For the generation maintenance scheduling (GMS) problem, a producer hopes to maximize its profit while ISO is to guarantee the system reliability. Thus, the GMS is a multi-objective optimization problem. In the GMS, there are large numbers of both continuous and integer variables, which complicates the resolving of the GMS. This paper proposes a new GMS model, which is suitable to be solved by the nondominated sorting genetic algorithm-II (NSGA-II). In the GMS model, the maintenance status of a generator is encoded into an integer variable and both the online status and the start-up status are represented by the generation variables. The GMS model on the IEEE reliability test system is solved by NSGA-II with a set of Pareto-optimal solutions obtained. The simulation results show that the GMS can be efficiently solved by NSGA-II. The simulation results also show that one producer's profit conflicts with another one's, and that the reliability objective is independent of the other objectives.

This paper presents a multi-agent structure and optimization algorithm to enhance energy utilization in a flexible jobshop environment under Time-of-Use (TOU) electricity pricing, in the presence of system changes. The proposed control structure based on multi-agent systems (MAS) takes into account re-scheduling in the workshop under TOU pricing. A multi-objective rescheduling model was developed to minimize due dates, makespan, total power consumption, and energy consumption costs. An improved genetic algorithm (GA) was designed to solve this model by converting the multi-objective into a single objective by optimizing each objective individually and combining the results. Simulation results demonstrate the effectiveness of the proposed algorithm and the application of the MAS structure.