Showing 2 results for Jalali Naini
S. G. Jalali Naini , M. B. Aryanezhad, A. Jabbarzadeh , H. Babaei ,
Volume 20, Issue 3 (IJIEPR 2009)
Abstract
This paper studies a maintenance policy for a system composed of two components, which are subject to continuous deterioration and consequently stochastic failure. The failure of each component results in the failure of the system. The components are inspected periodically and their deterioration degrees are monitored. The components can be maintained using different maintenance actions (repair or replacement) with different costs. Using stochastic regenerative properties of the system, a stochastic model is developed in order to analyze the deterioration process and a novel approach is presented that simultaneously determines the time between two successive inspection periods and the appropriate maintenance action for each of the components based on the observed degrees of deterioration. This approach considers different criteria like reliability and long-run expected cost of the system. A numerical example is provided in order to illustrate the implementation of the proposed approach.
Hessam Nedaei, Seyed Gholamreza Jalali Naini, Ahmad Makui,
Volume 32, Issue 1 (IJIEPR 2021)
Abstract
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.
