Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2022, , 479 - 498, 16.01.2023
https://doi.org/10.26650/JTL.2022.1096085

Öz

Kaynakça

  • Algethami, H., & Landa-Silva, D. (2015). A study of genetic operators for the workforce scheduling and routing problem. In Proceedings of the 11th Metaheuristics International Conference (MIC 2015, pp. 1-11). Agadir; Morocco.
  • Allaoua, H., Borne, S., Létocart, L., & Calvo, R. W. (2013). A matheuristic approach for solving a home health care problem. Electronic Notes in Discrete Mathematics, 41, 471-478. https://doi.org/10.1016/j. endm.2013.05.127
  • Bektas, T. (2006). The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega, 34(3), 209-219. https://doi.org/10.1016/j.omega.2004.10.004
  • Berkhout, C. (2015). Retail marketing strategy: Delivering shopper delight. Kogan Page Publishers, USA.
  • Bredström, D., & Rönnqvist, M. (2008). Combined vehicle routing and scheduling with temporal precedence and synchronization constraints. European Journal of Operational Research, 191(1), 19-31. https://doi. org/10.1016/j.ejor.2007.07.033
  • Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2016). Workforce scheduling and routing problems: literature survey and computational study. Annals of Operations Research, 239(1), 39-67. https://doi. org/10.1007/s10479-014-1687-2
  • Chandran, N., Narendran, T. T., & Ganesh, K. (2006). A clustering approach to solve the multiple traveling salesmen problem. International Journal of Industrial and Systems Engineering, 1(3), 372-387. https:// doi.org/10.1504/IJISE.2006.009794
  • Davendra, D. (Ed.). (2010). Traveling salesman problem: Theory and applications. BoD–Books on Demand.
  • Friske, M. W., Buriol, L. S., & Camponogara, E. (2022). A relax-and-fix and fix-and-optimize algorithm for a Maritime Inventory Routing Problem. Computers & Operations Research, 137, 105520. https:// doi.org/10.1016/j.cor.2021.105520
  • Gilbert, K. C., & Hofstra, R. B. (1992). A new multiperiod multiple traveling salesman problem with heuristic and application to a scheduling problem. Decision Sciences, 23(1), 250-259. https://doi. org/10.1111/j.1540-5915.1992.tb00387.x
  • Goel, V., Furman, K. C., Song, J. H., & El-Bakry, A. S. (2012). Large neighborhood search for LNG inventory routing. Journal of Heuristics, 18(6), 821-848. https://doi.org/10.1007/s10732-012-9206-6
  • Ip, W. H., Wang, D., & Cho, V. (2012). Aircraft ground service scheduling problems and their genetic algorithm with hybrid assignment and sequence encoding scheme. IEEE Systems Journal, 7(4), 649-657. https:// doi.org/10.1109/JSYST.2012.2196229
  • Johnson, D. S., & McGeoch, L. A. (2018). The traveling salesman problem: a case study. In E. Aarts & J. Lenstra (Ed.), Local Search in Combinatorial Optimization (pp. 215-310). Princeton: Princeton University Press, USA. https://doi.org/10.1515/9780691187563-011
  • Kara, I., & Bektas, T. (2006). Integer linear programming formulations of multiple salesman problems and its variations. European Journal of Operational Research, 174(3), 1449-1458. https://doi.org/10.1016/j. ejor.2005.03.008
  • Kovacs, A. A., Parragh, S. N., Doerner, K. F., & Hartl, R. F. (2012). Adaptive large neighborhood search for service technician routing and scheduling problems. Journal of Scheduling, 15(5), 579-600. https:// doi.org/10.1007/s10951-011-0246-9
  • Liu, X., Luo, M., & Zhao, Y. (2019). Integrating route optimisation with vehicle and unloading dock scheduling in LCL cargo collection. International Journal of Shipping and Transport Logistics, 11(2-3), 262-280. https://doi.org/10.1504/IJSTL.2019.099273
  • Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. https://doi.org/10.1109/TIT.1982.1056489
  • MacKay, D. (2003). An example inference task: Clustering. Information Theory, Inference and Learning Algorithms, 20, 284-292.
  • Mankowska, D. S., Meisel, F., & Bierwirth, C. (2014). The home health care routing and scheduling problem with interdependent services. Health Care Management Science, 17(1), 15-30. https://doi.org/10.1007/ s10729-013-9243-1
  • Masutti, T. A., & de Castro, L. N. (2008, July). A Clustering Approach Based on Artificial Neural Networks to Solve Routing Problems. In 2008 11th IEEE International Conference on Computational Science and Engineering (pp. 285-292). IEEE. https://doi.org/10.1109/CSE.2008.58
  • Misir, M., Smet, P., Verbeeck, K., & Vanden Berghe, G. (2011). Security personnel routing and rostering: a hyper-heuristic approach. In Proceedings of the 3rd International Conference on Applied Operational Research (Vol. 3, pp. 193-205). Tadbir; Canada.
  • Necula, R., Raschip, M., & Breaban, M. (2018). Balancing the subtours for multiple TSP approached with ACS: Clustering-based approaches vs. MinMax formulation. In EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI (pp. 210-223). Springer, Cham. https://doi. org/10.1007/978-3-319-69710-9_15
  • Nuriyev, U., Ugurlu, O., & Nuriyeva, F. (2018). A Simple Algorithm for The Multi-Depot Multiple Traveling Salesman Problem. In. Proceedings of the 6th International Conference on Control and Optimization with Industrial Applications (Vol. 2, pp. 235-237). Baku; Azerbaijan.
  • Pratap, S., Zhang, M., Shen, C. L., & Huang, G. Q. (2019). A multi-objective approach to analyse the effect of fuel consumption on ship routing and scheduling problem. International Journal of Shipping and Transport Logistics, 11(2-3), 161-175. https://doi.org/10.1504/IJSTL.2019.099270
  • Song, J. H., & Furman, K. C. (2013). A maritime inventory routing problem: Practical approach. Computers & Operations Research, 40(3), 657-665. https://doi.org/10.1016/j.cor.2010.10.031
  • Uggen, K. T., Fodstad, M., & Nørstebø, V. S. (2013). Using and extending fix-and-relax to solve maritime inventory routing problems. TOP, 21(2), 355-377. https://doi.org/10.1007/s11750-011-0174-z
  • Ugurlu, O. (2018). Rotalama problemleri için algoritmik yaklaşımlar. Doktora Tezi, Ege Üniversitesi, Fen Bilimleri Enstitüsü, İzmir.
  • Venkateswara Reddy, P., Kumar, A. C. S., Bhat, M. S., Dhanalakshmi, R., & Parthiban, P. (2010). Balanced centroids (BC) k-means clustering algorithm to transform MTSP to TSP. International Journal of Logistics Economics and Globalisation, 2(3), 187-197. https://doi.org/10.1504/IJLEG.2010.036299

A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study

Yıl 2022, , 479 - 498, 16.01.2023
https://doi.org/10.26650/JTL.2022.1096085

Öz

This study investigated the operational workforce scheduling and routing problem of a leading international retail company. Currently, the company plans to launch a new product into the Turkish market, which will be used in all its retail stores across the country. For the best marketing outcome, branding of all retail stores needs to be renewed by an outsourced workforce with a minimum of cost and time. We framed this as a workforce scheduling and routing optimization problem. Therefore, a two-stage solution was proposed. The retail stores were partitioned into disjoint regions in the first stage, and the schedules were optimized in the second stage. We employed the k-means clustering algorithm for constructing these regions. Two different heuristic approaches were applied to solve regional scheduling in the second stage of the algorithm since the resulting scheduling problem is NP-hard. Finally, a computational analysis was performed with real data and the results are discussed.

Kaynakça

  • Algethami, H., & Landa-Silva, D. (2015). A study of genetic operators for the workforce scheduling and routing problem. In Proceedings of the 11th Metaheuristics International Conference (MIC 2015, pp. 1-11). Agadir; Morocco.
  • Allaoua, H., Borne, S., Létocart, L., & Calvo, R. W. (2013). A matheuristic approach for solving a home health care problem. Electronic Notes in Discrete Mathematics, 41, 471-478. https://doi.org/10.1016/j. endm.2013.05.127
  • Bektas, T. (2006). The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega, 34(3), 209-219. https://doi.org/10.1016/j.omega.2004.10.004
  • Berkhout, C. (2015). Retail marketing strategy: Delivering shopper delight. Kogan Page Publishers, USA.
  • Bredström, D., & Rönnqvist, M. (2008). Combined vehicle routing and scheduling with temporal precedence and synchronization constraints. European Journal of Operational Research, 191(1), 19-31. https://doi. org/10.1016/j.ejor.2007.07.033
  • Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2016). Workforce scheduling and routing problems: literature survey and computational study. Annals of Operations Research, 239(1), 39-67. https://doi. org/10.1007/s10479-014-1687-2
  • Chandran, N., Narendran, T. T., & Ganesh, K. (2006). A clustering approach to solve the multiple traveling salesmen problem. International Journal of Industrial and Systems Engineering, 1(3), 372-387. https:// doi.org/10.1504/IJISE.2006.009794
  • Davendra, D. (Ed.). (2010). Traveling salesman problem: Theory and applications. BoD–Books on Demand.
  • Friske, M. W., Buriol, L. S., & Camponogara, E. (2022). A relax-and-fix and fix-and-optimize algorithm for a Maritime Inventory Routing Problem. Computers & Operations Research, 137, 105520. https:// doi.org/10.1016/j.cor.2021.105520
  • Gilbert, K. C., & Hofstra, R. B. (1992). A new multiperiod multiple traveling salesman problem with heuristic and application to a scheduling problem. Decision Sciences, 23(1), 250-259. https://doi. org/10.1111/j.1540-5915.1992.tb00387.x
  • Goel, V., Furman, K. C., Song, J. H., & El-Bakry, A. S. (2012). Large neighborhood search for LNG inventory routing. Journal of Heuristics, 18(6), 821-848. https://doi.org/10.1007/s10732-012-9206-6
  • Ip, W. H., Wang, D., & Cho, V. (2012). Aircraft ground service scheduling problems and their genetic algorithm with hybrid assignment and sequence encoding scheme. IEEE Systems Journal, 7(4), 649-657. https:// doi.org/10.1109/JSYST.2012.2196229
  • Johnson, D. S., & McGeoch, L. A. (2018). The traveling salesman problem: a case study. In E. Aarts & J. Lenstra (Ed.), Local Search in Combinatorial Optimization (pp. 215-310). Princeton: Princeton University Press, USA. https://doi.org/10.1515/9780691187563-011
  • Kara, I., & Bektas, T. (2006). Integer linear programming formulations of multiple salesman problems and its variations. European Journal of Operational Research, 174(3), 1449-1458. https://doi.org/10.1016/j. ejor.2005.03.008
  • Kovacs, A. A., Parragh, S. N., Doerner, K. F., & Hartl, R. F. (2012). Adaptive large neighborhood search for service technician routing and scheduling problems. Journal of Scheduling, 15(5), 579-600. https:// doi.org/10.1007/s10951-011-0246-9
  • Liu, X., Luo, M., & Zhao, Y. (2019). Integrating route optimisation with vehicle and unloading dock scheduling in LCL cargo collection. International Journal of Shipping and Transport Logistics, 11(2-3), 262-280. https://doi.org/10.1504/IJSTL.2019.099273
  • Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. https://doi.org/10.1109/TIT.1982.1056489
  • MacKay, D. (2003). An example inference task: Clustering. Information Theory, Inference and Learning Algorithms, 20, 284-292.
  • Mankowska, D. S., Meisel, F., & Bierwirth, C. (2014). The home health care routing and scheduling problem with interdependent services. Health Care Management Science, 17(1), 15-30. https://doi.org/10.1007/ s10729-013-9243-1
  • Masutti, T. A., & de Castro, L. N. (2008, July). A Clustering Approach Based on Artificial Neural Networks to Solve Routing Problems. In 2008 11th IEEE International Conference on Computational Science and Engineering (pp. 285-292). IEEE. https://doi.org/10.1109/CSE.2008.58
  • Misir, M., Smet, P., Verbeeck, K., & Vanden Berghe, G. (2011). Security personnel routing and rostering: a hyper-heuristic approach. In Proceedings of the 3rd International Conference on Applied Operational Research (Vol. 3, pp. 193-205). Tadbir; Canada.
  • Necula, R., Raschip, M., & Breaban, M. (2018). Balancing the subtours for multiple TSP approached with ACS: Clustering-based approaches vs. MinMax formulation. In EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI (pp. 210-223). Springer, Cham. https://doi. org/10.1007/978-3-319-69710-9_15
  • Nuriyev, U., Ugurlu, O., & Nuriyeva, F. (2018). A Simple Algorithm for The Multi-Depot Multiple Traveling Salesman Problem. In. Proceedings of the 6th International Conference on Control and Optimization with Industrial Applications (Vol. 2, pp. 235-237). Baku; Azerbaijan.
  • Pratap, S., Zhang, M., Shen, C. L., & Huang, G. Q. (2019). A multi-objective approach to analyse the effect of fuel consumption on ship routing and scheduling problem. International Journal of Shipping and Transport Logistics, 11(2-3), 161-175. https://doi.org/10.1504/IJSTL.2019.099270
  • Song, J. H., & Furman, K. C. (2013). A maritime inventory routing problem: Practical approach. Computers & Operations Research, 40(3), 657-665. https://doi.org/10.1016/j.cor.2010.10.031
  • Uggen, K. T., Fodstad, M., & Nørstebø, V. S. (2013). Using and extending fix-and-relax to solve maritime inventory routing problems. TOP, 21(2), 355-377. https://doi.org/10.1007/s11750-011-0174-z
  • Ugurlu, O. (2018). Rotalama problemleri için algoritmik yaklaşımlar. Doktora Tezi, Ege Üniversitesi, Fen Bilimleri Enstitüsü, İzmir.
  • Venkateswara Reddy, P., Kumar, A. C. S., Bhat, M. S., Dhanalakshmi, R., & Parthiban, P. (2010). Balanced centroids (BC) k-means clustering algorithm to transform MTSP to TSP. International Journal of Logistics Economics and Globalisation, 2(3), 187-197. https://doi.org/10.1504/IJLEG.2010.036299
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yöneylem
Bölüm Araştırma Makalesi
Yazarlar

Uğur Eliiyi 0000-0002-5584-891X

Onur Uğurlu 0000-0003-2743-5939

Sel Özcan Tatari 0000-0002-4711-6663

Yayımlanma Tarihi 16 Ocak 2023
Gönderilme Tarihi 30 Mart 2022
Kabul Tarihi 20 Ekim 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Eliiyi, U., Uğurlu, O., & Özcan Tatari, S. (2023). A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study. Journal of Transportation and Logistics, 7(2), 479-498. https://doi.org/10.26650/JTL.2022.1096085
AMA Eliiyi U, Uğurlu O, Özcan Tatari S. A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study. JTL. Ocak 2023;7(2):479-498. doi:10.26650/JTL.2022.1096085
Chicago Eliiyi, Uğur, Onur Uğurlu, ve Sel Özcan Tatari. “A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study”. Journal of Transportation and Logistics 7, sy. 2 (Ocak 2023): 479-98. https://doi.org/10.26650/JTL.2022.1096085.
EndNote Eliiyi U, Uğurlu O, Özcan Tatari S (01 Ocak 2023) A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study. Journal of Transportation and Logistics 7 2 479–498.
IEEE U. Eliiyi, O. Uğurlu, ve S. Özcan Tatari, “A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study”, JTL, c. 7, sy. 2, ss. 479–498, 2023, doi: 10.26650/JTL.2022.1096085.
ISNAD Eliiyi, Uğur vd. “A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study”. Journal of Transportation and Logistics 7/2 (Ocak 2023), 479-498. https://doi.org/10.26650/JTL.2022.1096085.
JAMA Eliiyi U, Uğurlu O, Özcan Tatari S. A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study. JTL. 2023;7:479–498.
MLA Eliiyi, Uğur vd. “A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study”. Journal of Transportation and Logistics, c. 7, sy. 2, 2023, ss. 479-98, doi:10.26650/JTL.2022.1096085.
Vancouver Eliiyi U, Uğurlu O, Özcan Tatari S. A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study. JTL. 2023;7(2):479-98.



The JTL is being published twice (in April and October of) a year, as an official international peer-reviewed journal of the School of Transportation and Logistics at Istanbul University.