Metaheuristics for Solving a Hybrid Flexible Flowshop Problem with Sequence-Dependent Setup Times

• Published in: Swarm Intelligence Based Optimization pp. 9 - 25
• Main Authors: Sioud, Aymen, Gagné, Caroline, Gravel, Marc
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 01.01.2014
• Series: Lecture Notes in Computer Science
• Subjects: ACO; Genetic Algorithm; Hybrid flowshop; Hybrid Metaheuristics; Makespan; Scheduling; Sequence-dependent setup times
• ISBN: 3319129694; 9783319129693
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-12970-9_2

In this paper, we propose three new metaheuristic implementations to address the problem of minimizing the makespan in a hybrid flexible flowshop with sequence-dependent setup times. The first metaheuristic is a genetic algorithm (GA) embedding two new crossover operators, and the second is an ant colony optimization (ACO) algorithm which incorporates a transition rule featuring lookahead information and past information based on archive concepts such as the multiobjective evolutionary computation. The third metaheuristic is a hybridization (HGA) of the GA and the ACO algorithms. Numerical experiments were performed to compare the performance of the proposed algorithms on different benchmarks from the literature. The algorithms are compared with the best algorithms from the literature. The results indicate that our algorithms generate better solutions than those of the known reference sets.