Application of NSGA-II with local search to multi-dock cross-docking sheduling problem

Abstract

Cross-docking is now widely applied to trucking industry, for which the optimal schedule of the trucks is a crucial issue. In the cross-docking scheduling problem, the objectives of minimizing the operation cost and maximizing the possibility of punctuality are both important. In this paper, a non-dominated sorting genetic algorithm version II (NSGA-II) with a novel greedy local search strategy is proposed to solve the multi-objective optimization problem. NSGA-II can provide decision makers with flexible choices among the different trade-off solutions, while the local-search strategy is employed to accelerate the convergence speed. In the experiments, four criteria are applied to evaluate the strengths of the proposed algorithm. Experimental results on both small and large size of problems show the accuracy and efficiency of the propose strategy.