Open AccessAn Improved Gray Wolf Optimization Algorithm for Solving Disassembly Sequencing Problems
Author(s) -
Laide Guo,
Fangjie Peng,
Dapeng Song,
Caihong Hu,
Dou Ling
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1944/1/012034
Subject(s) - gray (unit) , reuse , mathematical optimization , genetic algorithm , computer science , generator (circuit theory) , environmental pollution , optimization algorithm , algorithm , engineering , mathematics , medicine , power (physics) , environmental protection , physics , environmental science , quantum mechanics , radiology , waste management
In recent years, the utilization of end-of-life products which may cause serious environmental pollution has received much attention from both industrial and academia. The recycling and reusing of waste products have an essential step that is disassembly. A disassembly sequencing planning problem is studied with minimizing disassembly time to get the near-optimal solution. This work uses an improved algorithm for solving disassembly sequencing problems based on an improved gray wolf optimization algorithm, which has a group optimization options that can simulate the gray wolfs’ predation behaviours. An initial solution generator, a new solution generator, and a random mutation operator are adopted to improve the proposed algorithm, which can promote its efficiency and effectiveness. Experimental data show its superiority to solve this work’s problem comparing with the classical genetic algorithm.