Cross-docking is a logistics process in which products are unloaded through receiving docks and then transferred to shipping docks with almost no storage in between. In this paper, a mixed integer linear programming model (MILP) is proposed to optimise the scheduling, storage, assignment and sequencing of trucks at receiving and shipping docks for a problem inspired from a multiple-door cross-dock facility of an industrial partner with multiple temporary storage zones. The multiple storage zones are separated and located in the centre of the cross-dock handling different types of products. The objective is to minimise the total tardiness of inbound and outbound trucks. A heuristic (H) is proposed to find an initial solution. Then, three meta-heuristics are developed, namely Random Search (RS), Tabu Search (TS) and Simulated Annealing (SA) to improve the scheduling of trucks in order to minimise the tardiness of inbound and outbound trucks. Experimental results indicate that the three meta-heuristics (RS, TS and SA) are able to find good quality results within reasonable computational times. Finally, since SA showed the best performance compared to RS and TS, it was chosen to be compared to the current manual method using discrete event simulation.

交叉配送是一种物流过程，在该过程中，产品通过收货码头卸货，然后转移到货运码头，而两者之间几乎没有任何存储。本文提出了一种混合整数线性规划模型（MILP），以针对受工业合作伙伴的多门跨码头设施启发而产生的问题，优化卡车在收货和发货码头的调度，存储，分配和排序。多个临时存储区。多个存储区是分开的，位于跨码头的中心，可处理不同类型的产品。目的是最大程度地减少入站和出站卡车的总拖延时间。建议使用启发式（H）来找到初始解。然后，开发了三种元启发法，即随机搜索（RS），禁忌搜索（TSU）和模拟退火（SA）改进了卡车的调度，以最大程度地减少进出卡车的拖车时间。实验结果表明，三种元启发式算法（RS，TS和SA）能够在合理的计算时间内找到良好的质量结果。最后，由于SA与RS和TS相比表现出最好的性能，因此被选择与使用离散事件模拟的当前手动方法进行比较。