Learning-Based Grey Wolf Optimizer for Stochastic Flexible Job Shop Scheduling-Reference-Cited by-同舟云学术

Learning-Based Grey Wolf Optimizer for Stochastic Flexible Job Shop Scheduling

Published:2022-10 Issue:4 Volume:19 Page:3659-3671
ISSN:1545-5955
Container-title:IEEE Transactions on Automation Science and Engineering
language:
Short-container-title:IEEE Trans. Automat. Sci. Eng.

Author:

Lin ChengRan¹^ORCID,Cao ZhengCai¹^ORCID,Zhou MengChu²^ORCID

Affiliation:

1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China

2. Institute of Systems Engineering and the Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Macao, China

Funder

Beijing Leading Talents Program

National Natural Science Foundation of China

Fundo para o Desenvolvimento das Ciencias e da Tecnologia

State Key Laboratory of Synthetical Automation for Process Industries

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/8856/9918179/09684016.pdf?arnumber=9684016

Reference55 articles.

1. Approximate Simulation Budget Allocation for Subset Ranking

2. Scheduling algorithms for a semiconductor probing facility

3. Particle swarm optimization for function optimization in noisy environment

4. Continuous control with deep reinforcement learning;lillicrap;arXiv 1509 02971,2015

5. Optimizing expectations: From deep reinforcement learning to stochastic computation graphs;schulman,2017

Cited by 34 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A multi-objective genetic algorithm based on two-stage reinforcement learning for green flexible shop scheduling problem considering machine speed;Expert Systems with Applications;2024-12

2. An efficient two-stage optimization algorithm for a flexible job shop scheduling problem with worker shift arrangement;Computers & Operations Research;2024-11

3. An Enhanced Multi-Objective Evolutionary Algorithm with Reinforcement Learning for Energy-Efficient Scheduling in the Flexible Job Shop;Processes;2024-09-13

4. A double-Q network collaborative multi-objective optimization algorithm for precast scheduling with curing constraints;Swarm and Evolutionary Computation;2024-08

5. An enhanced teaching–learning-based optimization for the flexible job shop scheduling problem considering worker behaviours;Soft Computing;2024-07-11