Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence

The strategic location of manufacturing plants and warehouses and the allocation of resources to the various stages of a supply chain using big data is of paramount importance in the era of internet of things. A multi-objective mathematical model is formulated in this paper to solve a location-alloc...

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Main Authors:	Doolun, Ian Shivraj, Ponnambalam, S. G., Subramanian, Nachiappan, Kanagaraj, G.
Format:	Article
Language:	English
Published:	Elsevier Ltd. 2018
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/22373/ http://umpir.ump.edu.my/id/eprint/22373/ http://umpir.ump.edu.my/id/eprint/22373/ http://umpir.ump.edu.my/id/eprint/22373/1/Data%20driven%20hybrid%20evolutionary%20analytical%20approach%20for%20multi%20objective%20location%20allocation%20decisions.pdf

id	ump-22373
recordtype	eprints
spelling	ump-223732018-11-21T03:00:35Z http://umpir.ump.edu.my/id/eprint/22373/ Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence Doolun, Ian Shivraj Ponnambalam, S. G. Subramanian, Nachiappan Kanagaraj, G. TS Manufactures The strategic location of manufacturing plants and warehouses and the allocation of resources to the various stages of a supply chain using big data is of paramount importance in the era of internet of things. A multi-objective mathematical model is formulated in this paper to solve a location-allocation problem in a multi-echelon supply chain network to optimize three objectives simultaneously such as minimization of total supply chain cost (TSCC), maximization of fill rate and minimization of CO2 emissions. Data driven hybrid evolutionary analytical approach is proposed by integrating Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) to handle multiple objectives into Differential Evolution (DE) algorithm. Five variants of the hybrid algorithm are evaluated in addition to comparing the performance with the existing Multi-Objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. Extensive computational experiments confirm the superiority of the proposed Data driven hybrid evolutionary analytical approach over the existing MOHPSO algorithm. This study identifies a specific variant that is capable of producing the best solution in a higher order simulated instances and complex realistic scenario such as an automotive electronic parts supply chain in Malaysia. Elsevier Ltd. 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22373/1/Data%20driven%20hybrid%20evolutionary%20analytical%20approach%20for%20multi%20objective%20location%20allocation%20decisions.pdf Doolun, Ian Shivraj and Ponnambalam, S. G. and Subramanian, Nachiappan and Kanagaraj, G. (2018) Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence. Computers and Operations Research, 98. pp. 265-283. ISSN 0305-0548 https://doi.org/10.1016/j.cor.2018.01.008 10.1016/j.cor.2018.01.008
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TS Manufactures
spellingShingle	TS Manufactures Doolun, Ian Shivraj Ponnambalam, S. G. Subramanian, Nachiappan Kanagaraj, G. Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence
description	The strategic location of manufacturing plants and warehouses and the allocation of resources to the various stages of a supply chain using big data is of paramount importance in the era of internet of things. A multi-objective mathematical model is formulated in this paper to solve a location-allocation problem in a multi-echelon supply chain network to optimize three objectives simultaneously such as minimization of total supply chain cost (TSCC), maximization of fill rate and minimization of CO2 emissions. Data driven hybrid evolutionary analytical approach is proposed by integrating Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) to handle multiple objectives into Differential Evolution (DE) algorithm. Five variants of the hybrid algorithm are evaluated in addition to comparing the performance with the existing Multi-Objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. Extensive computational experiments confirm the superiority of the proposed Data driven hybrid evolutionary analytical approach over the existing MOHPSO algorithm. This study identifies a specific variant that is capable of producing the best solution in a higher order simulated instances and complex realistic scenario such as an automotive electronic parts supply chain in Malaysia.
format	Article
author	Doolun, Ian Shivraj Ponnambalam, S. G. Subramanian, Nachiappan Kanagaraj, G.
author_facet	Doolun, Ian Shivraj Ponnambalam, S. G. Subramanian, Nachiappan Kanagaraj, G.
author_sort	Doolun, Ian Shivraj
title	Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence
title_short	Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence
title_full	Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence
title_fullStr	Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence
title_full_unstemmed	Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence
title_sort	data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: automotive green supply chain empirical evidence
publisher	Elsevier Ltd.
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22373/ http://umpir.ump.edu.my/id/eprint/22373/ http://umpir.ump.edu.my/id/eprint/22373/ http://umpir.ump.edu.my/id/eprint/22373/1/Data%20driven%20hybrid%20evolutionary%20analytical%20approach%20for%20multi%20objective%20location%20allocation%20decisions.pdf
first_indexed	2023-09-18T22:33:16Z
last_indexed	2023-09-18T22:33:16Z
_version_	1777416428049137664

Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence

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