• DocumentCode
    501757
  • Title

    A Solution to Resource-Constrained Project Scheduling Problem: Based on Ant Colony Optimization Algorithm

  • Author

    Yuan, Yongbo ; Wang, Kai ; Ding, Le

  • Author_Institution
    Sch. of Civil & Hydraulic Eng., Dalian Univ. of Technol., Dalian, China
  • Volume
    1
  • fYear
    2009
  • fDate
    12-14 Aug. 2009
  • Firstpage
    446
  • Lastpage
    450
  • Abstract
    Ant colony optimization (ACO) is a popular-based, artificial agent, general-search technique for the solution of difficult combinatorial problems. This paper presents a solution to the resource-constraint project scheduling problem based on ACO algorithm. The method considers the quantified duration and resource as the heuristic information to calculate the accurate state transition probability and finally reaches the scheduling optimization. The described ACO algorithm is tested on a sample case taken from the literature and the parameters in ACO are determined by tests. The computational results validate the effectiveness of the proposed algorithm.
  • Keywords
    artificial life; combinatorial mathematics; optimisation; probability; project management; scheduling; search problems; ant colony optimization; artificial agent; combinatorial problem; general-search technique; quantified duration; resource-constrained project scheduling problem; scheduling optimization; state transition probability; Ant colony optimization; Dynamic programming; Heuristic algorithms; Hybrid intelligent systems; Mathematical programming; Probability; Processor scheduling; Resource management; Scheduling algorithm; Testing; Ant Colony Optimization; Constraint Resource; Project Scheduling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on
  • Conference_Location
    Shenyang
  • Print_ISBN
    978-0-7695-3745-0
  • Type

    conf

  • DOI
    10.1109/HIS.2009.92
  • Filename
    5254400