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Locating Collection and Delivery Points Using the p -Median Location Problem

Author

Listed:
  • Snežana Tadić

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Beograd, Serbia)

  • Mladen Krstić

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Beograd, Serbia)

  • Željko Stević

    (Faculty of Transport and Traffic Engineering Doboj, University of East Sarajevo, 71123 Lukavica, Bosnia and Herzegovina)

  • Miloš Veljović

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Beograd, Serbia)

Abstract

Background : Possible solutions to overcome the many challenges of home delivery are collection and delivery points (CDPs). In addition to commercial facilities, the role of CDPs can also be played by users’ households, providing a crowd storage service. Key decisions regarding CDPs relate to their location, as well as the allocation of users to selected locations, so that the distance of users from CDPs is minimal. Methods : In this paper, the described problem is defined as a p -median problem and solved for the area of the city of Belgrade, using the heuristic “greedy” and the simulated annealing algorithm. Results : Fifty locations of CDPs were selected and the users allocated to them were distributed in over 950 zones. The individual distances between users and the nearest CDPs and the sum of these distances, multiplied by the number of requests, were obtained. An example of modification of the number of CDPs is presented as a way of obtaining solutions that correspond to different preferences of operators and/or users in terms of their distances from the CDPs. Conclusions : User households can be used as CDPs to achieve various benefits. Locating CDPs, i.e., selecting households, can be solved as a p -median problem, using a combination of heuristic and metaheuristic algorithms. In addition, by modifying the number of medians, the total and average distances between users and CDPs can be better managed. The main contributions of the paper are the establishment of users’ households as potential locations of CDPs, the establishment of a framework for analysis of impact of the number of CDPs on the sum and average distances from the customers, as well as the creation of a basis for upgrading and modifying the model for implementation in the business practice.

Suggested Citation

  • Snežana Tadić & Mladen Krstić & Željko Stević & Miloš Veljović, 2023. "Locating Collection and Delivery Points Using the p -Median Location Problem," Logistics, MDPI, vol. 7(1), pages 1-17, February.
    • Handle: RePEc:gam:jlogis:v:7:y:2023:i:1:p:10-:d:1058811
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    1. Rolland, Erik & Schilling, David A. & Current, John R., 1997. "An efficient tabu search procedure for the p-Median Problem," European Journal of Operational Research, Elsevier, vol. 96(2), pages 329-342, January.
    2. Zhen-Hua Che & Tzu-An Chiang & Yun-Jhen Luo, 2022. "Multiobjective Optimization for Planning the Service Areas of Smart Parcel Locker Facilities in Logistics Last Mile Delivery," Mathematics, MDPI, vol. 10(3), pages 1-22, January.
    3. Cheng Cheng & Takanori Sakai & André Alho & Lynette Cheah & Moshe Ben-Akiva, 2021. "Exploring the Relationship between Locational and Household Characteristics and E-Commerce Home Delivery Demand," Logistics, MDPI, vol. 5(2), pages 1-13, May.
    4. Alfred A. Kuehn & Michael J. Hamburger, 1963. "A Heuristic Program for Locating Warehouses," Management Science, INFORMS, vol. 9(4), pages 643-666, July.
    5. Devari, Aashwinikumar & Nikolaev, Alexander G. & He, Qing, 2017. "Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 105-122.
    6. Jesica Armas & Angel A. Juan & Joan M. Marquès & João Pedro Pedroso, 2017. "Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1161-1176, October.
    7. Ahmad Alharbi & Chantal Cantarelli & Andrew Brint, 2022. "Crowd Models for Last Mile Delivery in an Emerging Economy," Sustainability, MDPI, vol. 14(3), pages 1-20, January.
    8. Mladenovic, Nenad & Brimberg, Jack & Hansen, Pierre & Moreno-Perez, Jose A., 2007. "The p-median problem: A survey of metaheuristic approaches," European Journal of Operational Research, Elsevier, vol. 179(3), pages 927-939, June.
    9. Wangshu Mu & Daoqin Tong, 2020. "On solving large p-median problems," Environment and Planning B, , vol. 47(6), pages 981-996, July.
    10. Yuen, Kum Fai & Wang, Xueqin & Ng, Li Ting Wendy & Wong, Yiik Diew, 2018. "An investigation of customers’ intention to use self-collection services for last-mile delivery," Transport Policy, Elsevier, vol. 66(C), pages 1-8.
    11. Roberta Alves & Renato da Silva Lima & David Custódio de Sena & Alexandre Ferreira de Pinho & José Holguín-Veras, 2019. "Agent-Based Simulation Model for Evaluating Urban Freight Policy to E-Commerce," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    12. Mehmann, Jens & Frehe, Volker & Teuteberg, Frank, 2015. "Crowd Logistics − A Literature Review and Maturity Model," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg Internationa, volume 20, pages 117-145, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    13. Osman Alp & Erhan Erkut & Zvi Drezner, 2003. "An Efficient Genetic Algorithm for the p-Median Problem," Annals of Operations Research, Springer, vol. 122(1), pages 21-42, September.
    14. S. L. Hakimi, 1964. "Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph," Operations Research, INFORMS, vol. 12(3), pages 450-459, June.
    15. L’udmila Jánošíková & Miloš Herda & Michal Haviar, 2017. "Hybrid genetic algorithms with selective crossover for the capacitated p-median problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(3), pages 651-664, September.
    16. Bruce L. Golden & Christopher C. Skiscim, 1986. "Using simulated annealing to solve routing and location problems," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 33(2), pages 261-279, May.
    17. Agnieszka Szmelter-Jarosz & Jagienka Rześny-Cieplińska, 2019. "Priorities of Urban Transport System Stakeholders According to Crowd Logistics Solutions in City Areas. A Sustainability Perspective," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    18. S. L. Hakimi, 1965. "Optimum Distribution of Switching Centers in a Communication Network and Some Related Graph Theoretic Problems," Operations Research, INFORMS, vol. 13(3), pages 462-475, June.
    19. Tong, Daoqin & Ren, Fang & Mack, James, 2012. "Locating farmers’ markets with an incorporation of spatio-temporal variation," Socio-Economic Planning Sciences, Elsevier, vol. 46(2), pages 149-156.
    20. Wang, Yuan & Zhang, Dongxiang & Liu, Qing & Shen, Fumin & Lee, Loo Hay, 2016. "Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 279-293.
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