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Implicit memory‐based technique in solving dynamic scheduling problems through Response Surface Methodology – Part I Model and method

Implicit memory‐based technique in solving dynamic scheduling problems through Response Surface... Purpose – This is the first part of a two‐part paper. The purpose of this paper is to report on methods that use the Response Surface Methodology (RSM) 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. Some of the methods are useful for investigating the performance (solution‐search abilities) of techniques (comprised of McBAR and other selected EA‐based techniques) for solving some multi‐objective dynamic resource‐constrained project scheduling problems with time‐varying number of tasks. Design/methodology/approach – The RSM is applied to: determine some EA parameters of the techniques, develop models of the performance of each technique, legitimize some algorithmic components of McBAR, manifest the relative performance of McBAR over the other techniques and determine the resiliency of McBAR against changes in the environment. Findings – The results of applying the methods are explored in the second part of this work. Originality/value – The models are composite and characterize an EA memory‐based technique. Further, the resiliency of techniques is determined by applying Lagrange optimization that involves the models. 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 I Model and method

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Publisher
Emerald Publishing
Copyright
Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/IJICC-12-2013-0053
Publisher site
See Article on Publisher Site

Abstract

Purpose – This is the first part of a two‐part paper. The purpose of this paper is to report on methods that use the Response Surface Methodology (RSM) 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. Some of the methods are useful for investigating the performance (solution‐search abilities) of techniques (comprised of McBAR and other selected EA‐based techniques) for solving some multi‐objective dynamic resource‐constrained project scheduling problems with time‐varying number of tasks. Design/methodology/approach – The RSM is applied to: determine some EA parameters of the techniques, develop models of the performance of each technique, legitimize some algorithmic components of McBAR, manifest the relative performance of McBAR over the other techniques and determine the resiliency of McBAR against changes in the environment. Findings – The results of applying the methods are explored in the second part of this work. Originality/value – The models are composite and characterize an EA memory‐based technique. Further, the resiliency of techniques is determined by applying Lagrange optimization that involves the models.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Jun 3, 2014

Keywords: Evolutionary computation; Genetic Algorithms; Multi‐objective optimization; Response Surface Methodology; Scheduling; Resource‐constrained project; Dynamic environments

References