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A dynamic model for engineering change propagations in multiple product development stages

Published online by Cambridge University Press:  16 February 2022

Yulaing Li Open the ORCID record for Yulaing Li [Opens in a new window] ,
Wei Zhao ,
Wenqi Zhang  and
Meng Chen
Yulaing Li*
Affiliation:
Institute of Artificial Intelligence and Shanghai Engineering Research Center of Industrial Big Data and Intelligent system, Donghua University, No. 2999 North Renmin Road, Songjiang District, Shanghai 201620, China
Wei Zhao
Affiliation:
Zhejiang University of Finance and Economics, 18 Xueyuan Street, Hangzhou, Zhejiang 310018, China
Wenqi Zhang
Affiliation:
Shanghai Aerospace System Engineering Institute, 3888 Yuanjiang Road, Shanghai 201108, China
Meng Chen
Affiliation:
Shanghai Aerospace System Engineering Institute, 3888 Yuanjiang Road, Shanghai 201108, China
*
Author for correspondence: Yuliang Li, E-mail: meyuliang@dhu.edu.cn
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Abstract

To accurately predict propagation dynamics for single or multiple change propagations across different product development stages in a sequential or concurrent way is critical for decision-making of implementing change requests. In this paper, a change propagation dynamic model is built based on the compartmentalization of engineering entities into susceptible engineering entities and affected engineering entities (SA), the ordinary differential equations for describing the rate of affected entities with respect to the total ones and the duration for resolving all the changes for every moment are presented by combining the calculations of change impacts with different split and joint junctions. Considering the difficulty of finding analytical solutions to the differential equations, algorithms for sequential and concurrent simulations of change propagations across different development stages, and random and GA (Genetic Algorithm)-based optimal selections of feasible propagation paths are developed to obtain numerical solutions for single and multiple change requests. Simulation results show that change ripples and blossoms can be observed in both sequential and concurrent change propagations, and these propagation patterns are not sensitive to the initial change effect and the threshold value for propagations, while critical change propagation paths and the number of initiated changes have important effects on both concurrent and sequential change propagation process. It is also demonstrated that concurrent propagation strategy is advantageous for processing single or few of initiated changes since it can shorten product redevelopment time, sequential propagation strategy has an advantage of robustness for handling multiple initiated change requests.

Keywords

Change propagationmultistage developmentpropagation dynamicssimulation
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e13
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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