Bottom-Up Bio-Inspired Algorithms for Optimizing Industrial Plants

M. Umlauft, M. Gojkovic, K. Harshina, M. Schranz

2023

Abstract

Scheduling in a production plant with a high product diversity is an NP-hard problem. In large plants, traditional optimization methods reach their limits in terms of computational time. In this paper, we use inspiration from two bio-inspired optimization algorithms, namely, the artificial bee colony (ABC) algorithm and the bat algorithm and apply them to the job shop scheduling problem. Unlike previous work using these algorithms for global optimization, we do not apply them to solutions in the solution space, though, but rather choose a bottom-up approach and apply them as literal swarm intelligence algorithms. We use the example of a semiconductor production plant and map the bees and bats to actual entities in the plant (lots, machines) using agent-based modeling using the NetLogo simulation platform. These agents then interact with each other and the environment using local rules from which the global behavior – the optimization of the industrial plant – emerges. We measure performance in comparison to a baseline algorithm using an engineered heuristics (FIFO, fill fullest batches first). Our results show that these types of algorithms, employed in a bottom-up manner, show promise of performance improvements using only low-effort local calculations.

Paper Citation

in Harvard Style

Umlauft M., Gojkovic M., Harshina K. and Schranz M. (2023). Bottom-Up Bio-Inspired Algorithms for Optimizing Industrial Plants. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-623-1, pages 59-70. DOI: 10.5220/0011693400003393

in Bibtex Style

@conference{icaart23,
author={M. Umlauft and M. Gojkovic and K. Harshina and M. Schranz},
title={Bottom-Up Bio-Inspired Algorithms for Optimizing Industrial Plants},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2023},
pages={59-70},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011693400003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Bottom-Up Bio-Inspired Algorithms for Optimizing Industrial Plants
SN - 978-989-758-623-1
AU - Umlauft M.
AU - Gojkovic M.
AU - Harshina K.
AU - Schranz M.
PY - 2023
SP - 59
EP - 70
DO - 10.5220/0011693400003393