Energies, 12(12), 2380. https://doi.org/10.3390/en12122380
  • Abu Aisha, T., Ouhimmou, M., & Paquet, M. (2020). Optimization of Container Terminal Layouts in the Seaport—Case of Port of Montreal. Sustainability, 12(3), 1165–1165. https://doi.org/10.3390/su12031165
  • Alharbi, A., Wang, S., & Davy, P. (2015). Schedule design for sustainable container supply chain networks with port time windows. Advanced Engineering Informatics, 29(3), 322–331. https://doi.org/10.1016/j.aei.2014.12.001
  • Alvarez, J. F., Longva, T., & Engebrethsen, E. S. (2010). A methodology to assess vessel berthing and speed optimization policies. Maritime Economics & Logistics, 12(4), 327–346. https://doi.org/10.1057/mel.2010.11
  • Ambrosino, D., & Sciomachen, A. (2021). Impact of Externalities on the Design and Management of Multimodal Logistic Networks. Sustainability, 13(9). https://doi.org/10.3390/su13095080
  • Aydin, N., Lee, H., & Mansouri, S. A. (2017). Speed optimization and bunkering in liner shipping in the presence of uncertain service times and time windows at ports. European Journal of Operational Research, 259(1), 143–154. https://doi.org/10.1016/j.ejor.2016.10.002
  • Caballini, C., Gracia, M. D., Mar-Ortiz, J., & Sacone, S. (2020). A combined data mining – optimization approach to manage trucks operations in container terminals with the use of a TAS: Application to an Italian and a Mexican port. Transportation Research Part E: Logistics and Transportation Review, 142, 102054–102054. https://doi.org/10.1016/j.tre.2020.102054
  • Cariou, P., Cheaitou, A., Larbi, R., & Hamdan, S. (2018). Liner shipping network design with emission control areas: A genetic algorithm-based approach. Transportation Research Part D: Transport and Environment, 63, 604–621. https://doi.org/10.1016/j.trd.2018.06.020
  • Caris, A., Macharis, C., & Janssens, G. K. (2008). Planning problems in intermodal freight transport: accomplishments and prospects. Transportation Planning and Technology, 31(3), 277–302. https://doi.org/10.1080/03081060802086397
  • Chang, Y.-T., Lee, P. T.-W., Kim, H.-J., & Shin, S.-H. (2010). Optimization model for transportation of container cargoes considering short sea shipping and external cost. Transportation Research Record: Journal of the Transportation Research Board, 2166(1), 99–108. https://doi.org/10.3141/2166-12
  • Cheaitou, A., & Cariou, P. (2019). Greening of maritime transportation: A multi-objective optimization approach. Annals of Operations Research, 273(1–2), 501–525. https://doi.org/10.1007/s10479-018-2786-2
  • Chen, G., Govindan, K., & Golias, M. M. (2013). Reducing truck emissions at container terminals in a low carbon economy: Proposal of a queueing-based bi-objective model for optimizing truck arrival pattern. Transportation Research Part E: Logistics and Transportation Review, 55, 3–22. https://doi.org/10.1016/j.tre.2013.03.008
  • Chen, J., Ye, J., Liu, A., Fei, Y., Wan, Z., & Huang, X. (2022). Robust optimization of liner shipping alliance fleet scheduling with consideration of sulfur emission restrictions and slot exchange. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04590-x
  • Chen, J., Ye, J., Zhuang, C., Qin, Q., & Shu, Y. (2022). Liner shipping alliance management: Overview and future research directions. Ocean & Coastal Management, 219, 106039–106039. https://doi.org/10.1016/j.ocecoaman.2022.106039
  • Chen, K., Xin, X., Niu, X., & Zeng, Q. (2020). Coastal transportation system joint taxation-subsidy emission reduction policy optimization problem. Journal of Cleaner Production, 247, 119096–119096. https://doi.org/10.1016/j.jclepro.2019.119096
  • Chen, L., Yip, T. L., & Mou, J. (2018). Provision of Emission Control Area and the impact on shipping route choice and ship emissions. Transportation Research Part D: Transport and Environment, 58, 280–291. https://doi.org/10.1016/j.trd.2017.07.003
  • Chen, R., Meng, Q., & Jia, P. (2022). Container port drayage operations and management: Past and future. Transportation Research Part E: Logistics and Transportation Review, 159, 102633–102633. https://doi.org/10.1016/j.tre.2022.102633
  • Chen, S., & Zeng, Q. (2021). Carbon-efficient scheduling problem of electric rubber-tyred gantry cranes in a container terminal. Engineering Optimization, 1–19. https://doi.org/10.1080/0305215X.2021.1972293
  • Chen, Y., Guo, D., Chen, Z., Fan, Y., & Li, X. (2018). Using a multi-objective programming model to validate feasibility of an underground freight transportation system for the Yangshan port in Shanghai. Tunnelling and Underground Space Technology, 81, 463–471. https://doi.org/10.1016/j.tust.2018.07.012
  • Christiansen, M., Hellsten, E., Pisinger, D., Sacramento, D., & Vilhelmsen, C. (2020). Liner shipping network design. European Journal of Operational Research, 286(1), 1–20. https://doi.org/10.1016/j.ejor.2019.09.057
  • Dai, Q., & Yang, J. (2020). A distributionally robust chance-constrained approach for modeling demand uncertainty in green port-hinterland transportation network optimization. Symmetry, 12(9). https://doi.org/10.3390/sym12091492
  • De, A., Wang, J., & Tiwari, M. K. (2021). Fuel bunker management strategies within sustainable container shipping operation considering disruption and recovery policies. IEEE Transactions on Engineering Management, 68(4), 1089–1111. https://doi.org/10.1109/TEM.2019.2923342
  • Digiesi, S., Facchini, F., & Mummolo, G. (2019). Dry port as a lean and green strategy in a container terminal hub: A mathematical programming model. Management and Production Engineering Review, 10(1), 14–28. https://doi.org/10.24425/mper.2019.128240
  • Do, N. A. D., Nielsen, I. E., Chen, G., & Nielsen, P. (2016). A simulation-based genetic algorithm approach for reducing emissions from import container pick-up operation at container terminal. Annals of Operations Research, 242(2), 285–301. https://doi.org/10.1007/s10479-014-1636-0
  • Dong, G., & Tae-Woo Lee, P. (2020). Environmental effects of emission control areas and reduced speed zones on container ship operation. Journal of Cleaner Production, 274, 122582–122582. https://doi.org/10.1016/j.jclepro.2020.122582
  • Du, Y., Chen, Q., Quan, X., Long, L., & Fung, R. Y. K. (2011). Berth allocation considering fuel consumption and vessel emissions. Transportation Research Part E: Logistics and Transportation Review, 47(6), 1021–1037. https://doi.org/10.1016/j.tre.2011.05.011
  • Du, Y., Meng, Q., & Wang, Y. (2015). Budgeting fuel consumption of container ship over round-trip voyage through robust optimization. Transportation Research Record: Journal of the Transportation Research Board, 2477(1), 68–75. https://doi.org/10.3141/2477-08
  • Duan, J., Li, L., Zhang, Q., Qin, J., & Zhou, Y. (2023). Integrated scheduling of automatic guided vehicles and automatic stacking cranes in automated container terminals considering landside buffer zone. Transportation Research Record. In Press. https://doi.org/10.1177/03611981231168862
  • Duan, J., Liu, Y., Zhang, Q., & Qin, J. (2021). Combined Configuration of Container Terminal Berth and Quay Crane considering Carbon Cost. Mathematical Problems in Engineering, 2021, 1–16. https://doi.org/10.1155/2021/6043846
  • Dulebenets, M. A. (2016). Advantages and disadvantages from enforcing emission restrictions within emission control areas. Maritime Business Review, 1(2), 107–132. https://doi.org/10.1108/MABR-05-2016-0011
  • Dulebenets, M. A. (2018a). A comprehensive multi-objective optimization model for the vessel scheduling problem in liner shipping. International Journal of Production Economics, 196, 293–318. https://doi.org/10.1016/j.ijpe.2017.10.027
  • Dulebenets, M. A. (2018b). The green vessel scheduling problem with transit time requirements in a liner shipping route with Emission Control Areas. Alexandria Engineering Journal, 57(1), 331–342. https://doi.org/10.1016/j.aej.2016.11.008
  • Dulebenets, M. A. (2022). Multi-objective collaborative agreements amongst shipping lines and marine terminal operators for sustainable and environmental-friendly ship schedule design. Journal of Cleaner Production, 342, 130897. https://doi.org/10.1016/j.jclepro.2022.130897
  • Dulebenets, M. A., Golias, M. M., & Mishra, S. (2017). The green vessel schedule design problem: Consideration of emissions constraints. Energy Systems, 8(4), 761–783. https://doi.org/10.1007/s12667-015-0183-3
  • Dulebenets, M. A., Moses, R., Ozguven, E. E., & Vanli, A. (2017). Minimizing carbon dioxide emissions due to container handling at marine container terminals via hybrid evolutionary algorithms. IEEE Access, 5, 8131–8147. https://doi.org/10.1109/ACCESS.2017.2693030
  • Dulebenets, M. A., & Ozguven, E. E. (2017). Vessel scheduling in liner shipping: Modeling transport of perishable assets. International Journal of Production Economics, 184, 141–156. https://doi.org/10.1016/j.ijpe.2016.11.011
  • Dulebenets, M. A., Pasha, J., Abioye, O. F., & Kavoosi, M. (2021). Vessel scheduling in liner shipping: A critical literature review and future research needs. 33(1), 106. https://doi.org/10.1007/s10696-019-09367-2
  • Duran, C., Derpich, I., & Carrasco, R. (2022). Optimization of port layout to determine greenhouse gas emission gaps. Sustainability, 14(20), 13517. https://doi.org/10.3390/su142013517
  • Fan, Ren, Guo, & Li. (2019). Truck scheduling problem considering carbon emissions under truck appointment system. Sustainability, 11(22), 6256–6256. https://doi.org/10.3390/su11226256
  • Fazili, M., Venkatadri, U., Cyrus, P., & Tajbakhsh, M. (2017). Physical Internet, conventional and hybrid logistic systems: A routing optimisation-based comparison using the Eastern Canada road network case study. International Journal of Production Research, 55(9), 2703–2730. https://doi.org/10.1080/00207543.2017.1285075
  • Feng, Y., Song, D.-P., Li, D., & Zeng, Q. (2020). The stochastic container relocation problem with flexible service policies. Transportation Research Part B: Methodological, 141, 116–163. https://doi.org/10.1016/j.trb.2020.09.006
  • Gao, C.-F., & Hu, Z.-H. (2021). Speed optimization for container ship fleet deployment considering fuel consumption. Sustainability, 13(9), 5242–5242. https://doi.org/10.3390/su13095242
  • Giovannini, M., & Psaraftis, H. N. (2019). The profit maximizing liner shipping problem with flexible frequencies: Logistical and environmental considerations. Flexible Services and Manufacturing Journal, 31(3), 567–597. https://doi.org/10.1007/s10696-018-9308-z
  • Golias, M. M., Saharidis, G. K., Boile, M., Theofanis, S., & Ierapetritou, M. G. (2009). The berth allocation problem: Optimizing vessel arrival time. Maritime Economics & Logistics, 11(4), 358–377. https://doi.org/10.1057/mel.2009.12
  • He, J. (2016). Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving. Advanced Engineering Informatics, 30(3), 390–405. https://doi.org/10.1016/j.aei.2016.04.006
  • He, J., Huang, Y., & Yan, W. (2015). Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption. Advanced Engineering Informatics, 29(1), 59–75. https://doi.org/10.1016/j.aei.2014.09.003
  • He, J., Huang, Y., Yan, W., & Wang, S. (2015). Integrated internal truck, yard crane and quay crane scheduling in a container terminal considering energy consumption. Expert Systems with Applications, 42(5), 2464–2487. https://doi.org/10.1016/j.eswa.2014.11.016
  • He, W., Jin, Z., Huang, Y., & Xu, S. (2021). The inland container transportation problem with separation mode considering carbon dioxide emissions. Sustainability, 13(3), 1573–1573. https://doi.org/10.3390/su13031573
  • Heilig, L., Lalla-Ruiz, E., & Voß, S. (2017). Multi-objective inter-terminal truck routing. Transportation Research Part E: Logistics and Transportation Review, 106, 178–202. https://doi.org/10.1016/j.tre.2017.07.008
  • Hu, Q., Gu, W., & Wang, S. (2022). Optimal subsidy scheme design for promoting intermodal freight transport. Transportation Research Part E: Logistics and Transportation Review, 157, 102561–102561. https://doi.org/10.1016/j.tre.2021.102561
  • Hu, Q.-M., Hu, Z.-H., & Du, Y. (2014). Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels. Computers & Industrial Engineering, 70, 1–10. https://doi.org/10.1016/j.cie.2014.01.003
  • Hu, Z.-H. (2020). Low-emission berth allocation by optimizing sailing speed and mooring time. Transport, 35(5), 486–499. https://doi.org/10.3846/transport.2020.14080
  • Irannezhad, E., Prato, C. G., & Hickman, M. (2018). The effect of cooperation among shipping lines on transport costs and pollutant emissions. Transportation Research Part D: Transport and Environment, 65(September), 312–323. https://doi.org/10.1016/j.trd.2018.09.008
  • Iris, Ç., & Lam, J. S. L. (2019). A review of energy efficiency in ports: Operational strategies, technologies and energy management systems. Renewable and Sustainable Energy Reviews, 112, 170–182. https://doi.org/10.1016/j.rser.2019.04.069
  • Jiang, X., Mao, H., Wang, Y., & Zhang, H. (2020). Liner shipping schedule design for near-sea routes considering big customers’ preferences on ship arrival time. Sustainability, 12(18), 7828–7828. https://doi.org/10.3390/su12187828
  • Kanellos, F. D. (2019). Multiagent-system-based operation scheduling of large ports’ power systems with emissions limitation. IEEE Systems Journal, 13(2), 1831–1840. https://doi.org/10.1109/JSYST.2018.2850970
  • Karakas, S., Kirmizi, M., & Kocaoglu, B. (2021). Yard block assignment, internal truck operations, and berth allocation in container terminals: Introducing carbon-footprint minimisation objectives. Maritime Economics & Logistics, 23(4), 750–771. https://doi.org/10.1057/s41278-021-00186-7
  • Kim, S., Park, M., & Lee, C. (2013). Multimodal freight transportation network design problem for reduction of greenhouse gas emissions. Transportation Research Record: Journal of the Transportation Research Board, 2340(1), 74–83. https://doi.org/10.3141/2340-09
  • Kurtuluş, E. (2022). Optimizing inland container logistics and dry port location-allocation from an environmental perspective. Research in Transportation Business & Management, 100839. https://doi.org/10.1016/j.rtbm.2022.100839
  • Lagemann, B., Lindstad, E., Fagerholt, K., Rialland, A., & Ove Erikstad, S. (2022). Optimal ship lifetime fuel and power system selection. Transportation Research Part D: Transport and Environment, 102, 103145. https://doi.org/10.1016/j.trd.2021.103145
  • Lam, J. S. L., & Gu, Y. (2013). Port hinterland intermodal container flow optimisation with green concerns: A literature review and research agenda. International Journal of Shipping and Transport Logistics, 5(3), 257–257. https://doi.org/10.1504/IJSTL.2013.054190
  • Lam, J. S. L., & Gu, Y. (2016). A market-oriented approach for intermodal network optimisation meeting cost, time and environmental requirements. International Journal of Production Economics, 171, 266–274. https://doi.org/10.1016/j.ijpe.2015.09.024
  • Lan, X., Tao, Q., & Wu, X. (2023). Liner-shipping network design with emission control areas: A real case study. Sustainability, 15(4), 3734. https://doi.org/10.3390/su15043734
  • Lan, X., Zuo, X., & Tao, Q. (2023). Container shipping optimization under different carbon emission policies: A case study. Sustainability, 15(10), 8388. https://doi.org/10.3390/su15108388
  • Lee, H., Aydin, N., Choi, Y., Lekhavat, S., & Irani, Z. (2018). A decision support system for vessel speed decision in maritime logistics using weather archive big data. Computers & Operations Research, 98, 330–342. https://doi.org/10.1016/j.cor.2017.06.005
  • Li, C., Qi, X., & Lee, C.-Y. (2015). Disruption recovery for a vessel in liner shipping. Transportation Science, 49(4), 900–921. https://doi.org/10.1287/trsc.2015.0589
  • Li, H., & Li, X. (2022). A branch-and-bound algorithm for the bi-objective quay crane scheduling problem based on efficiency and energy. Mathematics, 10(24), 4705. https://doi.org/10.3390/math10244705
  • Li, L., Zhu, J., Ye, G., & Feng, X. (2018). Development of green ports with the consideration of coastal wave energy. Sustainability, 10(11), 4270. https://doi.org/10.3390/su10114270
  • Li, M., & Sun, X. (2022). Path optimization of low-carbon container multimodal transport under uncertain conditions. Sustainability, 14(21), 14098. https://doi.org/10.3390/su142114098
  • Li, S., Tang, L., Liu, J., Zhao, T., & Xiong, X. (2023). Vessel schedule recovery strategy in liner shipping considering expected disruption. Ocean & Coastal Management, 237, 106514. https://doi.org/10.1016/j.ocecoaman.2023.106514
  • Li, S., Wu, W., Ma, X., Zhong, M., & Safdar, M. (2023). Modelling medium- and long-term purchasing plans for environment-orientated container trucks: A case study of Yangtze River port. Transportation Safety and Environment, 5(1), tdac043. https://doi.org/10.1093/tse/tdac043
  • Li, X., Kuang, H., & Hu, Y. (2019). Carbon mitigation strategies of port selection and multimodal transport operations—A case study of northeast China. Sustainability, 11(18), 4877. https://doi.org/10.3390/su11184877
  • Li, X., Peng, Y., Wang, W., Huang, J., Liu, H., Song, X., & Bing, X. (2019). A method for optimizing installation capacity and operation strategy of a hybrid renewable energy system with offshore wind energy for a green container terminal. Ocean Engineering, 186, 106125. https://doi.org/10.1016/j.oceaneng.2019.106125
  • Li, X., Sun, B., Guo, C., Du, W., & Li, Y. (2020). Speed optimization of a container ship on a given route considering voluntary speed loss and emissions. Applied Ocean Research, 94, 101995. https://doi.org/10.1016/j.apor.2019.101995
  • Li, X., Sun, B., Jin, J., & Ding, J. (2022). Speed optimization of container ship considering route segmentation and weather data loading: Turning point-time segmentation method. Journal of Marine Science and Engineering, 10(12), 1835. https://doi.org/10.3390/jmse10121835
  • Lin, D., & Leong, P. (2022). A stochastic sailing speed optimization and vessel deployment problem in liner shipping. Journal of Marine Science and Technology-Taiwan, 30(3), 249–259. https://doi.org/10.51400/2709-6998.2580
  • Liu, D., & Ge, Y.-E. (2018). Modeling assignment of quay cranes using queueing theory for minimizing CO2 emission at a container terminal. Transportation Research Part D: Transport and Environment, 61, 140–151. https://doi.org/10.1016/j.trd.2017.06.006
  • Liu, M., Liu, R., Zhang, E., & Chu, C. (2022). Eco-friendly container transshipment route scheduling problem with repacking operations. Journal of Combinatorial Optimization, 43(5), 1010–1035. https://doi.org/10.1007/s10878-020-00619-8
  • Liu, M., Liu, X., Chu, F., Zhu, M., & Zheng, F. (2020). Liner ship bunkering and sailing speed planning with uncertain demand. Computational and Applied Mathematics, 39(1), 22–22. https://doi.org/10.1007/s40314-019-0994-2
  • Liu, S. (2023). Multimodal transportation route optimization of cold chain container in time-varying network considering carbon emissions. Sustainability, 15(5), 4435. https://doi.org/10.3390/su15054435
  • Liu, Y., Xin, X., Yang, Z., Chen, K., & Li, C. (2021). Liner shipping network—Transaction mechanism joint design model considering carbon tax and liner alliance. Ocean & Coastal Management, 212, 105817. https://doi.org/10.1016/j.ocecoaman.2021.105817
  • Liu, Y., Zhao, X., & Huang, R. (2022). Research on comprehensive recovery of liner schedule and container flow with hard time windows constraints. Ocean & Coastal Management, 224, 106171. https://doi.org/10.1016/j.ocecoaman.2022.106171
  • Lu, J., Wu, X., & Wu, Y. (2023). The construction and application of dual-objective optimal speed model of liners in a changing climate: Taking Yang Ming route as an example. Journal of Marine Science and Engineering, 11(1), 157. https://doi.org/10.3390/jmse11010157
  • Ma, J., Wang, X., Yang, K., & Jiang, L. (2023). Uncertain programming model for the cross-border multimodal container transport system based on inland ports. Axioms, 12(2), 132. https://doi.org/10.3390/axioms12020132
  • Ma, M., Fan, H., Jiang, X., & Guo, Z. (2019). Truck arrivals scheduling with vessel dependent time windows to reduce carbon emissions. Sustainability, 11(22), 6410. https://doi.org/10.3390/su11226410
  • Ma, Q., Wang, W., Peng, Y., & Song, X. (2018). An optimization approach to the intermodal transportation network in fruit cold chain, considering cost, quality degradation and carbon dioxide footprint. Polish Maritime Research, 25(1), 61–69. https://doi.org/10.2478/pomr-2018-0007
  • Ma, W., Hao, S., Ma, D., Wang, D., Jin, S., & Qu, F. (2021). Scheduling decision model of liner shipping considering emission control areas regulations. Applied Ocean Research, 106, 102416. https://doi.org/10.1016/j.apor.2020.102416
  • Ma, W., Zhang, J., Han, Y., Zheng, H., Ma, D., & Chen, M. (2022). A chaos-coupled multi-objective scheduling decision method for liner shipping based on the NSGA-III algorithm. Computers & Industrial Engineering, 174, 108732. https://doi.org/10.1016/j.cie.2022.108732
  • Maia, L. C., & Couto, A. (2013). Strategic rail network optimization model for freight transportation. Transportation Research Record: Journal of the Transportation Research Board, 2378(1), 1–12. https://doi.org/10.3141/2378-01
  • Mansouri, S. A., Lee, H., & Aluko, O. (2015). Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions. Transportation Research Part E: Logistics and Transportation Review, 78, 3–18. https://doi.org/10.1016/j.tre.2015.01.012
  • Martínez-López, A. (2021). A multi-objective mathematical model to select fleets and maritime routes in short sea shipping: A case study in Chile. Journal of Marine Science and Technology, 26(3), 673–692. https://doi.org/10.1007/s00773-020-00757-y
  • Martínez-López, A., Caamaño Sobrino, P., Chica González, M., & Trujillo, L. (2018). Optimization of a container vessel fleet and its propulsion plant to articulate sustainable intermodal chains versus road transport. Transportation Research Part D: Transport and Environment, 59, 134–147. https://doi.org/10.1016/j.trd.2017.12.021
  • Martínez-López, A., Caamaño Sobrino, P., Chica González, M., & Trujillo, L. (2019). Choice of propulsion plants for container vessels operating under Short Sea Shipping conditions in the European Union: An assessment focused on the environmental impact on the intermodal chains. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 233(2), 653–669. https://doi.org/10.1177/1475090218797179
  • Martínez-López, A., & Chica, M. (2020). Joint optimization of routes and container fleets to design sustainable intermodal chains in Chile. Sustainability, 12(6), 2221. https://doi.org/10.3390/su12062221
  • Martínez-López, A., Sobrino, P. C., & González, M. M. (2016). Influence of external costs on the optimisation of container fleets by operating under motorways of the sea conditions. International Journal of Shipping and Transport Logistics, 8(6), 653–686. https://doi.org/10.1504/IJSTL.2016.079293
  • Matsukura, H., Udommahuntisuk, M., Yamato, H., & Dinariyana, A. A. B. (2010). Estimation of CO2 reduction for Japanese domestic container transportation based on mathematical models. Journal of Marine Science and Technology, 15(1), 34–43. https://doi.org/10.1007/s00773-009-0069-y
  • Meng, Q., Wang, S., Andersson, H., & Thun, K. (2014). Containership routing and scheduling in liner shipping: overview and future research directions. Transportation Science, 48(2), 265–280. https://doi.org/10.1287/trsc.2013.0461
  • Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 339, b2535. https://doi.org/10.1136/bmj.b2535
  • Nadi, A., Nugteren, A., Snelder, M., Van Lint, J. W. C., & Rezaei, J. (2022). Advisory-based time slot management system to mitigate waiting time at container terminal gates. Transportation Research Record: Journal of the Transportation Research Board, 2676(10), 036119812210909. https://doi.org/10.1177/03611981221090940
  • Niu, Y., Yu, F., Yao, H., & Yang, Y. (2022). Multi-equipment coordinated scheduling strategy of U-shaped automated container terminal considering energy consumption. Computers & Industrial Engineering, 174, 108804. https://doi.org/10.1016/j.cie.2022.108804
  • Omran, M., Ghousi, R., & Kadkhodaei, A. (2023). Sustainable model of port-hinterland freight distribution network considering uncertainty: A case study of Iran. Scientia Iranica, 30(2), 784–802. https://doi.org/10.24200/sci.2021.55884.4447
  • Palacio, A., Adenso-Díaz, B., & Lozano, S. (2015). A decision-making model to design a sustainable container depot logistic network: The case of the port of Valencia. Transport, 33(1), 119–130. https://doi.org/10.3846/16484142.2015.1107621
  • Palacio, A., Adenso-Díaz, B., Lozano, S., & Furió, S. (2016). Bicriteria optimization model for locating maritime container depots: Application to the Port of Valencia. Networks and Spatial Economics, 16(1), 331–348. https://doi.org/10.1007/s11067-013-9205-7
  • Pasha, J., Dulebenets, M. A., Fathollahi-Fard, A. M., Tian, G., Lau, Y., Singh, P., & Liang, B. (2021). An integrated optimization method for tactical-level planning in liner shipping with heterogeneous ship fleet and environmental considerations. Advanced Engineering Informatics, 48, 101299. https://doi.org/10.1016/j.aei.2021.101299
  • Peng, Y., Li, X., Wang, W., Wei, Z., Bing, X., & Song, X. (2019). A method for determining the allocation strategy of on-shore power supply from a green container terminal perspective. Ocean & Coastal Management, 167, 158–175. https://doi.org/10.1016/j.ocecoaman.2018.10.007
  • Peng, Y., Wang, W., Song, X., & Zhang, Q. (2016). Optimal allocation of resources for yard crane network management to minimize carbon dioxide emissions. Journal of Cleaner Production, 131, 649–658. https://doi.org/10.1016/j.jclepro.2016.04.120
  • Pian, F., Shi, Q., Yao, X., Zhu, H., & Luan, W. (2021). Joint optimization of a dry port with multilevel location and container transportation: The case of northeast China. Complexity, 2021, 5584600. https://doi.org/10.1155/2021/5584600
  • Pourmohammad-Zia, N., Schulte, F., Gonzalez-Ramirez, R., Voss, S., & Negenborn, R. (2023). A robust optimization approach for platooning of automated ground vehicles in port hinterland corridors. Computers & Industrial Engineering, 117, 109046. https://doi.org/10.1016/j.cie.2023.109046
  • Psaraftis, H. N., & Kontovas, C. A. (2013). Speed models for energy-efficient maritime transportation: A taxonomy and survey. Transportation Research Part C: Emerging Technologies, 26, 331–351. https://doi.org/10.1016/j.trc.2012.09.012
  • Qi, J., & Wang, S. (2023). LNG bunkering station deployment problem-A case study of a Chinese container shipping network. Mathematics, 11(4), 813. https://doi.org/10.3390/math11040813
  • Qi, X., & Song, D.-P. (2012). Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times. Transportation Research Part E: Logistics and Transportation Review, 48(4), 863–880. https://doi.org/10.1016/j.tre.2012.02.001
  • Rajkovic, R., Zrnic, N., Kirin, S., & Dragovic, B. (2016). A review of multi-objective optimization of container flow using sea and land legs together. FME Transaction, 44(2), 204–211. https://doi.org/10.5937/fmet1602204R
  • Reinhardt, L. B., Pisinger, D., Sigurd, M. M., & Ahmt, J. (2020). Speed optimizations for liner networks with business constraints. European Journal of Operational Research, 285(3), 1127–1140. https://doi.org/10.1016/j.ejor.2020.02.043
  • Sáinz Bernat, N., Schulte, F., Voß, S., & Böse, J. (2016). Empty container management at ports considering pollution, repair options, and street-turns. Mathematical Problems in Engineering, 2016, 1–13. https://doi.org/10.1155/2016/3847163
  • Schmidt, J., Meyer-Barlag, C., Eisel, M., Kolbe, L. M., & Appelrath, H.-J. (2015). Using battery-electric AGVs in container terminals—Assessing the potential and optimizing the economic viability. Research in Transportation Business & Management, 17, 99–111. https://doi.org/10.1016/j.rtbm.2015.09.002
  • Schulte, F., Lalla-Ruiz, E., González-Ramírez, R. G., & Voß, S. (2017). Reducing port-related empty truck emissions: A mathematical approach for truck appointments with collaboration. Transportation Research Part E: Logistics and Transportation Review, 105, 195–212. https://doi.org/10.1016/j.tre.2017.03.008
  • Shi, H., Xu, P., & Yang, Z. (2016). Optimization of transport network in the Basin of Yangtze River with minimization of environmental emission and transport/investment costs. Advances in Mechanical Engineering, 8(8), 168781401666092. https://doi.org/10.1177/1687814016660923
  • Shiri, S., & Huynh, N. (2018). Assessment of U.S. chassis supply models on drayage productivity and air emissions. Transportation Research Part D: Transport and Environment, 61, 174–203. https://doi.org/10.1016/j.trd.2017.04.024
  • Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
  • Song, D.-P., Li, D., & Drake, P. (2015). Multi-objective optimization for planning liner shipping service with uncertain port times. Transportation Research Part E: Logistics and Transportation Review, 84, 1–22. https://doi.org/10.1016/j.tre.2015.10.001
  • Sun, Y. (2020). Green and reliable freight routing problem in the road-rail intermodal transportation network with uncertain parameters: A fuzzy goal programming approach. Journal of Advanced Transportation, 2020, 1–21. https://doi.org/10.1155/2020/7570686
  • Sun, Y., Hrušovský, M., Zhang, C., & Lang, M. (2018). A time-dependent fuzzy programming approach for the green multimodal routing problem with rail service capacity uncertainty and road traffic congestion. Complexity, 2018, 1–22. https://doi.org/10.1155/2018/8645793
  • Sun, Y., & Lang, M. (2015). Modeling the multicommodity multimodal routing problem with schedule-based services and carbon dioxide emission costs. Mathematical Problems in Engineering, 2015, 1–21. https://doi.org/10.1155/2015/406218
  • Sun, Y., Zheng, J., Han, J., Liu, H., & Zhao, Z. (2022). Allocation and reallocation of ship emission permits for liner shipping. Ocean Engineering, 266, 112976. https://doi.org/10.1016/j.oceaneng.2022.112976
  • Tan, R., Duru, O., & Thepsithar, P. (2020). Assessment of relative fuel cost for dual fuel marine engines along major Asian container shipping routes. Transportation Research Part E: Logistics and Transportation Review, 140, 102004. https://doi.org/10.1016/j.tre.2020.102004
  • Tan, R., Psaraftis, H., & Wang, D. (2022). The speed limit debate: Optimal speed concepts revisited under a multi-fuel regime. Transportation Research Part D-Transport and Environment, 111, 103445. https://doi.org/10.1016/j.trd.2022.103445
  • Tan, Z., Wang, Y., Meng, Q., & Liu, Z. (2018). Joint ship schedule design and sailing speed optimization for a single inland shipping service with uncertain dam transit time. Transportation Science, 52(6), 1570–1588. https://doi.org/10.1287/trsc.2017.0808
  • Tan, Z., Zeng, X., Shao, S., Chen, J., & Wang, H. (2022). Scrubber installation and green fuel for inland river ships with non-identical streamflow. Transportation Research Part E: Logistics and Transportation Review, 161, 102677. https://doi.org/10.1016/j.tre.2022.102677
  • Tao, Y., Zhang, S., Lin, C., & Lai, X. (2023). A bi-objective optimization for integrated truck operation and storage allocation considering traffic congestion in container terminals. Ocean & Coastal Management, 232, 106417. https://doi.org/10.1016/j.ocecoaman.2022.106417
  • Tran, N. K., Haasis, H.-D., & Buer, T. (2017). Container shipping route design incorporating the costs of shipping, inland/feeder transport, inventory and CO2 emission. Maritime Economics & Logistics, 19(4), 667–694. https://doi.org/10.1057/mel.2016.11
  • Trapp, A. C., Harris, I., Sanchez Rodrigues, V., & Sarkis, J. (2020). Maritime container shipping: Does coopetition improve cost and environmental efficiencies? Transportation Research Part D: Transport and Environment, 87, 102507. https://doi.org/10.1016/j.trd.2020.102507
  • Tsao, Y., & Linh, V. (2018). Seaport-dry port network design considering multimodal transport and carbon emissions. Journal of Cleaner Production, 199, 481–492. https://doi.org/10.1016/j.jclepro.2018.07.137
  • Tsao, Y., & Thanh, V. (2019). A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment. Transportation Research Part E: Logistics and Transportation Review, 124, 13–39. https://doi.org/10.1016/j.tre.2019.02.006
  • UNCTAD. (2021). Review of Maritime Transport.
  • Venturini, G., Iris, Ç., Kontovas, C. A., & Larsen, A. (2017). The multi-port berth allocation problem with speed optimization and emission considerations. Transportation Research Part D: Transport and Environment, 54, 142–159. https://doi.org/10.1016/j.trd.2017.05.002
  • Wang, C., & Chen, J. (2017). Strategies of refueling, sailing speed and ship deployment of containerships in the low-carbon background. Computers & Industrial Engineering, 114, 142–150. https://doi.org/10.1016/j.cie.2017.10.012
  • Wang, C., Yu, S., & Xu, L. (2022). Decisions on sailing frequency and ship type in liner shipping with the consideration of carbon dioxide emissions. Regional Studies in Marine Science, 52, 102371–102371. https://doi.org/10.1016/j.rsma.2022.102371
  • Wang, S. (2016). Fundamental properties and pseudo-polynomial-time algorithm for network containership sailing speed optimization. European Journal of Operational Research, 250(1), 46–55. https://doi.org/10.1016/j.ejor.2015.10.052
  • Wang, S., Alharbi, A., & Davy, P. (2014). Liner ship route schedule design with port time windows. Transportation Research Part C: Emerging Technologies, 41, 1–17. https://doi.org/10.1016/j.trc.2014.01.012
  • Wang, S., & Meng, Q. (2012). Sailing speed optimization for container ships in a liner shipping network. Transportation Research Part E: Logistics and Transportation Review, 48(3), 701–714. https://doi.org/10.1016/j.tre.2011.12.003
  • Wang, S., & Meng, Q. (2015). Robust bunker management for liner shipping networks. European Journal of Operational Research, 243(3), 789–797. https://doi.org/10.1016/j.ejor.2014.12.049
  • Wang, S., & Meng, Q. (2017). Container liner fleet deployment: A systematic overview. Transportation Research Part C: Emerging Technologies, 77, 389–404. https://doi.org/10.1016/j.trc.2017.02.010
  • Wang, S., Meng, Q., & Liu, Z. (2013). Containership scheduling with transit-time-sensitive container shipment demand. Transportation Research Part B: Methodological, 54, 68–83. https://doi.org/10.1016/j.trb.2013.04.003
  • Wang, S., Qu, X., & Yang, Y. (2015). Estimation of the perceived value of transit time for containerized cargoes. Transportation Research Part A: Policy and Practice, 78, 298–308. https://doi.org/10.1016/j.tra.2015.04.014
  • Wang, S., & Wang, X. (2016). A polynomial-time algorithm for sailing speed optimization with containership resource sharing. Transportation Research Part B: Methodological, 93, 394–405. https://doi.org/10.1016/j.trb.2016.08.003
  • Wang, S., Zhuge, D., Zhen, L., & Lee, C.-Y. (2021). Liner shipping service planning under sulfur emission regulations. Transportation Science, 55(2), 491–509. https://doi.org/10.1287/trsc.2020.1010
  • Wang, T., Wang, X., & Meng, Q. (2018). Joint berth allocation and quay crane assignment under different carbon taxation policies. Transportation Research Part B: Methodological, 117, 18–36. https://doi.org/10.1016/j.trb.2018.08.012
  • Wang, W., Peng, Y., Li, X., Qi, Q., Feng, P., & Zhang, Y. (2019). A two-stage framework for the optimal design of a hybrid renewable energy system for port application. Ocean Engineering, 191, 106555. https://doi.org/10.1016/j.oceaneng.2019.106555
  • Wang, Y., Meng, Q., & Kuang, H. (2018). Jointly optimizing ship sailing speed and bunker purchase in liner shipping with distribution-free stochastic bunker prices. Transportation Research Part C: Emerging Technologies, 89, 35–52. https://doi.org/10.1016/j.trc.2018.01.020
  • Wen, X., Ge, Y.-E., Yin, Y., & Zhong, M. (2022). Dynamic recovery actions in multi-objective liner shipping service with buffer times. Proceedings of the Institution of Civil Engineers - Maritime Engineering, 175(2), 46–62. https://doi.org/10.1680/jmaen.2021.005
  • Wong, E. Y. C., Tai, A. H., Lau, H. Y. K., & Raman, M. (2015). An utility-based decision support sustainability model in slow steaming maritime operations. Transportation Research Part E: Logistics and Transportation Review, 78, 57–69. https://doi.org/10.1016/j.tre.2015.01.013
  • Wong, E. Y. C., Tai, A. H., & So, S. (2020). Container drayage modelling with graph theory-based road connectivity assessment for sustainable freight transportation in new development area. Computers & Industrial Engineering, 149, 106810. https://doi.org/10.1016/j.cie.2020.106810
  • Wu, Y., Huang, Y., Wang, H., & Zhen, L. (2022a). Joint planning of fleet deployment, ship refueling, and speed optimization for dual-fuel ships considering methane slip. Journal of Marine Science and Engineering, 10(11), 1690. https://doi.org/10.3390/jmse10111690
  • Wu, Y., Huang, Y., Wang, H., & Zhen, L. (2022b). Nonlinear programming for fleet deployment, voyage planning and speed optimization in sustainable liner shipping. Electronic Research Archive, 31(1), 147–168. https://doi.org/10.3934/era.2023008
  • Wu, Y., Huang, Y., Wang, H., Zhen, L., & Shao, W. (2023). Green technology adoption and fleet deployment for new and aged ships considering maritime decarbonization. Journal of Marine Science and Engineering, 11(1), 36. https://doi.org/10.3390/jmse11010036
  • Xing, Y., Yang, H., Ma, X., & Zhang, Y. (2019). Optimization of ship speed and fleet deployment under carbon emissions policies for container shipping. Transport, 34(3), 260–274. https://doi.org/10.3846/transport.2019.9317
  • Xu, B., Liu, X., Li, J., Yang, Y., Wu, J., Shen, Y., & Zhou, Y. (2022). Dynamic appointment rescheduling of trucks under uncertainty of arrival time. Journal of Marine Science and Engineering, 10(5), 695. https://doi.org/10.3390/jmse10050695
  • Yang, Y., Zhu, X., & Haghani, A. (2019). Multiple equipment integrated scheduling and storage space allocation in rail–water intermodal container terminals considering energy efficiency. Transportation Research Record: Journal of the Transportation Research Board, 2673(3), 199–209. https://doi.org/10.1177/0361198118825474
  • Yang, Z., Xin, X., Chen, K., & Yang, A. (2021). Coastal container multimodal transportation system shipping network design—Toll policy joint optimization model. Journal of Cleaner Production, 279, 123340. https://doi.org/10.1016/j.jclepro.2020.123340
  • Yu, D., Li, D., Sha, M., & Zhang, D. (2019). Carbon-efficient deployment of electric rubber-tyred gantry cranes in container terminals with workload uncertainty. European Journal of Operational Research, 275(2), 552–569. https://doi.org/10.1016/j.ejor.2018.12.003
  • Yu, H., Deng, Y., Zhang, L., Xiao, X., & Tan, C. (2022). Yard operations and management in automated container terminals: A review. Sustainability, 14(6), 3419. https://doi.org/10.3390/su14063419
  • Yu, H., Fang, Z., Fu, X., Liu, J., & Chen, J. (2021). Literature review on emission control-based ship voyage optimization. Transportation Research Part D: Transport and Environment, 93, 102768. https://doi.org/10.1016/j.trd.2021.102768
  • Yu, H., Ge, Y.-E., Chen, J., Luo, L., Tan, C., & Liu, D. (2017). CO2 emission evaluation of yard tractors during loading at container terminals. Transportation Research Part D: Transport and Environment, 53, 17–36. https://doi.org/10.1016/j.trd.2017.03.014
  • Yu, H., Huang, M., Zhang, L., & Tan, C. (2022). Yard template generation for automated container terminal based on bay sharing strategy. Annals of Operations Research, In Press. https://doi.org/10.1007/s10479-022-04657-9
  • Yu, J., Voß, S., & Song, X. (2022). Multi-objective optimization of daily use of shore side electricity integrated with quayside operation. Journal of Cleaner Production, 351, 131406. https://doi.org/10.1016/j.jclepro.2022.131406
  • Yu, J., Voß, S., & Tang, G. (2019). Strategy development for retrofitting ships for implementing shore side electricity. Transportation Research Part D: Transport and Environment, 74, 201–213. https://doi.org/10.1016/j.trd.2019.08.004
  • Yu, M.-M., & Chen, L.-H. (2016). Centralized resource allocation with emission resistance in a two-stage production system: Evidence from a Taiwan’s container shipping company. Transportation Research Part A: Policy and Practice, 94, 650–671. https://doi.org/10.1016/j.tra.2016.10.003
  • Yu, Y., Tu, J., Shi, K., Liu, M., & Chen, J. (2021). Flexible Optimization of International Shipping Routes considering Carbon Emission Cost. Mathematical Problems in Engineering, 2021, 6678473. https://doi.org/10.1155/2021/6678473
  • Zacharioudakis, P. G., Iordanis, S., Lyridis, D. V., & Psaraftis, H. N. (2011). Liner shipping cycle cost modelling, fleet deployment optimization and what-if analysis. Maritime Economics & Logistics, 13(3), 278–297. https://doi.org/10.1057/mel.2011.11
  • Zhang, M., Wiegmans, B., & Tavasszy, L. (2013). Optimization of multimodal networks including environmental costs: A model and findings for transport policy. Computers in Industry, 64(2), 136–145. https://doi.org/10.1016/j.compind.2012.11.008
  • Zhang, Q., Wang, S., & Zhen, L. (2022). Yard truck retrofitting and deployment for hazardous material transportation in green ports. Annals of Operations Research, In Press. https://doi.org/10.1007/s10479-021-04507-0
  • Zhang, X., Lam, J. S. L., & Iris, Ç. (2020). Cold chain shipping mode choice with environmental and financial perspectives. Transportation Research Part D: Transport and Environment, 87, 102537. https://doi.org/10.1016/j.trd.2020.102537
  • Zhang, Y., Atasoy, B., & Negenborn, R. R. (2022). Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search. Transportation Research Record: Journal of the Transportation Research Board, 2676(3), 71–87. https://doi.org/10.1177/03611981211049148
  • Zhang, Y., Liang, C., Shi, J., Lim, G., & Wu, Y. (2022). Optimal port microgrid scheduling incorporating onshore power supply and berth allocation under uncertainty. Applied Energy, 313, 118856. https://doi.org/10.1016/j.apenergy.2022.118856
  • Zhao, S., Duan, J., Li, D., & Yang, H. (2022). Vessel scheduling and bunker management with speed deviations for liner shipping in the presence of collaborative agreements. IEEE Access, 10, 107669–107684. https://doi.org/10.1109/ACCESS.2022.3211311
  • Zhao, W., Wang, Y., Zhang, Z., & Wang, H. (2021). Multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm. Journal of Marine Science and Engineering, 9(4), 357. https://doi.org/10.3390/jmse9040357
  • Zhao, Y., Chen, Y., Fagerholt, K., Lindstad, E., & Zhou, J. (2023). Pathways towards carbon reduction through technology transition in liner shipping. Maritime Policy & Management, In Press. https://doi.org/10.1080/03088839.2023.2224813
  • Zhao, Y., Ye, J., & Zhou, J. (2021). Container fleet renewal considering multiple sulfur reduction technologies and uncertain markets amidst COVID-19. Journal of Cleaner Production, 317, 128361. https://doi.org/10.1016/j.jclepro.2021.128361
  • Zhao, Y., Zhou, J., Fan, Y., & Kuang, H. (2020). An expected utility-based optimization of slow steaming in sulphur emission control areas by applying big data analytics. IEEE Access, 8, 3646–3655. https://doi.org/10.1109/ACCESS.2019.2962210
  • Zhen, L., Hu, Z., Yan, R., Zhuge, D., & Wang, S. (2020). Route and speed optimization for liner ships under emission control policies. Transportation Research Part C: Emerging Technologies, 110, 330–345. https://doi.org/10.1016/j.trc.2019.11.004
  • Zhen, L., Jin, Y., Wu, Y., Yuan, Y., & Tan, Z. (2022). Benders decomposition for internal truck renewal decision in green ports. Maritime Policy & Management, 1–23. https://doi.org/10.1080/03088839.2021.2021596
  • Zhen, L., Lin, S., & Zhou, C. (2022). Green port oriented resilience improvement for traffic-power coupled networks. Reliability Engineering & System Safety, 225, 108569. https://doi.org/10.1016/j.ress.2022.108569
  • Zhen, L., Sun, Q., Zhang, W., Wang, K., & Yi, W. (2021). Column generation for low carbon berth allocation under uncertainty. Journal of the Operational Research Society, 72(10), 2225–2240. https://doi.org/10.1080/01605682.2020.1776168
  • Zhen, L., Wang, S., & Wang, K. (2016). Terminal allocation problem in a transshipment hub considering bunker consumption. Naval Research Logistics (NRL), 63(7), 529–548. https://doi.org/10.1002/nav.21717
  • Zhen, L., Wang, S., & Zhuge, D. (2017). Dynamic programming for optimal ship refueling decision. Transportation Research Part E: Logistics and Transportation Review, 100, 63–74. https://doi.org/10.1016/j.tre.2016.12.013
  • Zhen, L., Wu, Y., Wang, S., & Laporte, G. (2020). Green technology adoption for fleet deployment in a shipping network. Transportation Research Part B: Methodological, 139, 388–410. https://doi.org/10.1016/j.trb.2020.06.004
  • Zheng, Y., Xu, M., Wang, Z., & Xiao, Y. (2023). A genetic algorithm for integrated scheduling of container handing systems at container terminals from a low-carbon operations perspective. Sustainability, 15(7), 6035. https://doi.org/10.3390/su15076035
  • Zhong, M., Yang, Y., Zhou, Y., & Postolache, O. (2020). Application of hybrid GA-PSO based on intelligent control fuzzy system in the integrated scheduling in automated container terminal. Journal of Intelligent & Fuzzy Systems, 39(2), 1525–1538. https://doi.org/10.3233/JIFS-179926
  • Zhu, M., Chen, M., & Kristal, M. (2018). Modelling the impacts of uncertain carbon tax policy on maritime fleet mix strategy and carbon mitigation. Transport, 33(3), 707–717. https://doi.org/10.3846/transport.2018.1579
  • Zhu, S., Gao, J., He, X., Zhang, S., Jin, Y., & Tan, Z. (2021). Green logistics oriented tug scheduling for inland waterway logistics. Advanced Engineering Informatics, 49, 101323. https://doi.org/10.1016/j.aei.2021.101323
  • Zhuge, D., Wang, S., & Wang, D. Z. W. (2021). A joint liner ship path, speed and deployment problem under emission reduction measures. Transportation Research Part B: Methodological, 144, 155–173. https://doi.org/10.1016/j.trb.2020.12.006
There are 192 citations in total.

Details

Primary Language English
Subjects Environmentally Sustainable Engineering, Transportation Engineering
Journal Section Review Paper
Authors

Ercan Kurtuluş 0000-0003-0585-9319

Publication Date September 28, 2023
Submission Date December 25, 2022
Acceptance Date August 13, 2023
Published in Issue Year 2023 Volume: 12 Issue: 3

Cite

APA Kurtuluş, E. (2023). Optimization for Green Container Shipping: A Review and Future Research Directions. Marine Science and Technology Bulletin, 12(3), 282-311. https://doi.org/10.33714/masteb.1224099
AMA Kurtuluş E. Optimization for Green Container Shipping: A Review and Future Research Directions. Mar. Sci. Tech. Bull. September 2023;12(3):282-311. doi:10.33714/masteb.1224099
Chicago Kurtuluş, Ercan. “Optimization for Green Container Shipping: A Review and Future Research Directions”. Marine Science and Technology Bulletin 12, no. 3 (September 2023): 282-311. https://doi.org/10.33714/masteb.1224099.
EndNote Kurtuluş E (September 1, 2023) Optimization for Green Container Shipping: A Review and Future Research Directions. Marine Science and Technology Bulletin 12 3 282–311.
IEEE E. Kurtuluş, “Optimization for Green Container Shipping: A Review and Future Research Directions”, Mar. Sci. Tech. Bull., vol. 12, no. 3, pp. 282–311, 2023, doi: 10.33714/masteb.1224099.
ISNAD Kurtuluş, Ercan. “Optimization for Green Container Shipping: A Review and Future Research Directions”. Marine Science and Technology Bulletin 12/3 (September 2023), 282-311. https://doi.org/10.33714/masteb.1224099.
JAMA Kurtuluş E. Optimization for Green Container Shipping: A Review and Future Research Directions. Mar. Sci. Tech. Bull. 2023;12:282–311.
MLA Kurtuluş, Ercan. “Optimization for Green Container Shipping: A Review and Future Research Directions”. Marine Science and Technology Bulletin, vol. 12, no. 3, 2023, pp. 282-11, doi:10.33714/masteb.1224099.
Vancouver Kurtuluş E. Optimization for Green Container Shipping: A Review and Future Research Directions. Mar. Sci. Tech. Bull. 2023;12(3):282-311.

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