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Advances in Operations Research
Volume 2015, Article ID 282178, 12 pages
http://dx.doi.org/10.1155/2015/282178
Research Article

Preventive Maintenance Scheduling for Multicogeneration Plants with Production Constraints Using Genetic Algorithms

1Technological Science Department, College of Technological Studies, PAAET, P.O. Box 42325, Shuwaikh 70654, Kuwait
2Automotive & Marine Engineering Technology Department, College of Technological Studies, PAAET, P.O. Box 42325, Shuwaikh 70654, Kuwait
3Manufacturing Engineering Technology Department, College of Technological Studies, PAAET, P.O. Box 42325, Shuwaikh 70654, Kuwait

Received 13 August 2014; Revised 20 November 2014; Accepted 9 January 2015

Academic Editor: Viliam Makis

Copyright © 2015 Khaled Alhamad et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper describes a method developed to schedule the preventive maintenance tasks of the generation and desalination units in separate and linked cogeneration plants provided that all the necessary maintenance and production constraints are satisfied. The proposed methodology is used to generate two preventing maintenance schedules, one for electricity and the other for distiller. Two types of crossover operators were adopted, 2-point and 4-point. The objective function of the model is to maximize the available number of operational units in each plant. The results obtained were satisfying the problem parameters. However, 4-point slightly produce better solution than 2-point ones for both electricity and water distiller. The performance as well as the effectiveness of the genetic algorithm in solving preventive maintenance scheduling is applied and tested on a real system of 21 units for electricity and 21 units for water. The results presented here show a great potential for utility applications for effective energy management over a time horizon of 52 weeks. The model presented is an effective decision tool that optimizes the solution of the maintenance scheduling problem for cogeneration plants under maintenance and production constraints.