A three-stage optimization method for logistics management - Fan, Yunqi (2016)
Logistics management is a complex engineering optimization problem involving multi-objective formulations. The management competences such as vehicle routes planning, delivery and pick-up jobs scheduling, and distribution centers location design are crucial skills for each logistics company. With the rapid development of economy, there is a significant demand from logistics industry to solve the above mentioned problems in a more intelligent way. In this project, a three-echelon model is proposed to address several traditional distinct logistics optimization problems in an integrated manner. In the first phase, a large number of delivery tasks are grouped into several segments for vehicles assignment using a set of clustering rules. After segmentation, each subgroup could be simplified as an independent travelling salesman problem (TSP). In the second phase, the customized genetic algorithm is employed to solve the TSP. Computational experiments conducted in Matlab show that this algorithm can obtain a generally satisfying solution for the TSP. Finally, a set of distribution centers are selected from the demand sites using immune-genetic algorithm to further enhance the distribution efficiency in the last phase. This multi-stage method improves the solution’s performance, and can be extended to deal with large-scale problems.