Adaptive large neighborhood search for the commodity constrained split delivery VRP

Wenjuan Gu 1 Diego Cattaruzza 1 Maxime Ogier 1 Frédéric Semet 1
1 INOCS - Integrated Optimization with Complex Structure
ULB - Université Libre de Bruxelles [Bruxelles], Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : This paper addresses the commodity constrained split delivery vehicle routing problem (C-SDVRP) where customers require multiple commodities. This problem arises when customers accept to be delivered separately. All commodities can be mixed in a vehicle as long as the vehicle capacity is satisfied. Multiple visits to a customer are allowed, but a given commodity must be delivered in one delivery. In this paper, we propose a heuristic based on the adaptive large neighborhood search (ALNS) to solve the C-SDVRP, with the objective of efficiently tackling medium and large sized instances. We take into account the distinctive features of the C-SDVRP and adapt several local search moves to improve a solution. Moreover, a mathematical programming based operator (MPO) that reassigns commodities to routes is used to improve a new global best solution. Computational experiments have been performed on benchmark instances from the literature. The results assess the efficiency of the algorithm, which can provide a large number of new best-known solutions in short computational times.
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Wenjuan Gu, Diego Cattaruzza, Maxime Ogier, Frédéric Semet. Adaptive large neighborhood search for the commodity constrained split delivery VRP. Computers and Operations Research, Elsevier, 2019, 112, pp.104761. ⟨10.1016/j.cor.2019.07.019⟩. ⟨hal-02317246⟩



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