SavedAdd your journal

Journals / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2018 / Cilt: 36 - Sayı: 3

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

Eren ÖZCEYLAN
Çağrı KOÇ
Mehmet ERBAŞ
Pages
741–766
Publication Date
2018-09-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper describes a Multi-Trip Heterogeneous Fixed Fleet Vehicle Routing Problem (MTHFFVRP) arising at one of the major retail chain in Turkey.
📊
Key Findings & Data:The solution method is applied on a real dataset of the supermarket store chain operates in Turkey.
💡
Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Geographical information system · vehicle routing · multi-trip

Abstract

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.

🔖

Cite This Article

ÖZCEYLAN, E., KOÇ, Ç., ERBAŞ, M. (2018). A GIS-BASED OPTIMIZATION METHOD FOR A VEHICLE ROUTING PROBLEM ARISING AT A SUPERMARKET STORE CHAIN. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 36 - Sayı: 3, 741–766. https://ajindex.com/articles/419332

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. ArcGIS, 2016. ArcGIS Network Analyst Tutorial
  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
  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,
  12. Glover, F.W., Laguna, M., 1998. Tabu search. Kluwer Academic, Massachusetts. Google Maps, 2017
  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