Iterated greedy algorithms enhanced by hyper-heuristic based learning for hybrid flexible flowshop scheduling problem with sequence dependent setup times: A case study at a manufacturing plant

Metaheuristic algorithms offer unique opportunities in problem solving. Although they do not guarantee optimality, it has been shown by numerous publications that they can achieve excellent results in acceptable time. Particularly in real-life production systems, which are mostly comprised of complex discrete optimization problems, the merit should be finding appropriate and efficient solutions in shorter periods rather than waiting for the optimum solution in whole shift. Accordingly, this paper presents a learning iterated greedy search metaheuristic to minimize the maximum completion time in a hybrid flexible flowshop problem with sequence dependent setup times encountered at a manufacturing plant. The proposed algorithm is comprised of four main phases. The first phase employs NEH heuristic to generate an initial solution. Additionally, in order to introduce diversity, some replications are occasionally allowed to start with random solutions. Destruction mechanism to perturb the current solution is used in the next phase. It is followed by a construction procedure, which is used to repair the partial solution obtained after destruction. Finally, a descent neighborhood search enhanced by a hyper-heuristic based learning is applied to the repaired solution in the fourth phase. Thus, algorithm adaptively learns and promotes the most efficient low-level heuristic out of a heuristics pool and encourages the metaheuristic algorithm in using the promoted low-level heuristic in the final phase. The proposed algorithm along with its several extensions is tested by using real data taken from the mentioned production system. Next, by making use of the same data, the developed algorithms are also compared to eight different algorithms, which are shown to be promising in the related literature. Finally, appropriate statistical tests are applied to demonstrate possible significant improvements among all tested algorithms. (C) 2020 Elsevier Ltd. All rights reserved.