Re-sequencing of Design Processes With Activity Stochastic Time and Cost: An Optimization-Simulation Approach

[+] Author and Article Information
Hisham M. E. Abdelsalam

Decision Support Department, Faculty of Computers and Information, Cairo University, 5 Dr. Ahmed Zewel St, Giza 12613, Egypt

Han P. Bao

Department of Mechanical Engineering, Old Dominion University, Norfolk, VA 23529

J. Mech. Des 129(2), 150-157 (Nov 12, 2005) (8 pages) doi:10.1115/1.2216730 History: Received March 16, 2005; Revised November 12, 2005

Background. By the mid-1990s, the importance of the early introduction of new products to both market share and profitability became fully understood. Thus, reducing product time-to-market became an essential requirement for continuous competition. Coupled with the fact that about 70% of the life cycle cost of a product is committed at early design phases, the motivation for developing and implementing more effective methodologies for managing the design process of new product development projects became very strong. Method of Approach. One tool that helps in understanding and analyzing such a project is the design structure matrix (DSM). This paper presents a framework that obtains an optimum sequence of project activities—presented by the DSM—that minimizes total time and cost given stochastic activity estimated time and cost. The framework interfaces a meta-heuristic optimization algorithm called simulated annealing with a commercial risk analysis software. Results. The proposed framework was applied to a design project and the results have shown a robust solution minimum was reached. Conclusions. Since much of the time and cost involved in the design process is attributable to its expensive iterative nature. The framework presented in this paper improves a design project via obtaining an optimum sequence of its activities that minimizes total time and cost.

Copyright © 2007 by American Society of Mechanical Engineers
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Figure 1

Design structure matrix

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Figure 2

The presented framework

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Figure 3

Iterative time and cost computation heuristic

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Figure 4

Initial versus final DSM

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Figure 5

Probability distribution of the objective functions

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Figure 6

Objective function measures

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Figure 7

Solution robustness

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Figure 8

Optimal solution sensitivity charts

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Figure 9

Deterministic case (1)

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Figure 10

Deterministic case (2)



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