A discrete whale swarm algorithm for hybrid flow-shop scheduling problem with limited buffers

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.