<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Energy saving has always been a concern in production scheduling, especially in distributed hybrid flow shop scheduling problems. This study proposes a shuffled frog leaping algorithm with Q-learning (QSFLA) to solve distributed hybrid flow shop scheduling problems with energy-saving(DEHFSP) for minimizing the maximum completion time and total energy consumption simultaneously. The mathematical model is provided, and the lower bounds of two optimization objectives are given and proved. A Q-learning process is embedded in the memeplex search of QSFLA. The state of the population is calculated based on the lower bound. Sixteen search strategy combinations are designed according to the four kinds of global search and four kinds of neighborhood structure. One combination is selected to be used in the memeplex search according to the population state. An energy-saving operator is presented to reduce total energy consumption without increasing the processing time. One hundred forty instances with different scales are tested, and the computational results show that QSFLA is a very competitive algorithm for solving DEHFSP.</p>
</abstract>

Energy saving has always been a concern in production scheduling, especially in distributed hybrid flow shop scheduling problems. This study proposes a shuffled frog leaping algorithm with Q-learning (QSFLA) to solve distributed hybrid flow shop scheduling problems with energy-saving(DEHFSP) for minimizing the maximum completion time and total energy consumption simultaneously. The mathematical model is provided, and the lower bounds of two optimization objectives are given and proved. A Q-learning process is embedded in the memeplex search of QSFLA. The state of the population is calculated based on the lower bound. Sixteen search strategy combinations are designed according to the four kinds of global search and four kinds of neighborhood structure. One combination is selected to be used in the memeplex search according to the population state. An energy-saving operator is presented to reduce total energy consumption without increasing the processing time. One hundred forty instances with different scales are tested, and the computational results show that QSFLA is a very competitive algorithm for solving DEHFSP.

A Shuffled Frog Leaping Algorithm with Q-Learning for Distributed Hybrid Flow Shop Scheduling Problem with Energy-Saving

School of Mechanical Engineering, Anhui Polytechnic University

AnHui Key Laboratory of Detection Technology and Energy Saving Devices

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Energy saving has always been a concern in production scheduling, especially in distributed hybrid flow shop scheduling problems. This study proposes a...

A Shuffled Frog Leaping Algorithm with Q-Learning for Distributed Hybrid Flow Shop Scheduling Problem with Energy-Saving

Online veröffentlicht: 19. März 2024

Seitenbereich: 101 - 120

Eingereicht: 11. Dez. 2023

Akzeptiert: 27. Feb. 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0006

Schlüsselwörter
energy-saving, distributed scheduling, hybrid flow shop, shuffled frog-leaping algorithm, reinforcement learning

© 2024 Jingcao Cai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Shuffled Frog Leaping Algorithm with Q-Learning for Distributed Hybrid Flow Shop Scheduling Problem with Energy-Saving

Online veröffentlicht: 19. März 2024

Seitenbereich: 101 - 120

Eingereicht: 11. Dez. 2023

Akzeptiert: 27. Feb. 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0006

Schlüsselwörterenergy-saving, distributed scheduling, hybrid flow shop, shuffled frog-leaping algorithm, reinforcement learning

© 2024 Jingcao Cai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
energy-saving, distributed scheduling, hybrid flow shop, shuffled frog-leaping algorithm, reinforcement learning