Adaptive Market-Based Dynamic Task Allocation Under Environmental Uncertainty

Ozturk H. B., Yavaş N. B., Bingül Z.

15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2025, Bilbao, Spain, 11 - 13 June 2025, vol.1, pp.70-80, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 1
  • Doi Number: 10.5220/0013522600003970
  • City: Bilbao
  • Country: Spain
  • Page Numbers: pp.70-80
  • Keywords: Decentralized Algorithms, Multi-Agent Systems, Swarm and Collective Intelligence, Uncertainty Theory
  • Kocaeli University Affiliated: Yes

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.