Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm. (1st November 2016)

• Record Type: Journal Article
• Authors: Li, Zixiang; Tang, Qiuhua; Zhang, LiPing
• Is Part Of: Journal of cleaner production. Volume 135(2016:Nov.)
• Page Start: 508
• Page End: 522
• Publication Date: 2016-11-01
• Subjects: Assembly line balancing -- Two-sided robotic assembly line -- Energy consumption -- Simulated annealing -- Pareto-optimal set; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2016.06.131
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Two-sided assembly lines are usually utilized to produce large-sized high-volume products. Recently robots are widely utilized in this line to replace the manual handling and manage the allocated tasks. For a robotic assembly line, the energy consumption is a major expense and the increased energy cost draws much more attentions from manufacturing enterprises. To the best knowledge of the authors, there is no research reported on the energy consumption of two-sided robotic assembly line. This paper presents a new mixed-integer programming model to minimize the energy consumption and cycle time simultaneously. A restarted simulated annealing algorithm is developed to deal with the complexity of the model, which utilizes new local search with three neighbor structures and restart phase based on the crowding distance assignment procedure to obtain well-spread Pareto-optimal set. Testing cases are designed to measure the performance of the proposed method and the restarted simulated annealing algorithm is compared with the fast elitist non-dominated sorting genetic algorithm. The computational results demonstrate that the proposed model is useful to reduce the total energy consumption and the restarted simulated annealing algorithm outperforms the non-dominated sorting genetic algorithm in both convergence and spread criteria. Highlights: Energy consumption in robotic two-sided assembly line is considered. A new mixed-integer programming model is developed. Restarted simulated annealing algorithm is developed. A restart phase is developed based on a crowding distance assignment procedure. Statistically significant results indicate the superiority over NSGA-II.