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Application of fuzzy numbers to the estimation of an ongoing project's completion time

100%
KUCHTA D.
Operations Research and Decisions
|
2012
|
vol. 22
|
issue 4
87-103
EN
A dynamic, interactive approach to the control of a project’s realization time is proposed. The duration time of the project’s activities is assumed to depend on certain factors whose influence may change in time. Based on the project’s history up to a certain moment, the change in influence of these factors has been evaluated and estimates of the duration of activities which have not been started yet are updated. The estimates of the duration time of the activities and project are expressed in the form of fuzzy numbers. This allows us to keep a constant track of the risk of the project not keeping the deadline and to be aware of which factors influence delays, thus where to act in order to minimize the final delay while it is not yet too late – in the course of the project’s realization, as early as possible.
2
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Generalization of the critical chain method supporting the management of projects with a high degree of uncertainty and imperfect information

100%
KUCHTA D.
Operations Research and Decisions
|
2010
|
vol. 20
|
issue 2
77-90
EN
In the critical chain method the fundamental notion is the project buffer, and its length is based on task estimation risk. This estimation is almost never unequivocal. If it is not correct, the whole method may turn out to be ineffective. Different experts may have different opinions about this risk. The critical chain method allows to take into account the opinion of only one expert, which may seriously falsify the image of the project situation. This paper proposes a generalization of the critical chain method allowing the use of the opinions of several experts – both while planning a project and while controlling it. Thanks to such an approach, in each phase of project planning and control we are aware of the opinions of various experts as to the correctness of the deadline which was agreed upon with the customer, as to the chances of meeting this deadline and as to the necessity of strengthening project control or introducing changes into the project.
3
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Risks characteristic to Agile project management methodologies and responses to them

100%
KUCHTA D.
Operations Research and Decisions
|
2013
|
vol. 23
|
issue 4
75-95
EN
Agile methods for project management are often treated as methods for mitigating risk. However, there is disagreement as to whether explicit methods of risk management should be used in projects which are executed according to Agile methodology or is the implicit risk management built into Agile methodologies sufficient. To contribute to the discussion, an attempt has been made to identify risks that are either caused by the introduction of an Agile methodology to a project or become more significant when such methodology is in use. If such risks exist, this would be evidence that explicit risk management is required, even in the case of Agile methodologies. The results of this research may be useful for any organization that is in the process of selecting a methodology for project management and is considering Agile methodologies.
4
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A method of variable selection for fuzzy regression – the possibility approach

63%
GŁADYSZ B., KUCHTA D.
Operations Research and Decisions
|
2011
|
vol. 21
|
issue 2
5-15
EN
A method of variable selection for fuzzy regression has been proposed. Using the method, the significance of fuzzy regression coefficients has been examined. The method presented is equivalent to the method of variable selection for classical regression based on an analysis of the confidence intervals for their coefficients. Illustrative examples are presented.
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