Ms. Yuying Rong Profile

Yuying Rong

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 28 February 2024 Paper

A deep reinforcement learning method for job shop scheduling integrated with automated guided vehicles

Shuting Huang, Baigang Du, Yuying Rong

Proceedings Volume 13071, 1307123 (2024) https://doi.org/10.1117/12.3025432

KEYWORDS: Transportation, Matrices, Mathematical optimization, Manufacturing, Feature extraction, Decision making, Signal processing, Neural networks, Genetics

Read Abstract +

With the emergence of the smart factory concept, Automated Guided Vehicles have been widely utilized in job shop transportation systems, drawing increased attention to the job shop scheduling problem integrated with multiple AGVs. In this problem, the sequential-logic constraint between transportation and processing tasks in each operation complicates the problem's decoupling compared to traditional job shop scheduling problems. In this paper, a deep reinforcement learning-based method is proposed to address this integrated scheduling problem. The problem is initially formulated as a Markov Decision Process, with state features designed from the perspectives of jobs and AGVs. Furthermore, a novel reward function is designed to maximize job shop resource utilization. The primary objective of the scheduling is to obtain an optimal solution that maximizes resource utilization, thereby enhancing the core competitiveness of the factory. Benchmark experiments demonstrate the effectiveness and competitiveness of this method in obtaining solutions for the integrated problems.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years