Rapid Production Rescheduling for Flow Shop Under Machine Failure Disturbance Using Hybrid Perturbation Population Genetic Algorithm-Artificial Neural Networks (PPGA-ANNs)-Reference-Cited by-同舟云学术

Rapid Production Rescheduling for Flow Shop Under Machine Failure Disturbance Using Hybrid Perturbation Population Genetic Algorithm-Artificial Neural Networks (PPGA-ANNs)

Published:2023 Issue: Volume:11 Page:75794-75817
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Saophan Pakkaporn¹^ORCID,Pannakkong Warut²,Singhaphandu Raveekiat¹,Huynh Van-Nam¹^ORCID

Affiliation:

1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Japan

2. School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Khlong Nueng, Thailand

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10179223.pdf?arnumber=10179223

Reference58 articles.

1. The two stage assembly flowshop scheduling problem to minimize total tardiness

2. A discrete Water Wave Optimization algorithm for no-wait flow shop scheduling problem

3. A mathematical model and extension algorithm for assembly flexible flow shop scheduling problem

4. An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

5. Optimal two- and three-stage production schedules with setup times included

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