Abstract
The parcel delivery, which is one of the most important components in urban last-mile logistics, has been an increasing challenge with the rapid growth of e-commerce. To improve the efficiency of urban last-mile logistics, parcel locker has been widely advocated as an innovative alternative to the parcel delivery. In this paper, we concentrate on a multi-objective parcel locker network design problem (MOPLNDP), which is motivated by a real-life network design problem in urban last-mile logistics. The ultimate goal of MOPLNDP is to optimize the total cost of the network and the accessibility of customers to parcel locker stations, which is different from previous network design problems in logistics systems. Firstly, we establish the mathematical model of MOPLNDP based on which relevant problem-specific solution properties and heuristics are presented. Secondly, an active-learning Pareto evolutionary algorithm (ALPEA) is proposed to solve MOPLNDP. The proposed ALPEA relies on an elitist nondominated sorting method to discover promising search regions and a novel active-learning refinement mechanism to intensively find high-quality solutions in given search regions. Finally, experimental results on 70 random instances with different scales and a real-life case study are reported, from which the encouraging numerical results and management insights are obtained.
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