This paper investigates a new automatic guided vehicle scheduling problem with pickup and delivery from the goods handling process in a matrix manufacturing workshop with multi-variety and small-batch production. The problem aims to determine a solution that maximizes customer satisfaction while minimizing distribution cost. For this purpose, a multi-objective mixed-integer linear programming model is first formulated. Then an effective multi-objective evolutionary algorithm is developed for solving the problem. In the algorithm, a constructive heuristic is presented and incorporated into the population initialization. A multi-objective local search based on an ideal-point is used to enforce the exploitation capability. A novel two-point crossover operator is designed to make full use of valuable information collected in the non-dominated solutions. A restart strategy is proposed to avoid the algorithm trapping into a local optimum. At last, a series of comparative experiments are implemented based on a number of real-world instances from an electronic equipment manufacturing enterprise. The results show that the proposed algorithm has a significantly better performance than the existing multi-objective algorithms for solving the problem under consideration.

本文研究了一个新的自动引导车辆调度问题，该问题涉及在具有多品种和小批量生产的矩阵制造车间中从货物处理过程中取货和交货。该问题旨在确定一种解决方案，以最大程度地提高客户满意度，同时最大程度地降低分销成本。为此，首先建立了一个多目标混合整数线性规划模型。然后，开发了一种有效的多目标进化算法来解决该问题。在该算法中，提出了一种建设性的启发式方法，并将其结合到总体初始化中。基于理想点的多目标局部搜索用于增强利用能力。设计了一种新颖的两点交叉算子，以充分利用在非支配解决方案中收集的有价值的信息。为了避免算法陷入局部最优状态，提出了一种重启策略。最后，根据电子设备制造企业的许多实际实例，进行了一系列比较实验。结果表明，所提出的算法在解决所考虑的问题上比现有的多目标算法具有明显更好的性能。