本文研究的是带有车辆容量限制以及时间窗口约束的越库配送车辆调度问题,该问题旨在通过车辆与仓门的合理分配来实现越库内部货物的最优调度从而达到高效的运作目标。由于该问题是强NP难的问题,本文基于遗传算法的思想,设计了单点交叉算子和两点交叉算子,并采用"交叉行为自适应选择机制"设计了一种自适应遗传算法来进行求解。在数值实验中,本文将该自适应遗传算法与分别采用单种交叉算子的遗传算法进行算法性能的比较,通过大量不同规模的数值算例的结果对比发现在这三种算法中,采用自适应机制的遗传算法在最终解的质量上总体表现最好,表明该算法对于求解此类问题具有良好的性能,同时也表明该自适应机制对于提升算法性能上具有显著的促进作用。As a just-in-time(JIT) logistics technology, crossdock refers to the operation of pickup-delivery and order-dealt activities at any intermediate points between upstream suppliers and downstream customers. Those intermediate points include transshipment warehouse or distribution center. They are used to achieve the elimination of goods storage, which can be temporarily stored and generally not more than one or two days, and can greatly reduce response time and inventory cost as well. Crossdock is increasingly being used to optimize the distribution network of supply chain, which can significantly reduce company's inventory levels by integrating inventory management strategy and distribution strategy. Thus, crossdock can reduce not only inventory management cost and cargo loss rate, but also speed up cash flow. It is reported that many well-known multi-national companies, such as Wal-Mart, Unilever, Dell, and Cisco, have implemented this technique successfully. The truck scheduling strategy plays a very important role in operations within the crossdock distribution center, which has a significant impact on the operational efficiency of crossdock. A good truck scheduling strategy can increase operational efficiency, improve customer service level, and reduce the total cost. Because crossdock-related problems are a hot topic in both academic and industrial areas nowadays, there is a lot of related research works in the previous literatures, which can be mainly divided into five categories:(1) the facility location problem of crossdocks;(2) the network flow problem in a multiple-crossdock distribution system;(3) the layout design problem within a crossdock;(4) the resource allocation problem within a crossdock;(5) the scheduling problem within a crossdock. In this paper, we try to extend the truck scheduling problem in a crossdock and consider the capacity constraint of outbound trucks as well as that of the crossdock. In reality, the number of inbound/outbound docks is limited, and the distance between inbound and outbound docks is different, and the amount of transshipment cargos and the time window for each inbound/outbound truck is different. Therefore, it is important to schedule these trucks to guarantee the number of trucks assigned to a door as many as possible so that cargos can be transshipped as many as possible and the transfer distance inside the crossdock can be minimum. These considerations will have a great impact on the efficiency and cost of crossdock operations. This paper tries to find a truck scheduling with a minimum cost under the time windows and capacity constraints of outbound truck and crossdock in order to increase the efficiency of crossdock operations. In the first part, we introduce this truck scheduling problem including constraints, assumptions, notations as well as decision variables, by which we propose a 0-1 Integer Programming Model. This kind of optimization problem is NP-hard in the strong sense. As a result, we try to develop a genetic algorithm by adopting single-point crossover(SPCO) and double-point crossover(DPCO) adaptively to solve it. In the second part, we introduce the steps of designing our adaptive genetic algorithm. We design the chromosome and adopt heuristic method to obtain a group of initial solutions at first rather than generate them randomly in order to obtain good quality ones because the quality of the initial solutions will influence the efficiency of the genetic algorithm greatly. SPCO and DPCO are developed as well as probability mutation operator. After that, an adaptive scheme is proposed to adopt the two crossover operators adaptively. The third part is numerical experiments, where we compare the performance of the adaptive genetic algorithm with those adopting SPCO and DPCO, respectively. We not only show the generation procedure of those parameters, but also develop three categories experiments including large, medium and small scale instances respectively. The results show that the adaptive genetic algorithm has the best performance in terms of final near-optimal solution quality in all scenarios. These findings mean that the proposed adaptive genetic algorithm is a good way to solve this kind of truck scheduling problem. Moreover, it shows that this adaptive scheme can improve the efficiency of the genetic algorithm for this problem. In summary, this paper tries to resolve the truck scheduling problem with the time windows and capacity constraints of outbound truck and crossdock, and develops an adaptive genetic algorithm to solve it. The numerical experiments show that our algorithm outperforms the others with a single crossover operator. This suggests that this adaptive genetic algorithm is an efficient way to solve this kind of problem.国家自然科学基金资助项目(71371158,71301032);; 教育部“新世纪优秀人才支持计划”资助项目(NCET-10-0712);; 中央高校基本科研业务费资金资助项目(2012221011
