Queuing theory-based optimization research on the multi-objective transportation problem of fourth party logistics

Abstract

Fourth party logistics is an urgent need for economic and social development, and its research focuses on path optimization. Comprehensively considering the choice of third party logistics suppliers, routes, and transportation modes, this study proposes a multi-objective transportation optimization model based on queuing theory, which minimizes cost, time, and carbon emissions. In addition, to solve the problems of infeasible solutions in fourth party logistics route optimization (4plrp), a priority-random based strengthen elitist genetic algorithm is proposed. Experiments show that the algorithm has good convergence and stability in solving small/large 4plrp and is superior to six typical algorithms, such as the elite genetic algorithm.