A fuzzy evolutionary programming-based solution methodology for security-constrained generation maintenance scheduling

El-Sharkh, M.Y.; El-Keib, A.A.; Chen, H.

doi:10.1016/s0378-7796(03)00076-2

Cited by 49 publications

(30 citation statements)

References 6 publications

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“…The scope of work on the maintenance scheduling literature includes approaches, optimality studies, algorithms, techniques, constraints and limitations. Approaches in Maintenance Scheduling research include, but are not limited to, multistage, system, probabilistic [48], fuzzy modeling [19,25] and CPM with a bill of materials. Several effective and sufficient techniques have been suggested as alternatives to traditional optimization-based techniques where the problem size is large and exact optimal solution is not available.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Various optimization studies have also been conducted, including by Billinton and Pan [6], Charles-Owaba [12], Christiaanse [16], El-Sharkh [25], Yamayee et al [74], and Zurn and Quintana [76,77]. Models have been developed for different situations: proportional intensities, full history, warranted products, finite source, uncertainty, deregulated systems and planning.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Inflation-Based Maintenance Scheduling Model

Oke

Charles-Owaba

2012

The South African Journal of Industrial Engineering

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This paper attempts to extend an original Gantt-charting model for optimal maintenance scheduling of multiple facilities in multiple periods credited to CharlesOwaba. In particular, an inflationary factor is incorporated into the period-dependent cost function element of the model. Some computational experimentation on the new model is tested. This work is perhaps the first to advance the extension of the Optimal Gantt Charting (OGC) model in terms of inflation. The work presents a wealth of research opportunities and has at least the modest potential to evaluate maintenance scheduling theory into the ranks of major theories of maintenance. OPSOMMINGDie artikel poog om inflasie te akkommodeer binne die raamwerk van 'n Ganttkaartmodel vir die bepaling van optimum skedulering van instandhouding vir meervoudige fasiliteite met die verloop van tyd. Inflasie vorm dus deel van die koste-element van die model. Die model is ook op 'n beperkte wyse gevalideer met behulp van 'n rekenaarstudie. Die verkreë resultate toon dat addisionele navorsing gedoen kan word om sodoende die metode te vestig in die voorste rang van instandhoudingsbeplanning

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Inflation-Based Maintenance Scheduling Model

Oke

Charles-Owaba

2012

The South African Journal of Industrial Engineering

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“…Price forecasts are obtained using an ANN approach [4]. The actual and forecasted market prices for the last 24 days just prior to the day of estimation d are used to estimate the covariance matrix at day d by using Equation (8).…”

Section: Case Studymentioning

confidence: 99%

“…Deterministic approaches include priority list (PL), dynamic programming (DP), Lagrangian Relaxation (LR), integer/ mixed-integer programming method and branch-and-bound method. Meta-heuristic approaches include expert systems (ES), fuzzy logic (FL), ANNs, genetic algorithm (GA), evolutionary programming (EP), simulated annealing (SA), tabu search (TS) and particle swarm optimizer (PSO) [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Risk‐based self‐scheduling for GenCos in the day‐ahead competitive electricity markets

Yamin

Altawil

Al-Ajlouni

et al. 2008

Int Trans Elec Energy Syst

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SUMMARYThis paper addresses the risk-based self-scheduling problem using a hybrid technique between Lagrangian relaxation (LR) and particle swarm optimizer (PSO). The paper analyses a self-scheduling model that accounts for profit and risk simultaneously. The effect of risk is explicitly modelled in the self-scheduling problem taking into account the variance of the market-clearing prices. The forecasted hourly probabilities that spinning and non-spinning reserves are called and generated are also considered in the formulation to simulate the reserve uncertainty. Artificial neural network (ANN) is applied for forecasting the hourly reserve probability. The proposed approach is applied to a 36 unit test system.

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“…To find solution for GMS of large scale systems in a reasonable amount of time, intelligent optimization techniques have been proposed. The objective of minimizing the cost is considered and schedule is obtained for maintenance of generators using combination of logic programming, constraint satisfaction technique and Branch and Bound (B & B) algorithm [1], duality theory [2], Simulated Annealing (SA) [3], Genetic Algorithm (GA) [4], Tabu Search (TS) [5], Memetic Algorithm (MA) [6], Evolutionary Programming (EP) [7] and Particle Swarm Optimization (PSO) [8]. The reliability 2 objective of GMS is as important as cost objective.…”

Section: Introductionmentioning

confidence: 99%

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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A fuzzy evolutionary programming-based solution methodology for security-constrained generation maintenance scheduling

Cited by 49 publications

References 6 publications

An Inflation-Based Maintenance Scheduling Model

An Inflation-Based Maintenance Scheduling Model

Risk‐based self‐scheduling for GenCos in the day‐ahead competitive electricity markets

Fuzzy Clustered Multi Objective Differential Evolution for Thermal Generator Maintenance Scheduling

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