Journal article
Data-driven joint optimization of maintenance and spare parts provisioning for deep space habitats: A stochastic programming approach
Acta astronautica, Vol.214, pp.167-181
01/2024
DOI: 10.1016/j.actaastro.2023.10.028
Abstract
The utilization of sensor data to make informed decisions on maintenance schedule and spare parts provisioning will be determinant to the development of self-aware habitable facilities–so called SmartHabs–in deep space regions. To address this novel and challenging problem, this work presents a chance constrained stochastic mixed integer program to jointly optimize maintenance and spare parts provisioning for ensuring reliable and cost-efficient operations of SmartHabs. A SmartHab is modeled as a multi-unit system with non-identical components that randomly degrade over time. The proposed optimization model is driven by telemetry sensor data as the uncertainty of the remaining lifetime of the components is estimated by data-driven prognostic algorithms. The model further incorporates characteristic operational conditions of deep space habitats such as a long planning horizon that requires multiple maintenance decisions for each component, maintenance task assignment to humans and robots, long lead times between resupply trips, and high penalties due to stock-outs of spare parts. The formulation introduces chance constraints restricting the unavailability of critical components and the number of corrective repairs with high probability under the uncertainty of component failures. The nonlinearities induced by the chance constraints are circumvented by the derivation of two linearization methods based on scenario representation and safe approximations, resulting in their tractable mixed integer linear reformulations. The proposed model is validated by simulations, showing reliable and effective plans against benchmark approaches while evidencing to be computationally scalable as the number of components increases.
•Joint optimization of maintenance and spare provisioning for deep space habitats.•Telemetry sensor data is used to describe the uncertainty of the optimization model.•Chance constraints for restricting the probability of unexpected failures.•Scalable optimization model validated by simulation studies.
Details
- Title: Subtitle
- Data-driven joint optimization of maintenance and spare parts provisioning for deep space habitats: A stochastic programming approach
- Creators
- Heraldo Rozas - Georgia Institute of TechnologyBeste Basciftci - University of IowaNagi Gebraeel - Georgia Institute of Technology
- Resource Type
- Journal article
- Publication Details
- Acta astronautica, Vol.214, pp.167-181
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.actaastro.2023.10.028
- ISSN
- 0094-5765
- eISSN
- 1879-2030
- Language
- English
- Date published
- 01/2024
- Academic Unit
- Business Analytics
- Record Identifier
- 9984500859502771
Metrics
6 Record Views