Optimization of a two-echelon supply network using multi-objective genetic algorithms

Fahimnia, B; Luong, L; Marian, R

Optimization of a two-echelon supply network using multi-objective genetic algorithms

Fahimnia, B Luong, L Marian, R

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Publisher:: IEEE Computer Society
Publication Type:: Conference Proceeding
Citation:: 2009 WRI World Congress on Computer Science and Information Engineering, 2009, 5 pp. 406 - 413
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Fahimnia, B	en_US
dc.contributor.author	Luong, L	en_US
dc.contributor.author	Marian, R	en_US
dc.contributor.editor	Burgin, M	en_US
dc.contributor.editor	Chowdhury, MH	en_US
dc.contributor.editor	Ham, CH	en_US
dc.contributor.editor	Ludwig, S	en_US
dc.contributor.editor	Su, W	en_US
dc.contributor.editor	Yenduri, S	en_US
dc.date	2009-03-31	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	2009 WRI World Congress on Computer Science and Information Engineering, 2009, 5 pp. 406 - 413	en_US
dc.identifier.isbn	978-0-7695-3507-4	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29516
dc.description.abstract	The overall performance of a supply-chain (SC) is influenced significantly by the decisions taken in its production-distribution (P-D) plan. A P-D plan integrates decisions in production, transport and warehousing as well as inventory management. One key issue in the performance evaluation of a Supply Network (SN) is the modeling and optimization of P-D planning problem considering its actual complexity. Based on the integration of Aggregate Production Planning and Distribution Planning, this paper firstly develops a mixed integer formulation for a twoechelon supply network considering the real-world variables and constraints. A multi-objective genetic algorithm (MOGA) is then designed for the optimization of the developed mathematical model. Finally, a real-world case study incorporating multiple products, multiple plants, multiple warehouses, multiple end-users, and multiple time periods will be considered for investigating the performance evaluation of the MOGA method against the traditional approaches of SC planning.	en_US
dc.publisher	IEEE Computer Society	en_US
dc.relation.ispartof	2009 WRI World Congress on Computer Science and Information Engineering	en_US
dc.relation.ispartof	IEEE World Congress on Computer Science and Information Engineering	en_US
dc.rights	© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Optimization of a two-echelon supply network using multi-objective genetic algorithms	en_US
dc.type	Conference Proceeding
utslib.citation.volume	5	en_US
utslib.location	USA	en_US
utslib.location.activity	Los Angeles, USA	en_US
utslib.for	1503 Business and Management	en_US
dc.location.activity	Los Angeles, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2009-03-31	en_US
pubs.volume	5	en_US

Abstract:

The overall performance of a supply-chain (SC) is influenced significantly by the decisions taken in its production-distribution (P-D) plan. A P-D plan integrates decisions in production, transport and warehousing as well as inventory management. One key issue in the performance evaluation of a Supply Network (SN) is the modeling and optimization of P-D planning problem considering its actual complexity. Based on the integration of Aggregate Production Planning and Distribution Planning, this paper firstly develops a mixed integer formulation for a twoechelon supply network considering the real-world variables and constraints. A multi-objective genetic algorithm (MOGA) is then designed for the optimization of the developed mathematical model. Finally, a real-world case study incorporating multiple products, multiple plants, multiple warehouses, multiple end-users, and multiple time periods will be considered for investigating the performance evaluation of the MOGA method against the traditional approaches of SC planning.

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http://hdl.handle.net/10453/29516