A novel approach for multi-objective truck scheduling problems in a cross-docking center.

Cross-docking implementation pursues different goals, including integrating transportation, shorter delivery time, and cost reduction. In this paper, we proposed an operational approach to schedule the trucks in a cross-docking system considering the breakdown probability of the trucks, capacity constraint, and earliness penalty under a just-in-time approach. The proposed objective functions, to be minimized, represent the total completion time and the outbound trucks' earliness and tardiness. As our problem is an NP-hard, we used genetic algorithm functions with a non-dominated sorting procedure, and particle swarm optimization algorithm to reach near-optimal solutions and compared these two algorithms using test problems with four different indexes; (1) quality (2) mean ideal distance, (3) diversity, and (4) the number of Pareto solutions. We performed sensitivity analyses to show the sensitivity of breakdown rate and acceptable truck earliness and tardiness parameters on the objective functions. Results show that, based on our metric measures, the non-dominated sorting genetic algorithm has a better Pareto boundary and performs better than the particle swarm optimization algorithm. Finally, sensitivity analyses are performed to study the effect of (1) an upper bound of earliness, (2) an upper bound for tardiness, and (3) a change of the failure rate on the objective functions. [ABSTRACT FROM AUTHOR]