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.

Sathiya, V., Chinnadurai, M., Ramabalan, S., Appolloni, A. (2021). Mobile robots and evolutionary optimization algorithms for green supply chain management in a used-car resale company. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY [10.1007/s10668-020-01015-2].

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

Appolloni A.
Membro del Collaboration Group
2021-01-01

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.
2021
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore SECS-P/08 - ECONOMIA E GESTIONE DELLE IMPRESE
English
Green supply chain management
Reverse supply chain
Low carbon logistics
Greenhouse gas (GHG) emissions
Robot
Automated guided vehicle (AGV)
Multi-objective optimization
NSGA-II
HMODE
Sathiya, V., Chinnadurai, M., Ramabalan, S., Appolloni, A. (2021). Mobile robots and evolutionary optimization algorithms for green supply chain management in a used-car resale company. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY [10.1007/s10668-020-01015-2].
Sathiya, V; Chinnadurai, M; Ramabalan, S; Appolloni, A
Articolo su rivista
File in questo prodotto:
File Dimensione Formato  
Sathiya2020_Article_MobileRobotsAndEvolutionaryOpt.pdf

solo utenti autorizzati

Tipologia: Versione Editoriale (PDF)
Licenza: Copyright dell'editore
Dimensione 2.41 MB
Formato Adobe PDF
2.41 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/255337
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 21
  • ???jsp.display-item.citation.isi??? 16
social impact