This paper develops an efficient variant of a Genetic Algorithm (GA) for a ship routing and scheduling problem (SRSP) with time-window in industrial shipping operation mode. This method addresses the problem of loading shipments for many customers using heterogeneous ships. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and ship capacities. The results of a computational investigation are presented and the solution quality and execution time are explored with respect to problem size. The proposed algorithm is compared, in terms of solution quality and computational time, with an exact method that uses Set Partitioning Problem (SPP). It is found that while the exact method solves small scale problem efficiently, treating large scale problems with the exact method becomes involved due to computational problem, a deficiency that the GA can encounter. Meantime, GA consistently returns better solution than other published work using Tabu Search method in term of solution quality.
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.
Purpose The purpose of this paper is to describe a method that has been set up to schedule preventive maintenance (PM) tasks for power and water plants with all constraints such as production and maintenance. Design/methodology/approach The proposed methodology relies on the zero-one integer programming model that finds the maximum number of power and water units available in separate generating units. To verify this, the model was implemented and tested as a case study in Kuwait for the Cogeneration Station. Findings An effective solution can be achieved for scheduling the PM tasks and production at the power and water cogeneration plant. Practical implications The proposed model offers a practical method to schedule PM of power and water units, which are expensive equipment. Originality/value This proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for power and water units in a cogeneration plant, effectively and complies with all constraints.
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.
Preventive maintenance (PM) is a maintenance program with activities created at a determined interval or according to certain principles, designed to reduce the likelihood of failure or deterioration of item performance. This aims to improve overall reliability and system availability. In this research, a preventive maintenance schedule (PMS) was designed for electricity and desalination of water in power plants, subject to meeting relevant constraints. The proposed methodology is used to generate a PMS for the boilers, turbines, and distillers. A nonlinear integer programming (NLIP) model was employed to address this problem. The results of the proposed method were compared with the PMS for a power station in Kuwait. The results were better in terms of the volume of production and in terms of the gap between the available production and demand in order to continue providing consumers with electricity and water without a shortage in the event of a breakdown in equipment. It produces an improvement of 12.12% and 16.58% respectively, for water and electricity. Furthermore, the sensitivity and robustness of the proposed method were analysed by increasing the maintenance duration for some equipment, increasing the demand, and adding various additional conditions. In addition, a comparison of additional conditions with a binary problem method in terms of computer time for the search for an optimal solution was carried out, where the model provided an optimal solution in a reasonable time. Among the most important benefits that the user can obtain for this technique are extending the life of the equipment, increasing efficiency, and reducing expenses.
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