A Hybrid Territory Defined evolutionary algorithm approach for closed loop green supply chain network design. (September 2016)

• Record Type: Journal Article
• Authors: Tiwari, Anurag; Chang, Pei-Chann; Tiwari, M.K.; Kandhway, Rishabh
• Is Part Of: Computers & industrial engineering. Volume 99(2016)
• Page Start: 432
• Page End: 447
• Publication Date: 2016-09
• Subjects: SCM -- GCLSP -- EDA -- NSGAII -- EDATDEA; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.05.018
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: A closed loop green supply chain. Highlights: The paper deals with the closed loop green supply chain problem (GCLSC). We considered reducing the waste by recycling waste electronic components. A hybrid of EDA and Territory Defined algorithm is applied to solve the GCLSC. We compare the results with those obtained by NSGA II on a same GCLSC problem. Abstract: The Closed loop Supply chain network distribution is one of the most important problems with much real world application in supply chain management area. Presently climate change problem is one of the major concerns for Researchers. Closed loop green supply chain (GCLSC) problem is the extension of closed loop supply chain problem. Semiconductor industries are one of the major industries and a number of waste products in semiconductor industries are quite high. We have considered reducing the waste in semiconductor by recycling the useful waste electronic equipment. In GCLSC, we consider to maximize the profit in forward supply chain whereas we attempt to minimize the Carbon footprints at the same time. In this paper we used a hybrid of Estimation of distribution algorithm (EDA) and Territory Defined multi-objective algorithm to select the optimum number of facilities in the closed loop supply chain network. To examine the effectiveness of our Hybrid Territory Defined algorithm (EDATDEA), we compare the results with those obtained by NSGA II on a same GCLSC problem with different problem sizes and the same data sets.