An effective shuffled frog-leaping algorithm for lot-streaming flow shop scheduling problem

This paper presents an effective shuffled frogleaping algorithm (SFLA) for solving a lot-streaming flow shop scheduling problem with equal-size sublots, where a criterion is to minimize maximum completion time (i.e., makespan) under both an idling and no-idling production cases. Unlike the original SFLA, the proposed SFLA represents an individual or frog as a job permutation and utilizes a position-based crossover operator to generate new candidate solutions. An efficient initialization scheme based on the Nawaz–Enscore–Ham heuristic is proposed to construct an initial population with a certain level of quality and diversity. A simple but effective local search approach is embedded in SFLA to enhance the local intensification capability. In addition, a speed-up method to evaluate insert neighborhood is presented to improve the algorithm’s efficiency. Extensive computational experiments and comparisons are provided, which demonstrate the effectiveness of the proposed SFLA against the best performing algorithms from the literature.