An alternative for robust estimation in Project Management

Martín, M.M. López; García, Catalina Beatriz García; Pérez, José Garcı́a; Granero, Miguel Ángel Sánchez

doi:10.1016/j.ejor.2012.01.058

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…Janak, Lin, and Floudas (2007) and Golenko-Ginzburg and Gonik (1998) describe activity time as a random variable with given density functions, such as uniform, normal or beta distributions. Although a well-known beta distribution has frequently been used to describe activity time in project scheduling, the mean and variance of activity time from this distribution does not sufficiently impact on the tail area of activity time (Lopez Martin, Garcia Garcia, Garcia Perez, & Sanchez Granero, 2012). Feng, Liu, and Burns (2000) suggest that the time and cost of each activity are normally distributed.…”

Section: Introductionmentioning

confidence: 99%

Stochastic project management via computer simulation and optimisation method

Chiang

Jeang

2015

International Journal of Systems Science: Operations & Logi

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The proposed approach combines program evaluation review technique (PERT) and critical path method (CPM) into a single-project scheduling problem. Given that activity time and cost are uncertain, a robust design for project scheduling using the response surface methodology (RSM) is presented in the proposed research. The controllable variables are the mean and tolerance of activity time, which are both cost related. In order to simulate an actual application, activity times, activity tolerances, activity time costs and activity tolerance costs are all randomly created from known probabilistic distributions. The probabilistic values of completion time within a promised date and of total cost under a given budget are also considered as response values for statistical optimisation. As is already known, the solutions found using RSM are solutions that are optimised for a single objective only. In the case of multiple objectives, actions for further optimisation are necessitated within a limited project budget, restricted completion time, allowable total cost probability, and acceptable completion time probability. Hence, after gathering response functions using RSM, an optimisation technique, mathematical programming, is used to formulate the problem in an integral fashion and solve multiple objectives under various constraints.

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Section: Introductionmentioning

confidence: 99%

Stochastic project management via computer simulation and optimisation method

Chiang

Jeang

2015

International Journal of Systems Science: Operations & Logi

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“…In the literature, some authors have proposed many different distributions other than beta, such as the normal distribution, doubly truncated normal distribution, lognormal distribution, the mixed beta and uniform distribution, triangular distribution, parkinson distribution, tilted beta distribution, weibull distribution, mixture distributions (BENDELL, et al 1995;PREMACHANDRA, 2001;ABDELKADER, 2004;MOHAN, et al, 2007;HAHN, 2008;MCCOMBS, et al, 2009;SHANKAR, et al, 2010;LÓPEZ MARTÍN, et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Pert using Fuzzy variables and probability distribution function randomly selected

Rodriguez

2019

Ind. Jour. Manag. & Prod.

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Program Evaluation and Review Technique (PERT) is widely used for project management in real world applications. The aim of this paper is to simulate and analyze a PERT network under conditions of uncertainty though a hybrid model. The basic assumption is that a project under extreme conditions of uncertainty can be satisfactorily modelled by using simple fuzzy linguistic variables to estimate activities durations, and a probability distribution function randomly selected in order to measure the activity times. Fuzzy linguistic expressions are used to estimate the activity time. Activity parameters are calculated by using basic operations between triangular fuzzy numbers and centroid method with classical Beta PERT definition. For each activity time a probability distribution function is randomly selected from a set of four possible distributions commonly cited in the literature. Hypothetical projects with 4, 40, 400 and 4000 activities using the proposed model are analyzed; the project duration is estimated through Monte Carlo Simulation. Finally, results are analyzed and compared with classical Beta PERT technique.

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