Araştırma Makalesi
BibTex RIS Kaynak Göster

A GIS-BASED OPTIMIZATION METHOD FOR A VEHICLE ROUTING PROBLEM ARISING AT A SUPERMARKET STORE CHAIN

Yıl 2018, Cilt: 36 Sayı: 3, 741 - 766, 01.09.2018

Öz

This paper describes a Multi-Trip Heterogeneous Fixed Fleet Vehicle Routing Problem (MTHFFVRP) arising at one of the major retail chain in Turkey. The paper presents a GIS-based optimization method, based on a tabu search algorithm, that can be used to store, analyze and visualize all data as well as model solutions in geographic format. The solution method is applied on a real dataset of the supermarket store chain operates in Turkey. The paper presents computational and managerial results by analyzing the trade-offs between various parameters such as demand, number of vehicles, vehicle speed and capacity, and also a single-trip version of the problem. According to the one of the results, the total en-route time is increased by 5.18%, 4.25% and 1.82%, when the capacity of each vehicle type is decreased by 30%, 20% and 10%, respectively.

Kaynakça

  • [1] ArcGIS, 2016. ArcGIS Network Analyst Tutorial. <http://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/about-the-network-analyst-tutorial-exercises.htm>.
  • [2] Baldacci, R., Battarra, M., Vigo, D. 2008. Routing a heterogeneous fleet of vehicles. In B. L. Golden, S. Raghavan, E. A. Wasil, eds. The Vehicle Routing Problem: Latest Advances and New Challenges (pp. 1–25). Springer, New York.
  • [3] BİM Inc., 2017. B˙IM Inc.’s hard-discount concept. <http://english.bim.com.tr/Category/627/bim-philosophy.aspx>.
  • [4] Bozkaya, B., Yanik, S., Balcisoy, S., 2010. A GIS-based optimization framework for competitive multi-facility location-routing problem. Networks and Spatial Economics 10, 297–320.
  • [5] Casas, I., Malik, A., Delmelle, E. M., Karwan, M. H., Batta, R., 2007. An automated network generation procedure for routing of unmanned aerial vehicles (UAVs) in a GIS environment. Networks and Spatial Economics 7, 153–176.
  • [6] Cattaruzza, D., Absi, N., Feillet, D., Vidal, T., 2014. A memetic algorithm for the multi trip vehicle routing problem. European Journal of Operational Research 236, 833–848.
  • [7] Cattaruzza, D., Absi, N., Feillet, D., 2016. Vehicle routing problems with multiple trips. 4OR, 1–37.
  • [8] Crainic, T. G., Gajpal, Y., Gendreau, M., 2015. Multi-zone multi-trip vehicle routing problem with time windows. INFOR: Information Systems and Operational Research 53, 49–67.
  • [9] Custodio, A. L., Oliveira, R. C., 2006. Redesigning distribution operations: a case study on integrating inventory management and vehicle routes design. International Journal of Logistics: Research and Applications 9, 169–187.
  • [10] Demirel, H., Korkutan, M., Shoman, W., Alganc, U., 2017. Geographic information system (GIS) based accessibility analysis for highway transportation. Sigma Journal of Engineering and Natural Sciences 8, 339–344.
  • [11] Fried, T., Munnich, L., Horan, T., Hilton, B., 2018. Evolving supply chains and local freight flows: A geographic information system analysis of Minnesota cereal grain movement. Transportation Research Record, <https://doi.org/10.1177/0361198118759952>.
  • [12] Glover, F.W., Laguna, M., 1998. Tabu search. Kluwer Academic, Massachusetts. Google Maps, 2017. <https://maps.google.com>.
  • [13] Koç C.¸ Karaoglan, I., 2016. The green vehicle routing problem: A heuristic based exact solution approach. Applied Soft Computing 39, 154–164.
  • [14] Koç C.¸ Bektas, T., Jabali, O., Laporte, G., 2015. A hybrid evolutionary algorithm for heterogeneous fleetvehicle routing problems with time windows. Computers & Operations Research, 64, 11–27.
  • [15] Koç C., Bektas, T., Jabali, O., Laporte, G., 2016. Thirty years of heterogeneous vehicle routing. European Journal of Operational Research 249, 1–21.
  • [16] Krichen, S., Faiz, S., Tlili, T., Tej, K., 2014. Tabu-based GIS for solving the vehicle routing problem. Expert Systems with Applications 41, 6483–6493.
  • [17] Laporte, G. 2009. Fifty years of vehicle routing. Transportation Science 43, 408–416.
  • [18] Min, H., Melachrinoudis, E., 2016. A model-based decision support system for solving vehicle routing and driver scheduling problems under hours of service regulations. International Journal of Logistics Research and Applications 19, 256–277.
  • [19] Mingozzi, A., Roberti, R., Toth, P., 2013. An exact algorithm for the multitrip vehicle routing problem.
  • [20] INFORMS Journal on Computing 25, 193–207.
  • [21] Olivera, A., Viera, O., 2007. Adaptive memory programming for the vehicle routing problem with multiple trips. Computers & Operations Research 34, 28–47.
  • [22] Pasha, U., Hoff, A., Hvattum, L. M., 2016. Simple heuristics for the multi-period fleet size and mix vehicle routing problem. INFOR: Information Systems and Operational Research 54, 97–120.
  • [23] Prins, C., 2002. Efficient heuristics for the heterogeneous fleet multitrip VRP with application to a large-scale real case. Journal of Mathematical Modelling and Algorithms 1, 135–150.
  • [24] Samanlioglu, F., 2013. A multi-objective mathematical model for the industrial hazardous waste locationrouting problem. European Journal of Operational Research 226, 332–340.
  • [25] Toth, P. Vigo, D., eds. 2014. Vehicle routing: Problems, methods, and applications. MOS-SIAM Series on Optimization, Philadelphia.
  • [26] Yanik, S., Bozkaya, B., deKervenoael, R., 2014. A new VRPPD model and a hybrid heuristic solution approach for e-tailing. European Journal of Operational Research 236, 879–890.
  • [27] Yu, B., Ma, N., Cai, W., Li, T., Yuan, X., Yao, B., 2013. Improved ant colony optimisation for the dynamic multi-depot vehicle routing problem. International Journal of Logistics Research and Applications 16, 144–157.
Yıl 2018, Cilt: 36 Sayı: 3, 741 - 766, 01.09.2018

Öz

Kaynakça

  • [1] ArcGIS, 2016. ArcGIS Network Analyst Tutorial. <http://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/about-the-network-analyst-tutorial-exercises.htm>.
  • [2] Baldacci, R., Battarra, M., Vigo, D. 2008. Routing a heterogeneous fleet of vehicles. In B. L. Golden, S. Raghavan, E. A. Wasil, eds. The Vehicle Routing Problem: Latest Advances and New Challenges (pp. 1–25). Springer, New York.
  • [3] BİM Inc., 2017. B˙IM Inc.’s hard-discount concept. <http://english.bim.com.tr/Category/627/bim-philosophy.aspx>.
  • [4] Bozkaya, B., Yanik, S., Balcisoy, S., 2010. A GIS-based optimization framework for competitive multi-facility location-routing problem. Networks and Spatial Economics 10, 297–320.
  • [5] Casas, I., Malik, A., Delmelle, E. M., Karwan, M. H., Batta, R., 2007. An automated network generation procedure for routing of unmanned aerial vehicles (UAVs) in a GIS environment. Networks and Spatial Economics 7, 153–176.
  • [6] Cattaruzza, D., Absi, N., Feillet, D., Vidal, T., 2014. A memetic algorithm for the multi trip vehicle routing problem. European Journal of Operational Research 236, 833–848.
  • [7] Cattaruzza, D., Absi, N., Feillet, D., 2016. Vehicle routing problems with multiple trips. 4OR, 1–37.
  • [8] Crainic, T. G., Gajpal, Y., Gendreau, M., 2015. Multi-zone multi-trip vehicle routing problem with time windows. INFOR: Information Systems and Operational Research 53, 49–67.
  • [9] Custodio, A. L., Oliveira, R. C., 2006. Redesigning distribution operations: a case study on integrating inventory management and vehicle routes design. International Journal of Logistics: Research and Applications 9, 169–187.
  • [10] Demirel, H., Korkutan, M., Shoman, W., Alganc, U., 2017. Geographic information system (GIS) based accessibility analysis for highway transportation. Sigma Journal of Engineering and Natural Sciences 8, 339–344.
  • [11] Fried, T., Munnich, L., Horan, T., Hilton, B., 2018. Evolving supply chains and local freight flows: A geographic information system analysis of Minnesota cereal grain movement. Transportation Research Record, <https://doi.org/10.1177/0361198118759952>.
  • [12] Glover, F.W., Laguna, M., 1998. Tabu search. Kluwer Academic, Massachusetts. Google Maps, 2017. <https://maps.google.com>.
  • [13] Koç C.¸ Karaoglan, I., 2016. The green vehicle routing problem: A heuristic based exact solution approach. Applied Soft Computing 39, 154–164.
  • [14] Koç C.¸ Bektas, T., Jabali, O., Laporte, G., 2015. A hybrid evolutionary algorithm for heterogeneous fleetvehicle routing problems with time windows. Computers & Operations Research, 64, 11–27.
  • [15] Koç C., Bektas, T., Jabali, O., Laporte, G., 2016. Thirty years of heterogeneous vehicle routing. European Journal of Operational Research 249, 1–21.
  • [16] Krichen, S., Faiz, S., Tlili, T., Tej, K., 2014. Tabu-based GIS for solving the vehicle routing problem. Expert Systems with Applications 41, 6483–6493.
  • [17] Laporte, G. 2009. Fifty years of vehicle routing. Transportation Science 43, 408–416.
  • [18] Min, H., Melachrinoudis, E., 2016. A model-based decision support system for solving vehicle routing and driver scheduling problems under hours of service regulations. International Journal of Logistics Research and Applications 19, 256–277.
  • [19] Mingozzi, A., Roberti, R., Toth, P., 2013. An exact algorithm for the multitrip vehicle routing problem.
  • [20] INFORMS Journal on Computing 25, 193–207.
  • [21] Olivera, A., Viera, O., 2007. Adaptive memory programming for the vehicle routing problem with multiple trips. Computers & Operations Research 34, 28–47.
  • [22] Pasha, U., Hoff, A., Hvattum, L. M., 2016. Simple heuristics for the multi-period fleet size and mix vehicle routing problem. INFOR: Information Systems and Operational Research 54, 97–120.
  • [23] Prins, C., 2002. Efficient heuristics for the heterogeneous fleet multitrip VRP with application to a large-scale real case. Journal of Mathematical Modelling and Algorithms 1, 135–150.
  • [24] Samanlioglu, F., 2013. A multi-objective mathematical model for the industrial hazardous waste locationrouting problem. European Journal of Operational Research 226, 332–340.
  • [25] Toth, P. Vigo, D., eds. 2014. Vehicle routing: Problems, methods, and applications. MOS-SIAM Series on Optimization, Philadelphia.
  • [26] Yanik, S., Bozkaya, B., deKervenoael, R., 2014. A new VRPPD model and a hybrid heuristic solution approach for e-tailing. European Journal of Operational Research 236, 879–890.
  • [27] Yu, B., Ma, N., Cai, W., Li, T., Yuan, X., Yao, B., 2013. Improved ant colony optimisation for the dynamic multi-depot vehicle routing problem. International Journal of Logistics Research and Applications 16, 144–157.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Eren Özceylan Bu kişi benim 0000-0002-5213-6335

Çağrı Koç Bu kişi benim 0000-0002-7377-204X

Mehmet Erbaş Bu kişi benim 0000-0002-3002-6135

Yayımlanma Tarihi 1 Eylül 2018
Gönderilme Tarihi 24 Şubat 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 36 Sayı: 3

Kaynak Göster

Vancouver Özceylan E, Koç Ç, Erbaş M. A GIS-BASED OPTIMIZATION METHOD FOR A VEHICLE ROUTING PROBLEM ARISING AT A SUPERMARKET STORE CHAIN. SIGMA. 2018;36(3):741-66.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/