‘Preventive maintenance scheduling of multi-cogeneration plants using integer programming’

Alidaee, Bahram; Wang, Haibo

doi:10.1057/jors.2009.37

Cited by 2 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For cogeneration systems, a panoply of optimization approaches varying from approximate to exact methods are available. Approximate approaches vary from simple heuristics, tabu search to genetic algorithms [12][13][14][16][17][18][19] whereas exact approaches are mostly integer programming based [20][21][22][23][24][25][26].…”

Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Program for Scheduling Preventive Maintenance of Cogeneration Plants with Production

2021

View full text Add to dashboard Cite

This paper considers the scheduling of preventive maintenance for the boilers, turbines, and distillers of power plants that produce electricity and desalinated water. It models the problem as a mathematical program (MP) that maximizes the sum of the minimal ratios of production to the demand of electricity and water during a planning time horizon. This objective encourages the plants’ production and enhances the chances of meeting consumers’ needs. It reduces the chance of power cuts and water shortages that may be caused by emergency disruptions of equipment on the network. To assess its performance and effectiveness, we test the MP on a real system consisting of 32 units and generate a preventive maintenance schedule for a time horizon of 52 weeks (one year). The generated schedule outperforms the schedule established by experts of the water plant; it induces, respectively, 16% and 12% increases in the surpluses while either matching or surpassing the total production. The sensitivity analysis further indicates that the generated schedule can handle unforeseen longer maintenance periods as well as a 120% increase in demand—a sizable realization in a country that heavily relies on electricity to acclimate to the harsh weather conditions. In addition, it suggests the robustness of the schedules with respect to increased demand. In summary, the MP model yields optimal systematic sustainable schedules.

show abstract