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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Production scheduling with multi-robot task allocation in a real industry 4.0 setting.

Zohreh Shakeri¹, Khaled Benfriha², Mohsen Varmazyar³

¹Laboratoire Conception de Produits et Innovation (LCPI), Arts et Metiers Institute of Technology, 75013, Paris, France. zohreh.shakeri@ensam.eu.

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Summary

This study optimizes Multi-Robot Flexible Job Shop scheduling for Industry 4.0 systems. A novel Proposed Genetic Algorithm (PGA) significantly improves efficiency over basic methods for complex manufacturing scheduling.

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Area of Science:

Operations Research
Industrial Engineering
Manufacturing Systems

Background:

Industry 4.0 demands efficient production systems, making robotic scheduling critical.
Existing research often simplifies robotic job shop problems, neglecting robot diversity and resource allocation.
Smart manufacturing requires precise scheduling for material transfer robots, especially with limited buffers and blocking conditions.

Purpose of the Study:

To address the complex Multi-Robot Flexible Job Shop (MRFJS) scheduling problem with limited buffers.
To develop and evaluate a novel Genetic Algorithm (GA) for optimizing production schedules in realistic Industry 4.0 scenarios.
To minimize makespan in a system with non-identical parallel machines and diverse robots.

Main Methods:

Formulation of a Mixed-Integer Programming (MILP) model to minimize makespan.
Development of a new Genetic Algorithm (GA) incorporating Roy and Sussman's Alternative Graph.
Computational testing using various scales and real manufacturing plant data.

Main Results:

The Proposed Genetic Algorithm (PGA) achieved an average Relative Deviation (ARD) of 0.25%.
The PGA demonstrated a 34% improvement over the Basic Genetic Algorithm (BGA), which had an ARD of 0.38%.
The algorithm proved effective in solving complex scheduling problems in real-world production settings.

Conclusions:

The developed PGA is highly effective for optimizing MRFJS scheduling in Industry 4.0 environments.
The study highlights the importance of considering robot diversity and buffer limitations for realistic scheduling.
The proposed method offers a significant advancement for smart manufacturing efficiency and complex scheduling solutions.