Mobile robots and evolutionary optimization algorithms for green supply chain management in a used-car resale company
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Mobile robots and evolutionary optimization algorithms for green supply chain management in a used‑car resale company V. Sathiya1 · M. Chinnadurai2 · S. Ramabalan3 · Andrea Appolloni4 Received: 31 March 2020 / Accepted: 25 September 2020 © Springer Nature B.V. 2020
Abstract To ensure environment friendly products in the international supply chain scenario, an important initiative is reverse supply chain (RSC). The benefits (environmental and financial) from a RSC are influenced by disposal of reusable parts, cost factors and emissions during transportation, collection, recovery facilities, recycling, disassembly and remanufacturing. During designing a network for reverse supply chain, some objectives related to social, economic and ecological concerns are to be considered. This paper suggests two strategies for reducing the costs and emissions in a network of RSC. This research work considers design of RSC for a used-car resale company. First strategy outlines the design of a mobile robot—solar-powered automated guided vehicle (AGV) for reducing logistic cost and greenhouse gas (GHG) emissions. The second strategy proposes a new multi-objective optimization model to reduce the costs and emissions of GHG. Strict carbon caps constraint is used as a guideline for reducing emissions. The proposed strategies are tested for a real-world problem at Maruti True Value network design in Tamil Nadu and Puducherry region of India. Two algorithms namely Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) and Heterogeneous Multi-Objective Differential Evolution algorithm (HMODE) are proposed. HMODE is a new improved multi-objective optimization algorithm. To select the best optimal solution from the Pareto-optimal front, normalized weighted objective functions (NWOF) method is used. The strength or weakness of a Pareto-optimal front is evaluated by the metrics namely ratio of non-dominated individuals (RNI) and solution spread measure (SSM). Also, Algorithm Effort (AE) and Optimiser Overhead (OO) are utilized to find the computational effort of multi-objective optimization algorithms. Results proved that proposed strategies are worth enough to reduce the GHG emissions and costs. Keywords Green supply chain management · Reverse supply chain · Low carbon logistics · Greenhouse gas (GHG) emissions · Robot · Automated guided vehicle (AGV) · Multiobjective optimization · NSGA-II · HMODE
* V. Sathiya [email protected] Extended author information available on the last page of the article
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1 Introduction The customer awareness regarding eco-friendly products is on the rise. This forces the original products manufacturers responsible for end-of-life products (Devika Kannan et al. 2012). Simultaneously, the number of products disposed by the consumers is also growing. The increasing energy prices, climate change, limited resources, increasing emissions, increasing population, disposal capacities and enhanced standard of life urge the research and industry people to design and deve
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