Research on project scheduling optimization is trending toward many-objective optimization, in which the number of objectives exceeds three. However, existing studies usually only consider the necessary logical constraints, ignoring descriptions of practical scenarios and corresponding complex constraints, which limit the designed algorithms in problems with such scenarios and constraints. This study focuses on the many-objective repetitive project scheduling problem considering practical scenarios with complex constraints (MRPSP-PSCC). Various constraints are described under flexible matching/mapping between multi-crew, multi-mode, and multi-section scenarios. A many-objective project scheduling model is proposed for synchronous optimization of time, cost, quality, resource usage, and interruption time. The multiphase balance of diversity and convergence nondominated sorting genetic algorithm III (B-NSGA-III) with unique advantages for continuous many-objective optimization problems is transformed for discrete many-objective optimization. A series of unique designs is employed in the algorithm, including three-layer coding rules, constraint handling, and local search, to improve the problem-solving efficiency of the algorithm. The effectiveness and superiority of the model and algorithm for MRPSP-PSCC were verified through a case study.

中文翻译：

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场景化重复项目调度优化

项目调度优化的研究正朝着多目标优化的方向发展，其中目标的数量超过三个。然而，现有的研究通常只考虑必要的逻辑约束，而忽略了对实际场景的描述和相应的复杂约束，这限制了设计的算法在具有此类场景和约束的问题上。本研究的重点是考虑具有复杂约束的实际场景的多目标重复项目调度问题 (MRPSP-PSCC)。在多机组、多模式和多部分场景之间的灵活匹配/映射下描述了各种约束。提出了一个多目标项目调度模型，用于时间、成本、质量、资源使用和中断时间的同步优化。将对连续多目标优化问题具有独特优势的多阶段平衡多样性收敛非支配排序遗传算法III(B-NSGA-III)转化为离散多目标优化问题。该算法采用了一系列独特的设计，包括三层编码规则、约束处理和局部搜索，以提高算法的求解效率。通过实例验证了该模型和算法对MRPSP-PSCC的有效性和优越性。和局部搜索，提高算法的求解效率。通过实例验证了该模型和算法对MRPSP-PSCC的有效性和优越性。和局部搜索，提高算法的求解效率。通过实例验证了该模型和算法对MRPSP-PSCC的有效性和优越性。