Composite multi-objective optimization on a new collaborative vehicle routing problem with shared carriers and depots

The collaborative vehicle routing problem for logistic optimization is a fundamental part of the sustainable supply chain, and has increasingly been of concern in recent years. This study proposes a novel logistics collaboration model to address the collaborative vehicle routing problem that involves shared carriers and depots (CVRP-SCD). This is inspired by the fact that a depot often has orders for multiple carriers, and a carrier often delivers orders to multiple depots. This problem involves decreasing transportation distances and improving capacity utilization by extending supply-side unilateral logistics collaborations to simultaneously consider collaborations between supply- and demand-sides. Further, the proposed CVRP-SCD model uses a composite objective that is a weighted sum of four objectives — including quality, reliability, cost, and time — to accurately evaluate and analyze the efficiency improvement. An extended variable neighborhood search algorithm is presented based on three matrices, including the carrier collaboration matrix, depot collaboration matrix, and transportation sequence matrix. This algorithm aims to address the trade-offs among multiple objectives for the CVRP-SCD model by incorporating new operators based on specific features involved in the problem. Simulation experiments are performed on two sets of instances to evaluate the proposed algorithm’s effectiveness as well as the savings produced from the proposed model. Further, a comparison experiment between different objective optimization also verifies the rationality of the proposed composite multi-objective optimization. •Proposing a new collaborative vehicle routing model with shared carriers and depots.•Incorporating multiple objectives for an optimum modeling of the proposed model.•Presenting an extended variable neighborhood search algorithm based on three matrices.•Performing experiments to verify the extended algorithm’s effectiveness.•Performing experiments to evaluate the savings produced from the proposed model.