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A chance constraint based multi-item production planning model using simulation optimization

Title	A chance constraint based multi-item production planning model using simulation optimization
Author	Albey, Erinç, Uzsoy, R., Kempf, K. G.
Publication Date:	2016
Publication Place	- IEEE
Subject	Semiconductor industry, Scheduling models, Evolution, Systems
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-5090-4486-3
Record ID	9509458d-b7a5-4044-87dc-e07a37dcc4bc
Library Location	Industrial Engineering
Date	2016
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	We consider a single stage multi-item production-inventory system under stochastic demand. We had previously proposed a production planning model integrating ideas from forecast evolution and inventory theory to plan work releases into a production facility in the face of stochastic demand. However, this model is tractable only if the capacity allocations are exogenous. This paper determines the capacity allocated to each product in each period using a genetic algorithm. Computational experiments reveal that the proposed algorithm outperforms the previous approach in both total cost and service level.
DOI	10.1109/WSC.2016.7822309

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Özyeğin University

A chance constraint based multi-item production planning model using simulation optimization

Author Albey, Erinç, Uzsoy, R., Kempf, K. G.

Publication Date 2016

Publication Place - IEEE

Subject Semiconductor industry, Scheduling models, Evolution, Systems

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-5090-4486-3

Record ID 9509458d-b7a5-4044-87dc-e07a37dcc4bc

Library Location Industrial Engineering

Date 2016

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text We consider a single stage multi-item production-inventory system under stochastic demand. We had previously proposed a production planning model integrating ideas from forecast evolution and inventory theory to plan work releases into a production facility in the face of stochastic demand. However, this model is tractable only if the capacity allocations are exogenous. This paper determines the capacity allocated to each product in each period using a genetic algorithm. Computational experiments reveal that the proposed algorithm outperforms the previous approach in both total cost and service level.

DOI 10.1109/WSC.2016.7822309