Abstract

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.

Highlights

  • Preventive maintenance (PM) is crucial to many service and manufacturing industries

  • This paper models the problem of generating a preventive maintenance schedule (PMS) for each unit of the power plants, using a mixed integer mathematical program (MP), where some of the decision variables are binary while others are integers and real

  • Preventive maintenance scheduling is a critical issue in energy systems

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Summary

Introduction

Preventive maintenance (PM) is crucial to many service and manufacturing industries. Their routine application avoids unexpected equipment breakdown that, otherwise, would negatively impact the reliability of the logistic chain, but would cause sizable losses, reduced productivity, and deteriorated quality of products and services. Many companies (from different industrial sectors) resort to scheduling PMs with the objective of optimizing service delivery, be it in terms of continuity or quality. In the transport sector, ships [1,2], trains [3,4], and airplanes [5] are subject to different types of PMs, with some of them being daily, monthly, or usage-dependent. Some plants resort to PMs to minimize the disruption of their supply [8]

Motivation
Problem Statement and Contribution
Literature Review
Problem Description and Mathematical Formulation
Case Study
Sensitivity Analysis
Findings
Conclusions
Full Text
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