A cooperative memetic algorithm for energy-aware distributed welding shop scheduling problem. (April 2023)

• Record Type: Journal Article
• Authors: Wang, Jing-jing; Wang, Ling; Xiu, Xia
• Is Part Of: Engineering applications of artificial intelligence. Volume 120(2023)
• Publication Date: 2023-04
• Subjects: Distributed manufacturing -- Welding shop scheduling -- Energy-aware -- Cooperative memetic algorithm -- Multi-objective optimization; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.105877
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Facing globalization trends and sustainable industrial development, energy-aware distributed manufacturing has become an emerging topic. Meanwhile, welding is a kind of indispensable processing in the development of manufacturing and its effective scheduling can improve production efficiency and reduce energy consumption. However, it is difficult to solve the energy-aware distributed welding shop scheduling problem (EADWSP) due to the characteristics of large scale and multiple objectives. Thus, this paper presents a mathematical model and a cooperative memetic algorithm (CMA) to addresses the EADWSP with minimization both makespan and total energy consumption. To improve the quality and diversity of initial population, a hybrid initialization is developed with a modified NEH based heuristic. Via taking full advantage of historical information, a cooperative search based on feedback is designed and a cooperative selection strategy is employed to balance the exploration and exploitation. In addition, multiple problem-specific operators are presented and a local intensification with Q-learning is designed to enhance exploitation capability. Numerical experiments are carried out and the results demonstrate the effectiveness of the above specific designs. The comparisons to the existing algorithms show superiority of the proposed CMA. Moreover, the application to a real-life case also verifies the effectiveness and practicability in solving the EADWSP. Highlights: Energy-aware distributed welding shop scheduling with is studied. Bi-objective mathematical model with economic and environmental indexes is presented. Cooperative memetic algorithm with feedback mechanism is proposed. Local intensification with Q-learning is developed. More effective than existing algorithm in solving datasets and real-life case.