Journal article
Stochastic Optimization of Maintenance and Operations Schedules Under Unexpected Failures
IEEE transactions on power systems, Vol.33(6), pp.6755-6765
11/01/2018
DOI: 10.1109/TPWRS.2018.2829175
Abstract
We develop a stochastic optimization framework for integrated condition-based maintenance and operations scheduling for a fleet of generators with explicit consideration of unexpected failures. Our approach is based on a model that uses condition-based failure scenarios derived from the remaining lifetime distributions of the generators, as well as a chance constraint to ensure a reliable maintenance plan. We derive a deterministic safe approximation of the difficult chance constraint. The large number of failure scenarios is handled by a combination of sample average approximation and an enhanced scenario decomposition algorithm in a distributed framework. We introduce a number of algorithmic improvements by exploiting the polyhedral structure of the problem, utilizing its time decomposability, and an analysis of the transmission line capacities. Finally, we present a case study demonstrating the significant cost savings and computational benefits of the proposed framework.
Details
- Title: Subtitle
- Stochastic Optimization of Maintenance and Operations Schedules Under Unexpected Failures
- Creators
- Beste BasciftciShahana Ahmed - H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USANagi Z. Gebraeel - H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USAMurat Yildirim - H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on power systems, Vol.33(6), pp.6755-6765
- Publisher
- IEEE
- DOI
- 10.1109/TPWRS.2018.2829175
- ISSN
- 0885-8950
- eISSN
- 1558-0679
- Language
- English
- Date published
- 11/01/2018
- Academic Unit
- Business Analytics
- Record Identifier
- 9984120460502771
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