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Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

Wenlong Cheng (College of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, China)
Wenjun Meng (College of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, China)

Robotic Intelligence and Automation

ISSN: 2754-6969

Article publication date: 2 January 2024

Issue publication date: 29 March 2024

165

Abstract

Purpose

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

Design/methodology/approach

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

Findings

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

Originality/value

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

Keywords

Acknowledgements

Institutional review board statement: Not applicable.

Informed consent statement: Not applicable.

Data availability statement: The data presented in this study are available on request from the first author.

Conflict of interest: The authors declare no conflict of interest.

Citation

Cheng, W. and Meng, W. (2024), "Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop", Robotic Intelligence and Automation, Vol. 44 No. 1, pp. 34-47. https://doi.org/10.1108/RIA-11-2022-0266

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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