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The fleet size and mix location-routing problem with time windows: Formulations and a heuristic algorithm

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  • Koç, Çağrı
  • Bektaş, Tolga
  • Jabali, Ola
  • Laporte, Gilbert
Abstract
This paper introduces the fleet size and mix location-routing problem with time windows (FSMLRPTW) which extends the location-routing problem by considering a heterogeneous fleet and time windows. The main objective is to minimize the sum of vehicle fixed cost, depot cost and routing cost. We present mixed integer programming formulations, a family of valid inequalities and we develop a powerful hybrid evolutionary search algorithm (HESA) to solve the problem. The HESA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle heterogeneous fleet dimensioning and location decisions. We evaluate the strengths of the proposed formulations with respect to their ability to find optimal solutions. We also investigate the performance of the HESA. Extensive computational experiments on new benchmark instances have shown that the HESA is highly effective on the FSMLRPTW.

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  • Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "The fleet size and mix location-routing problem with time windows: Formulations and a heuristic algorithm," European Journal of Operational Research, Elsevier, vol. 248(1), pages 33-51.
    • Handle: RePEc:eee:ejores:v:248:y:2016:i:1:p:33-51
    DOI: 10.1016/j.ejor.2015.06.082
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    5. Wu, Yuehui & Qureshi, Ali Gul & Yamada, Tadashi, 2022. "Adaptive large neighborhood decomposition search algorithm for multi-allocation hub location routing problem," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1113-1127.
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    9. Longlong Leng & Yanwei Zhao & Jingling Zhang & Chunmiao Zhang, 2019. "An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem," IJERPH, MDPI, vol. 16(11), pages 1-28, June.
    10. Darvish, Maryam & Archetti, Claudia & Coelho, Leandro C. & Speranza, M. Grazia, 2019. "Flexible two-echelon location routing problem," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1124-1136.
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    15. Chen Chao & Tian Zhihui & Yao Baozhen, 2019. "Optimization of two-stage location–routing–inventory problem with time-windows in food distribution network," Annals of Operations Research, Springer, vol. 273(1), pages 111-134, February.
    16. Vanga, Ratnaji & Venkateswaran, Jayendran, 2020. "Fleet sizing of reusable articles under uncertain demand and turnaround times," European Journal of Operational Research, Elsevier, vol. 285(2), pages 566-582.
    17. Wang, Sihan & Sun, Wei & Baldacci, Roberto & Elomri, Adel, 2025. "Exact solution of location–routing problems with heterogeneous fleet and weight-based carbon emissions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).
    18. Sina Abbasi & Maryam Moosivand & Ilias Vlachos & Mohammad Talooni, 2023. "Designing the Location–Routing Problem for a Cold Supply Chain Considering the COVID-19 Disaster," Sustainability, MDPI, vol. 15(21), pages 1-24, October.
    19. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "The impact of depot location, fleet composition and routing on emissions in city logistics," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 81-102.

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