Many supply chain stakeholders rely on the cross-docking concept, according to which products delivered in specific transportation management units to the cross-docking terminal (CDT) undergo decomposition, sorting based on the end customer preferences, consolidation, and then transported to the final destinations. Scheduling of the inbound and outbound trucks for service at the CDT doors is considered as one of the convoluted decision problems faced by the CDT operators. This study proposes a new Adaptive Polyploid Memetic Algorithm (APMA) for the problem of scheduling CDT trucks that can assist with proper CDT operations planning. APMA directly relies on the polyploidy concept, where copies of the parent chromosomes (i.e., solutions) are stored before performing the crossover operations and producing the offspring chromosomes. The number of chromosome copies is controlled through the adaptive polyploid mechanism based on the objective function improvements achieved and computational time changes. Moreover, a number of problem-specific hybridization techniques are used within the algorithm to facilitate the search process. Computational experiments show that the application of adaptive polyploidy alone may not be sufficient for the considered decision problem. Hybridization techniques that directly consider problem-specific properties are required in order to improve solution quality at convergence. Furthermore, the APMA algorithm developed in this article substantially outperforms some of the well-known state of the art metaheuristics with regards to solution quality and returns truck schedules that have lower total truck service cost.

许多供应链利益相关者都依赖交叉配送概念，根据这种概念，特定运输管理单元中交付给交叉配送终端（CDT）的产品会进行分解，根据最终客户的偏好进行分类，合并，然后再运输到最终目的地。在CDT门口安排进出卡车的服务安排，被认为是CDT运营商面临的棘手的决策问题之一。这项研究针对调度CDT卡车的问题提出了一种新的自适应多倍体模因算法（APMA），可以帮助正确进行CDT运营计划。APMA直接依赖于多倍体概念，在执行交叉操作并产生后代染色体之前，应先存储亲本染色体（即溶液）的副本。基于实现的目标函数的改进和计算时间的变化，通过自适应多倍体机制控制染色体的拷贝数。此外，在算法中使用了许多特定于问题的杂交技术来促进搜索过程。计算实验表明，仅应用自适应多倍体可能不足以解决所考虑的决策问题。为了提高收敛时的解决方案质量，需要直接考虑特定问题属性的杂交技术。此外，在解决方案质量方面，本文开发的APMA算法在本质上优于某些众所周知的元启发式方法，并且可以返回卡车总服务成本较低的卡车时间表。