This paper addresses a new multiple automatic guided vehicle dispatching problem (AGVDP) from material handling process in a matrix manufacturing workshop. The problem aims to determine a solution with the objective of minimizing the transportation cost including travel cost, penalty cost for violating time and AGV cost. For this purpose, a mixed integer linear programming model is first formulated based on a comprehensive investigation. Then, a discrete artificial bee colony algorithm (DABC) is presented together with some novel and advanced techniques for solving the problem. In the proposed DABC algorithm, a nearest-neighbor-based heuristic based on the problem-specific characteristics is presented to generate an initial solution with a high level of quality. Five effective neighborhood operators are presented to generated neighboring solutions with a high level of diversity. Four theorems are proposed to avoid the unfeasible solutions generated by the neighborhood operators. Two new control parameters are introduced. One is to balance the global exploration and local exploitation in employed bee and onlooker bee phases. The other is to enhance the local exploitation capability of the neighborhood operators. Besides, an insertion-based local search method is provided for the scout bee phase to lead the algorithm to a promising region of the solution space. A comprehensive and thorough evaluation with 110 instances collected from a real-world factory shows that the presented algorithm produces superior results which are also demonstrated to be statistically significant than the existing algorithms in the close related literature.

本文从矩阵制造车间的物料搬运过程中解决了一个新的多重自动引导车辆调度问题（AGVDP）。该问题旨在确定一种解决方案，其目的是将包括旅行成本，违反时间的罚款成本和AGV成本在内的运输成本降至最低。为此，首先在全面研究的基础上制定了混合整数线性规划模型。然后，提出了一种离散的人工蜂群算法（DABC），以及一些新颖和先进的技术来解决该问题。在提出的DABC算法中，提出了基于特定问题特征的基于最近邻居的启发式算法，以生成具有高质量水平的初始解决方案。介绍了五个有效的邻域算子，以生成具有高度多样性的邻域解决方案。为了避免邻域算子产生不可行的解，提出了四个定理。引入了两个新的控制参数。一种是在受雇蜂和旁观蜂阶段平衡全球勘探和本地开采。二是提高邻里经营者的本地开发能力。此外，为侦察蜂阶段提供了基于插入的局部搜索方法，以将算法引导至解空间的有希望的区域。