 An inverse optimization approach for a capacitated vehicle routing problemLu Chen, Yuyi Chen and André Langevin European Journal of Operational Research, 2021, vol. 295, issue 3, 1087-1098 Abstract: The capacitated vehicle routing problem studied in this paper stems from an e-commerce company in China. Efficient delivery is crucial for the logistic service of the company. It was observed that experienced drivers (experts) planned better delivery routes than those from optimization tools. This is largely due to the fact that the objectives (or the cost matrices) in real life are highly complicated and the experts use more practical objectives while making decisions. In this paper, we propose an inverse optimization formulation to derive a proper cost matrix by learning from the experts experience. Thus, the routing model with respect to the learned cost matrix could provide solutions as good as those given by experts. A multiplicative weights updates algorithm is applied to achieve a fast and convergent learning process. Experimental analyses based on randomly generated instances and real-world instances demonstrate the effectiveness of the approach. Keywords: Routing; Inverse optimization; On-line learning; Last-mile delivery (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:295:y:2021:i:3:p:1087-1098 DOI: 10.1016/j.ejor.2021.03.031 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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