RANKING OF TOTAL TIME RESERVES FOR DETERMINATION OF THE CRITICAL PATH IN FUZZY NETWORK PLAN
Abstract
The Critical Path Method, as a method of network planning, is used in the process of planning and control of complex projects from various fields. For their successful implementation, a clear determination of the duration of each project activity is necessary. However, in construction sector it is very difficult to fulfil this requirement, because the realization of many activities is accompanied by a certain degree of uncertainty and risk. Since construction projects are characterized by uniqueness and unrepeatability, there are often no available historical data for a clear and precise determination of the duration of project activities. Given that, the duration should be predicted by expert and experienced persons, who, in the conditions of unique influencing factors and facing many inaccuracies, should define their decisions during the whole process of project management. Due to the shortcomings of classical planning methods, related to the calculation of the duration of project activities, which does not offer the possibility of modelling complex construction projects, there was a real need for the definition and implementation of new concepts of planning. As an alternative way to model uncertainty and risk, the fuzzy concept was introduced in the planning process, with which the durations of project activities are presented through fuzzy numbers. This fundamental approach, which is based on the application of fuzzy theory, enables advanced and successful modelling of real situations and projects, which overcomes the shortcomings characteristic of classical planning methods. This paper gives an overview of one practical example of application of fuzzy logic in the planning process. One method of ranking fuzzy numbers has been applied for calculation of the earliest and the latest fuzzy time of project activities, and the total time reserves, in order to determine the critical path in fuzzy network diagrams.
References
Jing-Rong C., Ching-Hsue C., Chen-Yi K. (2006). Conceptual procedure for ranking fuzzy numbers based on adaptive two-dimensions dominance. Soft Computing. p. 94-103. DOI:10.1007/s00500-004-0429-9.
Chen Sh. Hsueh Y. (2008). A simple approach to fuzzy critical path analysis in project networks. Applied Mathematical Modelling. vol.32, no.7. p.1289-1297. DOI: 10.1016/j.apm.2007.04.009.
Detyniecki M. Yager R. (2001). Ranking fuzzy numbers using alpha-weighted valuations. International journal of Uncertainty, fuzziness and knowledge-based systems. Vol. 8(5). pp. 573- 592. DOI: 10.1142/S021848850000040X.
Hellman M. (2005). Fuzzy logic introduction. University of Rennes, France.
Han TC. Chung Cheng-Chi. Liang Gin-Shuh. (2006). Application of fuzzy critical path method to airport’s cargo ground operation systems. Journal of Marine science and technology. Vol. 14, No. 3. pp. 139-146. DOI: 10.51400/2709-6998.2067.
Shankar N. Ravi. Sireesha V. Srinivasa K. Vani N. (2010). Fuzzy critical path method based on matric distance ranking of fuzzy numbers. International journal of Mathematical analyses. Vol. 4, no. 17, 995-1006.
Shankar N. Ravi. Sireesha V. Rao P. Phani Bushan. (2010). An analytical method for finding critical path in a fuzzy project network. International journal of Contemp. Math. Sciences. Vol. 5, no. 20. pp. 953-962.
Shankar R. Sireesha V. Rao P. (2010). Critical path analysis in the fuzzy project network. Advances in Fuzzy Mathematics. ISSN 0973-533X. Vol. 5 (3). P. 285-294.
Sireesha V. Shankar N. Ravi. (2010). A new approach to find total float time and critical path in a fuzzy project network. International journal of Engineering science and technology. Vol. 2(4). pp.600-609. ISSN: 0975-5462.
Zadeh L. (2018). Fuzzy logic theory and applications: Part I and Part II, Part I – Fuzzy logic theory, Part II – Applications and advanced topics of fuzzy logic. World scientific. New Jersey. ISBN(s) 9789813238176. DOI: 10.1142/10936.
