Abstract: Manufacturing process evaluation is an important part to improve the efficiency of the manufacturing system. Data Envelopment Analysis(DEA)is an effective tool to measure the relative efficiencies of the manufacturing system. However, the traditional DEA models ignore the production information embedded in the intermediate products as well as the allocation information of various inputs among the individual sub-process and can't reflect the real underlying production technology. To solve this problem, we have proposed a DEA-efficiency measurement and decomposition models of two-stage manufacturing system with exogenous inputs under both constant and variable returns to scale. The modeling method does not need the pre-specified aggregation way of sub-effciencies into the system efficiency. The evaluation results for the iron&steel manufacturing system show that the model can do more to find inefficient decision-making units(DMUs)and possible potential for performance improvement than the traditional approaches, and help decision makers to detect the origination of inefficiency for those inefficient DMUs. The numerical example proves the reasonability of our method. This study provides an analytical method for the efficiency measurement and decomposition of such complex manufacturing system.

Key words: operational research, efficiency analysis, network data envelopment analysis, two-stage manufacturing system with exogenous inputs

摘要： 制造过程评价是改善制造系统效率的重要一环,传统的评价方法将每个制造系统决策单元视为黑箱来研究整体效率,忽略了中间产品转化信息及投入要素在各子过程中的配置信息。针对两阶段(第二阶段有外源性新投入)制造系统的效率评估问题,分别在固定规模报酬和可变规模报酬假设下,充分利用制造系统中间产品的转化及外源投入要素的配置信息,建立了制造系统网络DEA效率测度及分解模型,建模方法遵循客观评价原则,无需事先主观确定子效率和系统效率之间的组合关系。并将其应用于钢铁制造系统效率测度与分解,研究结果表明该方法能够挖掘决策单元内部子单元的效率情况,帮助决策者发现复杂制造过程非有效的根源,为复杂制造过程的整体效率测度及分解提供了有效的分析方法。

关键词: 运筹学, 效率分析, 网络数据包络分析, 外源投入型两阶段制造系统