Title :
Benchmarks for maintenance scheduling problems in power generation
Author :
Almakhlafi, Ahmad ; Knowles, Joshua
Author_Institution :
Sch. of Comput. Sci., Univ. of Manchester, Manchester, UK
Abstract :
We present a test suite of 23 instances of a preventive maintenance scheduling problem from the power industry, which we also make available online. The formulation of the problem and the suite are derived from real-world data collected recently. A first study of the landscape characteristics of these problem instances based on three different types of adaptive walk reveals a generally rugged landscape, with little global fitness-distance correlation. Initial results from a simple evolutionary algorithm shows indifferent performance compared to adaptive walks, suggesting that intensive local search may be an important component of a successful optimizer for this problem.
Keywords :
electricity supply industry; evolutionary computation; power generation scheduling; preventive maintenance; search problems; adaptive walk; benchmarks; evolutionary algorithm; generally rugged landscape; global fitness-distance correlation; intensive local search; landscape characteristics; maintenance scheduling problems; power generation; power industry; preventive maintenance scheduling problem; problem instances; real-world data; Correlation; Generators; Job shop scheduling; Maintenance engineering; Optimization; Planning; Power generation; Maintenance scheduling; benchmarks; fitness landscape analysis; genetic algorithms;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6252988