Collaborative vehicle routing problem with rough location using extended ant colony optimization algorithm

Horizontal cooperation in logistics refers to several logistics service providers cooperating to accomplish common goals, which usually involves a collaborative vehicle routing problem. In this paper, we present a new collaborative vehicle routing problem with rough location (CVRPRL), which considers both the security of sharing detailed customer information and the configuration of shared resource. We utilize the rough location of the customer to replace the detailed customer location and introduce the concept of collaborative logistics sharing degree in the CVRPRL model. Subsequently, the cost allocation mechanism is designed based on an extended Shapley value, which allocates fixed costs and risks in different ways. Further, an extended ant colony optimization (EACO) algorithm is proposed to solve the CVRPRL. The EACO algorithm combines both large neighborhood search and local search strategies. Finally, we perform a series of simulation experiments to verify the effectiveness of EACO compared with other meta-heuristic algorithms.