| Summary: | 碩士 === 國立交通大學 === 工業工程與管理系所 === 105 === We need to assess a group of selected firms in an industry and their production lines contain two stages. Each stage has a dedicated bundle of inputs and outputs and the two stages share the other bundle of inputs and outputs. There is a set of intermediate items, (links) between the two stages. The lower and upper bounds of the ratios of the sharing inputs and outputs are prespecified. All firms take turns be the one to evaluate itself. Let firm o evaluates itself against the peer firms. We employed linear programming DEA model to measure the shadow prices of the inputs, outputs and links to obtain the best efficiency score. The dual model of GBM-CRS is to measure the shadow slacks of inputs to be reduced and the outputs to be added so that firm o would improve himself to the best imaginary frontier of the peer firms. In Phase-I of our procedure, we treat the links as non-discretionary in the linear programming model of GBM-bc (Liu, 2017b). The optimal best efficiency score is computed and the links are partitioned into analog-as-input (aa-inputs) and analog-as-outputs (aa-outputs). The ratio of each sharing inputs and outputs are determined as well. In Phase-II, a linear programming model of GBM-bc model (Liu, 2017a) aims to measure the shadow prices and shadow slacks of the inputs, aa-inputs, outputs and aa-outputs. The model determines the best efficiency score of the overall production system as well. Reducing the evaluated shadow slacks, the targets of inputs and aa-inputs on the best efficiency frontier are defined. On the other hand, adding the obtained shadow slacks, the locations of outputs and aa-outputs on the best efficiency frontier are pointed. The benchmark firms of firm o are identified as well. Once each firm assessed himself, all the firms are ranked according to their efficiency scores.
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