基于学习期望的DEA方法测量团队调整效率:在油气钻井中的应用

Q3 Decision Sciences
H. Nedaei, S. G. J. Naini, A. Makui
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引用次数: 0

摘要

数据包络分析(Data Envelopment Analysis, DEA)衡量决策单元在多输入多输出情况下的相对效率。在将工作团队视为DMU的情况下,它通常由多个职位和几个员工组成。然而,没有办法衡量员工个人的效率,同时考虑到队友的影响。本文提出了一个模型来衡量员工的效率,这样他们就可以公平地评估他们的队友的相对表现。并将学习期望和作业中断导致的学习损失效应纳入DEA模型。因此,该模型能够对每个职位的员工进行排名,然后可以在奖励系统中使用。然后,通过对South Pars气田20口井、160个不同作业的案例研究,探索了所提出模型的能力,这是DEA在油气井钻井性能分析中的首次应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A DEA approach to measure teammate-adjusted efficiencies incorporating learning expectations: An application to oil & gas wells drilling
Data Envelopment Analysis (DEA) measures the relative efficiency of Decision-Making Units (DMUs) with multiple inputs and multiple outputs. In the case of considering a working team as a DMU, it often comprises multiple positions with several employees. However, there is no method to measure the efficiency of employees individually taking account the effect of teammates. This paper presents a model to measure the efficiency of employees such that they are fairly evaluated regarding relative performances of their teammates. Moreover, the learning expectations and the effect of learning lost due to operation breaks are incorporated into the DEA model. This model is thus able to rank the employees working in each position that can then be utilized within award systems. The capabilities of the proposed model are then explored by a case study of 20 wells with 160 distinct operations in the South Pars gas field, which is the first application of DEA in the oil and gas wells drilling performance analysis.
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来源期刊
International Journal of Industrial Engineering and Production Research
International Journal of Industrial Engineering and Production Research Engineering-Industrial and Manufacturing Engineering
CiteScore
1.60
自引率
0.00%
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审稿时长
10 weeks
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