Automated Generation of Precedence Graphs in Digital Value Chains for Automotive Production

2025-04-28

AI Summary

In automotive manufacturing, digital value chain activities—including ECU identification, software flashing, customization, and debugging—suffer from strong configuration dependencies, time-consuming manual precedence graph construction, redundant workstations, and poor adaptability to evolving network topologies. Method: This study proposes a vehicle-configuration-adaptive approach for automatic precedence graph generation and scheduling, integrating digital value chain modeling, mixed-integer linear programming (MILP), and automated scheduling algorithms. Contribution/Results: The method enables precedence graph generation in ≤2 minutes, eliminates backup workstations, and supports rapid integration of new network topologies. Empirical evaluation demonstrates a 50% reduction in setup time, decreased hardware station count, improved capacity utilization, and significantly enhanced system throughput and responsiveness.

Technology Category

• Planning, Routing, and Scheduling: Scheduling
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This study examines the digital value chain in automotive manufacturing, focusing on the identification, software flashing, customization, and commissioning of electronic control units in vehicle networks. A novel precedence graph design is proposed to optimize this process chain using an automated scheduling algorithm that employs mixed integer linear programming techniques. The results show significant improvements in key metrics. The algorithm reduces the number of production stations equipped with expensive hardware and software to execute digital value chain processes, while increasing capacity utilization through efficient scheduling and reduced idle time. Task parallelization is optimized, resulting in streamlined workflows and increased throughput. Compared to the traditional method, the automated approach has reduced preparation time by 50% and reduced scheduling activities, as it now takes two minutes to create the precedence graph. The flexibility of the algorithm's constraints allows for vehicle-specific configurations while maintaining high responsiveness, eliminating backup stations and facilitating the integration of new topologies. Automated scheduling significantly outperforms manual methods in efficiency, functionality, and adaptability.

Problem

• Automated scheduling algorithm optimizes digital value chain in automotive production
• Reduces production stations and idle time while increasing capacity utilization
• Enables vehicle-specific configurations with high responsiveness and flexibility

Innovation

• Automated scheduling algorithm using mixed integer linear programming
• Optimized task parallelization for streamlined workflows
• Flexible constraints for vehicle-specific configurations