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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 398123, 12 pages
Research Article

A Dynamic Intelligent Decision Approach to Dependency Modeling of Project Tasks in Complex Engineering System Optimization

1College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
2Institute of Systems Engineering, Huazhong University of Science and Technology, Wuhan, Hubei Province 430074, China

Received 2 January 2013; Revised 25 March 2013; Accepted 25 March 2013

Academic Editor: Reza Jazar

Copyright © 2013 Tinggui Chen and Renbin Xiao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Complex engineering system optimization usually involves multiple projects or tasks. On the one hand, dependency modeling among projects or tasks highlights structures in systems and their environments which can help to understand the implications of connectivity on different aspects of system performance and also assist in designing, optimizing, and maintaining complex systems. On the other hand, multiple projects or tasks are either happening at the same time or scheduled into a sequence in order to use common resources. In this paper, we propose a dynamic intelligent decision approach to dependency modeling of project tasks in complex engineering system optimization. The approach takes this decision process as a two-stage decision-making problem. In the first stage, a task clustering approach based on modularization is proposed so as to find out a suitable decomposition scheme for a large-scale project. In the second stage, according to the decomposition result, a discrete artificial bee colony (ABC) algorithm inspired by the intelligent foraging behavior of honeybees is developed for the resource constrained multiproject scheduling problem. Finally, a certain case from an engineering design of a chemical processing system is utilized to help to understand the proposed approach.