| 1. |
- Jakobsson, Stefan, 1970, et al.
(författare)
-
A method for simulation based optimization using radial basis functions
- 2010
-
Ingår i: Optimization & Engineering. - : Springer Science and Business Media LLC. - 1573-2924 .- 1389-4420. ; 11:4, s. 501-532
-
Tidskriftsartikel (refereegranskat)abstract
- We propose an algorithm for the global optimization of expensive and noisy black box functions using a surrogate model based on radial basis functions (RBFs). A method for RBF-based approximation is introduced in order to handle noise. New points are selected to minimize the total model uncertainty weighted against the surrogate function value. The algorithm is extended to multiple objective functions by instead weighting against the distance to the surrogate Pareto front; it therefore constitutes the first algorithm for expensive, noisy and multiobjective problems in the literature. Numerical results on analytical test functions show promise in comparison to other (commercial) algorithms, as well as results from a simulation based optimization problem.
|
|
| 2. |
- Nilsson, Julia, et al.
(författare)
-
An evaluation approach for opportunistic maintenance optimization models for nuclear power plants
- 2010
-
Ingår i: Power and Energy Society General Meeting, 2010 IEEE. - : IEEE. - 9781424483570
-
Konferensbidrag (refereegranskat)abstract
- Nuclear power constitutes one of the main electricity production sources in several countries, e.g. in Sweden, with about half of today's electricity production. Driving forces for cost-efficiency have pushed the development of new methods for maintenance planning and optimization forward. Reliability Centered Asset Management (RCAM) is one of these new approaches, and maintenance optimization is one way to perform quantitative analysis which is a feature of RCAM. This paper proposes a stochastic framework for evaluating opportunistic maintenance optimization models for the generation of replacement schedules for shaft seals in feed-water pump systems in nuclear power plants. Results show that the extended discounted model has a good overall performance, when compared to other strategies. Moreover, it is shown that it is the best strategy for high values of the constant cost for maintenance and the second best for lower values of the constant cost.
|
|
| 3. |
- Wojciechowski, Adam, 1982
(författare)
-
On the optimization of opportunistic maintenance activities
- 2010
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Maintenance is a source of large costs; in the EU the maintenance costs amountto between 4% and 8% of the total sales turnover. Opportunistic maintenance is anattempt to lower the maintenance cost by considering the failure of onecomponent as an opportunity to replace yet non-failed components in order toprevent future failures. At the time of failure of one component, a decision is to be made on which additional components toreplace in order to minimize the expected maintenance cost over a planningperiod.This thesis continues the work of Dickman et. al. (1991) and Andreasson (2004) on the opportunistic replacement problem. In Paper I, we show thatthe problem with time-dependent costs is NP-hard and present a mixed integerlinear programming model for the problem. We apply the model to problems withdeterministic and stochastic component lives with data originating from theaviation and wind power industry. The model is applied in a stochastic settingby employing the expected values of component lives. In Paper II, a first steptowards a stochastic programming model that considers components with uncertain lives istaken by extending the problem to allow non-identical lives for component individuals.This problem is shown to be NP-hard even with time-independent costs. We present amixed integer linear programming model of the problem. The solution time of the model is substantially reduced compared to the model presented in Andreasson (2004). In Paper III, we then study the opportunisticreplacement problem with uncertain component lives and present a two-stagestochastic programming approach. We present a deterministic equivalent model anddevelop a decomposition method. Numerical studies on the same data as in Paper I from the aviation and wind power industry show that the stochastic programming approachproduces maintenance decisions that are on average less costly than decisions obtained from simple maintenance policies and the approach used in Paper I. The decomposition method requires less CPU-time than solving the deterministic equivalent on three out of four problems.
|
|
