Modeling truck scheduling problem at a cross-dock facility through a bi-objective bi-level optimization approach

Uncertainty and non-deterministic nature of the real world makes planning and scheduling in cross-docks a very complicated task for decision makers. These constant changes that happen all the time, often, lead to an increase in costs and/or a decrease in efficiency. Most of the uncertainty in cross-docks is caused by un-known truck arrival times. In this study we address the problem of scheduling incoming and outgoing trucks at a cross-dock facility, when vehicle arrival times are unknown, through a cost-stable scheduling strategy. Two meta-heuristics, MODE and NSGA-II, are used for solving the designed sample problems and are compared with a random search based genetic algorithm existing in the literature. Finally, performance of each algorithm is measured and analyzed using four metrics: quality, spacing, diversification and mean ideal distance. The results indicate that the proposed model MODE algorithm performs better in comparison with the other two methods.