A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multi-Objective Stochastic Optimization for Preventive Maintenance Planning
Presentation ; Maintenance is an essential part of mechanical integrity programs and aims to prevent the occurrence of process safety incidents and costly unplanned shutdowns. Maintenance can increase the reliability of equipment in productive systems and effective preventive maintenance programs enable maintenance activities to be planned proactively. However, maintenance planning is subject to resource scarcity and is rendered nontrivial due to system complexity, reliability model nonlinearity, and parametric uncertainty. Multi-objective stochastic mixed-integer nonlinear programming is well suited to addressing these challenges and is adopted here to optimize the time intervals in which to perform maintenance on different pieces of equipment. Following presentation of an optimal maintenance planning framework, a model is formulated and optimized accounting for: the effect of imperfect repair using an effective age model, equipment failure behavior using a Weibull reliability model, endogenous uncertainty in reliability model parameters, and the simultaneous need to satisfy the competing objectives of cost minimization and reliability maximization using the ε-constraint method. The results of the research consist of optimal maintenance plans, plots of resultant equipment and system reliability over time, and a Pareto frontier of optimal solutions from which the decision maker can select. The approach adopted here is illustrated with a case study and can be extended to improving the overall availability, effectiveness, and resilience of a variety of productive systems.
Multi-Objective Stochastic Optimization for Preventive Maintenance Planning
Presentation ; Maintenance is an essential part of mechanical integrity programs and aims to prevent the occurrence of process safety incidents and costly unplanned shutdowns. Maintenance can increase the reliability of equipment in productive systems and effective preventive maintenance programs enable maintenance activities to be planned proactively. However, maintenance planning is subject to resource scarcity and is rendered nontrivial due to system complexity, reliability model nonlinearity, and parametric uncertainty. Multi-objective stochastic mixed-integer nonlinear programming is well suited to addressing these challenges and is adopted here to optimize the time intervals in which to perform maintenance on different pieces of equipment. Following presentation of an optimal maintenance planning framework, a model is formulated and optimized accounting for: the effect of imperfect repair using an effective age model, equipment failure behavior using a Weibull reliability model, endogenous uncertainty in reliability model parameters, and the simultaneous need to satisfy the competing objectives of cost minimization and reliability maximization using the ε-constraint method. The results of the research consist of optimal maintenance plans, plots of resultant equipment and system reliability over time, and a Pareto frontier of optimal solutions from which the decision maker can select. The approach adopted here is illustrated with a case study and can be extended to improving the overall availability, effectiveness, and resilience of a variety of productive systems.
Multi-Objective Stochastic Optimization for Preventive Maintenance Planning
Gordon, Christopher K. (author) / Mannan, M. Sam (author)
2018-01-01
Miscellaneous
Electronic Resource
English
DDC:
690
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