Project Scheduling Problem with Stochastic Resource-Dependent Activity Duration

Chun Jie Zhong; Ying Yu; Yun Lang Jia

doi:10.4028/www.scientific.net/AMM.483.607

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 483Project Scheduling Problem with Stochastic...

Project Scheduling Problem with Stochastic Resource-Dependent Activity Duration

Abstract:

A resource-constrained project scheduling problem with stochastic resource-dependent activity durations is presented in this paper,and the two-point method is employed to simulate the uncertain property.Furthermore a genetic algorithm combined with this method is provided to solve the problem. Compared with the results from the genetic with Monte Carlo simulation, the proposed method is verified to be effective and more efficient.

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Periodical:

Applied Mechanics and Materials (Volume 483)

Pages:

607-610

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.483.607

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Online since:

December 2013

Authors:

Chun Jie Zhong, Ying Yu*, Yun Lang Jia

Keywords:

Genetic Algorithm (GA), Monte Carlo Simulation, RCPSP, Two-Point Method

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References

[1] E. Demeulemeester, W. Herroelen. A branch and bound procedure for the Multiple Resource Constrained Project Scheduling Problem, Management Science, vol. 38, 1992, pp.1803-1818.

DOI: 10.1287/mnsc.38.12.1803

Google Scholar

[2] A. Viana, J. Pinho, D. Sousa. Using metaheuristics in mulitiobjective resource constrained project scheduling, European Journal of Operational Research, vol. 120, 2000, pp.359-374.

DOI: 10.1016/s0377-2217(99)00163-0

Google Scholar

[3] M. Mori, C. C. Tseng. A genetic algorithm for multi-mode resource constrained project scheduling problem, European Journal of Operational Research, vol. 100, 1997, pp.134-141.

DOI: 10.1016/s0377-2217(96)00180-4

Google Scholar

[4] T. Yang, C. Chang. Stochastic resource-constrained scheduling for repetitive construction projects with uncertain supply of resources and funding,. International Journal of Project Management, vol. 23, 2005, pp.546-553.

DOI: 10.1016/j.ijproman.2005.03.003

Google Scholar

[5] J. Xiong, Y. Chen, K. Yang, et al. A hybrid multiobjective genetic algorithm for robust resource-constrained project scheduling with stochastic durations, Mathematical Problems in Engineering, 2012, pp.1-22.

DOI: 10.1155/2012/786923

Google Scholar

[6] D Golenko—Ginzburg, A Gonik. A heuristic for network project scheduling with random activity durations depending on the resource allocation, International Journal of Production Research, vol. 55, 1998, pp.149-162.

DOI: 10.1016/s0925-5273(98)00044-9

Google Scholar

[7] E. Rosenblueth. Two-point estimates in probabilities, Applied Mathematical Modelling, vol. 5, 1981, pp.329-335.

DOI: 10.1016/s0307-904x(81)80054-6

Google Scholar

[8] K R Maccrimmon, C A Ryavec. Analytical study of the PERT assumptions, Operations Research, vol. 12, 1964, pp.16-36.

DOI: 10.1287/opre.12.1.16

Google Scholar

[9] G. Verbic, C. A. Canizares. Probabilistic optimal power flow in electricity markets based on a two-point estimate method, IEEE Transactions on Power System, vol. 21, 2006, pp.1883-1893.

DOI: 10.1109/tpwrs.2006.881146

Google Scholar

[10] Tounsi. M, David.P. Local search algorithm to improve the local search, Tools with Artificial Intelligence, 2002, pp.438-443.

DOI: 10.1109/tai.2002.1180836

Google Scholar