An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model

In this paper, five variants of Differential Evolution (DE) algorithms are proposed to solve the multi-echelon supply chain network optimization problem. Supply chain network composed of different stages which involves products, services and information flow between suppliers and customers, is a val...

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Main Authors:	Ayda, Emdadian, Ponnambalam, S. G., Kanagaraj, G.
Format:	Article
Language:	English
Published:	Universiti Malaysia Pahang 2018
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/22211/ http://umpir.ump.edu.my/id/eprint/22211/ http://umpir.ump.edu.my/id/eprint/22211/ http://umpir.ump.edu.my/id/eprint/22211/1/An%20implementation%20of%20differential%20evolution%20algorithm.pdf

id	ump-22211
recordtype	eprints
spelling	ump-222112018-11-12T01:43:16Z http://umpir.ump.edu.my/id/eprint/22211/ An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model Ayda, Emdadian Ponnambalam, S. G. Kanagaraj, G. TS Manufactures In this paper, five variants of Differential Evolution (DE) algorithms are proposed to solve the multi-echelon supply chain network optimization problem. Supply chain network composed of different stages which involves products, services and information flow between suppliers and customers, is a value-added chain that provides customers products with the quickest delivery and the most competitive price. Hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between several objectives. The supply chain problem utilized in this study is taken from literature which incorporates demand, capacity, raw-material availability, and sequencing constraints in order to maximize total profitability. The performance of DE variants has been investigated by solving three stage multi-echelon supply chain network optimization problems for twenty demand scenarios with each supply chain settings. The objective is to find the optimal alignment of procurement, production, and distribution while aiming towards maximizing profit. The results show that the proposed DE algorithm is able to achieve better performance on a set of supply chain problem with different scenarios those obtained by well-known classical GA and PSO. Universiti Malaysia Pahang 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22211/1/An%20implementation%20of%20differential%20evolution%20algorithm.pdf Ayda, Emdadian and Ponnambalam, S. G. and Kanagaraj, G. (2018) An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model. Journal of Modern Manufacturing Systems and Technology, 1. pp. 1-14. ISSN 2636-9575 http://journal.ump.edu.my/jmmst/article/view/196 DOI: https://doi.org/10.15282/jmmst.v1i1.196
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 Ayda, Emdadian Ponnambalam, S. G. Kanagaraj, G. An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model
description	In this paper, five variants of Differential Evolution (DE) algorithms are proposed to solve the multi-echelon supply chain network optimization problem. Supply chain network composed of different stages which involves products, services and information flow between suppliers and customers, is a value-added chain that provides customers products with the quickest delivery and the most competitive price. Hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between several objectives. The supply chain problem utilized in this study is taken from literature which incorporates demand, capacity, raw-material availability, and sequencing constraints in order to maximize total profitability. The performance of DE variants has been investigated by solving three stage multi-echelon supply chain network optimization problems for twenty demand scenarios with each supply chain settings. The objective is to find the optimal alignment of procurement, production, and distribution while aiming towards maximizing profit. The results show that the proposed DE algorithm is able to achieve better performance on a set of supply chain problem with different scenarios those obtained by well-known classical GA and PSO.
format	Article
author	Ayda, Emdadian Ponnambalam, S. G. Kanagaraj, G.
author_facet	Ayda, Emdadian Ponnambalam, S. G. Kanagaraj, G.
author_sort	Ayda, Emdadian
title	An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model
title_short	An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model
title_full	An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model
title_fullStr	An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model
title_full_unstemmed	An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model
title_sort	implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model
publisher	Universiti Malaysia Pahang
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22211/ http://umpir.ump.edu.my/id/eprint/22211/ http://umpir.ump.edu.my/id/eprint/22211/ http://umpir.ump.edu.my/id/eprint/22211/1/An%20implementation%20of%20differential%20evolution%20algorithm.pdf
first_indexed	2023-09-18T22:32:56Z
last_indexed	2023-09-18T22:32:56Z
_version_	1777416406716907520

An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model

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