A Boltzmann-Based Estimation of Distribution Algorithm for a General Resource Scheduling Model

Most researchers employed common functional models when managing scheduling problems with controllable processing times. However, in many complicated manufacturing systems with a high diversity of jobs, these functional resource models fail to reflect their specific characteristics. To fulfill these...

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Bibliographic Details
Published inIEEE transactions on evolutionary computation Vol. 19; no. 6; pp. 793 - 806
Main Authors Xinle Liang, Huaping Chen, Lozano, Jose A.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Approximation methods
Boltzmann distribution
Complexity theory
Controllable processing times
Discrete model
Estimation
Estimation of distribution algorithm
Production scheduling
Resource management
Scheduling
Simulation
Sociology
Boltzmann distribution
scheduling
controllable processing times
discrete model
estimation of distribution algorithm (EDA)
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ISSN1089-778X
1941-0026
DOI10.1109/TEVC.2014.2382135

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Summary:Most researchers employed common functional models when managing scheduling problems with controllable processing times. However, in many complicated manufacturing systems with a high diversity of jobs, these functional resource models fail to reflect their specific characteristics. To fulfill these requirements, we apply a more general model, the discrete model. Traditional functional models can be viewed as special cases of such model. In this paper, the discrete model is implemented on a problem of minimizing the weighted resource allocation subject to a common deadline on a single machine. By reducing the problem to a partition problem, we demonstrate that it is NP-complete, which addresses the difficult issue of the guarantee of both the solution quality and time cost. In order to tackle the problem, we develop an estimation of distribution algorithm based on an approximation of the Boltzmann distribution. The approximation strategy represents a tradeoff between complexity and solution accuracy. The results of the experiments conducted on benchmarks show that, compared with other alternative approaches, the proposed algorithm has competitive behavior, obtaining 74 best solutions out of 90 instances.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2014.2382135