Genetic algorithms for condition-based maintenance optimization under uncertainty

Compare, Michele; Martini, Fabio; Zio, Enrico

doi:10.1016/j.ejor.2015.01.057

Cited by 57 publications

(29 citation statements)

References 50 publications

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“…Some success in the application of evolutionary algorithms to various decision support application areas has been achieved by several investigators [5] [6], but the problem is far from being resolved. Factories are scheduled by production planners who have a wealth of experience and understanding of the intricacies of day to day operations.…”

Section: Introductionmentioning

confidence: 99%

“…Unwanted characteristics could include two small batch runs of a part separated by a run of another part in a manufacturing schedule or a void in a packing problem that could easily have been filled. Evolutionary algorithms possess the trait of discovery [5], but the discovery process is heavily dependent upon randomness built into the search algorithm. For most decision support applications, specific domain knowledge is available which, if embodied in the problem encoding, could be used to greatly enhance both the convergence of the solution process and the quality of the final strategy.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

Alobaidi¹,

Webb²,

Sandgren³

2017

OJOp

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Genetic or evolutionary search algorithms seek and exploit the structure of a problem by operating on an encoding which represents the problem variables. The algorithms employed are generally designed to handle a wide variety of problems and while exploitation and progress can be rapid at the initial stages, the algorithms ultimate convergence rate can be slow. In order to speed up the solution process as well as produce a more refined solution, a rule based encoding is proposed. The rule based structure injects domain specific knowledge into the optimization process. This allows for an intuitive encoding and mimics the process utilized in many decision support applications such as scheduling. The encoding for the approach is often reduced in size as well. Specific rules for a particular problem class are coded into the formulation. This requires additional programming effort, but is valuable for specific applications which are repeated over time. During the solution process, a record is maintained concerning which rules or sequences of rules were successfully applied. This allows the rules to be continuously updated over time. An approach for implementing rules within the design encoding is demonstrated and several simple game problems are solved using this technique. The results are compared to a solution generated by a traditional encoding.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

Alobaidi¹,

Webb²,

Sandgren³

2017

OJOp

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“…An example of a corroded existing structure is presented; it is based on non-stationary POMDPs, for an infinite and a finite horizon case with 332 and 14,009 states, respectively. Comparea et al (2015) propose and compare maintenance optimization techniques based on genetic algorithms (GA), the parameters of the maintenance model being affected by uncertainty and the fitness values represented by cumulative distributions. A method to rank the uncertain fitness values and a novel Pareto dominance concept are developed.…”

Section: Literature Surveymentioning

confidence: 99%

The stochastic opportunistic replacement problem, part III: improved bounding procedures

2019

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We consider the problem to find a schedule for component replacement in a multi-component system, whose components possess stochastic lives and economic dependencies, such that the expected costs for maintenance during a pre-defined time period are minimized. The problem was considered in Patriksson et al. (Ann Oper Res 224:51-75, 2015b), in which a two-stage approximation of the problem was optimized through decomposition (denoted the optimization policy). The current paper improves the effectiveness of the decomposition approach by establishing a tighter bound on the value of the recourse function (i.e., the second stage in the approximation). A general lower bound on the expected maintenance cost is also established. Numerical experiments with 100 simulation scenarios for each of four test instances show that the tighter bound yields a decomposition generating fewer optimality cuts. They also illustrate the quality of the lower bound. Contrary to results presented earlier, an age-based policy performs on par with the optimization policy, although most simple policies perform worse than the optimization policy.

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“…For example, for an activity, the implementation of measure 7 will result in a shorter duration and a smaller risk loss than the implementation of measure 2. So, the monotonic decreasing function is used to represent the duration function that is shown in formula (29), and risk loss function is shown in formula (30), respectively,…”

Section: Examplementioning

confidence: 99%

“…For example, GA has been successfully applied in solving traveling salesman problem, knapsack problem, bin packing problem, and so on. However, the disadvantage of GA is that the local search capability is not strong [29][30][31][32][33]. Simulated annealing (SA) is a general random search algorithm, which is an extension of the local search algorithm [34][35][36][37].…”

Section: Algorithm Designmentioning

confidence: 99%

Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Project

Huang

et al. 2017

Mathematical Problems in Engineering

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IT outsourcing is an effective way to enhance the core competitiveness for many enterprises. But the schedule risk of IT outsourcing project may cause enormous economic loss to enterprise. In this paper, the Distributed Decision Making (DDM) theory and the principal-agent theory are used to build a model for schedule risk management of IT outsourcing project. In addition, a hybrid algorithm combining simulated annealing (SA) and genetic algorithm (GA) is designed, namely, simulated annealing genetic algorithm (SAGA). The effect of the proposed model on the schedule risk management problem is analyzed in the simulation experiment. Meanwhile, the simulation results of the three algorithms GA, SA, and SAGA show that SAGA is the most superior one to the other two algorithms in terms of stability and convergence. Consequently, this paper provides the scientific quantitative proposal for the decision maker who needs to manage the schedule risk of IT outsourcing project.

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Genetic algorithms for condition-based maintenance optimization under uncertainty

Cited by 57 publications

References 50 publications

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

RETRACTED: A Rule Based Evolutionary Algorithm for Intelligent Decision Support

The stochastic opportunistic replacement problem, part III: improved bounding procedures

Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Project

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