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Updated: Jun 30, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Modular scheduling of tightly coupled production lines.

Joan Marcè I Igual¹, Marc Geilen¹, Mitra Nasri¹

¹Eindhoven University of Technology, Eindhoven, The Netherlands.

Journal of Intelligent Manufacturing

|June 29, 2026

Summary

This study introduces a distributed scheduling method for modular production lines. It enables modules to reach consensus on job handover times, improving overall schedule feasibility and productivity.

Keywords:

Collaborative manufacturing Consensus Constraint graphs Distributed decision making Flow shops Modular production lines Scheduling

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Last Updated: Jun 30, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

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Published on: October 1, 2019

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Area of Science:

Industrial Engineering
Operations Research
Computer Science

Background:

Optimizing tightly coupled modular production lines is complex due to diverse products, varied constraints, and precise timing needs.
Modular setups offer flexibility and cost savings but complicate scheduling due to distributed decision-making and limited system views.
Local scheduling decisions in modular systems can significantly impact global schedule feasibility.

Purpose of the Study:

To propose a distributed scheduling method for tightly coupled modular sequential production lines.
To address the challenge of coordinating modules with limited local information for global schedule optimization.
To develop a multi-agent framework that facilitates consensus on job handover times.

Main Methods:

Developed a multi-agent framework with a central system agent and local agents.
The system agent propagates timing constraints and seeks consensus on job handover times between modules.
Integrated existing local schedulers without modification through local agents.

Main Results:

The distributed scheduling method was tested on complex re-entrant flow-shop instances with setup times and due dates.
Schedules generated by the distributed method showed near-optimal performance, with an average makespan only 1.15% larger than a hypothetical optimum.
The approach effectively managed timing constraints and achieved global schedule feasibility in a modular environment.

Conclusions:

The proposed distributed scheduling method is effective for tightly coupled modular sequential production lines.
The multi-agent framework successfully enables consensus-building for globally feasible schedules.
This approach offers a practical solution for optimizing productivity in industries like semiconductor manufacturing.