This paper studies a hybrid flow-shop scheduling problem with limited buffers and two process routes that comes from an engine hot-test production line in a diesel engine assembly plant. It extends the classical hybrid flow-shop scheduling problem by considering practical constraints on buffer area resources and alternative process routes. Because of its NP-hardness and large scale, traditional optimization methods and heuristic rules cannot obtain satisfactory solutions. A discrete whale swarm algorithm (DWSA) is proposed to identify near-optimal solutions efficiently. The proposed algorithm adopts an encoding method based on the problem characteristic and a greedy delayed decoding strategy to avoid infeasible solutions. A hybrid initialization is used to ensure the quality of the initial population and diversity. A new way of computing distances and a movement rule between individuals are designed. Five mutation operators and a deduplication strategy are proposed to improve the population diversity. The effectiveness of the proposed DWSA is validated on three groups of instances and a real-world industrial case.

本文研究了一个有限的缓冲区和两条工艺路线的混合流水车间调度问题，该问题来自柴油发动机装配厂的发动机热试生产线。它通过考虑对缓冲区资源和替代工艺路线的实际约束，扩展了经典的混合流水车间调度问题。传统的优化方法和启发式规则由于其NP难解性和大规模性而无法获得令人满意的解决方案。提出了一种离散鲸群算法（DWSA）来有效地识别近似最优解。提出的算法采用基于问题特征的编码方法和贪婪的延迟解码策略来避免不可行的解决方案。混合初始化用于确保初始种群和多样性的质量。设计了一种计算距离和个体之间运动规则的新方法。提出了五个变异算子和一个重复数据删除策略来改善种群多样性。提议的DWSA的有效性在三组实例和一个实际的工业案例中得到了验证。