Project network analysis with fuzzy activity times

McCahon, Cynthia S.; Lee, E.S.

doi:10.1016/0898-1221(88)90120-4

Cited by 54 publications

(27 citation statements)

References 1 publication

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“…Indeed, one can obtain all the optimal lower and upper bounds of earliest starting dates of tasks by applying the forward recursion (formulae: (1), (2)) for configurations Ω − A and Ω + A , respectively. But the backward recursion fails to compute the set of possible latest starting dates [38,39,43] and in consequence floats can no longer be recovered from this procedure. Let us illustrate the point that the backward recursion can not be used to compute the latest starting dates and the floats.…”

Section: Interval Valued Problem Definitionmentioning

confidence: 99%

“…However the overwhelming part of the literature devoted to this topic adopts an orthodox stochastic approach, thus leading to a very complex problem that is still partially unsolved to-date [24]. To the best of the authors' knowledge, interval-valued PERT analysis seems to have existed only as a special case of fuzzy PERT studies that have been proposed since the late seventies [5,10,25,26,35,39,38,42,43,44]. Recently, Conde [8] has used the min-max regret criterion to find a robust set of critical tasks in a project with interval task durations.…”

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confidence: 99%

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Criticality analysis of activity networks under interval uncertainty

Fortin

Zieliński

Dubois

et al. 2010

J Sched

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Section: Interval Valued Problem Definitionmentioning

confidence: 99%

mentioning

confidence: 99%

Criticality analysis of activity networks under interval uncertainty

Fortin

Zieliński

Dubois

et al. 2010

J Sched

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“…There is a particular emphasize on the fact that actually distribution of probabilities allows simulating variability of repetitive tasks, but not the uncertainty due to a lack of the information (e.g., Dubois, Prade and Smets (1996), Ferson and Ginzburg (1996), et al). Only as an approximate solution to the mentioned range of problems we consider approaches by Chanas and Kamburowski (1981), Hapke, Jaszkiewicz and Słowiński (1994), Loostma (1997), McCahon and Lee (1988), as well as Prade (1979) and Rommelfanger (1994).…”

Section: Literature Reviewmentioning

confidence: 99%

Logic of Interval Uncertainty

Kuzmin¹

2014

MAS

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The scientific category of uncertainty refers to that group of terms, an interpretation of which is not unambiguous and exact. In non-eliminability of the category soft content barrier there is an objective transition to the interval uncertainty. This research is an attempt to solve the issue of estimating the interval uncertainty based on methods of a logical analysis and a comparison. The approach presented by the paper is opposed to known methods of a mechanical selection of values following a given function. In the course of the research, there has been introduced a concept of the "tenversion uncertainty" for scientific use. Overall results obtained from the research allow calculating values of the interval uncertainty and assess their quality. The scientific competency of methods is achieved in theoretically tested solutions.

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“…McCahon and Lee [10] perform forward and backward calculation and propose the application of the comparison method, instead of using the extended max and extended min.…”

Section: Published By Atlantis Pressmentioning

confidence: 99%

“…Fuzzy sets were then applied among others to real algebra, where the term fuzzy number was coined. A method that utilizes fuzzy sets in a network is presented by several researchers, for example, Dubois and Prade [2], Chanas and Kamburowski [5], Chanas [6], Buckley [7], Mares [8], Gazdik [9], McCahon and Lee [10], Tsujimura et al, [11], Chanas and Zielinski [12], Han et.al. [13], Liang and Han [14], Nasution [16,17], and Slyeptsov and Tyshchuk [18].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Maintenance Planning Using Fuzzy Critical Path Analysis

Atli¹,

Kahraman²

2012

IJCIS

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Aircraft's availability is certainly one of the most important features of modern avionic industry. The aircraft maintenance scheduling is one of the major decisions an airline has to make during its operation. When an aircraft maintenance event occurs, the overhaul tasks management process requires the execution of all tasks to perform and has to guarantee the on-time aircraft delivery and the respect of the daily flight schedule. Though maintenance scheduling comes as an end stage in an airline operation, it has potential for cost savings. Maintenance scheduling is an easily understood but difficult to solve problem. Given a flight schedule with aircraft assigned to it, the aircraft maintenance-scheduling problem is to determine which aircraft should fly which segment and when and where each aircraft should undergo different levels of maintenance check. The objective of this paper is to minimize the aircraft maintenance planning time and to show how to create a plan with critical path analyses under fuzzy environment. We use trapezoidal fuzzy numbers for activity times and Activity-on-Node (AON) representation in fuzzy critical path method (FCPM). An illustrative example is given for Aircraft Gas Turbine Engine Repair/Overhaul problem.

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Project network analysis with fuzzy activity times

Cited by 54 publications

References 1 publication

Criticality analysis of activity networks under interval uncertainty

Criticality analysis of activity networks under interval uncertainty

Logic of Interval Uncertainty

Aircraft Maintenance Planning Using Fuzzy Critical Path Analysis

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