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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

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Feasibility Study for an Automated Engineering Change Process.

M E Sharp¹, T D Hedberg¹, W Z Bernstein¹

¹Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.

International Journal of Production Research

|January 9, 2023

Summary

This summary is machine-generated.

Automating engineering change requests can significantly reduce project costs and improve efficiency. This study demonstrates the feasibility of using genetic algorithms for this process, promising substantial time and cost savings for industries managing numerous changes.

Keywords:

Design Automation Design-for-Inspection Engineering Change Request Inspectability

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Area of Science:

Engineering Management
Computational Engineering
Product Lifecycle Management

Background:

Engineering changes represent a substantial cost in project management.
While change mitigation is ideal, design decisions evolve, necessitating change management.
Current processes for engineering change requests can be inefficient and resource-intensive.

Purpose of the Study:

To analyze the feasibility and performance of automating engineering change requests.
To demonstrate the potential for increased speed, efficiency, and effectiveness in product-lifecycle-wide change processes.
To highlight the need for advanced search algorithms over brute-force methods for change request management.

Main Methods:

A case study was used to mimic a typical change request scenario.
Genetic algorithms were selected to demonstrate the feasibility of automation.
The performance of the genetic algorithm was analyzed for efficiency and effectiveness.

Main Results:

The study confirmed the feasibility of using genetic algorithms for automating engineering change requests.
Analysis indicated that automation can significantly reduce the human effort required for low-level changes.
The genetic algorithm approach proved more efficient than brute-force methods for the examined case.

Conclusions:

Automating engineering change requests shows promise for substantial industry gains in time and cost.
Genetic algorithms offer a viable and deployable solution for enhancing change request processes.
Further development of sophisticated algorithms could greatly improve process efficiency and industry competitiveness.