| Summary: | 碩士 === 國立交通大學 === 工業工程與管理學系 === 100 === Data envelopment analysis (DEA) is a method, utilizing linear programming (LP), to compute relative efficiencies of all decision making units (DMUs). Solving LP problems is easy in theory. However, in practice, computational loading cannot be ignored for large-scale data. This thesis proposes an algorithm that significantly improves computational effort for solving large-scale DEA problems. Specifically, the proposed algorithm is able to control the size of individual LP problems, e.g. no more than 300 DMUs are used in every LP problem, for computing relative efficiency. As a result, computational efficiency is improved from LP problem size reduction (e.g. from 10,000 to 300 DMUs). This work can also be the theoretical foundation of using trial version or free software (e.g. AMPL and GAMS) to solve DEA problems in any scale.
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