An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots-Reference-Cited by-同舟云学术

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An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots

Published:2022-02 Issue: Volume:238 Page:107819
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Zhang Biao,Pan Quan-ke,Meng Lei-lei,Lu Chao,Mou Jian-hui,Li Jun-qing

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference48 articles.

1. The hybrid flow shop scheduling problem;Ruiz;European J. Oper. Res.,2010

2. Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times;Pan;Appl. Math. Comput.,2017

3. Metaheuristic algorithms for the hybrid flowshop scheduling problem;Öztop;Comput. Oper. Res.,2019

4. Modeling and multi-neighborhood iterated greedy algorithm for distributed hybrid flow shop scheduling problem;Shao;Knowl-Based Syst.,2020

5. Automatic algorithm design for hybrid flowshop scheduling problems;Alfaro-Fernández;European J. Oper. Res.,2020

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1. Tri-objective lot-streaming scheduling optimization for hybrid flow shops with uncertainties in machine breakdowns and job arrivals using an enhanced genetic programming hyper-heuristic;Computers & Operations Research;2024-12

2. Combining meta-heuristics and Q-learning for scheduling lot-streaming hybrid flow shops with consistent sublots;Swarm and Evolutionary Computation;2024-12

3. A matheuristic with re-lot-sizing strategies for flexible job-shop rescheduling problem with lot-streaming and machine reconfigurations;European Journal of Operational Research;2024-12

4. Reinforcement learning for distributed hybrid flowshop scheduling problem with variable task splitting towards mass personalized manufacturing;Journal of Manufacturing Systems;2024-10

5. Adaptive projection plane and reference point strategy for multi-objective particle swarm optimization;Alexandria Engineering Journal;2024-10

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