A comparative study of GA and PSO approach for cost optimisation in product recovery systems

A product recovery system is proposed to reduce the bulk of waste sent to landfills by retrieving materials and parts of obsolete products for using them in remanufacturing and recycling. Product recovery is a significant strategy for enhancing customer satisfaction with regard to environmental conc...

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Published inInternational journal of production research Vol. 61; no. 4; pp. 1283 - 1297
Main Authors Dwivedi, Ashish, Madaan, Jitender, Chan, Felix T. S., Dalal, Mohit
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 16.02.2023
Taylor & Francis LLC
Subjects
Chains
Comparative studies
Customer satisfaction
Fast moving consumer goods (FMCG)
genetic algorithm (GA)
Genetic algorithms
Heuristic methods
Integer programming
Linear programming
Mixed integer
mixed integer linear programming (MILP)
Obsolescence
particle swarm optimisation (PSO)
Particle swarm optimization
Product recovery system (PRS)
Remanufacturing
Online AccessGet full text
ISSN0020-7543
1366-588X
DOI10.1080/00207543.2022.2035008

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Abstract A product recovery system is proposed to reduce the bulk of waste sent to landfills by retrieving materials and parts of obsolete products for using them in remanufacturing and recycling. Product recovery is a significant strategy for enhancing customer satisfaction with regard to environmental concerns. Considering the fact that some products are returned, it becomes challenging to analyse whether to manufacture a new product or to rework the returned product at every step of the product recovery chain. Our approach uses a mixed integer linear programming model with the genetic algorithm and particle swarm optimisation, where two meta-heuristic algorithms are introduced for solving the MILP problem. Here, a recovery scenario is modelled, subject to the time and type of product to be processed. The study is intended to enhance the overall productivity of the product recovery chain. To demonstrate the approach, a case study is presented in the fast-moving consumer goods industry in which the proposed model demonstrates a reduction in the overall cost in the product recovery chain.
AbstractList A product recovery system is proposed to reduce the bulk of waste sent to landfills by retrieving materials and parts of obsolete products for using them in remanufacturing and recycling. Product recovery is a significant strategy for enhancing customer satisfaction with regard to environmental concerns. Considering the fact that some products are returned, it becomes challenging to analyse whether to manufacture a new product or to rework the returned product at every step of the product recovery chain. Our approach uses a mixed integer linear programming model with the genetic algorithm and particle swarm optimisation, where two meta-heuristic algorithms are introduced for solving the MILP problem. Here, a recovery scenario is modelled, subject to the time and type of product to be processed. The study is intended to enhance the overall productivity of the product recovery chain. To demonstrate the approach, a case study is presented in the fast-moving consumer goods industry in which the proposed model demonstrates a reduction in the overall cost in the product recovery chain.
Author Madaan, Jitender
Dalal, Mohit
Dwivedi, Ashish
Chan, Felix T. S.
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Snippet A product recovery system is proposed to reduce the bulk of waste sent to landfills by retrieving materials and parts of obsolete products for using them in...
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SubjectTerms Chains
Comparative studies
Customer satisfaction
Fast moving consumer goods (FMCG)
genetic algorithm (GA)
Genetic algorithms
Heuristic methods
Integer programming
Linear programming
Mixed integer
mixed integer linear programming (MILP)
Obsolescence
particle swarm optimisation (PSO)
Particle swarm optimization
Product recovery system (PRS)
Remanufacturing
Title A comparative study of GA and PSO approach for cost optimisation in product recovery systems
URI https://www.tandfonline.com/doi/abs/10.1080/00207543.2022.2035008
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