An Iterated Greedy Algorithm for a Parallel Machine Scheduling Problem with Re-entrant and Group Processing Features

• Published in: Tehnički vjesnik Vol. 30; no. 4; pp. 1241 - 1252
• Main Authors: Yuan, Shuaipeng, Wang, Bailin, Li, Tike
• Format: Journal Article Paper
• Language: English
• Published: Slavonski Baod University of Osijek 01.08.2023; Josipa Jurja Strossmayer University of Osijek; Strojarski fakultet u Slavonskom Brodu; Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek; Građevinski i arhitektonski fakultet Osijek; Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
• Subjects: Algorithms; Greedy algorithms; group processing; Integer programming; iterated greedy algorithm; Linear programming; Manufacturing; Milling (machining); Mixed integer; parallel machine scheduling; programming model; re-entrant; Scheduling; Steel industry; Steel making; China
• ISSN: 1330-3651; 1848-6339
• DOI: 10.17559/TV-20230602000692

This research paper addresses a novel parallel machine scheduling problem with re-entrant and group processing features, specifically motivated by the hot milling process in the modern steel manufacturing industry. The objective is to minimize the makespan. As no existing literature exists on this problem, the paper begins by analyzing the key characteristics of the problem. Subsequently, a mixed integer linear programming model is formulated. To tackle the problem, an improved iterated greedy algorithm (IGA) is proposed. The IGA incorporates a problem-specific heuristic to construct the initial solution. Additionally, it incorporates an effective destruction and reconstruction procedure. Furthermore, an acceptance rule is developed to prevent the IGA from getting stuck in local optima. The proposed approach is evaluated through computational experiments. The results demonstrate that the proposed IGA outperforms three state-of-the-art meta-heuristics, highlighting its high effectiveness. Overall, this research contributes to the understanding and solution of the parallel machine scheduling problem with re-entrant and group processing features in the context of the hot milling process. The proposed algorithm provides insights for practical applications in the steel manufacturing industry.