Benchmarks for maintenance scheduling problems in power generation

Ahmad Almakhlafi; Joshua Knowles

doi:10.1109/CEC.2012.6252988

Benchmarks for maintenance scheduling problems in power generation

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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. © 2012 IEEE.

Original language	English
Title of host publication	2012 IEEE Congress on Evolutionary Computation, CEC 2012\|IEEE Congr. Evol. Comput., CEC
Publisher	IEEE
ISBN (Print)	9781467315098
DOIs	https://doi.org/10.1109/CEC.2012.6252988
Publication status	Published - 2012
Event	2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD Duration: 1 Jul 2012 → …

Conference

Conference	2012 IEEE Congress on Evolutionary Computation, CEC 2012
City	Brisbane, QLD
Period	1/07/12 → …

Keywords

benchmarks
fitness landscape analysis
genetic algorithms
Maintenance scheduling

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10.1109/CEC.2012.6252988

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title = "Benchmarks for maintenance scheduling problems in power generation",

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. {\textcopyright} 2012 IEEE.",

keywords = "benchmarks, fitness landscape analysis, genetic algorithms, Maintenance scheduling",

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note = "2012 IEEE Congress on Evolutionary Computation, CEC 2012 ; Conference date: 01-07-2012",

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N2 - 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. © 2012 IEEE.

AB - 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. © 2012 IEEE.

KW - benchmarks

KW - fitness landscape analysis

KW - genetic algorithms

KW - Maintenance scheduling

U2 - 10.1109/CEC.2012.6252988

DO - 10.1109/CEC.2012.6252988

M3 - Conference contribution

SN - 9781467315098

BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012|IEEE Congr. Evol. Comput., CEC

PB - IEEE

T2 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012

Y2 - 1 July 2012

ER -

Benchmarks for maintenance scheduling problems in power generation

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Keywords

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