A new method for unit maintenance scheduling considering reliability and operation expense

Leou, Rong‐Ceng

doi:10.1016/j.ijepes.2006.02.009

Cited by 57 publications

(34 citation statements)

References 13 publications

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“…The average value of reliability index is calculated using (2). The membership function value for cost and reliability objective is calculated using (10) and (11) and the normalized membership function value for every individual population k, is calculated using (17). The vector for which the normalized membership function value µ k attains maximum can be chosen as a best compromise non dominated solution.…”

Section: Crossovermentioning

confidence: 99%

“…In [16], Kim et al have considered two objectives cost and reliability and converted into single objective with the help of weighting coefficients and maintenance timetable is obtained using hybrid GA, SA and TS. Leou [17] proposed a new formulation in which the cost and reliability are considered as an index and GA is combined with SA and is implemented for solving the problem. Multi objective formulation of GMS model is considered in [18] and non inferior solution for GMS is attained.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Clustered Multi Objective Differential Evolution for Thermal Generator Maintenance Scheduling

Balaji¹,

Balamurugan²,

Lakshminarasimman³

2016

IJIES

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Generator Maintenance Scheduling (GMS) problem in a vertically integrated electric utility system is considered in this paper. The cost and reliability objectives are included in the problem formulation. The GMS is formulated as a non linear, heavily constrained multi-objective optimization problem and the non dominated solutions are obtained using multi objective hybrid integer coded differential evolution (MOHDE) algorithm. To attain the best compromise non dominated solution between cost and reliability, fuzzy based mechanism has been used in MOHDE. In this paper, fuzzy clustered multi objective hybrid differential evolution (FC-MOHDE) is proposed for solving thermal generator maintenance scheduling. To validate the performance of the proposed algorithm, two systems having 4 and 22 generating units are considered. The test results confirm the superiority of the algorithm in determining the best compromise solution for the multi objective GMS problem.

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Section: Crossovermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Clustered Multi Objective Differential Evolution for Thermal Generator Maintenance Scheduling

Balaji¹,

Balamurugan²,

Lakshminarasimman³

2016

IJIES

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“…The literature about the SA is wide, but it deals with some specific applications (Kia et al, 2012;Leung et al, 2012), as well for multi-objective problems (Lin and Ying, 2013), or proposals of utilization of SA matched with other heuristics, as the Ant Colony (AC) algorithms (Sitarz, 2009), the TS or, more often, the GAs (Rong-Ceng, 2006;Zahrani et al, 2008).…”

Section: Simulated Annealing and Cooling Schedulementioning

confidence: 99%

A New Innovative Cooling Law for Simulated Annealing Algorithms

Certa¹,

Lupo²,

Passannanti³

2015

American Journal of Applied Sciences

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The present paper proposes an original and innovative cooling law in the field of Simulated Annealing (SA) algorithms. Particularly, such a law is based on the evolution of different initial seeds on which the algorithm works in parallel. The efficiency control of the new proposal, executed on problems of different kind, shows that the convergence quickness by using such a new cooling law is considerably greater than that obtained by traditional laws. Furthermore, it is shown that the effectiveness of the SA algorithm arising from the proposed cooling law is independent of the problem type. This last feature reduces the number of parameters to be initially fixed, so simplifying the preliminary calibration process necessary to optimize the algorithm efficiency.

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“…The category of convenience criteria is the least used and we could not find any reference in the literature to single objective formula tions in this category. However, objectives from this category ap pear in multiobjective formulations [13,14]. Objectives within this category include minimising the degree of constraint viola tions or minimising possible disruptions to the existing schedule.…”

Section: Gms Model Considerationsmentioning

confidence: 99%

An investigation into the effectiveness of simulated annealing as a solution approach for the generator maintenance scheduling problem

Schlünz

Vuuren

2013

International Journal of Electrical Power & Energy Systems

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The generator maintenance scheduling (GMS) problem is the difficult combinatorial optimisation problem of finding a schedule for the planned maintenance outages of generating units in a power system. The GMS model considered in this paper is formulated as a mixed integer program, with a reliability optimality criterion, subject to a number of constraints. A new version of the simulated annealing (SA) method for solving the GMS problem is presented. Four cooling schedules (the geometric and three adaptive schedules), two neighbourhood move operators (an elementary move and an ejection chain move operator), and a hybrid local search heuristic/SA algorithm are compared. To our knowledge, this is the first study considering a different SA cooling schedule and move operator in a GMS context. A new 32-unit GMS test system is established and used in conjunction with a benchmark test system from the literature in this investigation. It is found that choosing a different cooling schedule and an ejection chain move operator yield improved results to that of the SA algorithm currently employed in the GMS literature. The hybrid SA algorithm performs very well compared to other methods on the benchmark test system from the literature, and an improved lower bound on the objective function value is presented for this test system.

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A new method for unit maintenance scheduling considering reliability and operation expense

Cited by 57 publications

References 13 publications

Fuzzy Clustered Multi Objective Differential Evolution for Thermal Generator Maintenance Scheduling

Fuzzy Clustered Multi Objective Differential Evolution for Thermal Generator Maintenance Scheduling

A New Innovative Cooling Law for Simulated Annealing Algorithms

An investigation into the effectiveness of simulated annealing as a solution approach for the generator maintenance scheduling problem

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