Marginal Model Synthesization Algorithm for Data Envelopment Analysis and its Application

In this paper, we propose a new approach for determining the unknown quantities in Banker–Charnes–Cooper models for data envelopment analysis by developing the marginal model synthesization algorithm. In this algorithm, several marginal fractional programming models are first constructed based on a simple numeric optimization. Then, a set of synthetic Banker–Charnes–Cooper models is obtained by compounding the marginal fractional programming models. A comparison of the proposed and existing approaches in terms of computational cost and stability of results shows that the former approach has distinct advantages. We also present an application of the proposed approach for analyzing the efficiency of industries in Japanese prefectures.