Optimisation of Ceramic Kiln Loading Problem Using Multi-Objective Genetic Algorithm

Derya Deliktaş, Ayşe Kaygısız

2024

Abstract

Efficient resource utilisation is paramount for boosting productivity and competitiveness within industrial contexts. In ceramic manufacturing, the Ceramic Kiln Loading Problem is critical, wherein the optimal arrangement of ceramic products within kilns significantly influences production efficiency. This study aims to enhance efficiency by maximising the utilisation of the oven vehicle through optimal loading of the ordered products. To achieve this objective, the Genetic Algorithm has been integrated with weighted sum and conic scalarisation methods, and the results obtained from each method have been compared. Additionally, since the algorithm’s parameters can significantly influence its performance, parameter tuning has been conducted using the irace method. The findings corroborate the superiority of results obtained by integrating the Genetic Algorithm with weighted sum scalarisation.

Paper Citation

in Harvard Style

Deliktaş D. and Kaygısız A. (2024). Optimisation of Ceramic Kiln Loading Problem Using Multi-Objective Genetic Algorithm. In Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-706-1, SciTePress, pages 248-257. DOI: 10.5220/0012807100003753

in Bibtex Style

@conference{icsoft24,
author={Derya Deliktaş and Ayşe Kaygısız},
title={Optimisation of Ceramic Kiln Loading Problem Using Multi-Objective Genetic Algorithm},
booktitle={Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2024},
pages={248-257},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012807100003753},
isbn={978-989-758-706-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Optimisation of Ceramic Kiln Loading Problem Using Multi-Objective Genetic Algorithm
SN - 978-989-758-706-1
AU - Deliktaş D.
AU - Kaygısız A.
PY - 2024
SP - 248
EP - 257
DO - 10.5220/0012807100003753
PB - SciTePress