Abstract
Delivering goods in an efficient and cost-effective way is always a challenging problem in logistics. In this paper, the multi-depot vehicle routing is focused. To cope with the conflicting requirements, an advanced multi-objective evolutionary algorithm is proposed. Local-search empowered genetic operations and a fuzzy cluster-based initialization process are embedded in the design for performance enhancement. Its outperformance, as compared to existing alternatives, is confirmed by extensive simulations based on numerical datasets and real traffic conditions with various customers’ distributions.
Highlights
The Internet shopping enjoys a surge of popularity in recent years, supported by the advances of mobile communications and Internet technology
VRP can be generalized as problem with multiple depots, referred as multi-depot vehicle routing problem (MDVRP)[4,5]
We focus on the MDVRP with delivery service time
Summary
The Internet shopping enjoys a surge of popularity in recent years, supported by the advances of mobile communications and Internet technology. For a one-day global shopping event, it could generate gross merchandise value up to USD17.8 billions[1]. The huge amounts of transactions imply billions of goods need to be delivered to customers all over the world in an efficient way, which is an opportunity and a challenge for the logistics industry. In face of the challenge, many research efforts have been made, while vehicle routing problem (VRP)[2,3] is a related topic in operational research, focusing on domestic delivery. VRP can be generalized as problem with multiple depots, referred as multi-depot vehicle routing problem (MDVRP)[4,5]. In MDVRP, customers are firstly assigned to one of the depots, and the vehicle at the depot is deployed to serve the dedicated set of customers based on a designated routing path
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