A multi-objective approach to welding shop scheduling for makespan, noise pollution and energy consumption. (20th September 2018)

• Record Type: Journal Article
• Authors: Lu, Chao; Gao, Liang; Li, Xinyu; Zheng, Jun; Gong, Wenyin
• Is Part Of: Journal of cleaner production. Volume 196(2018)
• Page Start: 773
• Page End: 787
• Publication Date: 2018-09-20
• Subjects: Energy efficiency -- Welding shop scheduling problem -- Noise pollution -- Multi-objective optimization; 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.2018.06.137
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Sustainable scheduling problems have attracted great attention from researchers and enterprises. Sustainable scheduling should simultaneously consider economic, environmental and social impacts. However, to date, most studies on sustainable scheduling problems have emphasized the balance between the economy (e.g., makespan) and the environment (e.g., energy consumption or carbon emissions). Noise pollution is an important social issue and harmful to human health, however, it was ignored in most previous studies. Thus, this study investigates a welding shop scheduling problem (WSSP) that considers noise pollution alongside more common energy consumption and productivity issues. The studied WSSP is unique because multiple welders could simultaneously carry out the same task. First, we present a new mathematical model of the multi-objective WSSP. A novel hybrid multi-objective grey wolf algorithm (HMOGWO) is then designed. A new local search strategy based on the problem's properties is proposed to improve exploitation capability of the HMOGWO. In addition, a new energy saving strategy is presented to extend the operational life span of welders and promote energy efficiency. Finally, to demonstrate the effectiveness of the HMOGWO and the new energy saving strategy, we compare our proposal with other multi-objective optimization algorithms through comparison experiments. The results indicate that the proposed HMOGWO and energy saving strategy are superior to the competitors on this problem. Additionally, this method is successfully applied to a real-world case. Highlights: A mathematical model for a welding shop scheduling problem is formulated. The noise pollution criterion is considered in this model. A multi-objective optimization algorithm is presented to solve this problem. An effective energy saving strategy is proposed. An initialization strategy and local search strategy are proposed.