PurposeThis paper describes a method developed to schedule the preventive maintenance tasks in separate and linked cogeneration plants while satisfying the maintenance and production constraints.Design/methodology/approachThe proposed methodology is based on a mixed integer programming model which finds the maximum number of available power and desalting units in separate and linked cogeneration plants. To verify that the model can be implemented for a real system, a case study of scheduling the preventive maintenance tasks of a cogeneration plant in Kuwait is illustrated.FindingsAn efficient solution can be achieved for scheduling the preventive maintenance tasks and production in cogeneration plants.Practical implicationsThe paper offers a practical model that can be used to schedule preventive maintenance for expensive equipment in cogeneration plans.Originality/valueThe model presented is an effective decision tool that optimises the solution of the maintenance scheduling problem for cogeneration plants under maintenance and production constraints.

Maintenance scheduling of cogeneration plants, which produce both electric power and desalinated water, is a typical complex process with long-term operations and planning problems. The plants' maintenance scheduling process has to determine the appropriate schedule for preventive maintenance, while satisfying all the system constraints and maintaining adequate system availability. It is an optimization problem and the maintenance and system constraints include the crew constraint, maintenance window constraint and time limitation constraint. In this paper, an integer linear-programming model, which has been developed, is described which schedules the preventive maintenance tasks in a multi-cogeneration plant. Results of a test example of such a plant situated in Kuwait are presented to show the applicability of the approach.

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.

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