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
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;
Conference_Titel :
Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-0-7695-3745-0
DOI :
10.1109/HIS.2009.92
