Harnessing the Power of Large Language Model for Natural Language-Driven Task Scheduling Optimization

Hpone Thant Kyaw, Khalifa Alyafei, Xuewu Dai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper is to develop an integrated system for task scheduling in projects management, leveraging advanced technologies of large language model (LLM) and mixed integer linear programming (MILP). The proposed design focuses on nature language interaction and integration, with the ultimate objective of automating task scheduling for enhanced project efficiency. The proposed solution comprises three main steps: generative models for user to input the task requests, MILP for solving the optimization problem, and project schedule results presentation. The project highlights the future-oriented LLM-driven optimization trends in project management and gives recommendations for future investigations in hybrids, user-friendly task scheduling solutions
Original languageEnglish
Title of host publication2025 30th International Conference on Automation and Computing (ICAC)
Place of PublicationPiscataway, US
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9798331525453
ISBN (Print)9798331525460
DOIs
Publication statusPublished - 27 Aug 2025
EventThe 30th International Conference on Automation and Computing (ICAC 2025) - University of Loughborough, Loughborough, United Kingdom
Duration: 27 Aug 202529 Aug 2025
https://cacsuk.co.uk/icac/

Conference

ConferenceThe 30th International Conference on Automation and Computing (ICAC 2025)
Country/TerritoryUnited Kingdom
CityLoughborough
Period27/08/2529/08/25
Internet address

Keywords

  • Task Scheduling
  • Construction Projects
  • Integrated System
  • Natural Language Processing (NLP)
  • Mixed Integer Linear Programming (MILP)

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