scholarly journals A Statistical Method for Estimating Activity Uncertainty Parameters to Improve Project Forecasting

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 952 ◽  
Author(s):  
Mario Vanhoucke ◽  
Jordy Batselier
Keyword(s):  
Probability Distributions ◽  
Statistical Hypothesis ◽  
Distribution Model ◽  
Project Schedule ◽  
Cost Overruns ◽  
Project Outcomes ◽  
Activity Duration ◽  
Calibration Methods ◽  
Uncertainty Parameters ◽  
Duration Uncertainty

Just like any physical system, projects have entropy that must be managed by spending energy. The entropy is the project’s tendency to move to a state of disorder (schedule delays, cost overruns), and the energy process is an inherent part of any project management methodology. In order to manage the inherent uncertainty of these projects, accurate estimates (for durations, costs, resources, …) are crucial to make informed decisions. Without these estimates, managers have to fall back to their own intuition and experience, which are undoubtedly crucial for making decisions, but are are often subject to biases and hard to quantify. This paper builds further on two published calibration methods that aim to extract data from real projects and calibrate them to better estimate the parameters for the probability distributions of activity durations. Both methods rely on the lognormal distribution model to estimate uncertainty in activity durations and perform a sequence of statistical hypothesis tests that take the possible presence of two human biases into account. Based on these two existing methods, a new so-called statistical partitioning heuristic is presented that integrates the best elements of the two methods to further improve the accuracy of estimating the distribution of activity duration uncertainty. A computational experiment has been carried out on an empirical database of 83 empirical projects. The experiment shows that the new statistical partitioning method performs at least as good as, and often better than, the two existing calibration methods. The improvement will allow a better quantification of the activity duration uncertainty, which will eventually lead to a better prediction of the project schedule and more realistic expectations about the project outcomes. Consequently, the project manager will be able to better cope with the inherent uncertainty (entropy) of projects with a minimum managerial effort (energy).

Genetic algorithm for determining the construction logistics of precast components

2019 ◽  
Vol 26 (10) ◽  
pp. 2289-2306 ◽  
Author(s):  
Yuan Fang ◽  
S. Thomas Ng
Keyword(s):  
Construction Materials ◽  
Activity Based Costing ◽  
Project Schedule ◽  
Start Time ◽  
Cost Overruns ◽  
Content Type ◽  
Factors Affecting ◽  
Planning Decisions ◽  
Project Outcomes ◽  
Precast Construction

Purpose Precast construction has become increasingly popular in the construction industry. Nonetheless, the logistics of construction materials has been a neglected topic, and this neglect has resulted in delays and cost overruns. Careful planning that considers all of the factors affecting construction logistics can ensure project success. The purpose of this paper is to examine the potential for using genetic algorithms (GAs) to derive logistics plans for materials production, supply and consumption. Design/methodology/approach The proposed GA model is based on the logistics of precast components from the supplier’s production yard, to the intermediate warehouse and then to the construction site. Using an activity-based costing (ABC) approach, the model not only considers the project schedule, but also takes into account the production and delivery schedule and storage of materials. Findings The results show that GAs are suitable for solving time-cost trade-off problems. The optimization process helps to identify the activity start time during construction and the delivery frequency that will result in the minimal cost. What-if scenarios can be introduced to examine the effects of changes in construction logistics on project outcomes. Originality/value This paper presents a method for using GAs and an ABC approach to support construction logistics planning decisions. It will help construction planners and materials suppliers to establish material consumption and delivery schedules, rather than relying on subjective judgment.

Risk Planning with Discrete Distribution Analysis Applied to Petroleum Spills

2013 ◽  
Vol 2 (4) ◽  
pp. 61-78 ◽  
Author(s):  
Roy L. Nersesian ◽  
Kenneth David Strang
Keyword(s):  
Probability Distribution ◽  
Probability Distributions ◽  
Low Cost ◽  
Discrete Distribution ◽  
Distribution Model ◽  
Distribution Analysis ◽  
Distribution Models ◽  
Discrete Probability ◽  
Nonparametric Statistical ◽  
Spreadsheet Software

This study discussed the theoretical literature related to developing and probability distributions for estimating uncertainty. A theoretically selected ten-year empirical sample was collected and evaluated for the Albany NY area (N=942). A discrete probability distribution model was developed and applied for part of the sample, to illustrate the likelihood of petroleum spills by industry and day of week. The benefit of this paper for the community of practice was to demonstrate how to select, develop, test and apply a probability distribution to analyze the patterns in disaster events, using inferential parametric and nonparametric statistical techniques. The method, not the model, was intended to be generalized to other researchers and populations. An interesting side benefit from this study was that it revealed significant findings about where and when most of the human-attributed petroleum leaks had occurred in the Albany NY area over the last ten years (ending in 2013). The researchers demonstrated how to develop and apply distribution models in low cost spreadsheet software (Excel).

scholarly journals Using the Bootstrap Method for a Statistical Significance Test of Differences between Summary Histograms

2006 ◽  
Vol 134 (5) ◽  
pp. 1442-1453 ◽  
Author(s):  
Kuan-Man Xu
Keyword(s):  
Euclidean Distance ◽  
Probability Distributions ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Bootstrap Method ◽  
Statistical Hypothesis ◽  
Object Size ◽  
Size Category ◽  
Significance Level ◽  
The Bootstrap Method

Abstract A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries–Matusita distance, and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called “cloud objects.” Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object, and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.

scholarly journals Best Fitted Distribution For Meteorological Data In Kuala Krai

2021 ◽  
Vol 3 (1) ◽  
pp. 16-25
Author(s):  
Siti Mariam Norrulashikin ◽  
Fadhilah Yusof ◽  
Siti Rohani Mohd Nor ◽  
Nur Arina Bazilah Kamisan
Keyword(s):  
Climate Change ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Meteorological Data ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Data Series ◽  
Distribution Models ◽  
Goodness Of Fit Tests

Modeling meteorological variables is a vital aspect of climate change studies. Awareness of the frequency and magnitude of climate change is a critical concern for mitigating the risks associated with climate change. Probability distribution models are valuable tools for a frequency study of climate variables since it measures how the probability distribution able to fit well in the data series. Monthly meteorological data including average temperature, wind speed, and rainfall were analyzed in order to determine the most suited probability distribution model for Kuala Krai district. The probability distributions that were used in the analysis were Beta, Burr, Gamma, Lognormal, and Weibull distributions. To estimate the parameters for each distribution, the maximum likelihood estimate (MLE) was employed. Goodness-of-fit tests such as the Kolmogorov-Smirnov, and Anderson-Darling tests were conducted to assess the best suited model, and the test's reliability. Results from statistical studies indicate that Burr distributions better characterize the meteorological data of our research. The graph of probability density function, cumulative distribution function as well as Q-Q plot are presented.

Project Schedule Uncertainty Analysis using Fuzzy Logic

2002 ◽  
Vol 33 (4) ◽  
pp. 15-22 ◽  
Author(s):  
Matthew J. Liberatore
Keyword(s):  
Probability Theory ◽  
Fuzzy Logic ◽  
Project Schedule ◽  
Computational Approaches ◽  
Activity Duration ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
And Coping ◽  
Quantifying Uncertainty ◽  
Straightforward Approach

There is continuing interest by academics and practitioners alike in measuring and coping with project schedule uncertainty. Fuzzy logic has been proposed as an alternate approach to probability theory for quantifying uncertainty related to activity duration. However, the fuzzy logic approach is not widely understood, and generally accepted computational approaches are not available. This paper describes the differences between the probabilistic and fuzzy approaches and the advantages of the latter. The paper also illustrates a straightforward approach for applying fuzzy logic to assess project schedule uncertainty.

scholarly journals A top-down model to generate ensembles of runoff from a large number of hillslopes

2013 ◽  
Vol 20 (5) ◽  
pp. 683-704 ◽  
Author(s):  
P. R. Furey ◽  
V. K. Gupta ◽  
B. M. Troutman
Keyword(s):  
Water Loss ◽  
Probability Distributions ◽  
Random Variable ◽  
Statistical Hypothesis ◽  
Generation Model ◽  
Loss Ratio ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Top Down ◽  
Runoff Event

Abstract. We hypothesize that total hillslope water loss for a rainfall–runoff event is inversely related to a function of a lognormal random variable, based on basin- and point-scale observations taken from the 21 km2 Goodwin Creek Experimental Watershed (GCEW) in Mississippi, USA. A top-down approach is used to develop a new runoff generation model both to test our physical-statistical hypothesis and to provide a method of generating ensembles of runoff from a large number of hillslopes in a basin. The model is based on the assumption that the probability distributions of a runoff/loss ratio have a space–time rescaling property. We test this assumption using streamflow and rainfall data from GCEW. For over 100 rainfall–runoff events, we find that the spatial probability distributions of a runoff/loss ratio can be rescaled to a new distribution that is common to all events. We interpret random within-event differences in runoff/loss ratios in the model to arise from soil moisture spatial variability. Observations of water loss during events in GCEW support this interpretation. Our model preserves water balance in a mean statistical sense and supports our hypothesis. As an example, we use the model to generate ensembles of runoff at a large number of hillslopes for a rainfall–runoff event in GCEW.

Effects of Tooth Indexing Errors on Load Distribution and Tooth Load Sharing of Splines Under Combined Loading Conditions

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
J. Hong ◽  
D. Talbot ◽  
A. Kahraman
Keyword(s):  
Load Distribution ◽  
Probability Distributions ◽  
Random Sequence ◽  
Load Sharing ◽  
Spur Gear ◽  
Combined Loading ◽  
Distribution Model ◽  
Loading Conditions ◽  
Manufacturing Tolerance ◽  
Single Tooth

In this paper, influences of tooth indexing errors on load distribution and tooth load sharing of spline joints are investigated by modifying an existing semi-analytical load distribution model for side-fit involute splines. Two commonly observed loading conditions, namely (i) combined torsion and radial loads representative of a spline joint of a spur gear with shaft and (ii) combined torsion, radial loads, and tilting moment representative of a spline joint of a helical gear with shaft are considered in this study. Numerical results of an example spline having (i) no tooth indexing error, (ii) a single tooth with indexing error, and (iii) a random sequence of tooth indexing errors under these two loading conditions are presented to demonstrate the effects of tooth indexing errors. In addition, a practical study of the robustness to manufacturing tolerances is also presented where probability distributions of load sharing factor of the critical tooth of an example spline designed to certain manufacturing tolerance classes are obtained with a large number of randomly generated indexing error sequences.

A Novel Scenario Reduction Method by 3D-Outputs Clustering for Condition-Based Maintenance Optimization

2017 ◽  
Vol 24 (04) ◽  
pp. 1750018
Author(s):  
Xinbo Qian ◽  
Qiuhua Tang ◽  
Bo Tao
Keyword(s):  
Computational Complexity ◽  
Reduction Method ◽  
Probability Distributions ◽  
Condition Based Maintenance ◽  
Maintenance Optimization ◽  
Scenario Reduction ◽  
Output Performance ◽  
Probabilistic Distribution ◽  
Small Set ◽  
Uncertainty Parameters

Condition-based maintenance (CBM) optimization involves considering inherent uncertainties and external uncertainties. Since computational complexity increases exponentially with the number of degradation uncertainties and stages, scenario reduction aims to select small set of typical scenarios which can maintain the probability distributions of outputs of possible scenarios. A novel scenario reduction method, 3D-outputs-clustering scenario reduction (3DOCS), is presented by considering the impacts of uncertainty parameters on the output performance for CBM optimization which have been overlooked. Since the output performance for CBM is much more essential than the inputs, the proposed scenario reduction method reduces degradation scenarios by [Formula: see text]-means clustering of the multiple outputs of degradations scenarios for CBM. It minimizes the probabilistic distribution distances of outputs between original and selected scenarios. Case studies show that 3DOCS has advantages as a smaller distance of output performance of selected scenarios compared to that of initial scenarios.

scholarly journals On the Discretization of Continuous Probability Distributions Using a Probabilistic Rounding Mechanism

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 555
Author(s):  
Chénangnon Frédéric Tovissodé ◽  
Sèwanou Hermann Honfo ◽  
Jonas Têlé Doumatè ◽  
Romain Glèlè Kakaï
Keyword(s):  
Poisson Distribution ◽  
Count Data ◽  
Probability Distributions ◽  
Continuous Distribution ◽  
Simple Expression ◽  
Distribution Model ◽  
Stochastic Representation ◽  
Gamma Distributions ◽  
The Family ◽  
Jensen Shannon Divergence

Most existing flexible count distributions allow only approximate inference when used in a regression context. This work proposes a new framework to provide an exact and flexible alternative for modeling and simulating count data with various types of dispersion (equi-, under-, and over-dispersion). The new method, referred to as “balanced discretization”, consists of discretizing continuous probability distributions while preserving expectations. It is easy to generate pseudo random variates from the resulting balanced discrete distribution since it has a simple stochastic representation (probabilistic rounding) in terms of the continuous distribution. For illustrative purposes, we develop the family of balanced discrete gamma distributions that can model equi-, under-, and over-dispersed count data. This family of count distributions is appropriate for building flexible count regression models because the expectation of the distribution has a simple expression in terms of the parameters of the distribution. Using the Jensen–Shannon divergence measure, we show that under the equidispersion restriction, the family of balanced discrete gamma distributions is similar to the Poisson distribution. Based on this, we conjecture that while covering all types of dispersions, a count regression model based on the balanced discrete gamma distribution will allow recovering a near Poisson distribution model fit when the data are Poisson distributed.

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