A Framework for a Fuzzy Sustainable Maintenance Strategy Selection Problem -- restricted access to full text

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Issue Date
2011-05-04
Embargo End Date
Authors
Nezami, Farnaz Ghazi
Advisor
Yildirim, Mehmet Bayram
Citation

Nezami, Farnaz Ghazi (2011). A Framework for a Fuzzy Sustainable Maintenance Strategy Selection Problem. -- In Proceedings: 7th Annual Symposium: Graduate Research and Scholarly Projects. Wichita, KS: Wichita State University, p. 116

Abstract

Maintenance cost constitutes 15-60% of the total production cost. Choosing the right maintenance strategy might provide significant savings in lost production, additional power consumption and lifecycle costs. An optimum maintenance strategy keeps facilities at the functionally desirable situations and decreases the failure rate. As a result, the downtime and the total energy consumption to achieve the same level of production may decrease significantly. Examples of such strategies are failure- based and preventive maintenance strategies. The traditional maintenance strategy selection problem (MSP) is a multi-criteria decision making problem with objectives in cost, resource utilizations, reliability, availability and safety. In this paper, we propose a sustainability based maintenance strategy selection model under uncertain conditions to address some nonmonetary objectives such as social and environmental goals as well. In the proposed framework, decision makers define the weight of each sustainability criteria as well as the ratings of alternatives (maintenance strategies) with respect to the criteria using linguistic variables. These linguistic variables are translated into fuzzy triangular numbers. Using a fuzzy VIKOR method, the most appropriate maintenance strategy is selected. The proposed framework is illustrated through a case study. Some numerical results and sensitivity analysis is presented.

Table of Content
Description
Paper presented to the 7th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Marcus Welcome Center, Wichita State University, May 4, 2011.
Research completed at the Industrial & Manufacturing Engineering
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