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Authors: Yahia Zakaria ; Ahmed BahaaElDin and Mayada Hadhoud

Affiliation: Computer Engineering Department, Faculty of Engineering, Cairo University, Giza and Egypt

Keyword(s): Feature Selection, Flexible Job Shop Scheduling, Dynamic Scheduling, Genetic Programming.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

Abstract: Dynamic flexible job shop scheduling is an optimization problem concerned with job assignment in dynamic production environments where future job arrivals are unknown. Job scheduling systems employ a pair of rules: a routing rule which assigns a machine to process an operation and a sequencing rule which determines the order of operation processing. Since hand-crafted rules can be time and effort-consuming, many papers employ genetic programming to generate optimum rule trees from a set of terminals and operators. Since the terminal set can be large, the search space can be huge and inefficient to explore. Feature selection techniques can reduce the terminal set size without discarding important information and they have shown to be effective for improving rule generation for dynamic job shop scheduling. In this paper, we extend a niching-based feature selection technique to fit the requirements of dynamic flexible job shop scheduling. The results show that our method can generate ru les that achieves significantly better performance compared to ones generated from the full feature set. (More)

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Paper citation in several formats:
Zakaria, Y.; BahaaElDin, A. and Hadhoud, M. (2019). Applying Feature Selection to Rule Evolution for Dynamic Flexible Job Shop Scheduling. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - ECTA; ISBN 978-989-758-384-1; ISSN 2184-3236, SciTePress, pages 139-146. DOI: 10.5220/0007957801390146

@conference{ecta19,
author={Yahia Zakaria. and Ahmed BahaaElDin. and Mayada Hadhoud.},
title={Applying Feature Selection to Rule Evolution for Dynamic Flexible Job Shop Scheduling},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - ECTA},
year={2019},
pages={139-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007957801390146},
isbn={978-989-758-384-1},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - ECTA
TI - Applying Feature Selection to Rule Evolution for Dynamic Flexible Job Shop Scheduling
SN - 978-989-758-384-1
IS - 2184-3236
AU - Zakaria, Y.
AU - BahaaElDin, A.
AU - Hadhoud, M.
PY - 2019
SP - 139
EP - 146
DO - 10.5220/0007957801390146
PB - SciTePress