An Efficient Multi-objective Evolutionary Algorithm for a Practical Dynamic Pickup and Delivery Problem

Junchuang Cai, Qingling Zhu, Qiuzhen Lin, Jianqiang Li, Jianyong Chen, Zhong Ming

Abstract: Recently, practical dynamic pickup and delivery problem (DPDP) has become a challenging problem in manufacturing enterprises, due to the uncertainties of customers’ requirements and production processes. This paper proposes a multi-objective evolutionary algorithm based on decomposition with four efficient local search strategies, called MOEA/D-ES, which can well solve a practical DPDP with constraints like dock, time windows, capacity and last-in-first-out loading. This method decomposes the problem under consideration into many subproblems. The experimental results on 40 real-world logistics problem instances, offered by Huawei in the competition at ICAPS 2021, validate the high efficiency and effectiveness of our proposed method.