An Entropy-Based Adaptive Hybrid Particle Swarm Optimization for Disassembly Line Balancing Problems

Abstract

Abstract:In order to improve the product disassembly efficiency, the disassembly line balancing problem (DLBP) is transformed into a problem of searching for the optimum path in the directed and weighted graph by constructing the disassembly hierarchy information graph (DHIG). Then, combining the characteristic of the disassembly sequence, an entropy-based adaptive hybrid particle swarm optimization algorithm (AHPSO) is presented. In this algorithm, entropy is introduced to measure the changing tendency of population diversity, and the dimension learning, crossover and mutation operator are used to increase the probability of producing feasible disassembly solutions (FDS). Performance of the proposed methodology is tested on the primary problem instances available in the literature, and the results are compared with other evolutionary algorithms. The results show that the proposed algorithm is efficient to solve the complex DLBP.

A Novel Simulated Annealing-Based Hyper-Heuristic Algorithm for Stochastic Parallel Disassembly Line Balancing in Smart Remanufacturing

“…In order to promote and enrich the application of automation disassembly in the real-world, the disassembly of actual EoL products is gradually introduced in DLBP. The majority of actual EoL products are focused on waste electric and electronic equipment (WEEE), such as personal computers (PC) [ 34 ], mobile phones [ 35 ], laptops [ 36 ], etc. These WEEE products are suitable for conducting experiments due to their variety and simple physical structure.…”

Disassembly Line Balancing of Electronic Waste Considering the Degree of Task Correlation

“…The extant literature well explored the tangible dimensions of this consideration-reconfiguring the layout to improve material flow [26], reducing the number of disassembly workstations [27][28][29][30][31], improving workload smoothness by making a balance between workstations' operating time [32,33], or revisiting the disassembly tasks assignment to minimize the total idle time [34][35][36], cycle time [15,37,38], and the number of direction changes [39] that are the considered utilization-based operational measures. To further improve the efficiency of disassembly operations, other studies suggested priority-based approaches-for example, early removal of valuable high-demand parts [27,40,41], easily accessible parts [16,18], and the parts that require longer disassembly times [42]. Environment-friendly considerations were more recently incorporated to improve operational sustainability-for example, by early removal of hazardous components [27,31,40,43] and maximizing CO 2 saving [44].…”

Disassembly Line Balancing by Using Simulation Optimization

“…Wang, Li, & Gao, 2019), tabu search (Kalayci & Gupta, 2014), ant colony optimization (Agrawal & Tiwari, 2008;L. P. Ding, Feng, Tan, & Gao, 2010;Kalayci & Gupta, 2013b;Seamus M. McGovern & Gupta, 2006), artificial bee colony algorithm (Kalayci & Gupta, 2013c;Kalayci, Hancilar, Gungor, & Gupta, 2015;Liu et al, 2018; Alphanumeric Journal Volume 9, Issue 1, 2021 Wang, Guo, & Liu, 2019;Wang, Li, Gao, Li, & Sutherland, 2021), particle swarm optimization (Kalayci & Gupta, 2013a;Xiao, Wang, Yu, & Nie, 2017), firefly algorithm (Zhu, et al, 2018)and artificial fish swarm algorithm (Zhang, Wang, Zhu, & Wang, 2017) are the most commonly used metaheuristic techniques.…”