Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a hol...Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life （EOL） strategy planning, specifically using remanufactur- ing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufactufing for a product and its compo- nents. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enu- meration of all EOL strategies is prohibitively time-con- suming, even for a product with a small number of components, genetic algorithm （GA）, specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology.展开更多
文摘Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life （EOL） strategy planning, specifically using remanufactur- ing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufactufing for a product and its compo- nents. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enu- meration of all EOL strategies is prohibitively time-con- suming, even for a product with a small number of components, genetic algorithm （GA）, specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better product design. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology.
