A fuzzy multi-objective optimization model for sustainable reverse logistics network design. (August 2016)

• Record Type: Journal Article
• Authors: Govindan, Kannan; Paam, Parichehr; Abtahi, Amir-Reza
• Is Part Of: Ecological indicators. Volume 67(2016)
• Page Start: 753
• Page End: 768
• Publication Date: 2016-08
• Subjects: Reverse logistics -- Sustainability -- Social responsibility -- Fuzzy mathematical programming -- Multi-objective metaheuristic algorithm -- Epsilon-constraint method; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2016.03.017
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Proposes a multiobjective model to design an optimized reverse logistic network. Optimizes economic, social and environmental objectives simultaneously. Fuzzy approach to dealing with uncertainties in many parameters of the model. Multi-objective genetic algorithm for solving the model efficiently. Abstract: Decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects. Developing a model, which can simultaneously consider these environmental, social, and economic aspects and their indicators, is an important problem for both researchers and practitioners. In this paper, we try to address this comprehensive approach by using indicators for measurement of aforementioned aspects and by applying fuzzy mathematical programming to design a multi-echelon multi-period multi-objective model for a sustainable reverse logistics network. To reflect all aspects of sustainability, we try to minimize the present value of costs, as well as environmental impacts, and optimize the social responsibility as objective functions of the model. In order to deal with uncertain parameters, fuzzy mathematical programming is used, and to obtain solutions on Pareto front, a customized multi-objective particle swarm optimization (MOPSO) algorithm is applied. The validity of the proposed solution procedure has been analyzed in small and large size test problems based on four comparison metrics and computational time using analysis of variance. Finally, in order to indicate the applicability of the suggested model and the practicality of the proposed solution procedure, the model has been implemented in a medical syringe recycling system. The results reveal that the suggested MOPSO algorithm overtakes epsilon-constraint method from the aspects of quality of the solutions as well as computational time. Proper use of the proposed process could help managers efficiently manage the flow of recycled products with regard to environmental and social considerations, and the process offers a sustainable competitive advantage to corporations.