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Mobile robots and evolutionary optimization algorithms for green supply chain management in a used-car resale company

Author

Listed:
  • V. Sathiya

    (E.G.S. Pillay Engineering College)

  • M. Chinnadurai

    (E.G.S. Pillay Engineering College)

  • S. Ramabalan

    (E.G.S. Pillay Engineering College)

  • Andrea Appolloni

    (University of Rome Tor Vergata)

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.

Suggested Citation

  • V. Sathiya & M. Chinnadurai & S. Ramabalan & Andrea Appolloni, 2021. "Mobile robots and evolutionary optimization algorithms for green supply chain management in a used-car resale company," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9110-9138, June.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:6:d:10.1007_s10668-020-01015-2
    DOI: 10.1007/s10668-020-01015-2
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    1. Mladen Krstić & Giulio Paolo Agnusdei & Pier Paolo Miglietta & Snežana Tadić & Violeta Roso, 2022. "Applicability of Industry 4.0 Technologies in the Reverse Logistics: A Circular Economy Approach Based on COmprehensive Distance Based RAnking (COBRA) Method," Sustainability, MDPI, vol. 14(9), pages 1-30, May.
    2. A’aeshah Alhakamy & Areej Alhowaity & Anwar Abdullah Alatawi & Hadeel Alsaadi, 2023. "Are Used Cars More Sustainable? Price Prediction Based on Linear Regression," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    3. Lia Nersesian & Ashkan Hafezalkotob & Raziyeh Reza-Gharehbagh, 2023. "Alternative governmental carbon policies on populations of green and non-green supply chains in a competitive market," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4139-4172, May.
    4. Shin-Cheng Yeh & Ai-Wei Wu & Hui-Ching Yu & Homer C. Wu & Yi-Ping Kuo & Pei-Xuan Chen, 2021. "Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-34, August.

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