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Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/74050

Type: Conference paper
Title: A reactive-proactive approach for solving dynamic scheduling with time-varying number of tasks
Author: Abello, M.
Michalewicz, Z.
Bui, L.
Citation: Proceedings of the 2012 IEEE Congress on Evolutionary Computation, held in Brisbane, 10-15 June, 2012: pp.1-10
Publisher: IEEE
Issue Date: 2012
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 9781467315081
Cover art
Conference Name: IEEE Congress on Evolutionary Computation (2012 : Brisbane, Qld.)
Statement of
Responsibility: 
Manuel Blanco Abello, Zbignew Michalewicz and Lam Thu Bui
Abstract: Any system (whether in the area of finance, manufacturing, administration, etc.) that operates in a dynamic environment needs to be adaptive to changes; it should also anticipate possible adverse events to remain competitive. In our previous research in this area we experimented with one particular approach: Mapping of Task ID for Centroid-Based Adaptation with Random Immigrants (McBAR) to address problems of environmental changes for Resource-Constrained Project Scheduling (RCPS) problem, especially when the latter involves changes in task numbers. However, at that time, McBAR was applied as reactive tool only. In this paper we extend McBAR approach to the RCPS problem in a proactive-reactive way. The system handles also three competing objectives: cost, makespan, and the risk of failure. We have not found any papers that deal with risk on the RCPS problem and utilize the attributes of plans from the past environmental changes. This particular aspect is incorporated in McBAR – experimental results indicate the efficiency of such approach in finding optimal solutions for a current change. In this paper we also analyze, under the effects of environmental dynamics, the variation of risk computed via McBAR and of parameters related to optimization. Further, we compare McBAR to other Evolutionary Algorithm approach in the same problem.
Keywords: Adaptation; dynamic environments; multiobjectiveoptimization; risk management
Rights: © Copyright 2012 IEEE - All rights reserved.
RMID: 0020122212
DOI: 10.1109/CEC.2012.6256484
Appears in Collections:Computer Science publications
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