An investigation into the effectiveness of simulated annealing as a solution approach for the generator maintenance scheduling problem
The original publication is available at http://www.sciencedirect.com/science/article/pii/S0142061513001786
The generator maintenance scheduling (GMS) problem is the difficult combinatorial optimisation problem of finding a schedule for the planned maintenance outages of generating units in a power system. The GMS model considered in this paper is formulated as a mixed integer program, with a reliability optimality criterion, subject to a number of constraints. A new version of the simulated annealing (SA) method for solving the GMS problem is presented. Four cooling schedules (the geometric and three adaptive schedules), two neighbourhood move operators (an elementary move and an ejection chain move operator), and a hybrid local search heuristic/SA algorithm are compared. To our knowledge, this is the first study considering a different SA cooling schedule and move operator in a GMS context. A new 32-unit GMS test system is established and used in conjunction with a benchmark test system from the literature in this investigation. It is found that choosing a different cooling schedule and an ejection chain move operator yield improved results to that of the SA algorithm currently employed in the GMS literature. The hybrid SA algorithm performs very well compared to other methods on the benchmark test system from the literature, and an improved lower bound on the objective function value is presented for this test system.