Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows

Yong Wang, Jiayi Zhe, Xiuwen Wang, Yaoyao Sun and Haizhong Wang
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Yong Wang: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Jiayi Zhe: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Xiuwen Wang: School of Management, Shanghai University, Shanghai 200444, China
Yaoyao Sun: School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Haizhong Wang: School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97330, USA

Sustainability, 2022, vol. 14, issue 11, 1-37

Abstract: Dynamic customer demands impose new challenges for vehicle routing optimization with time windows, in which customer demands appear dynamically within the working periods of depots. The delivery routes should be adjusted for the new customer demands as soon as possible when new customer demands emerge. This study investigates a collaborative multidepot vehicle routing problem with dynamic customer demands and time windows (CMVRPDCDTW) by considering resource sharing and dynamic customer demands. Resource sharing of multidepot across multiple service periods can maximize logistics resource utilization and improve the operating efficiency of delivery logistics networks. A bi-objective optimization model is constructed to optimize the vehicle routes while minimizing the total operating cost and number of vehicles. A hybrid algorithm composed of the improved k -medoids clustering algorithm and improved multiobjective particle swarm optimization based on the dynamic insertion strategy (IMOPSO-DIS) algorithm is designed to find near-optimal solutions for the proposed problem. The improved k -medoids clustering algorithm assigns customers to depots in terms of specific distances to obtain the clustering units, whereas the IMOPSO-DIS algorithm optimizes vehicle routes for each clustering unit by updating the external archive. The elite learning strategy and dynamic insertion strategy are applied to maintain the diversity of the swarm and enhance the search ability in the dynamic environment. The experiment results with 26 instances show that the performance of IMOPSO-DIS is superior to the performance of multiobjective particle swarm optimization, nondominated sorting genetic algorithm-II, and multiobjective evolutionary algorithm. A case study in Chongqing City, China is implemented, and the related results are analyzed. This study provides efficient optimization strategies to solve CMVRPDCDTW. The results reveal a 32.5% reduction in total operating costs and savings of 29 delivery vehicles after optimization. It can also improve the intelligence level of the distribution logistics network, promote the sustainable development of urban logistics and transportation systems, and has meaningful implications for enterprises and government to provide theoretical and decision supports in economic and social development.

Keywords: multidepot vehicle routing problem with time windows; dynamic customer demands; resource sharing; IMOPSO-DIS algorithm; collaborative network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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