An expert system for generator maintenance scheduling using operation index

Lin, Chin E.; Huang, Chiung-Yi; Huang, Ching-Lien; Liang, Chih-Chien; Lee, S.-Y.

doi:10.1109/59.207327

Cited by 35 publications

(18 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each unit , a maintenance starting time period is chosen randomly, according to a uniform distribution, between the unit's earliest and latest maintenance starting time periods. Any possible constraint violations are calculated, and the total penalty value is then calculated according to (13). The local search heuristic may be applied to the random initial solution to potentially obtain a 'good' initial solution for the algorithm.…”

Section: Implementation Of the Hybrid Sa Algorithmmentioning

confidence: 99%

“…In an exact solution approach, solution techniques include methods such as branch-and-bound algorithms, dynamic programming, and Benders' decomposition method [10]. Approximate solution approaches include solution techniques such as basic search heuristics, metaheuristics (including genetic algorithm [5], tabu search, simulated annealing [11], ant colony optimisation, particle swarm optimisation [12]), fuzzy logic modelling, and expert system approaches [13]. General purpose decision support systems such as the spreadsheet-based optimisation tool developed by Savić et al [14] may also be used to solve the GMS problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Application of a Computerised Decision Support System for Generator Maintenance Scheduling: A South African Case Study

Schlünz

Vuuren

2012

sajie

View full text Add to dashboard Cite

In the generator maintenance scheduling (GMS) problem, a schedule is sought 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 quadratic program assuming a reliability objective, subject to certain constraints. A generic GMS solution methodology is derived, capable of solving general GMS problem instances that conform to this model structure, and implemented in a computerised decision support system. The decision support system is then used to solve a real-life case study in the South African national power system. OPSOMMINGIn die generator-instandhoudingskeduleringsprobleem (GISP) word daar gesoek na 'n skedule vir die beplande instandhouding van kragopwekkingseenhede binne 'n kragstelsel. Die GISP-model wat in hierdie artikel beskou word, is 'n gemengde, heeltallige, kwadratiese program, en bevat 'n betroubaarheidsdoel, onderhewig aan sekere beperkings. 'n Generiese GISP-oplossingsmetodiek is ontwikkel om enige GISP van die vorm soos in hierdie model te kan oplos. Die oplossingsmetodiek word in 'n gerekenariseerde besluitsteunstelsel geïmplementeer, waarna 'n realistiese gevallestudie, binne die konteks van die SuidAfrikaanse nasionale kragstelsel, daarmee opgelos word.

show abstract

Section: Implementation Of the Hybrid Sa Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Application of a Computerised Decision Support System for Generator Maintenance Scheduling: A South African Case Study

Schlünz

Vuuren

2012

sajie

View full text Add to dashboard Cite

show abstract

“…To meet this demand, the development of power system technology has become increasingly important in order to maintain a reliable and economic electric power supply (Lin et al, 1992). One major concern of such development is the optimization of power plant maintenance scheduling.…”

Section: Introductionmentioning

confidence: 99%

Power Plant Maintenance Scheduling Using Ant Colony Optimization

Foong¹,

Maier²,

Simpson³

2007

Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

View full text Add to dashboard Cite

“…Various mathematical models focusing on coordinating production and maintenance plans are proposed in (Lin et al, 1992;Gurevich et al, 1996;Agogino et al, 1997;Ben-Daya and Rahim, 2000;El-Amin et al, 2000;Kiyoshi et al, 2002;Chattopadhyay, 2004;Martorell et al, 2005;Aghezzaf et al, 2007;Dahal and Chakpitak, 2007;El-Ferik, 2008;Fitouhi and Nourelfath, 2012;Wang, 2013). A wide variety of solution techniques and algorithms including the whole spectrum of heuristic techniques, dynamic programming, tabusearch multi-objective optimization, expert systems and many other hybrid techniques are also proposed, see for example (Lin et al, 1992;Gurevich et al, 1996;Agogino et al, 1997;Ben-Daya and Rahim, 2000;Kiyoshi et al, 2002;Chattopadhyay, 2004;Martorell et al, 2005;Dahal and Chakpitak, 2007). Integrated production and imperfect preventive maintenance planning models were also proposed, see for example (Chung and Krajewski, 1984;Ben-Daya and Rahim, 2000;Sana and Chaudhuri, 2010;Fitouhi and Nourelfath, 2012;Aghezzaf et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Integrated Production and Imperfect Preventive Maintenance Planning - An Effective MILP-based Relax-and-Fix/Fix-and-Optimize Method

Tam

Aghezzaf

Khatab³

et al. 2017

Proceedings of the 6th International Conference on Operations Research and Enterprise Systems

View full text Add to dashboard Cite

Abstract:This paper investigates the integrated production and imperfect preventive maintenance planning problem. The main objective is to determine an optimal combined production and maintenance strategy that concurrently minimizes production as well as maintenance costs during a given finite planning horizon. To enhance the quality of the solution and improve the computational time, we reconsider the reformulation of the problem proposed in (Aghezzaf et al., 2016) and then solved it with an effective MILP-based Relax-and-Fix/Fix-andOptimize method (RFFO). The results of this Relax-and-Fix/Fix-and-Optimize technique were also compared to those obtained by a Dantzig-Wolfe Decomposition (DWD) technique applied to this same reformulation of the problem. The results of this analysis show that the RFFO technique provides quite good solutions to the test problems with a noticeable improvement in computational time. DWD on the other hand exhibits a good improvement in terms of computational times, however, the quality of the solution still requires some more improvements.

show abstract

An expert system for generator maintenance scheduling using operation index

Cited by 35 publications

References 6 publications

The Application of a Computerised Decision Support System for Generator Maintenance Scheduling: A South African Case Study

The Application of a Computerised Decision Support System for Generator Maintenance Scheduling: A South African Case Study

Power Plant Maintenance Scheduling Using Ant Colony Optimization

Integrated Production and Imperfect Preventive Maintenance Planning - An Effective MILP-based Relax-and-Fix/Fix-and-Optimize Method

Contact Info

Product

Resources

About