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Performance evaluation of supply chain in stochastic environment: using a simulation based DEA framework

Performance evaluation of supply chain in stochastic environment: using a simulation based DEA... Supply chain operates in a dynamic platform and its performance measurement requires intensive data collection from the entire value chain. The task of collecting data in supply chain is not trivial and it often faces with uncertainties. This paper develops a simple tool to measure supply chain performance in the real environment, which is stochastic. Firstly, it introduces the data envelopment analysis (DEA) supply chain model in combination with Monte Carlo simulation to measure the supply chain performance in the stochastic environment. Secondly, a GA-based heuristic technique will be presented to improve the prediction of the performance measurement. This methodology offers an alternative to handle uncertainties in supply chain efficiency measurement and could also be used in other relevant fields, to measure efficiency. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Business Performance and Supply Chain Modelling Inderscience Publishers

Performance evaluation of supply chain in stochastic environment: using a simulation based DEA framework

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1758-9401
eISSN
1758-941X
DOI
10.1504/IJBPSCM.2009.030642
Publisher site
See Article on Publisher Site

Abstract

Supply chain operates in a dynamic platform and its performance measurement requires intensive data collection from the entire value chain. The task of collecting data in supply chain is not trivial and it often faces with uncertainties. This paper develops a simple tool to measure supply chain performance in the real environment, which is stochastic. Firstly, it introduces the data envelopment analysis (DEA) supply chain model in combination with Monte Carlo simulation to measure the supply chain performance in the stochastic environment. Secondly, a GA-based heuristic technique will be presented to improve the prediction of the performance measurement. This methodology offers an alternative to handle uncertainties in supply chain efficiency measurement and could also be used in other relevant fields, to measure efficiency.

Journal

International Journal of Business Performance and Supply Chain ModellingInderscience Publishers

Published: Jan 1, 2009

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