A survival duration-guided NSGA-III for sustainable flexible job shop scheduling problem considering dual resources

作     者：Li Hongyu Wu Xiuli 

作者机构：Univ Sci & Technol Beijing Sch Mech Engn Dept Logist Engn Beijing Peoples R China 

出 版 物：《IET COLLABORATIVE INTELLIGENT MANUFACTURING》 (IET Collab. Intell. Manuf.)

年 卷 期：2021年第3卷第2期

页      面：119-130页

核心收录：

基　　金：National Science Foundation of China National Key RD plan [2020YFB1712902] 

主　　题：decoding algorithms sustainable flexible job shop scheduling dual resources ergonomic risk sustainable development job shop scheduling ergonomics energy consumption survival duration-guided NSGA-III algorithm cross-generation selection minimisation SDG-NSGA-III algorithm nondominated sorting SFJSPCDR next-generation population genetic algorithms 

摘      要：Considering the increasing concern on sustainable development from manufacturers, focus is given to three kinds of indicators of sustainable development, that is, economy, environment, and society, and schedule two types of resource, that is machines and workers, simultaneously in the classical flexible job shop scheduling problem. The authors define it as a sustainable flexible job shop scheduling problem considering dual resources (SFJSPCDR). First, a model of the SFJSPCDR is formulated to optimise the makespan, the energy consumption, and the ergonomic risk simultaneously. Second, an improved survival duration-guided NSGA-III algorithm (SDG-NSGA-III) is proposed to solve SFJSPCDR. The survival duration of each individual determines whether it takes part in generating offspring. In order to balance the energy consumption and ergonomic risk while minimising the makespan, a double-low decoding algorithm is proposed, which is composed of two decoding algorithms. Cross-generation selection is employed with the non-dominated sorting, and the next-generation population is selected according to the reference point-based selection strategy. In addition, a restart strategy is also integrated to improve the exploration and exploitation performance of the SDG-NSGA-III algorithm. Finally, a group of experiments are carried out and the results prove the effectiveness of the proposed algorithm.