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Research Papers

Evaluation of Acceleration Effect of Dynamic Sequencing of Design Process in a Multiproject Environment

[+] Author and Article Information
Changmuk Kang

Department of Industrial Engineering, Seoul National University, Shillim-dong, Kwanak-gu, Seoul 151-744, Republic of Koreamuk83@snu.ac.kr

Yoo S. Hong1

Department of Industrial Engineering, Seoul National University, Shillim-dong, Kwanak-gu, Seoul 151-744, Republic of Koreayhong@snu.ac.kr

1

Corresponding author.

J. Mech. Des 131(2), 021008 (Jan 21, 2009) (11 pages) doi:10.1115/1.3066599 History: Received June 29, 2007; Revised November 21, 2008; Published January 21, 2009

The design process is difficult to accelerate due to its iterative nature, which increases project cost and delays completion time of a design project. Many previous studies tried to find the optimal structure and sequence of a design process minimizing iteration. In a multiproject environment, however, waiting time caused by resource shortage is a more critical reason for a lengthy project than iteration time. In this paper, we propose a novel sequencing method that reduces waiting time in a multiproject environment by dynamically changing the sequence of design tasks, according to availability of resources. It is called a dynamic sequencing method, as opposed to the traditional static sequencing method by which every design project follows a predefined optimal sequence. In order to evaluate the effect of this method, we developed a design-process model for simulating iteration and waiting in a multiproject environment. The simulation results show that dynamic sequencing is significantly better than traditional static sequencing with respect to average duration of projects. It is noted that more significant improvements can be obtained for the bottlenecked and unbalanced processes, both of which conditions would otherwise have negative effects on process performance.

Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

(a) DSM and (b) iteration mechanism

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Figure 2

Open tandem queuing network model

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Figure 3

Three coupled tasks

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Figure 4

Illustration of (a) static sequencing and (b) dynamic sequencing

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Figure 5

Procedure of (a) static sequencing and (b) dynamic sequencing

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Figure 6

Rework-probability matrix (RP) and rework-impact matrix (RI) for optical mouse design process

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Figure 7

(a) Average duration and (b) standard deviation of two sequencing methods

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Figure 8

Distribution of durations

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Figure 9

(a) Utilization and (b) average number in queue for each sequencing method

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Figure 10

(a) Average duration and (b) standard deviation of durations for different project types

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Figure 11

Distribution of durations of various types of projects

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Figure 12

(a) Average durations and (b) waiting times of the balanced process

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Figure 13

(a) Utilization and (b) average number in queue in a balanced process

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Figure 14

Improvement of the dynamic sequencing over (a) SMI and (b) NZF (an arrowed rectangle indicates the mouse design process)

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Figure 15

Comparison of the sequencing methods for asymmetric RP and RI matrices with mode values of (a) 0.3, (b) 0.5, and (c) 0.7

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