A multi-objective optimization of closed-loop supply chain problem with vehicle routing

Shahul Hamid Khan; Vivek Kumar Chouhan; Santhosh Srinivasan

doi:10.30574/gjeta.2021.6.2.0009

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A multi-objective optimization of closed-loop supply chain problem with vehicle routing

Shahul Hamid Khan ^{1, *}, Vivek Kumar Chouhan ¹and Santhosh Srinivasan ²

1 Department of Mechanical Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram (IIITDM), Chennai, India.

2 Department of Mechanical Engineering, Sri Krishna College of Technology, Coimbatore, India

Research Article

Global Journal of Engineering and Technology Advances, 2021, 06(02), 121-130.

Article DOI: 10.30574/gjeta.2021.6.2.0009

DOI url: https://doi.org/10.30574/gjeta.2021.6.2.0009

Publication history

Received on 05 January 2021; revised on 30 January 2021; accepted on 02 February 2021

Abstract

Product recovery has become significant business strategies to increase a competitive edge in business and also in the society. Parts from discarded products due to rapid advancement and post-consumer products before & after end-of-life (EOL) are recovered to reduce landfill waste and to have become a part of circular economy. Product recovery is made possible with the help of Closed-loop supply chain (CLSC). This paper concentrates on multi-period, multi-product, and multi-echelon Closed Loop Green Supply Chain (CLGSC) network. A bi-objective (cost and emission) Mixed Integer Linear Programming (MILP) model has been formulated for the network and has been optimized using Goal Programming approach and Genetic Algorithm. Results are discussed for providing some managerial insights of the model.

Keywords

Closed Loop Supply Chain; Product Recovery; Goal Programming; Emission; Vehicle Routing; Green Supply Chain

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How to cite this article

Shahul Hamid Khan, Vivek Kumar Chouhan and Santhosh Srinivasan. A multi-objective optimization of closed-loop supply chain problem with vehicle routing. Global Journal of Engineering and Technology Advances, 2021, 6(2), 121-130. Article DOI: https://doi.org/10.30574/gjeta.2021.6.2.0009

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