Using artificial bee colony to solve stochastic resource constrained project scheduling problem

Authors:
Amin Tahooneh;Koorush Ziarati
Affiliations:
Department of Mathematics, College of Science, Science, Shiraz University, Shiraz, Iran;Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Year:
2011

Citing 3
Cited 1

A Stochastic Branch-and-Bound Approach to Activity Crashing in Project Management

INFORMS Journal on Computing
When it is worthwhile to work with the stochastic RCPSP?

Journal of Scheduling
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

Journal of Global Optimization

Artificial bee colony algorithm: a survey

International Journal of Advanced Intelligence Paradigms

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Abstract

Resource constrained project scheduling (RCPSP) is one of the most crucial problems in project problem. The aim of RCPSP, which is NP-hard, is to minimize the project duration. Sometimes the activity durations are not known in advance and are random variables. These problems are called stochastic resource constrained project scheduling problems or stochastic RCPSP. Various algorithms such as genetic algorithm and GRASP have been applied on stochastic RCPSP. Bee algorithm is a metaheuristic based on the intelligent behavior of honey bee swarms. The goal of this study is adopting the artificial bee colony (ABC) algorithm to solve stochastic RCPSP and investigating its performance on the stochastic RCPSP. Simulation results show that proposed algorithm is an effective method for solving the stochastic resource constrained project scheduling problem. With regard to the problems with high distribution variability, the ABC algorithm is more effective than the other algorithms in the literature.