Volume 32, Issue 1 (IJIEPR 2021)                   IJIEPR 2021, 32(1): 47-64 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Nedaei H, Jalali Naini S G, Makui A. A DEA approach to measure teammate-adjusted efficiencies incorporating learning expectations: An application to oil & gas wells drilling. IJIEPR 2021; 32 (1) :47-64
URL: http://ijiepr.iust.ac.ir/article-1-1039-en.html
1- Department of Industrial Engineering, Iran University of Science and Technology
2- Department of Industrial Engineering, Iran University of Science and Technology , sgjalali@iust.ac.ir
Abstract:   (2520 Views)
Data envelopment analysis (DEA) measures the relative efficiency of decision-making units (DMU) 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 in a way that they are fairly evaluated regarding their teammates’ relative performances. 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.
Full-Text [PDF 818 kb]   (1053 Downloads)    
Type of Study: Research | Subject: Productivity Improvement
Received: 2020/03/28 | Accepted: 2020/09/21 | Published: 2020/12/11

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.