ACID: Ant Colony Inspired Deadline-Aware Task Allocation and Planning

EasyChair Preprint 12291

Abstract

Efficient task allocation in multi-worker service environments, coupled with the necessity to adhere to strict deadlines, poses a multifaceted challenge in fields like service operations, operations management, logistics, and resource management. This study tackles the novel problem of 'deadline-aware multi-worker task planning.' This entails optimizing the allocation and planning of tasks among multiple workers to maximize task completion within specific time constraints. We introduce ACID, a novel flexible graph optimization meta-heuristic algorithm, inspired by and extending traditional ant colony optimization. ACID uniquely incorporates heuristic information from task features and generational or iteration performance data. This includes task order, completion time, and both individual and swarm-level performance metrics, to devise an effective worker task plan. Through extensive experiments and simulations across varied scenarios, ACID demonstrates a significant improvement in task completion rates under tight deadlines compared to conventional methods. This research offers a valuable tool for industries requiring efficient task distribution while ensuring adherence to deadlines, with broad applications in logistics, e-commerce, manufacturing, and service industries.

Keyphrases: Ant Colony Optimization, decision making, task allocation, task planning

@booklet{EasyChair:12291,
  author    = {Chia E. Tungom and Chen Jiamu and Chang Kexin},
  title     = {ACID: Ant Colony Inspired Deadline-Aware Task Allocation and Planning},
  howpublished = {EasyChair Preprint 12291},
  year      = {EasyChair, 2024}}