Hybrid energy-efficient scheduling measures for flexible job-shop problem with variable machining speeds. (1st July 2022)

• Record Type: Journal Article
• Authors: Wei, Zhenzhen; Liao, Wenzhu; Zhang, Liuyang
• Is Part Of: Expert systems with applications. Volume 197(2022)
• Publication Date: 2022-07-01
• Subjects: Green manufacturing scheduling -- Energy-efficient -- Flexible job shop -- Variable machining speeds; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116785
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Energy-efficient flexible job-shop problem with variable machining speeds is investigated. Productional and environmental objectives are considered simultaneously. An energy-aware estimation model considering five running conditions on machines is proposed. Hybrid energy-efficient scheduling measures are developed as four implementing rules. Results proved the superior performance of these energy-efficient scheduling measures. Abstract: Production scheduling has a significant impact on energy savings in manufacturing system from the viewpoint of operation management. Taking the flexibility of machining speeds taken into account, the flexible job-shop scheduling problem will get closer to the real manufacturing environment, and the associated energy consumption for shifting speeds should also be noticeable. In this paper, a novel energy-aware estimation model is proposed to compute different energy consumptions for different running conditions of a machine. Then, a multi-objective optimization model is formulated for the dual flexible job-shop scheduling problem to minimize the makespan and total energy consumption. Hybrid energy-efficient scheduling measures are developed to reduce each kind of energy consumption on the machines. Finally, experimental results show the consistency between optimization objectives and demonstrate superior performance of these energy-efficient scheduling measures.