This paper addresses a many-objective flexible job-shop scheduling problem with transportation and setup times (MaOFJSP_T/S) where the objective is to minimize the makespan, total workload, workload of the critical machine, and penalties of earliness/tardiness. We first present a mathematical model as the representation of the problem, and then establish a network graph model to describe the structural characteristics of the problem and develop a new neighborhood structure. The neighborhood structure defines four move types for different objectives. Next, we propose a hybrid many-objective evolutionary algorithm (HMEA), which is designed to better balance exploitation and exploration. In this algorithm, the tabu search with the neighborhood structure is proposed to improve the local search ability. A reference-point based non-dominated sorting selection is presented to guide the algorithm to search towards the Pareto-optimal front and maintain diversity of solutions. Through three sets of experiments based on 28 benchmark instances, the partial and overall effects of this algorithm are evaluated. The experimental results demonstrate the effectiveness of the proposed HMEA in solving the MaOFJSP_T/S.

本文针对具有运输和设置时间（MaOFJSP_T / S）的多目标灵活作业车间调度问题进行了研究，其目标是最大程度地减少工期，总工作量，关键机器的工作量以及提早/延误的惩罚。我们首先提出一个数学模型来表示问题，然后建立一个网络图模型来描述问题的结构特征并开发一种新的邻域结构。邻域结构为不同目标定义了四种移动类型。接下来，我们提出了一种混合多目标进化算法（HMEA），旨在更好地平衡开发和探索之间的关系。该算法提出了一种具有邻域结构的禁忌搜索算法，以提高局部搜索能力。提出了一种基于参考点的非支配排序选择，以指导算法向Pareto最优前沿搜索并保持解的多样性。通过基于28个基准实例的三组实验，评估了该算法的部分和整体效果。实验结果证明了所提出的HMEA在解决MaOFJSP_T / S方面的有效性。