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Optimization of the integrated fleet-level imperfect selective maintenance and repairpersons assignment problem.
- Source :
- Journal of Intelligent Manufacturing; Mar2022, Vol. 33 Issue 3, p703-718, 16p
- Publication Year :
- 2022
-
Abstract
- Industrial environments such as manufacturing and transportation industries usually involve fleets of identical systems that must carry out several missions interspersed with scheduled finite breaks. Given the limited amount of maintenance resources and time available, only a restricted number of maintenance actions can be performed on selected components to ensure a pre-specified performance level of the fleet for the next mission. Such a maintenance strategy is known as fleet-level selective maintenance (FSM). The FSM is more complex than the selective maintenance problem as it adds the total number of systems in the fleet as another level of combinations to be explored during the optimization process. Most FSM models consider the replacement or perfect repair of system components as the only maintenance option. Furthermore, they consider a single repair channel and disregard the assignment of repairpersons and the impact of their variable skillsets on the maintenance costs and duration. In this paper, an approach is proposed to help in more realistic decision making for FSM where several imperfect maintenance levels and multiple repair channels are available. A novel integrated non-linear programming formulation of the FSM problem where maintenance and repairpersons assignment decisions are made jointly is proposed. All relevant parameters and terms of this non-linear optimization problem are developed and discussed. A two-phase modeling approach is then used to transform the original nonlinear problem into a binary integer optimization model. To demonstrate the validity and the added value of the proposed approach, multiple sets of numerical experiments are investigated and managerial implications are provided. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09565515
- Volume :
- 33
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Journal of Intelligent Manufacturing
- Publication Type :
- Academic Journal
- Accession number :
- 155185671
- Full Text :
- https://doi.org/10.1007/s10845-020-01672-0