Adaptive Market-Based Dynamic Task Allocation Under Environmental Uncertainty

Hasan Berke Ozturk, Nezih Bora Yavas, Zafer Bingul

2025

Abstract

This paper presents a novel consensus-based adaptive genetic-optimized auction (CAGA) algorithm to solve the dynamic task allocation (DTA) problem for a fleet of autonomous vehicles. The algorithm employs an auction routine for task assignment and a genetic algorithm (GA) to optimize task prices subject to the price update rule. The proposed algorithm is devised to achieve superior solutions in real-world applications. Hence, uncertainty theory was adopted to model uncertainties in task positions to create a realistic environment. In addition, Monte Carlo (MC) simulations are performed to effectively determine the degree of uncertainty. Several test scenarios have been carried out using other market-based methods, and the results illustrate the effectiveness of the algorithm.

Paper Citation

in Harvard Style

Ozturk H., Yavas N. and Bingul Z. (2025). Adaptive Market-Based Dynamic Task Allocation Under Environmental Uncertainty. In Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-759-7, SciTePress, pages 70-80. DOI: 10.5220/0013522600003970

in Bibtex Style

@conference{simultech25,
author={Hasan Ozturk and Nezih Yavas and Zafer Bingul},
title={Adaptive Market-Based Dynamic Task Allocation Under Environmental Uncertainty},
booktitle={Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2025},
pages={70-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013522600003970},
isbn={978-989-758-759-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - Adaptive Market-Based Dynamic Task Allocation Under Environmental Uncertainty
SN - 978-989-758-759-7
AU - Ozturk H.
AU - Yavas N.
AU - Bingul Z.
PY - 2025
SP - 70
EP - 80
DO - 10.5220/0013522600003970
PB - SciTePress