A hybrid genetic algorithm for the hybrid flow shop scheduling problem with nighttime work and simultaneous work constraints: A case study from the transformer industry

Abstract

This paper addresses a hybrid flow shop scheduling problem with real-world constraints, and proposes a novel algorithm for its solution. We first discuss the distinguishing characteristics of nighttime and simultaneous work in the transformer manufacturing process. To solve the problem within a reasonable time, we propose a hybrid genetic algorithm. This algorithm combines the Nawaz–Enscore–Ham (NEH) heuristic, a local search algorithm, and a machine allocation rule with the aim of minimizing the total tardiness. Our experimental results show that the proposed algorithm outperforms the NEH algorithm, a simple genetic algorithm, and five existing dispatching rules in terms of average total tardiness performance and relative deviation index. The proposed algorithm is also shown to be competitive with respect to its efficiency and robustness.