A tailored Benders decomposition approach for last-mile delivery with autonomous robots

Laurent Alfandari, Ivana Ljubić and Marcos De Melo da Silva

European Journal of Operational Research, 2022, vol. 299, issue 2, 510-525

Abstract: This work addresses an operational problem of a logistics service provider that consists of finding an optimal route for a vehicle carrying customer parcels from a central depot to selected facilities, from where autonomous devices like robots are launched to perform last-mile deliveries. The objective is to minimize a tardiness indicator based on the customer delivery deadlines. This article provides a better understanding of how three major tardiness indicators can be used to improve the quality of service by minimizing the maximum tardiness, the total tardiness, or the number of late deliveries. We study the problem complexity, devise a unifying Mixed Integer Programming formulation and propose an efficient branch-and-Benders-cut scheme to deal with instances of realistic size. Numerical results show that this novel Benders approach with a tailored combinatorial algorithm for generating Benders cuts largely outperforms all other alternatives. In our managerial study, we vary the number of available facilities, the coverage radius of autonomous robots and their speed, to assess their impact on the quality of service and environmental costs.

Keywords: Integer Programming; Last-mile delivery; Self-driving robots; Benders decomposition (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:299:y:2022:i:2:p:510-525

DOI: 10.1016/j.ejor.2021.06.048

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