带反馈的两阶段生产过程效率评价:改进的DEA模型

IF 1.1 4区计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

Infor Pub Date : 2022-07-28 DOI:10.1080/03155986.2022.2104573

Dawei Wang, Fangqing Wei, Fengwei Yang

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引用次数: 4

摘要

摘要:本文探讨了反馈系统中总效率是两阶段效率的加权平均的加性分解方法。在加性分解法中，权重通常表示为投入各阶段的总资源所占的比例，反映各阶段的相对重要性。将这种确定权重的方法应用于带反馈的可加性两阶段数据包络分析(DEA)模型时，我们发现第一阶段的权重从未低于第二阶段，表明第一阶段更受青睐，这导致了效率评价的偏倚。另外，随着一级效率的提高，一级的权重也随之降低，不符合为了使整体效率最大化，应该给效率较高的一级分配更大的权重的观点。在本研究中，我们建立了一个改进的常权反馈两阶段DEA模型，并开发了一种启发式求解方法。以2019年中国大陆30个地区高技术产业的实证数据为例，验证了改进模型的适用性和优越性。

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Efficiency evaluation of a two-stage production process with feedback: an improved DEA model

Abstract This study explores the additive decomposition method in which the overall efficiency is a weighted average of the two stage efficiencies in a feedback system. In the additive decomposition method, the weight is often expressed as the proportion of total resources devoted to each stage, reflecting the relative importance of the stage. When this approach of determining weights is used in the additive two-stage data envelopment analysis (DEA) model with feedback, we find that the weight of the first stage is never less than that of the second stage, indicating that the first stage is favored, which causes a biased efficiency evaluation. Additionally, the weight of the first stage decreases when its efficiency increases, which does not conform to the belief that to maximize the overall efficiencies, a larger weight should be assigned to the stage with higher efficiency. In this study, we build an improved feedback two-stage DEA model with constant weights and develop a heuristic method to solve it. An empirical dataset covering the high-tech industry of 30 regions in mainland China in 2019 is studied to illustrate the applicability and superiority of our improved model.

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来源期刊

Infor 管理科学-计算机：信息系统

CiteScore

2.60

自引率

7.70%

发文量

审稿时长

>12 weeks

期刊介绍： INFOR: Information Systems and Operational Research is published and sponsored by the Canadian Operational Research Society. It provides its readers with papers on a powerful combination of subjects: Information Systems and Operational Research. The importance of combining IS and OR in one journal is that both aim to expand quantitative scientific approaches to management. With this integration, the theory, methodology, and practice of OR and IS are thoroughly examined. INFOR is available in print and online.