Optimisation and Control of Engineering Change Schedules in the Automotive Industry with Metaheuristics...

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Ognjen Radišić-Aberger

Optimisation and Control of Engineering Change Schedules in the Automotive Industry with Metaheuristics and Machine Learning

Name: Optimisation and Control of Engineering Change Schedules in the Automotive Industry with Metaheuristics and Machine Learning
Price: 201.58 AUD
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ISBN: 9783658510732

Ognjen Radišić-Aberger

$251.95 $201.58

Paperback

Forthcoming
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English

Springer Vieweg
03 April 2026

Production & quality control management; Business mathematics & systems; Manufacturing industries; Production engineering; Machine learning

Series: Findings from Production Management Research

Adaptation and change are imperative for products and companies to remain competitive. Managing these changes, however, is increasingly difficult and requires thorough planning and management. Especially in complex production systems, the efficient handling of these engineering changes becomes a competitive edge. This book embarks upon the task to manage the increasingly difficult optimisation and control of engineering changes through artificial intelligence. Based on a knowledge base gained from a systematic literature review, it is shown how AI methods can be applied to resolve challenges faced in production environments. Based on metaheuristic algorithms, optimal EC effectivity dates are determined, which are then validated and controlled by machine learning based business process monitoring. These advances provide significant support for change coordinators and material planners by reducing administrative effort end ensuring complexity control.

By: Ognjen Radišić-Aberger
Imprint: Springer Vieweg
Country of Publication: Germany
Dimensions: Height: 210mm, Width: 148mm,
ISBN: 9783658510732
ISBN 10: 3658510730
Series: Findings from Production Management Research
Pages: 283
Publication Date: 03 April 2026
Audience: College/higher education , Further / Higher Education
Format: Paperback
Publisher's Status: Forthcoming

1. Introduction.- 2. Theoretical Background.- 3. Publication I: AI-Artifacts in Engineering Change Management – A Systematic Literature Review.- 4. Publication II: Deciding on When to Change – A Benchmark of Metaheuristic Algorithms for Timing Engineering Changes.- 5. Publication III: Predicting Schedule Adherence of Engineering Changes - A case study on effectivity date adherence prediction using machine learning.- 6. Publication IV: Evaluating Early Predictive Performance of Machine Learning Approaches for Engineering Change Schedule – A Case Study Using Predictive Process Monitoring Techniques.- 7. Critical Reflection and Future Perspective.- 8. Summary.

Ognjen Radišić-Aberger works in production planning. His academic research focuses on AI approaches to optimise production processes and production management.