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Resource allocation based on context-dependent data envelopment analysis and a multi-objective linear programming approach

delete2016-11-01
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PRE
AI
J
Jie Wu
Q
Qingyuan Zhu
Q
Qingxian An *
J
Junfei Chu
X
Xiang Ji
DOI:10.1016/j.cie.2016.08.025delete
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Abstract

Abstract

En 中文
This paper discusses a mechanism for the allocation of resources among a set of decision making units (DMUs) which are managed by a centralized control unit in an organization. Data envelopment analysis (DEA) and multi-objective linear programming (MOLP) are integrated to deal with this resource allocation problem. Also, context-dependent DEA is introduced to identify the changed production possibility set after resource allocation, which determines the production plans that are feasible with input increase or decrease in general. For the centralized unit, the MOLP approach is proposed to simultaneously maximize total output and effectiveness while minimizing the total allocated variable input consumption. Among these objectives, the effectiveness is determined by the output growth rate for all DMUs, which can reflect the effects obtained by allocating the input resources that are not used up, such as new equipment. In addition, we restrict the production of limited resources to the new most productive scale size (MPSS) region where the DMUs have the best economic characteristics. Finally, an example is employed to illustrate the approach. (C) 2016 Published by Elsevier Ltd.
Keywords:
Resource allocation
Data envelopment analysis
Multi-objective linear programming
Most productive scale size
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Computers and Industrial Engineering cover
Computers and Industrial Engineering
IF:
6.5
Papers:
1.0W
Citations:
3.8W

Organization

U
university of science & technology of china, cas
Scholars:
3.2W
Papers: 2.7W
Citations: 74
C
chinese academy of sciences
Scholars:
56.7W
Papers: 45.0W
Citations: 704
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