Maintenance Scheduling Optimization Using a Genetic Algorithm (GA) with a Probabilistic Fitness Function

Abdulwhab, A.; Billinton, R.; Eldamaty, A.A.; Faried, S.O.

doi:10.1080/15325000490446601

Cited by 17 publications

(7 citation statements)

References 30 publications

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“…Yare et al [26] introduced multiple swarm concepts for the modified discrete PSO to form a robust algorithm for solving the preventive maintenance schedule problem. Abdulwhab et al [27] used the GA optimization technique to maximize the overall system reliability for a specified future time period, in which a number of generating units are to be removed from service for preventive maintenance. Berrichi et al [28] presented an algorithm based on the ant colony optimization paradigm to solve the joint production and maintenance scheduling problem.…”

Section: Preventivementioning

confidence: 99%

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

Guo

Sun

Tang

et al. 2016

Discrete Dynamics in Nature and Society

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All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

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Section: Preventivementioning

confidence: 99%

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

Guo

Sun

Tang

et al. 2016

Discrete Dynamics in Nature and Society

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“…Levitin and Lisnianski [22] used GA to minimize the sum of costs of system modernization actions over the study period while satisfying reliability constraints at each stage. Abdulwhab et al [23] used GA optimization technique to maximize the overall system reliability for a specified future time period in which a number of generating units are to be removed from service for preventive maintenance.…”

Section: Introductionmentioning

confidence: 99%

Determining Optimal Replacement Policy with an Availability Constraint via Genetic Algorithms

Zong

Chai

Su³

2017

Mathematical Problems in Engineering

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We develop a model and a genetic algorithm for determining an optimal replacement policy for power equipment subject to Poisson shocks. If the time interval of two consecutive shocks is less than a threshold value, the failed equipment can be repaired. We assume that the operating time after repair is stochastically nonincreasing and the repair time is exponentially distributed with a geometric increasing mean. Our objective is to minimize the expected average cost under an availability requirement. Based on this average cost function, we propose the genetic algorithm to locate the optimal replacement policy N to minimize the average cost rate. The results show that the GA is effective and efficient in finding the optimal solutions. The availability of equipment has significance effect on the optimal replacement policy. Many practical systems fit the model developed in this paper.

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“…Evolutions of algorithms, beside more powerful computers, have caused these promotions [1,2]. These, also lead to complex designs on maintenance scheduling and offer new strategies for solving the problem and new approaches to organize the scheduling [3][4][5][6]. Recently, researchers are focused on production quality, availability of equipments, safety and the relationship between maintenance and production quality.…”

Section: Introductionmentioning

confidence: 99%

“…Since the nature of the problem is a nonlinear mixed integer programming and thus hard to solve, attempts have been done to solve it efficiently, even if it is not necessarily optimum Some has tried to solve the UMS problem by means of population based methods. In [4], Genetic Algorithm (GA) is used, but the fitness in this paper is considered as a stochastic function. GA is also used in [5] to solve the UMS problem.…”

Section: Introductionmentioning

confidence: 99%

Unit maintenance scheduling by means of fuzzy-game theory, considering uncertainty in rival-Genco's data

Bozorgi¹,

Pedram²,

Yousefi

2010

2010 18th Iranian Conference on Electrical Engineering

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Gencos, in a restructured power system, try to schedule their generators' maintenance in order to maximize their profit. Besides, Unit Maintenance Scheduling (UMS) as a mid-term plan has a significant effect on the Genco's profit in a power market. In a regulated power system, UMS is usually determined by a central system, such as System Operator (SO). On the other hand, in a de-regulated power system, UMS is determined through multiple interactions between the market players, mainly Gencos and SO. Considering these, it would be a desire to solve both short term and mid-term problems, in a single framework. In this case, Gencos can offer a price curve with an outlook to mid-term goals in a game-theoretic approach. The approach would be based on some predictions of parameters such as production cost factors. This article, addresses the uncertainty in the cost factors of a Rival-Genco in a fuzzy game theoretic scheme and experimental results show the effectiveness of the proposed approach.

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Maintenance Scheduling Optimization Using a Genetic Algorithm (GA) with a Probabilistic Fitness Function

Cited by 17 publications

References 30 publications

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

Determining Optimal Replacement Policy with an Availability Constraint via Genetic Algorithms

Unit maintenance scheduling by means of fuzzy-game theory, considering uncertainty in rival-Genco's data

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