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

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