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Rich vehicle routing problem with last-mile outsourcing decisions

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  • Alcaraz, Juan J.
  • Caballero-Arnaldos, Luis
  • Vales-Alonso, Javier

Abstract

This paper addresses a Rich Vehicle Routing Problem (RVRP) characterized by the following attributes: long-haul transport, driver hours regulation, incompatibility among goods, multiple depots and pickup locations, heterogeneous vehicles, time windows, and outsourcing decisions for last-mile delivery. Addressing the latter aspect is especially challenging, since it requires substantial changes in the algorithms for generating initial feasible solutions (construction heuristic) and for modifying existing solutions (improvement heuristics). Our work develops new heuristics adapted to the above attributes, and evaluates their performance in combination with common solving metaheuristics. We also assess the impact and the cost-effectiveness of the outsourcing attribute.

Suggested Citation

  • Alcaraz, Juan J. & Caballero-Arnaldos, Luis & Vales-Alonso, Javier, 2019. "Rich vehicle routing problem with last-mile outsourcing decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 263-286.
    • Handle: RePEc:eee:transe:v:129:y:2019:i:c:p:263-286
    DOI: 10.1016/j.tre.2019.08.004
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    3. Naihui Wang & Yulong Pei & Hao Fu, 2022. "Public Acceptance of Last-Mile Shuttle Bus Services with Automation and Electrification in Cold-Climate Environments," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    4. Nadia Giuffrida & Jenny Fajardo-Calderin & Antonio D. Masegosa & Frank Werner & Margarete Steudter & Francesco Pilla, 2022. "Optimization and Machine Learning Applied to Last-Mile Logistics: A Review," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
    5. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    6. Alcaraz, Juan J. & Losilla, Fernando & Caballero-Arnaldos, Luis, 2022. "Online model-based reinforcement learning for decision-making in long distance routes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    7. Daiane Maria Genaro Chiroli & Sérgio Fernando Mayerle & João Neiva Figueiredo, 2022. "Using state-space shortest-path heuristics to solve the long-haul point-to-point vehicle routing and driver scheduling problem subject to hours-of-service regulatory constraints," Journal of Heuristics, Springer, vol. 28(1), pages 23-59, February.
    8. Sun, Yanshuo & Kirtonia, Sajeeb & Chen, Zhi-Long, 2021. "A survey of finished vehicle distribution and related problems from an optimization perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    9. Yang, Fei & Dai, Ying & Ma, Zu-Jun, 2020. "A cooperative rich vehicle routing problem in the last-mile logistics industry in rural areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    10. Subrat Sarangi & Sudipta Sarangi & Nasim S. Sabounchi, 2023. "How managerial perspectives affect the optimal fleet size and mix model: a multi-objective approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 1-23, March.

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