Solving a disassembly-to-order system by using genetic algorithm and weighted fuzzy goal programming

In this paper, Genetic Algorithm (GA) is used to solve the disassembly-to-order (DTO) problem. DTO is a system where a variety of returned products are disassembled to fulfill the demand for specified numbers of components and materials. The main objective is to determine the optimal number of take-...

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Online Access:http://hdl.handle.net/2047/d10010063
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spelling ndltd-NEU--neu-3780142016-04-25T16:16:17ZSolving a disassembly-to-order system by using genetic algorithm and weighted fuzzy goal programmingIn this paper, Genetic Algorithm (GA) is used to solve the disassembly-to-order (DTO) problem. DTO is a system where a variety of returned products are disassembled to fulfill the demand for specified numbers of components and materials. The main objective is to determine the optimal number of take-back EOL (end-of-life) products for the DTO system which satisfy the desirable criteria of the system. We implement the Weighted Fuzzy Goal Programming (WFGP) to calculate the fitness values in GA process. We also consider product deterioration which affects the yield rates (e.g., older products tend to have lower yield rates for usable components) and use heuristic procedure to transform the stochastic disassembly yields into their deterministic equivalents. A numerical example is also considered.http://hdl.handle.net/2047/d10010063
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description In this paper, Genetic Algorithm (GA) is used to solve the disassembly-to-order (DTO) problem. DTO is a system where a variety of returned products are disassembled to fulfill the demand for specified numbers of components and materials. The main objective is to determine the optimal number of take-back EOL (end-of-life) products for the DTO system which satisfy the desirable criteria of the system. We implement the Weighted Fuzzy Goal Programming (WFGP) to calculate the fitness values in GA process. We also consider product deterioration which affects the yield rates (e.g., older products tend to have lower yield rates for usable components) and use heuristic procedure to transform the stochastic disassembly yields into their deterministic equivalents. A numerical example is also considered.
title Solving a disassembly-to-order system by using genetic algorithm and weighted fuzzy goal programming
spellingShingle Solving a disassembly-to-order system by using genetic algorithm and weighted fuzzy goal programming
title_short Solving a disassembly-to-order system by using genetic algorithm and weighted fuzzy goal programming
title_full Solving a disassembly-to-order system by using genetic algorithm and weighted fuzzy goal programming
title_fullStr Solving a disassembly-to-order system by using genetic algorithm and weighted fuzzy goal programming
title_full_unstemmed Solving a disassembly-to-order system by using genetic algorithm and weighted fuzzy goal programming
title_sort solving a disassembly-to-order system by using genetic algorithm and weighted fuzzy goal programming
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url http://hdl.handle.net/2047/d10010063
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