Title: Mathematical programming model to optimise an environmentally constructed supply chain: a genetic algorithm approach

Authors: Sejal Satish Dhage; Vaibhav S. Narwane; Rakesh D. Raut; Niraj Kishore Dere; Bhaskar B. Gardas; Balkrishna E. Narkhede

Addresses: K.J. Somaiya College of Engineering, Vidyanagar, Vidyavihar East, Ghatkopar East, Mumbai, Maharashtra 400077, India ' Department of Production Engineering, Veermata Jijabai Technological Institute (VJTI), HR Mahajani Marg, Matunga, Mumbai, Maharashtra, 400019, India ' Department of Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Vihar Lake, NITIE, Powai, Mumbai, Maharashtra, 400087, India ' K.J. Somaiya College of Engineering, Vidyanagar, Vidyavihar East, Ghatkopar East, Mumbai, Maharashtra 400077, India ' Department of Production Engineering, Veermata Jijabai Technological Institute (VJTI), HR Mahajani Marg, Matunga, Mumbai, Maharashtra, 400019, India ' Department of Industrial Engineering and Management Systems, National Institute of Industrial Engineering (NITIE), Vihar Lake, NITIE, Powai, Mumbai, Maharashtra, 400087, India

Abstract: The purpose of the study is to develop a network model for effective decision making from the sustainability aspect. The study proposes a mathematical programming model to optimise an environmentally constructed supply chain. The effect on the environment by components such as carbon monoxide, nitrogen dioxide and volatile organic particles formed during transportation in the supply chain has been considered. The multi-objective genetic algorithm optimises total cost and total environmental impact, which were subjected to constraints of demand, return, flow balance and capacity. The total cost consists of purchase cost, fixed cost, transportation cost, manufacturing cost, processing cost and inventory cost. Environmental impact of production, transportation, handling, lead reclamation, and plastic recycling process was considered. The model also uses life cycle assessment-based method for quantification of environmental impact and establishes Pareto optimal solutions for minimisation of economic as well as environmental impact. Results show a considerable reduction in closed-loop supply chain cost.

Keywords: reverse logistics; RL; closed-loop supply chain; CLSC; life cycle assessment; LCA; battery recycling; SLI batteries; environmental supply chain impact; multi-objective optimisation; genetic algorithm; artificial intelligence.

DOI: 10.1504/IJOR.2022.123395

International Journal of Operational Research, 2022 Vol.44 No.2, pp.226 - 253

Received: 27 Apr 2019
Accepted: 05 Aug 2019
Published online: 20 Jun 2022 *

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