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Multi-period capacitated facility location under delayed demand satisfaction

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  • Correia, Isabel
  • Melo, Teresa

Abstract

We address an extension of the classical multi-period facility location problem in which customers are sensitive to delivery lead times. Accordingly, two customer segments are considered. The first segment comprises customers that require timely demand satisfaction, whereas customers accepting delayed deliveries make up the second segment. Each customer belonging to the latter segment specifies a maximum delivery time. A tardiness penalty is incurred to each unit of demand that is not satisfied on time. In the problem that we study, a network is already in place with a number of facilities being operated at fixed locations. The network can be expanded by establishing new facilities at a finite set of potential sites and selecting their capacity levels from a set of available discrete sizes. In addition, existing facilities may be closed over the time horizon. Two mixed-integer linear programming formulations are proposed to re-design the network at minimum cost and a theoretical comparison of their linear relaxations is provided. We also extend the mathematical models to the case in which each customer accepting delayed demand satisfaction requires late shipments to occur at most once over the delivery lead time. To gain insight into how challenging these problems are to solve, a computational study is performed with randomly generated instances and using a general-purpose solver. Useful insights are derived from analyzing the impact of different delivery lead time restrictions on the network structure and cost.

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  • Correia, Isabel & Melo, Teresa, 2016. "Multi-period capacitated facility location under delayed demand satisfaction," European Journal of Operational Research, Elsevier, vol. 255(3), pages 729-746.
  • Handle: RePEc:eee:ejores:v:255:y:2016:i:3:p:729-746
    DOI: 10.1016/j.ejor.2016.06.039
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    1. Correia, Isabel & Melo, Teresa, 2016. "A computational comparison of formulations for a multi-period facility location problem with modular capacity adjustments and flexible demand fulfillment," Technical Reports on Logistics of the Saarland Business School 11, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    2. Marianov, Vladimir & Eiselt, H.A. & Lüer-Villagra, Armin, 2018. "Effects of multipurpose shopping trips on retail store location in a duopoly," European Journal of Operational Research, Elsevier, vol. 269(2), pages 782-792.
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    4. Laureano F. Escudero & Celeste Pizarro Romero, 2017. "On solving a large-scale problem on facility location and customer assignment with interaction costs along a time horizon," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 601-622, October.
    5. Fattahi, Mohammad & Govindan, Kannan & Keyvanshokooh, Esmaeil, 2017. "Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 176-200.
    6. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    7. Han, Jialin & Zhang, Jiaxiang & Zeng, Bing & Mao, Mingsong, 2021. "Optimizing dynamic facility location-allocation for agricultural machinery maintenance using Benders decomposition," Omega, Elsevier, vol. 105(C).
    8. S. Nobakhtian & A. Raeisi Dehkordi, 2018. "A fast algorithm for the rectilinear distance location problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 88(1), pages 81-98, August.

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