A lion optimization algorithm for an integrating maintenance planning and production scheduling problem with a total absolute deviation of completion times objective

• Published in: Soft computing (Berlin, Germany) Vol. 26; no. 24; pp. 13953 - 13968
• Main Authors: Yazdani, Reza, Alipour-Vaezi, Mohammad, Kabirifar, Kamyar, Salahi Kojour, Azim, Soleimani, Faezeh
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022
• Subjects: Application of Soft Computing; Artificial Intelligence; Computational Intelligence; Control; Engineering; Mathematical Logic and Foundations; Mechatronics; Robotics; Maintenance activity; Lion optimization algorithm; Scheduling; Total absolute deviation completion times (TADC)
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-022-07436-7

In today’s competitive business environment, the need for continuous production, quality improvement, and fast delivery necessitates highly reliable production and delivery processes. A more reliable system can be ensured by performing routine maintenance on the equipment. Maintenance, on the other hand, causes a temporary reduction in production capacity. To ensure a high level of system performance, it is essential to coordinate maintenance and production. The study integrates maintenance and production decisions in order to maximise efficiency by ensuring high-quality output and efficient resource utilisation; however, limited studies have been carried out addressing this type of scheduling problem with the objective function of total absolute deviation of completion times (TADC). Thus, this study aims to investigate the scheduling problem on a parallel machine under periodic maintenance in order to minimise the TADC of the jobs. Due to the complexity of the problem, a metaheuristic method called the lion optimization algorithm (LOA) is presented to solve the problem. This study performs a comprehensive comparative analysis to demonstrate the proposed algorithm’s reliability. The dragonfly algorithm, the grasshopper optimization algorithm, the multi-verse optimization algorithm, the sine cosine algorithm, the salp swarm algorithm, and the whale optimization algorithm are presented and their performance is compared to the LOA in this study The results demonstrate that the LOA consistently outperforms the other presented algorithms across all size ranges. The study’s findings contribute new theoretical and practical insights to the growing body of knowledge about manufacturing environments and have implications for planners and managers, particularly in businesses where unplanned production wastes financial resources.