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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Dynamic Intelligent Scheduling in Low-Carbon Heterogeneous Distributed Flexible Job Shops with Job Insertions and

Yi Chen1,2, Xiaojuan Liao1,2, Guangzhu Chen1,2

  • 1College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

This study addresses the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP) by developing a novel deep reinforcement learning approach. The Rainbow DQN method effectively minimizes tardiness and energy consumption in dynamic smart manufacturing environments.

Keywords:
Rainbow DQNdeep reinforcement learningdynamic schedulingheterogeneous distributed flexible job shoplow-carbon

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Manufacturing Systems Engineering

Background:

  • Economic globalization and green manufacturing drive the evolution of job shop scheduling.
  • Smart manufacturing faces dynamic events and complex scheduling challenges.
  • Existing research inadequately addresses LHDFJSP factors like heterogeneity, dynamic insertions, and low-carbon goals.

Purpose of the Study:

  • To establish a multi-objective mathematical model for the LHDFJSP.
  • To minimize total weighted tardiness and total energy consumption.
  • To develop an adaptive scheduling strategy for dynamic manufacturing environments.

Main Methods:

  • Formulation of a multi-objective mathematical model for LHDFJSP.
  • Development of diverse composite scheduling rules.
  • Application of a Rainbow deep-Q network (Rainbow DQN) for dynamic scheduling strategy learning.

Main Results:

  • The proposed Rainbow DQN-based method demonstrates effectiveness in solving the LHDFJSP.
  • Evaluation on an extended dataset confirms the method's generalization capabilities.
  • The approach proves robust in handling dynamic events and complex scheduling factors.

Conclusions:

  • The Rainbow DQN framework provides an effective solution for the LHDFJSP.
  • The method successfully balances minimizing tardiness and energy consumption.
  • The approach is suitable for dynamic smart manufacturing environments requiring adaptive scheduling.