Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/16390
Title: Monte Carlo DEA and budget allocation for data collection: An application to measure supply chain efficiency
Authors: WONG WAI PENG
Keywords: DEA, supply chain efficiency, data uncertainties, simulation optimization, genetic algorithm, gradient.
Issue Date: 16-Jul-2009
Source: WONG WAI PENG (2009-07-16). Monte Carlo DEA and budget allocation for data collection: An application to measure supply chain efficiency. ScholarBank@NUS Repository.
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 thesis is divided into two parts. First, we develop a DEA model to measure supply chain efficiency and provide an alternative way to measure efficiency in stochastic environment, which is Monte Carlo DEA. Second, in the context where data collection is needed and expensive, we provide a way on how to intelligently allocate the resources in data collection in order to get a better estimation of the efficiency score. As there is no explicit model to address this question, this thesis will introduce few methods based on the optimization simulation technique, which are the two-phase gradient technique and the GA (genetic algorithm) based technique to solve the problem.
URI: http://scholarbank.nus.edu.sg/handle/10635/16390
Appears in Collections:Ph.D Theses (Open)

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