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

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Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy.

KeYi Xing¹, LiBin Han, MengChu Zhou

¹State Key Laboratory for Manufacturing Systems Engineering and the Systems Engineering Institute, Xi’an Jiaotong University, Xi’an, China. kyxing@sei.xjtu.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|November 23, 2011

Summary

This study introduces a novel genetic algorithm for deadlock-free scheduling in automated manufacturing systems (AMSs). The method ensures efficient resource utilization and prevents system stalls, optimizing performance.

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Area of Science:

Manufacturing Engineering
Operations Research
Computer Science

Background:

Automated Manufacturing Systems (AMSs) require efficient control and scheduling for optimal performance.
Shared resources and flexible routing in AMSs can lead to deadlocks, hindering productivity.
Existing scheduling methods may not adequately address deadlock avoidance in complex AMS environments.

Purpose of the Study:

To develop a novel deadlock-free genetic scheduling algorithm for automated manufacturing systems (AMSs).
To integrate an optimal deadlock avoidance policy within a genetic algorithm framework.
To enhance the performance and reliability of AMSs through effective scheduling.

Main Methods:

Petri net models were used to represent the automated manufacturing systems (AMSs).
An optimal deadlock avoidance policy was embedded into a genetic algorithm.
A chromosome representation (permutation with repetition) was employed for scheduling solutions.
A one-step look-ahead method was utilized for checking and amending chromosome feasibility.

Main Results:

A novel deadlock-free genetic scheduling algorithm for AMSs was successfully developed.
The algorithm effectively checks and amends infeasible schedules, ensuring deadlock avoidance.
The chromosome representation and checking procedures facilitate cooperative genetic search.
Polynomial complexity of procedures supports efficient scheduling.

Conclusions:

The proposed genetic algorithm provides an effective solution for deadlock-free scheduling in AMSs.
The integration of optimal deadlock avoidance policies enhances system reliability.
The method offers a computationally efficient approach to optimizing AMS performance.