Artificial Neural Networks for Finite Capacity Scheduling: A Comparative Study

Authors

  • Ali Fuat Guneri Yildiz Technical University
  • Alev Taskin Gumus Yildiz Technical University

DOI:

https://doi.org/10.23055/ijietap.2008.15.4.183

Keywords:

Finite capacity scheduling, Artificial neural networks, Branch-and-bound algorithm.

Abstract

In this study artificial neural networks are applied for finite capacity scheduling. Utilisation of artificial neural networks on solving finite scheduling problems is examined. Also a comparative model is proposed by using multi layer perceptron (MLP) neural networks and branch-and-bound algorithm, and carried out to solve a real world problem in a job shop scheduling system.

Author Biographies

Ali Fuat Guneri, Yildiz Technical University

A

Alev Taskin Gumus, Yildiz Technical University

Alev Taskin Gumus is a Research Assistant in the Department of Industrial Engineering at Yildiz Technical University. Dr. Taskin Gumus received her MSc. Degree in Industrial Engineering from Yildiz Technical University, in 2003, MBA degree from Istanbul Technical University, in 2004, and PhD degree in Industrial Engineering fom Yildiz Technical University, in 2007. Her post-doctoral studies mainly focus on supply chain management, production and inventory systems, artificial neural networks, and fuzzy logic applications in industrial engineering and management science.

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Published

2022-02-26

How to Cite

Guneri, A. F., & Gumus, A. T. (2022). Artificial Neural Networks for Finite Capacity Scheduling: A Comparative Study. International Journal of Industrial Engineering: Theory, Applications and Practice, 15(4), 349–359. https://doi.org/10.23055/ijietap.2008.15.4.183

Issue

Vol. 15 No. 4 (2008)

Section

Data Sciences and Computational Intelligence

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