Optimal Production Scheduling by Integer Form of Population-Based Incremental Learning with Initial Probability Matrix Setting Methods and a Practical Production Simulator-Reference-Cited by-同舟云学术

Optimal Production Scheduling by Integer Form of Population-Based Incremental Learning with Initial Probability Matrix Setting Methods and a Practical Production Simulator

Published:2023-12-05 Issue: Volume: Page:565-572
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Container-title:2023 IEEE Symposium Series on Computational Intelligence (SSCI)
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Author:

Katagiri Ryusei¹,Fukuyama Yoshikazu¹,Kawaguchi Shuhei²,Takahashi Kenjiro³,Sato Takaomi⁴

Affiliation:

1. Graduate School of Advanced Mathematical Sciences, Meiji University,Tokyo,Japan

2. Corporate R&D Headquarters in Mitsubishi Electric Co., Ltd, Meiji University,Kanagawa,Japan

3. Corporate R&D Headquarters in Mitsubishi Electric Co., Ltd,Kanagawa,Japan

4. Corporate R&D Headquarters in Mitsubishi Electric Co., Ltd, The University of Tokyo,Kanagawa,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10371778/10371788/10371949.pdf?arnumber=10371949

Reference13 articles.

1. The job shop scheduling problem: Conventional and new solution techniques

2. Conference of the Parties to the United Nations Framework Convention on Climate Change (COP24)

3. A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction

4. Energy-aware parameters in job-shop scheduing problems;Salido,2013

5. An investigation into minimising total energy consumption and total weighted tardiness in job shops

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