Title :
Ant Colony Algorithm for Scheduling Resource Constrained Projects with Discounted Cash Flows
Author_Institution :
Sch. of Manage., Zhejiang Univ., Hangzhou
Abstract :
For long-term projects with considerable cash flows, the project managers aim to maximize the net present values instead of to minimize project durations. An ant colony algorithm is proposed to coordinate the allocation of scarce resources to improve the net present values. The algorithm adaptively adjusts resource allocation according to the pheromone generated by artificial ants employed to search for suitable schedules. The crossover operation, inverse mutation and elitist strategy are applied to accelerate the searching, and a backward scheduling technique is adopted to postpone cash outflows. An experimental testing indicates that the proposed algorithm helps to improve the net present values of resource constrained projects
Keywords :
artificial life; constraint theory; minimisation; project management; resource allocation; scheduling; ant colony algorithm; artificial ants; backward scheduling technique; crossover operation; discounted cash flows; elitist strategy; inverse mutation; project management; resource constrained project scheduling; scarce resource allocation; Acceleration; Ant colony optimization; Conference management; Cybernetics; Dynamic programming; Genetic mutations; Heuristic algorithms; Machine learning; Machine learning algorithms; Optimization methods; Project management; Resource management; Scheduling algorithm; Testing; Project scheduling; ant colony optimization; discounted cash flow; resource constraint;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258892