Multi-attribute bottleneck identification method for hybrid flow shops in panel furniture intelligent manufacturing - Xinyi Yue; Xianqing Xiong; Mei Zhang; Xiutong Xu (Complex & Intelligent Systems)
Abstract With the widespread application of theory of constraints (TOC) in the workshop, bottleneck machines are seen as a positive element in manufacturing system performance improvement. To accurately identify the bottleneck machine in a hybrid flow shop, we propose a multi-attribute bottleneck identification (MABI) method containing a two-layer framework. Firstly, an improved genetic algorithm (IGA) is used to solve the hybrid flow shop scheduling problem (HFSP), and entropy-weighted technique for order preference by similarity to ideal solution (EW-TOPSIS) method is selected to achieve the bottleneck machine identification. Further, to evaluate the feasibility and efficiency of the method, this paper compares the implementation of the two phases: comparison of IGA with five advanced algorithms called QIA, PSO, AIS, ACO and B&B and comparison of EW-TOPSIS with traditional bottleneck identification methods. Extensive numerical experiments on 77 benchmarks of different sizes not only validate the effectiveness of the IGA, but also demonstrate that the proposed IGA is more effective in bottleneck identification and converges stably to the optimal schedule. Finally, we successfully apply the MABI method to a real case of a hybrid flow shop of panel furniture, presenting challenges and opportunities for bottleneck machines faced by production managers and providing 3 bottleneck-driven scheduling ideas.