In industry, production is often organized in the form of a hybrid flow shop, and there is great interest in methods and algorithms for optimizing such production processes. While, thus far, methods have focused mostly on optimizing a single selected objective, it is increasingly important to address several objectives simultaneously in order to move from extreme to balanced solutions that consider diverse operational requirements. Following this, we classify and characterize the literature dealing with multi-objective hybrid flow shop scheduling problems (HFSP). We identify those features in metaheuristics that require particular attention during the process of finding Pareto solutions for HFSP (especially coding and decoding schemes, Pareto archives, and Pareto dominance concepts). To promote the evaluation of the suitability of algorithms for solving multi-criteria HFSP, we provide an overview of the test instances used in the literature and propose a systematization of performance criteria for the evaluation of Pareto fronts in order to create clear and consistent conceptual and semantic understanding. Based on this, recommendations are derived that can also be helpful for various multi-objective optimization problems and other application contexts for assessing solution quality as accurately and comparably as possible. Finally, current challenges and possible future research directions are highlighted.

在工业中，生产通常以混合流水车间的形式组织，人们对优化此类生产过程的方法和算法非常感兴趣。虽然到目前为止，方法主要集中在优化单个选定目标，但同时解决多个目标越来越重要，以便从极端解决方案转变为考虑不同操作要求的平衡解决方案。在此之后，我们对处理多目标混合流水车间调度问题 (HFSP) 的文献进行分类和描述。我们确定了在寻找 HFSP 帕累托解决方案的过程中需要特别注意的元启发式特征（尤其是编码和解码方案、帕累托档案和帕累托优势概念）。为了促进对解决多标准 HFSP 算法的适用性的评估，我们提供了文献中使用的测试实例的概述，并提出了用于评估帕累托前沿的性能标准的系统化，以创建清晰一致的概念和语义理解。基于此，得出的建议也有助于各种多目标优化问题和其他应用环境，以尽可能准确和可比较地评估解决方案质量。最后，强调了当前的挑战和未来可能的研究方向。我们提供了文献中使用的测试实例的概述，并提出了用于评估帕累托前沿的性能标准的系统化，以创建清晰一致的概念和语义理解。基于此，得出的建议也有助于各种多目标优化问题和其他应用环境，以尽可能准确和可比较地评估解决方案质量。最后，强调了当前的挑战和未来可能的研究方向。我们提供了文献中使用的测试实例的概述，并提出了用于评估帕累托前沿的性能标准的系统化，以创建清晰一致的概念和语义理解。基于此，得出的建议也有助于各种多目标优化问题和其他应用环境，以尽可能准确和可比较地评估解决方案质量。最后，强调了当前的挑战和未来可能的研究方向。