An am-TSPTW transformation and a RTS algorithm for commodity selection and vehicle routing planning in full truckload industry

This paper opts for a metaheuristic for the commodity selection and vehicle routing planning with full truckloads in an empty return context. The problem consists of selecting a subset of commodities available in the transportation network and assigning them to optimal routes for truckers in their return journeys to reduce the empty miles. The objective is to increase truckers' total profit while respecting the constraints of availability and time windows. To simplify the complex constraints of the problem, we transform it into a kind of am-TSPTW. Then, based on this transformation, we adapt a reactive tabu search (RTS) to solve it. The offered RTS is assessed and compared with the CPLEX solver on randomly generated data from the literature. The results prove that the RTS significantly beats the CPLEX regarding solution quality and CPU time.