Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm-Reference-Cited by-同舟云学术

Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

Published:2024-10 Issue:5 Volume:29 Page:1390-1408
ISSN:1007-0214
Container-title:Tsinghua Science and Technology
language:
Short-container-title:Tsinghua Sci. Technol.

Author:

Li Xixing¹,Zhao Qingqing¹,Tang Hongtao²,Guo Xing¹,Zhuang Mengzhen²,Li Yibing²,Wang Xi Vincent³

Affiliation:

1. School of Mechanical Engineering, Hubei University of Technology,Hubei Key Laboratory of Modern Manufacturing and Quality Engineering,Wuhan,China,430068

2. School of Mechanical and Electronic Engineering, Wuhan University of Technology,Hubei Digital Manufacturing Key Laboratory,Wuhan,China,430070

3. KTH Royal Institute of Technology,Department of Production Engineering,Stockholm,Sweden,SE-10044

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Publisher

Tsinghua University Press

Link

http://xplorestaging.ieee.org/ielx7/5971803/10517916/10517917.pdf?arnumber=10517917

Reference47 articles.

1. Surprisingly Popular-Based Adaptive Memetic Algorithm for Energy-Efficient Distributed Flexible Job Shop Scheduling

2. Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks

3. Optimal Design of Flexible Job Shop Scheduling Under Resource Preemption Based on Deep Reinforcement Learning

4. Hybrid particle swarm optimization algorithm based on entropy theory for solving DAR scheduling problem

5. Survey of integrated flexible job shop scheduling problems

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