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Implicit memory‐based technique in solving dynamic scheduling problems through Response Surface Methodology – Part II Experiments and analysis

Implicit memory‐based technique in solving dynamic scheduling problems through Response Surface... Purpose – This is the second part of a two‐part paper. The purpose of this paper is to report the results on the application of the methods that use the Response Surface Methodology to investigate an evolutionary algorithm (EA) and memory‐based approach referred to as McBAR – the Mapping of Task IDs for Centroid‐Based Adaptation with Random Immigrants. Design/methodology/approach – The methods applied in this paper are fully explained in the first part. They are utilized to investigate the performances (ability to determine solutions to problems) of techniques composed of McBAR and some EA‐based techniques for solving some multi‐objective dynamic resource‐constrained project scheduling problems with a variable number of tasks. Findings – The main results include the following: first, some algorithmic components of McBAR are legitimate; second, the performance of McBAR is generally superior to those of the other techniques after increase in the number of tasks in each of the above‐mentioned problems; and third, McBAR has the most resilient performance among the techniques against changes in the environment that set the problems. Originality/value – This paper is novel for investigating the enumerated results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

Implicit memory‐based technique in solving dynamic scheduling problems through Response Surface Methodology – Part II Experiments and analysis

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Publisher
Emerald Publishing
Copyright
Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/IJICC-12-2013-0054
Publisher site
See Article on Publisher Site

Abstract

Purpose – This is the second part of a two‐part paper. The purpose of this paper is to report the results on the application of the methods that use the Response Surface Methodology to investigate an evolutionary algorithm (EA) and memory‐based approach referred to as McBAR – the Mapping of Task IDs for Centroid‐Based Adaptation with Random Immigrants. Design/methodology/approach – The methods applied in this paper are fully explained in the first part. They are utilized to investigate the performances (ability to determine solutions to problems) of techniques composed of McBAR and some EA‐based techniques for solving some multi‐objective dynamic resource‐constrained project scheduling problems with a variable number of tasks. Findings – The main results include the following: first, some algorithmic components of McBAR are legitimate; second, the performance of McBAR is generally superior to those of the other techniques after increase in the number of tasks in each of the above‐mentioned problems; and third, McBAR has the most resilient performance among the techniques against changes in the environment that set the problems. Originality/value – This paper is novel for investigating the enumerated results.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Jun 3, 2014

Keywords: Evolutionary computation; Multi‐objective optimization; Genetic algorithms; Response surface methodology; Dynamic environments; Resource‐constrained project scheduling

References