Title | ||
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A Simplified Teaching-Learning-Based Optimization Algorithm for Disassembly Sequence Planning |
Abstract | ||
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Disassembly plays an important role in recovery and remanufacturing of Waste Electrical and Electronic Equipment (WEEE). A novel Simplified Teaching-Learning-Based Optimization (STLBO) algorithm is proposed for optimization of Disassembly Sequence Planning (DSP). The proposed STLBO is on the basis of a teaching-learning-based optimization method which is a new population based meta-heuristic algorithms. In the proposed STLBO algorithm, three operators are designed namely Feasible Solution Generator (FSG), Teacher Phase Operator (TPO) and Learner Phase Operator (LPO). The proposed algorithm is successfully tested against previous best known solutions for a set of public benchmarks. |
Year | DOI | Venue |
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2013 | 10.1109/ICEBE.2013.60 | ICEBE |
Keywords | Field | DocType |
industrial waste,optimisation,teacher phase operator,waste electrical and electronic equipment,stlbo algorithm,feasible solution,feasible solution generator,simplified teaching-learning-based optimization algorithm,meta-heuristic algorithms,recycling,dsp,teaching,tpo,recovery,weee directive,population based meta-heuristic algorithms,electronic equipment,learner phase operator,proposed algorithm,disassembly sequence planning,fsg,remanufacturing,learner phase,assembly planning,lpo,teaching-learning-based optimization method,proposed stlbo algorithm,design for disassembly,disassembly,meta-heuristic algorithm,simplified teachinglearning-based optimization,weee,proposed stlbo,mechanical engineering | Data mining,New population,Digital signal processing,Assembly planning,Sequence planning,Industrial engineering,Computer science,Optimization algorithm,Operator (computer programming),Electronic equipment,Remanufacturing | Conference |
Citations | PageRank | References |
2 | 0.36 | 6 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Kai Xia | 1 | 14 | 2.11 |
Liang Gao | 2 | 1493 | 128.41 |
Lihui Wang | 3 | 619 | 61.77 |
Weidong Li | 4 | 136 | 13.50 |
Kuo-Ming Chao | 5 | 1123 | 130.82 |