An Improved Moth-Flame Algorithm for Human-Robot Collaborative Parallel Disassembly Line Balancing Problem

被引：1

作者：

Zhang, Qi ^{[1
]}

Xu, Bin ^{[1
]}

Yao, Man ^{[2
]}

Wang, Jiacun ^{[3
]}

Guo, Xiwang ^{[4
]}

Qin, Shujin ^{[5
]}

Qi, Liang ^{[6
]}

Lu, Fayang ^{[4
]}

机构：

[1] Shenyang Univ Chem Technol, Coll Informat Engn, Shenyang 110142, Peoples R China

[2] He Univ, Sch Basic Med, Shenyang 110163, Peoples R China

[3] Monmouth Univ, Dept Comp Sci & Software Engn, West Long Branch, NJ 07764 USA

[4] Liaoning Petrochem Univ, Coll Informat & Control Engn, Fushun 113001, Peoples R China

[5] Shangqiu Normal Univ, Coll Econ & Management, Shangqiu 476000, Peoples R China

[6] Shandong Univ Sci & Technol, Dept Comp Sci & Technol, Qingdao 266590, Peoples R China

来源：

MATHEMATICS | 2024年 / 12卷 / 06期

关键词：

human-robot collaborative disassembly; parallel disassembly line; improved moth-flame algorithm; OPTIMIZATION ALGORITHM;

D O I：

10.3390/math12060816

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In the context of sustainable development strategies, the recycling of discarded products has become increasingly important with the development of electronic technology. Choosing the human-robot collaborative disassembly mode is the key to optimizing the disassembly process and ensuring maximum efficiency and benefits. To solve the problem of human-robot cooperative parallel dismantling line balance, a mixed integer programming model is established and verified by CPLEX. An improved Moth-Flame Optimization (IMFO) algorithm is proposed to speed up convergence and optimize the disassembly process of various products. The effectiveness of IMFO is evaluated through multiple cases and compared with other heuristics. The results of these comparisons can provide insight into whether IMFO is the most appropriate algorithm for the problem presented.

引用

页数：17

共 34 条

[11] Capacitated disassembly scheduling with random demand [J].

Kim, Hwa-Joong ;

Xirouchakis, Paul .

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2010, 48 (23) :7177-7194

[12] An Improved Artificial Bee Colony Algorithm With Q-Learning for Solving Permutation Flow-Shop Scheduling Problems [J].

Li, Hanxiao ;

Gao, Kaizhou ;

Duan, Pei-Yong ;

Li, Jun-Qing ;

Zhang, Le .

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 53 (05) :2684-2693

[13] Minimizing energy consumption in multi-objective two-sided disassembly line balancing problem with complex execution constraints using dual-individual simulated annealing algorithm [J].

Liang, Junyong ;

Guo, Shunsheng ;

Du, Baigang ;

Li, Yibing ;

Guo, Jun ;

Yang, Zhijie ;

Pang, Shibao .

JOURNAL OF CLEANER PRODUCTION, 2021, 284 (284)

[14] Spinel/Lithium-Rich Manganese Oxide Hybrid Nanofibers as Cathode Materials for Rechargeable Lithium-Ion Batteries [J].

Liu, Junxiang ;

Wang, Jiaqi ;

Ni, Youxuan ;

Zhang, Yudong ;

Luo, Jun ;

Cheng, Fangyi ;

Chen, Jun .

SMALL METHODS, 2019, 3 (12)

[15] A human-cyber-physical system enabled sequential disassembly planning approach for a human-robot collaboration cell in Industry 5.0 [J].

Lou, Shanhe ;

Zhang, Yiran ;

Tan, Runjia ;

Lv, Chen .

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2024, 87

[16]

Lu FaYang, 2021, Journal of Physics: Conference Series, V2024, DOI 10.1088/1742-6596/2024/1/012058

[17] Combinatorial optimization methods for disassembly line balancing [J].

McGovern, SM ;

Gupta, SM .

ENVIRONMENTALLY CONSCIOUS MANUFACTURING IV, 2004, 5583 :53-66

[18] A carnivorous plant algorithm for solving global optimization problems [J].

Meng, Ong Kok ;

Pauline, Ong ;

Kiong, Sia Chee .

APPLIED SOFT COMPUTING, 2021, 98

[19] Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm [J].

Mirjalili, Seyedali .

KNOWLEDGE-BASED SYSTEMS, 2015, 89 :228-249

[20] A novel, cost efficient identification method for disassembly planning of waste electrical and electronic equipment [J].

Nowakowski, Piotr .

JOURNAL OF CLEANER PRODUCTION, 2018, 172 :2695-2707

← 1 2 3 4 →