Abstract
The bicriteria optimization problem of many projects developments’ schedules with many competitive constraints on resources and interval constraints on the execution time and cost of operations is formulated in this article. Optimization is carried out according to the maximizing performance and the total cost of project execution criteria. The problem is NP-hard MILP and an efficient hybrid parametric algorithm that combines the critical path algorithm and ant colony optimization has been developed for its approximate solution. The actual performance and solutions’ quality of the hybrid algorithm’s software implementation have been compared with the results of IBM CPLEX on test problems. The effectiveness of the toolkit is confirmed experimentally by testing.
The research is supported by Ministry of Science and Higher Education of Russian Federation (project No. FSUN-2020-0009).
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References
Oleinikova, S.A.: Matematicheskaya model i optimizacionnaya zadacha sostavleniya raspisaniya dlya multiproektnoi sistemi s vremennimi i resursnimi ogranicheniyami i kriteriem ravnomernoi zagruzki. Vestnik Voronejskogo gosudarstvennogo tehnicheskogo universiteta 6(3) (2013)
Merkle, D., Middendorf, M., Schmeck, H.: Ant colony optimization for resource-constrained project scheduling. IEEE Trans. Evol. Comput. 6(4), 333–346 (2002)
Trojet, M., H’Mida, F., Lopez, P.: Project scheduling under resource constraints: application of the cumulative global constraint. In: 2009 International Conference on Computers and Industrial Engineering, pp. 62–67 IEEE. Troyes, France (2009). https://doi.org/10.1109/ICCIE.2009.5223894
Yassine, A., Meier, C., Browning, T.: Multi-Project Scheduling Using Competent Genetic Algorithms. University of Illinois Department of Industrial and Enterprise Systems Engineering, Illinois (2007)
Gonçalves, J.F., Mendes, J.J.M., Resende, M.G.C.: A genetic algorithm for the resource constrained multi-project scheduling problem. Eur. J. Oper. Res. 189(3), 1171–1190 (2008)
Li F., Xu Z. : A multi-agent system for distributed multi-project scheduling with two-stage decomposition. PloS One 13(10) (2018). https://doi.org/10.1371/journal.pone.0205445
Hanchate, D.B., Thorat, M.Y.A., Ambole, M.R.H.: Review on multimode resource constrained project scheduling problem. Int. J. Comput. Sci. Eng. Technol. 3(5), 155–159 (2012)
Zaree, M., et al.: Project scheduling optimization for contractor’s Net present value maximization using meta-heuristic algorithms: a case study. J. Ind. Eng. Manag. Stud. 7(2), 36–55 (2020). https://doi.org/10.22116/JIEMS.2020.221672.1342
Araujo, J.A.S., et al.: Strong bounds for resource constrained project scheduling: preprocessing and cutting planes. Comput. Oper. Res. 113 (2020)
Taha, H.A.: Operations Research: An Introduction, 8th edn. Upper Saddle River, New Jersey (2007)
Phillips, D.T., Garcia-Diaz, A.: Fundamentals of Network Analysis. Wiley Periodicals, Englewood Cliffs (2007)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)
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Mezentsev, Y.A., Chubko, N.Y. (2021). On One Bicriterion Discrete Optimization Problem and a Hybrid Ant Colony Algorithm for Its Approximate Solution. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_26
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