Reducing the number of paths in a minimized project-network with given bounds on the durations of activities

• Autorzy: Viattchenin D.

Identyfikatory

DOI

10.5277/ord150405

• Języki publikacji: EN

Abstrakty

EN

This paper deals in a preliminary way with the problem of selecting the smallest possible number of dominant paths in a minimized project-network with given bounds on the permissible values of the durations of activities. For this purpose, a classification technique is proposed. This technique is based on a heuristic possibilistic clustering of interval-valued data. The basic concepts of heuristic possibilistic clustering are defined and methods for preprocessing interval-valued data are described. An illustrative example is considered in detail and some conclusions are formulated.

Słowa kluczowe

EN

project-network; dominant path; heuristic possibilistic clustering; interval-valued data; stationary clustering structure; typical point

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2015
• Tom: Vol. 25, No. 4
• Strony: 71--87
• Opis fizyczny: Bibliogr. 12 poz., rys., tab.

Twórcy

autor

Viattchenin D.

• Department of Software Information Technology, Belarusian State University of Informatics and Radio-Electronics, P. Brovka St. 6, 220013 Minsk, Belarus

Bibliografia

• [1] BRUCKER F., DREXL A., MÖHRING R., NEUMANN K., PESCH E., Resource-constrained project schedul-ing: notation, classification, models, and methods, European Journal of Operation Research, 1999, 112 (1), 3.
• [2] BURILLO P., BUSTINCE H., Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets, Fuzzy Sets and Systems, 1996, 78 (3), 305.
• [3] GRZEGORZEWSKI P., Distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on Hausdorff metric, Fuzzy Sets and Systems, 2004, 148 (2), 319.
• [4] JU H., YUAN X.H., Similarity measures on interval-valued fuzzy sets and application to pattern recognition, [in:] D.Y. Cao (Ed.), Fuzzy Information and Engineering, Springer-Verlag, Heidelberg 2007, 384.
• [5] KAUFMANN A., Introduction to the Theory of Fuzzy Subsets, Academic Press, New York 1975.
• [6] KREINOVICH V., KOSHELEVA O., Towards dynamical systems approach to fuzzy clustering, [in:] D.A. Viatt- chenin (Ed.), Developments in Fuzzy Clustering, VEVER Publishing House, Minsk 2009, 10.
• [7] LAI T.-C., SOTSKOV Y.N., Sequencing with uncertain numerical data for makespan minimization, Jour-nal of the Operations Research Society, 1999, 50 (3), 230.
• [8] ORLOVSKY S.A., Problems of Decision Making for Fuzzy Initial Information, Nauka, Moscow 1981 (in Russian).
• [9] SOTSKOV Y.N., SHILAK A.N., Minimization of project-network with given bounds on permissible val-ues of activity durations, Technical Report No. 2, Institute of Engineering Cybernetics of the National Academy of Sciences of Belarus, Minsk 2000 (in Russian).
• [10] SOTSKOV Y.N., SHILAK A.N., Minimization of project-network with given bounds on activity durations, [in:] B. Fleischmann, R. Lasch, U. Dengs, W. Domschke (Eds.), Operations Research Proceedings 2000, Springer-Verlag, Heidelberg 2001, 384.
• [11] VIATTCHENIN D.A., Detecting outliers in interval-valued data using heuristic possibilistic clustering, Journal of Computer Science and Control Systems, 2012, 5 (2), 39.
• [12] VIATTCHENIN D.A., A Heuristic Approach to Possibilistic Clustering. Algorithms and Applications, Springer-Verlag, Heidelberg 2013.