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Autonomous decision-making method of transportation process for flexible job shop scheduling problem based on reinforcement learning | IEEE Conference Publication | IEEE Xplore

Autonomous decision-making method of transportation process for flexible job shop scheduling problem based on reinforcement learning


Abstract:

With the development of intelligent manufacturing technology, automatic guided vehicle (AGV) has been widely used in workshop logistics transportation. However, the compl...Show More

Abstract:

With the development of intelligent manufacturing technology, automatic guided vehicle (AGV) has been widely used in workshop logistics transportation. However, the complex and changeable production process requires efficient and dynamic transportation and distribution. In this paper, based on the results of process scheduling, a method of independent decision-making transportation process is studied. A transportation strategy training method with breakpoint continuation and hierarchical feedback is proposed based on deep Q-network (DQN). Transportation scheduling can be quickly decided and adjusted to adapt to dynamic and changeable orders. The method is applied to the FJSP problem, and the data experiments show the effectiveness of the method.
Date of Conference: 09-11 July 2021
Date Added to IEEE Xplore: 25 November 2021
ISBN Information:
Conference Location: Chongqing, China

Funding Agency:


I. Introduction

The rapid development of intelligent manufacturing technology has promoted the transformation of manufacturing methods from single-variety production to multi-variety and small-batch mixed production. More and more complex production process brings new challenges to production scheduling and logistics transportation. Especially, once the scheduling plan changes, the transportation and distribution also need to change. Therefore, it is necessary to develop the dynamic, efficient and fast response logistics transportation strategy to improve production efficiency.

References

References is not available for this document.