An agent-based, self-tuning, population learning algorithm for the resource constrained project scheduling

• Autorzy: Jędrzejowicz P. , Ratajczak-Ropel E.
• Języki publikacji: EN

Abstrakty

EN

The authors propose an agent-based population-learning algorithm (PLA) designed for solving the RCPSP and the MRCPSP. The paper contains problem formulation and a description of the proposed implementation of the PLA. The resulting multiple-agent system has been implemented using the JABAT environment designed with a view to facilitate development of a-teams. To validate the approach a computational experiment has been earned out. It has involved instances obtained from the available benchmark data sets. Results of the experiment show that the proposed implementation can serve as an effective tool for solving the resource-constrained project scheduling problems.

Słowa kluczowe

EN

project scheduling; evolutionary computations; tabu search; heuristics; artificial intelligence; multiple-agent systems

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2007
• Tom: Vol. 32, No. 3
• Strony: 213--225
• Opis fizyczny: Bibliogr. 11 poz.

Twórcy

autor

Jędrzejowicz P.

autor

Ratajczak-Ropel E.

• Department of Information Systems, Gdynia Maritime University, Poland,

Bibliografia

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• [8] Jedrzejowicz P., Ratajczak E., Population learning algorithm for resource-constrained project scheduling in D.W. Pearson, N.C. Steele, R.F.Albrecht (eds.), Artificial Neural Nets and Genetic Algorithms, Springer Computer Science, Wien, 2003, 223-228.
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• [10] Jedrzejowicz P., Ratajczak E., Population learning algorithm for the resource-constrained project scheduling, Jozefowska J., Węglarz J. (eds.), Perspectives in Modern Project Scheduling, Springer Science+Business Media LLC, 2006.
• [11] PSPLIB, http://129.187.I06.231/psplib.