Journal Article10.1016/j.eswa.2024.125616
An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming
Yarong Chen,Jia Yan Du,Jabir Mumtaz,Jingyan Zhong,Mudassar Rauf +4 more
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About: This article is published in Expert systems with applications. The article was published on 30 Oct 2024. The article focuses on the topics: Computer science & Flow shop scheduling.
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Citations
Food pickling workshop lot streaming scheduling with resource constraints using Q-learning-based fruit fly optimisation
Xixing Li,Chenming Liu,Rui Wu,Qingqing Zhao,Jing Wang,Qi Li,Hongtao Tang,Yibing Li,Xixing Li,Chenming Liu,Rui Wu,Qingqing Zhao,Jing Wang,Qi Li,Hongtao Tang,Yibing Li +15 more
GenAI job scheduling system for solving a flexible job shop scheduling problem
Toly Chen,Min Chi Chiu,Hsin-Chieh Wu,Toly Chen,Min Chi Chiu,Hsin-Chieh Wu +5 more
Abstract: Abstract Generative artificial intelligence (GenAI) applications in job scheduling are expected to help schedulers embed their requirements into scheduling models in a more user-friendly way to generate customized scheduling results. However, there are still very few such applications, while using existing general-purpose GenAI services is inconvenient and prone to data leakage risks. To solve these problems, this study established a GenAI job scheduling system. By hosting the GenAI job scheduling system locally, schedulers can avoid the leakage of order- or recipe-related information that may occur when uploading to the cloud-based GenAI service. In the GenAI job scheduling system, a user interface is designed for users to enter queries in natural language. The user’s query is then analyzed to extract his/her requirements related to the scheduling task, thereby building an extended three-field notation (ETFN) of the scheduling problem. A customized genetic algorithm (GA) is generated to help solve the mathematical programming (MP) model corresponding to the ETFN, thereby updating invalid code or adding new code to the basic GA application. The effectiveness of the GenAI job scheduling system has been tested in a flexible job shop case.
An adaptive multi-population algorithm with variable-speed mechanism for multi-objective hybrid lot-streaming flow shop scheduling problem
Fuqing Zhao,Jianlin Zhang,Tian-Peng Xu +2 more
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Hierarchical Multiobjective Heuristic for PCB Assembly Optimization in a Beam-Head Surface Mounter
01 Jul 2022
TL;DR: In this paper , a hierarchical multiobjective heuristic (HMOH) is proposed to optimize printed-circuit board assembly (PCBA) in a single beam-head surface mounter.
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A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time
TL;DR: An enhanced artificial bee colony algorithm (NEABC) with strong intensification is proposed to generate promising swarm and a restart strategy is designed in the scout bee stage with the consideration of the special onlooker bee stage of the algorithm.
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An Improved Artificial Bee Colony Algorithm With Q-Learning for Solving Permutation Flow-Shop Scheduling Problems
TL;DR: In this article , an improved artificial bee colony (ABC) algorithm was proposed to solve the permutation flow shop scheduling problem with minimizing the maximum completion time (makespan) by using learning.
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A review of lot streaming
TL;DR: Lot streaming is a technique that accelerates the flow of a product through a production system by splitting its production lot into sub-lots and then processing the sublots simultaneously over the machines.
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