A Promoted Hybrid Heuristic Algorithm for Two-dimensional Multi-depots Vehicle Routing Problem

In this paper, a multi-depots capacitated vehicle routing problem where client demand is composed of two-dimensional weighted items (2L-MDCVRP) is addressed. The problem calls for the minimization of the cost of transportation needed for the delivery of the goods demanded by the clients, and carried out by a fleet of vehicles based at several depots. The overall problem, denoted as 2L-MDCVRP, is NP-hard and it is very difficult to get a good performance solution in practice. We propose a quantum-behaved particle swarm optimization (QPSO) and exploration heuristic local search algorithm (EHLSA). It has been proved that particle swarm optimization is a very efficient approach for the CVRP, and the quantum-behaved can help particle swarm optimization to escape effectively from local optimum. Furthermore, we propose an exploration heuristic local search to solve the loading constraints in 2L-MDCVRP. The effectiveness of the proposed algorithm is demonstrated through computational experiments on benchmark instances. (Received, processed and accepted by the Chinese Representative Office.)