Chapter 16: Program Evaluation and Review Techniques (PERT)
|
|
- Lindsay Quinn
- 5 years ago
- Views:
Transcription
1 Chapter 16: Program Evaluation and Review Techniques (PERT) PERT PERT is a method for determining the length of a construction project and the probability of project completion by a specified date PERT is based on probabilistic activity durations 1
2 Recall that AON diagrams were based on deterministic activity durations When we assume that the duration of activity rebar columns is 10 days, what does that really mean? will rebar columns take exactly 10 days to complete? or will the actual duration vary from the estimated duration? It could mean that, on average, the duration is 10 days To accommodate the uncertainty associated with activity duration estimates, PERT is based on probabilistic activity durations 2
3 Since construction companies engage in work that they have done in the past, this results in multiple occurrences of the same activity and a historical record of durations or productivities PERT relies on activity durations that are established either by an analysis of historical data or through estimates of the range of probable activity durations Such data can be shown as a frequency histogram like the one shown below Histogram of Duration (days), with Normal Curve for a construction activity Duration (days) Source: Weber (2005, p.226) 3
4 No matter of the actual distribution, there are three measures of central tendency: mean, mode, and median Histogram of Duration (days), with Normal Curve for a construction activity Duration (days) Source: Weber (2005, p.226) Histogram of Duration (days), with Normal Curve for a construction activity Duration (days) Mean = Mode = 10 (most frequent occurrence) Median = 11 (equal number of observations above it and equal number of observations below it) Note also that the range of observations = 16 8 = 8 4
5 If all activities have been performed multiple times in the past enough times to generate a frequency histogram, a sample can be taken from each distribution that will give a duration for each activity Activity durations in PERT are based on three time estimates: Optimistic duration Most likely duration Pessimistic duration Histogram of Duration (days), with Normal Curve for a construction activity Duration (days) Optimistic duration: assumes maximum productivity How many days in this example? Pessimistic duration: assumes the worst productivity How many days in this example? 5
6 Histogram of Duration (days), with Normal Curve for a construction activity Duration (days) Most likely: most often based on historical performance How many days in this example? Calculating the mean estimate of duration The mean estimate of the activity duration is computed as follows t e = t o + 4 m p t 6 + t 6
7 t e = t o + 4 m p t 6 + t t e = mean or expected activity duration t o :optimistic activity duration t m : most likely activity duration t p : pessimistic activity duration Network calculations In PERT, project duration is called project mean duration (T e ) T e is calculated based on the regular forward pass using the activity mean durations t e for every activity 7
8 Example Calculating the Standard deviation Note that the mean value of the activity duration does not convey any information about the degree of uncertainty It would be helpful to have a measure to describe the extent to which the duration is expected to vary from the derived mean value Such a measure is known as the Standard Deviation (S) 8
9 We can use S to describe the extent to which the duration is expected to vary from the derived mean Standard deviation (S) = Range of activity durations 6 S = 6 tp to Note that 6 in the equation refers to ±3 standard deviations from the mean of a normal distribution, which contains 99.73% of all population values The variance Variance( V ) = S 2 = ( 6 tp to ) 2 S = V Note that S Pr oject = SCP = V CP 9
10 Example Slack In PERT, what we used to know as float is called slack Activity Total Slack = ATS Activity Free Slack = AFS 10
11 Calculating the probability of meeting deadline dates Based on the normal distribution, we can calculate the probability of project completion within certain duration The probabilities of occurrence of a specific duration can be determined by simply knowing the number of standard deviations that the value in question is away from the mean The standard normal curve areas table is set up to give information of the probability that a particular duration will be less than some specified value that is given in terms of the number of standard deviations that the value extends beyond the mean 11
12 Te = 24 days Ts = 27 days This is the normal distribution The probability to complete the project in 24 days (mean duration) or less = 50%, which is the area under the curve Te = 24 days Ts = 27 days Now to find the probability of completing the project in 27 days, we need to find out the number of standard deviations that T s (specified date) is away from T e Z Ts T = SCP e 12
13 Z = Ts T SCP e Z = = 1.43 Te = 24 days Ts = 27 days From the table, Z=1.43; probability = Therefore the probability of project completion in 27 days or less = 92.4% PERT 13
14 PERT The Program Evaluation and Review Technique, commonly abbreviated PERT, is a statistical tool, used in project management, that is designed to analyze and represent the tasks involved in completing a given project. Example For the following project determine the following! The Critical Path ( C.P) What is The probability of finishing the project before or on day number 21? What is the finish time for the project with a probability of 95 %? 14
15 Activity Pre. Duration To Tm Tp A B A C A D A B E C C F D E G F B H F D I F G N H I To Tm Tp Activity Te 15
16 4 6 7 B E H A C G N F D I 2.17 Determination of Critical Path : - Each Path Give us a specific duration, and we will take the longest : Days 17.34Days 19 Days Days Days Days 13.34Days 13.51Days C.P 16
17 Critical Path Activities Are : A, B, E, H, N Te (project) = 19 days 2) What is The probability of finishing the project before or on day number 21? Z = (Ts Te ) / Scp Standard Deviation For The Project (Scp) = S², where S : standard deviation for each Critical activity S for each activity = (Tp To ) / 6 Activity S A 0.50 B 0.50 E 0.50 H 0.50 N
18 So, : Scp = (.5²) + (.5²) + (.5²) + (.5²) + (.33²) = And : Z = (21 19 ) / = 1.9 So, the probability of completing the project In 21 days is 97.13% 3) What is the finish time for the project with a probability of 95 %? From the Z-Table we find that the Z value that has a probability of 95% equals to = 1.65 So, Z = (Ts Te) / Scp 1.65 = ( Ts 19 ) / We Get Ts = Days 18
Project Time Planning. Ahmed Elyamany, PhD
Project Time Planning Ahmed Elyamany, PhD 1 What is Planning The Development of a workable program of operations to accomplish established objectives when put into action. Done before project starts Planning
More informationSAMPLE STUDY MATERIAL. GATE, IES & PSUs Civil Engineering
SAMPLE STUDY MATERIAL Postal Correspondence Course GATE, IES & PSUs Civil Engineering CPM & CONSTRUCTION EQUIPMENT C O N T E N T 1. CPM AND PERT... 03-16 2. CRASHING OF NETWORK... 17-20 3. ENGINEERING
More informationChapter 7 (Cont d) PERT
Chapter 7 (Cont d) PERT Project Management for Business, Engineering, and Technology Prepared by John Nicholas, Ph.D. Loyola University Chicago & Herman Steyn, PhD University of Pretoria Variability of
More information11/8/2018. Overview. PERT / CPM Part 2
/8/08 PERT / CPM Part BSAD 0 Dave Novak Fall 08 Source: Anderson et al., 0 Quantitative Methods for Business th edition some slides are directly from J. Loucks 0 Cengage Learning Overview Last class introduce
More informationProject Planning & Control Prof. Koshy Varghese Department of Civil Engineering Indian Institute of Technology, Madras
Project Planning & Control Prof. Koshy Varghese Department of Civil Engineering Indian Institute of Technology, Madras (Refer Slide Time: 00:16) Lecture - 52 PERT Background and Assumptions, Step wise
More information. Introduction to CPM / PERT Techniques. Applications of CPM / PERT. Basic Steps in PERT / CPM. Frame work of PERT/CPM. Network Diagram Representation. Rules for Drawing Network Diagrams. Common Errors
More information07/09/2011. A.AMRANI-ZOUGGAR IMS-Lab, University Bordeaux1. These products specific management
A.AMRANI-ZOUGGAR IMS-Lab, University Bordeaux These products specific management What leads the industrialist to start projects? BENEFITS Ageing of the range Benchmarking, comparing the market and industrial
More informationCHAPTER 12. (The interpretation of the symbols used in the equations is given in page 3)
CHAPTER 12 The equations needed: (The interpretation of the symbols used in the equations is given in page 3) 1. ATI = t D 35 4. ws = I average weekly CU 2. I turn = CU I average 5. ds = I average daily
More informationA scheme developed by Du Pont to figure out
CPM Project Management scheme. A scheme developed by Du Pont to figure out Length of a normal project schedule given task durations and their precedence in a network type layout (or Gantt chart) Two examples
More informationST. JOSEPH S COLLEGE OF ARTS & SCIENCE (AUTONOMOUS) CUDDALORE-1
ST. JOSEPH S COLLEGE OF ARTS & SCIENCE (AUTONOMOUS) CUDDALORE-1 SUB:OPERATION RESEARCH CLASS: III B.SC SUB CODE:EMT617S SUB INCHARGE:S.JOHNSON SAVARIMUTHU 2 MARKS QUESTIONS 1. Write the general model of
More informationUsing Neutrosophic Sets to Obtain PERT Three-Times Estimates in Project Management
Using Neutrosophic Sets to Obtain PERT Three-Times Estimates in Project Management Mai Mohamed 1* Department of Operations Research Faculty of Computers and Informatics Zagazig University Sharqiyah Egypt
More informationProject Management Prof. Raghunandan Sengupta Department of Industrial and Management Engineering Indian Institute of Technology Kanpur
Project Management Prof. Raghunandan Sengupta Department of Industrial and Management Engineering Indian Institute of Technology Kanpur Module No # 07 Lecture No # 35 Introduction to Graphical Evaluation
More informationPROGRAMMING CPM. Network Analysis. Goal:
PROGRAMMING CPM 5/21/13 Network Analysis Goal: Calculate completion time Calculate start and finish date for each activity Identify critical activities Identify requirements and flows of resources (materials,
More informationQUESTION ONE Let 7C = Total Cost MC = Marginal Cost AC = Average Cost
ANSWER QUESTION ONE Let 7C = Total Cost MC = Marginal Cost AC = Average Cost Q = Number of units AC = 7C MC = Q d7c d7c 7C Q Derivation of average cost with respect to quantity is different from marginal
More informationJAC Conjunction Assessment
JAC Conjunction Assessment SSA Operators Workshop Denver, Colorado November 3-5, 2016 François LAPORTE Operational Flight Dynamics CNES Toulouse Francois.Laporte@cnes.fr SUMMARY CA is not an easy task:
More informationToward the Resolution of Resource Conflict in a MPL-CCPM Representation Approach
Toward the Resolution of Resource Conflict in a MPL-CCPM Representation Approach MUNENORI KASAHARA Nagaoka University of Technology Nagaoka, Niigata 940-2188 JAPAN s063349@ics.nagaokaut.ac.jp HIROTAKA
More informationNeutrosophic Sets, Project, Project Management, Gantt chart, CPM, PERT, Three-Time Estimate.
Using Neutrosophic Sets to Obtain PERT Three-Times Estimates in Project Management Excerpt from NEUTROSOPHIC OPERATIONAL RESEARCH, Volume I. Editors: Prof. Florentin Smarandache, Dr. Mohamed Abdel-Basset,
More informationNetwork Techniques - I. t 5 4
Performance:Slide.doc Network Techniques - I. t s v u PRT time/cost : ( Program valuation & Review Technique ) vent-on-node typed project-model with probabilistic (stochastic) data set as weights (inp)
More informationSample Chapter. Basic Hard Systems. Engineering: Part I
Basic Hard Systems 10 Engineering: Part I 10.1 Introduction: Hard Systems Analysis In Chapter 1 we dealt briefly with the rudiments of the systems approach to problem solving. In this chapter, we begin
More informationP8130: Biostatistical Methods I
P8130: Biostatistical Methods I Lecture 2: Descriptive Statistics Cody Chiuzan, PhD Department of Biostatistics Mailman School of Public Health (MSPH) Lecture 1: Recap Intro to Biostatistics Types of Data
More informationTIMES SERIES INTRODUCTION INTRODUCTION. Page 1. A time series is a set of observations made sequentially through time
TIMES SERIES INTRODUCTION A time series is a set of observations made sequentially through time A time series is said to be continuous when observations are taken continuously through time, or discrete
More informationA Critical Path Problem Using Triangular Neutrosophic Number
A Critical Path Problem Using Triangular Neutrosophic Number Excerpt from NEUTROSOPHIC OPERATIONAL RESEARCH, Volume I. Editors: Prof. Florentin Smarandache, Dr. Mohamed Abdel-Basset, Dr. Yongquan Zhou.
More informationBPSC Main Exam 2019 ASSISTANT ENGINEER. Test 4. CIVIL ENGINEERING Subjective Paper. Detailed Solutions
Detailed Solutions BPSC Main Exam 09 ASSISTANT ENGINEER CIVIL ENGINEERING Subjective Paper Test 4 Q. Solution: (i) Difference between Activity and Event Activity: Activity is the actual performance of
More informationComputers and Mathematics with Applications. Project management for arbitrary random durations and cost attributes by applying network approaches
Computers and Mathematics with Applications 56 (2008) 2650 2655 Contents lists available at ScienceDirect Computers and Mathematics with Applications journal homepage: www.elsevier.com/locate/camwa Project
More informationDescribing distributions with numbers
Describing distributions with numbers A large number or numerical methods are available for describing quantitative data sets. Most of these methods measure one of two data characteristics: The central
More informationSTATISTICS. 1. Measures of Central Tendency
STATISTICS 1. Measures o Central Tendency Mode, median and mean For a sample o discrete data, the mode is the observation, x with the highest requency,. 1 N F For grouped data in a cumulative requency
More informationA Critical Path Problem Using Triangular Neutrosophic Number
A ritical Path Problem Using Triangular Neutrosophic Number Mai Mohamed 1 Department of Operations Research Faculty of omputers and Informatics Zagazig University, Sharqiyah, Egypt Yongquan Zhou 2 ollege
More informationBasics of Uncertainty Analysis
Basics of Uncertainty Analysis Chapter Six Basics of Uncertainty Analysis 6.1 Introduction As shown in Fig. 6.1, analysis models are used to predict the performances or behaviors of a product under design.
More informationA Critical Path Problem in Neutrosophic Environment
A Critical Path Problem in Neutrosophic Environment Excerpt from NEUTROSOPHIC OPERATIONAL RESEARCH, Volume I. Editors: Prof. Florentin Smarandache, Dr. Mohamed Abdel-Basset, Dr. Yongquan Zhou. Foreword
More informationConfidence Intervals for the Sample Mean
Confidence Intervals for the Sample Mean As we saw before, parameter estimators are themselves random variables. If we are going to make decisions based on these uncertain estimators, we would benefit
More informationProactive Algorithms for Job Shop Scheduling with Probabilistic Durations
Journal of Artificial Intelligence Research 28 (2007) 183 232 Submitted 5/06; published 3/07 Proactive Algorithms for Job Shop Scheduling with Probabilistic Durations J. Christopher Beck jcb@mie.utoronto.ca
More informationOnline Scheduling Switch for Maintaining Data Freshness in Flexible Real-Time Systems
Online Scheduling Switch for Maintaining Data Freshness in Flexible Real-Time Systems Song Han 1 Deji Chen 2 Ming Xiong 3 Aloysius K. Mok 1 1 The University of Texas at Austin 2 Emerson Process Management
More informationOn-line scheduling of periodic tasks in RT OS
On-line scheduling of periodic tasks in RT OS Even if RT OS is used, it is needed to set up the task priority. The scheduling problem is solved on two levels: fixed priority assignment by RMS dynamic scheduling
More informationFor instance, we want to know whether freshmen with parents of BA degree are predicted to get higher GPA than those with parents without BA degree.
DESCRIPTIVE ANALYSIS For instance, we want to know whether freshmen with parents of BA degree are predicted to get higher GPA than those with parents without BA degree. Assume that we have data; what information
More informationChapter. Numerically Summarizing Data. Copyright 2013, 2010 and 2007 Pearson Education, Inc.
Chapter 3 Numerically Summarizing Data Section 3.1 Measures of Central Tendency Objectives 1. Determine the arithmetic mean of a variable from raw data 2. Determine the median of a variable from raw data
More informationContinuous Random Variables
Continuous Random Variables MATH 130, Elements of Statistics I J. Robert Buchanan Department of Mathematics Fall 2018 Objectives During this lesson we will learn to: use the uniform probability distribution,
More informationb) Explain about charts for attributes and explain their uses. 4) a) Distinguish the difference between CUSUM charts and Shewartz control charts.
ASSIGNMENT - 1, DEC - 2016. PAPER- I : STATISTICAL QUALITY CONTROL (DMSTT 21) 1) a) Explain about Midrange control chart and median control chart. b) Explain about average run length for X chart. 2) a)
More informationOperation management
Operation management Vigneron Loic December 3, 2008 1 Operations and productivity 1.1 Productivity Productivity = Units produced Imput used Units produced Labor productivity = Labor hours used One resource
More informationThe space complexity of approximating the frequency moments
The space complexity of approximating the frequency moments Felix Biermeier November 24, 2015 1 Overview Introduction Approximations of frequency moments lower bounds 2 Frequency moments Problem Estimate
More informationAverage case Complexity
February 2, 2015 introduction So far we only studied the complexity of algorithms that solve computational task on every possible input; that is, worst-case complexity. introduction So far we only studied
More informationPROBABILITY AND INFERENCE
PROBABILITY AND INFERENCE Progress Report We ve finished Part I: Problem Solving! Part II: Reasoning with uncertainty Part III: Machine Learning 1 Today Random variables and probabilities Joint, marginal,
More informationSolving Fuzzy PERT Using Gradual Real Numbers
Solving Fuzzy PERT Using Gradual Real Numbers Jérôme FORTIN a, Didier DUBOIS a, a IRIT/UPS 8 route de Narbonne, 3062, Toulouse, cedex 4, France, e-mail: {fortin, dubois}@irit.fr Abstract. From a set of
More informationStatistical Quality Control IE 3255 Spring 2005 Solution HomeWork #2
Statistical Quality Control IE 3255 Spring 25 Solution HomeWork #2. (a)stem-and-leaf, No of samples, N = 8 Leaf Unit =. Stem Leaf Frequency 2+ 3-3+ 4-4+ 5-5+ - + 7-8 334 77978 33333242344 585958988995
More informationPrediction of Power System Balancing Requirements and Tail Events
Prediction of Power System Balancing Requirements and Tail Events PNNL: Shuai Lu, Yuri Makarov, Alan Brothers, Craig McKinstry, Shuangshuang Jin BPA: John Pease INFORMS Annual Meeting 2012 Phoenix, AZ
More informationLecturer: Olga Galinina
Renewal models Lecturer: Olga Galinina E-mail: olga.galinina@tut.fi Outline Reminder. Exponential models definition of renewal processes exponential interval distribution Erlang distribution hyperexponential
More informationCMSC 451: Lecture 7 Greedy Algorithms for Scheduling Tuesday, Sep 19, 2017
CMSC CMSC : Lecture Greedy Algorithms for Scheduling Tuesday, Sep 9, 0 Reading: Sects.. and. of KT. (Not covered in DPV.) Interval Scheduling: We continue our discussion of greedy algorithms with a number
More informationReal-Time Calculus. LS 12, TU Dortmund
Real-Time Calculus Prof. Dr. Jian-Jia Chen LS 12, TU Dortmund 09, Dec., 2014 Prof. Dr. Jian-Jia Chen (LS 12, TU Dortmund) 1 / 35 Arbitrary Deadlines The worst-case response time of τ i by only considering
More informationSemantics of Ranking Queries for Probabilistic Data and Expected Ranks
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks Graham Cormode AT&T Labs Feifei Li FSU Ke Yi HKUST 1-1 Uncertain, uncertain, uncertain... (Probabilistic, probabilistic, probabilistic...)
More informationDescribing distributions with numbers
Describing distributions with numbers A large number or numerical methods are available for describing quantitative data sets. Most of these methods measure one of two data characteristics: The central
More informationAMS 5 NUMERICAL DESCRIPTIVE METHODS
AMS 5 NUMERICAL DESCRIPTIVE METHODS Introduction A histogram provides a graphical description of the distribution of a sample of data. If we want to summarize the properties of such a distribution we can
More informationSOLVING STOCHASTIC PERT NETWORKS EXACTLY USING HYBRID BAYESIAN NETWORKS
SOLVING STOCHASTIC PERT NETWORKS EXACTLY USING HYBRID BAYESIAN NETWORKS Esma Nur Cinicioglu and Prakash P. Shenoy School of Business University of Kansas, Lawrence, KS 66045 USA esmanur@ku.edu, pshenoy@ku.edu
More informationCS 361: Probability & Statistics
January 24, 2018 CS 361: Probability & Statistics Relationships in data Standard coordinates If we have two quantities of interest in a dataset, we might like to plot their histograms and compare the two
More informationLecture 6. Real-Time Systems. Dynamic Priority Scheduling
Real-Time Systems Lecture 6 Dynamic Priority Scheduling Online scheduling with dynamic priorities: Earliest Deadline First scheduling CPU utilization bound Optimality and comparison with RM: Schedulability
More informationM.SC. MATHEMATICS - II YEAR
MANONMANIAM SUNDARANAR UNIVERSITY DIRECTORATE OF DISTANCE & CONTINUING EDUCATION TIRUNELVELI 627012, TAMIL NADU M.SC. MATHEMATICS - II YEAR DKM24 - OPERATIONS RESEARCH (From the academic year 2016-17)
More informationLecture 8 Sampling Theory
Lecture 8 Sampling Theory Thais Paiva STA 111 - Summer 2013 Term II July 11, 2013 1 / 25 Thais Paiva STA 111 - Summer 2013 Term II Lecture 8, 07/11/2013 Lecture Plan 1 Sampling Distributions 2 Law of Large
More information2) Find CS if CI = 6x 2 and IS = x + 3 A) 45 B) 15 C) 22.5 D) ) Find TR if TR = 2x + 17 and JR = 2x + 5 A) 16 B) 8 C) 12 D) 24
eometry Assignment : 1 Name ate eriod ach figure shows a triangle with one or more of its medians. 1) ind if = 7x 1 and = 4x + 2) ind if = 6x 2 and = x + 3 A) 13 ) 19. ) 8.67 ) 26 A A) 4 ) 1 ) 22. ) 7.
More informationCost Analysis and Estimating for Engineering and Management
Cost Analysis and Estimating for Engineering and Management Chapter 6 Estimating Methods Ch 6-1 Overview Introduction Non-Analytic Estimating Methods Cost & Time Estimating Relationships Learning Curves
More informationSeismic Analysis of Structures Prof. T.K. Datta Department of Civil Engineering Indian Institute of Technology, Delhi. Lecture 03 Seismology (Contd.
Seismic Analysis of Structures Prof. T.K. Datta Department of Civil Engineering Indian Institute of Technology, Delhi Lecture 03 Seismology (Contd.) In the previous lecture, we discussed about the earth
More informationChapter 3 Statistics for Describing, Exploring, and Comparing Data. Section 3-1: Overview. 3-2 Measures of Center. Definition. Key Concept.
Chapter 3 Statistics for Describing, Exploring, and Comparing Data 3-1 Overview 3- Measures of Center 3-3 Measures of Variation Section 3-1: Overview Descriptive Statistics summarize or describe the important
More informationFor a stochastic process {Y t : t = 0, ±1, ±2, ±3, }, the mean function is defined by (2.2.1) ± 2..., γ t,
CHAPTER 2 FUNDAMENTAL CONCEPTS This chapter describes the fundamental concepts in the theory of time series models. In particular, we introduce the concepts of stochastic processes, mean and covariance
More informationSimulation & Modeling Event-Oriented Simulations
Simulation & Modeling Event-Oriented Simulations Outline Simulation modeling characteristics Concept of Time A DES Simulation (Computation) DES System = model + simulation execution Data Structures Program
More information4.12 Sampling Distributions 183
4.12 Sampling Distributions 183 FIGURE 4.19 Sampling distribution for y Example 4.22 illustrates for a very small population that we could in fact enumerate every possible sample of size 2 selected from
More informationFUZZY ASSOCIATION RULES: A TWO-SIDED APPROACH
FUZZY ASSOCIATION RULES: A TWO-SIDED APPROACH M. De Cock C. Cornelis E. E. Kerre Dept. of Applied Mathematics and Computer Science Ghent University, Krijgslaan 281 (S9), B-9000 Gent, Belgium phone: +32
More informationProbability and Statistics
Probability and Statistics Kristel Van Steen, PhD 2 Montefiore Institute - Systems and Modeling GIGA - Bioinformatics ULg kristel.vansteen@ulg.ac.be CHAPTER 4: IT IS ALL ABOUT DATA 4a - 1 CHAPTER 4: IT
More informationEmbedded systems engineering Distributed real-time systems
Embedded systems engineering Distributed real-time systems David Kendall David Kendall CM0605/KF6010 Lecture 09 1 / 15 Time is Central to Real-time and Embedded Systems Several timing analysis problems:
More informationRecent Probabilistic Computational Efficiency Enhancements in DARWIN TM
Recent Probabilistic Computational Efficiency Enhancements in DARWIN TM Luc Huyse & Michael P. Enright Southwest Research Institute Harry R. Millwater University of Texas at San Antonio Simeon H.K. Fitch
More informationReview. DS GA 1002 Statistical and Mathematical Models. Carlos Fernandez-Granda
Review DS GA 1002 Statistical and Mathematical Models http://www.cims.nyu.edu/~cfgranda/pages/dsga1002_fall16 Carlos Fernandez-Granda Probability and statistics Probability: Framework for dealing with
More informationProbability Distributions for Continuous Variables. Probability Distributions for Continuous Variables
Probability Distributions for Continuous Variables Probability Distributions for Continuous Variables Let X = lake depth at a randomly chosen point on lake surface If we draw the histogram so that the
More informationProbabilistic Planning. George Konidaris
Probabilistic Planning George Konidaris gdk@cs.brown.edu Fall 2017 The Planning Problem Finding a sequence of actions to achieve some goal. Plans It s great when a plan just works but the world doesn t
More informationPractical Tips for Modelling Lot-Sizing and Scheduling Problems. Waldemar Kaczmarczyk
Decision Making in Manufacturing and Services Vol. 3 2009 No. 1 2 pp. 37 48 Practical Tips for Modelling Lot-Sizing and Scheduling Problems Waldemar Kaczmarczyk Abstract. This paper presents some important
More informationModifications to Risk-Targeted Seismic Design Maps for Subduction and Near-Fault Hazards
Modifications to Risk-Targeted Seismic Design Maps for Subduction and Near-Fault Hazards Abbie B. Liel Assistant Prof., Dept. of Civil, Environ. and Arch. Eng., University of Colorado, Boulder, CO, USA
More informationHistogram Processing
Histogram Processing The histogram of a digital image with gray levels in the range [0,L-] is a discrete function h ( r k ) = n k where r k n k = k th gray level = number of pixels in the image having
More information1.0 Continuous Distributions. 5.0 Shapes of Distributions. 6.0 The Normal Curve. 7.0 Discrete Distributions. 8.0 Tolerances. 11.
Chapter 4 Statistics 45 CHAPTER 4 BASIC QUALITY CONCEPTS 1.0 Continuous Distributions.0 Measures of Central Tendency 3.0 Measures of Spread or Dispersion 4.0 Histograms and Frequency Distributions 5.0
More informationTrajectory planning and feedforward design for electromechanical motion systems version 2
2 Trajectory planning and feedforward design for electromechanical motion systems version 2 Report nr. DCT 2003-8 Paul Lambrechts Email: P.F.Lambrechts@tue.nl April, 2003 Abstract This report considers
More informationAnalysis of Disruption Causes and Effects in a Heavy Rail System
Third LACCEI International Latin American and Caribbean Conference for Engineering and Technology (LACCEI 25) Advances in Engineering and Technology: A Global Perspective, 8-1 June 25, Cartagena de Indias,
More informationNetwork analysis. A project is a temporary endeavor undertaken to create a "unique" product or service
Network analysis Introduction Network analysis is the general name given to certain specific techniques which can be used for the planning, management and control of projects. One definition of a project
More informationReport on EN6 DTC Ensemble Task 2014: Preliminary Configuration of North American Rapid Refresh Ensemble (NARRE)
Report on EN6 DTC Ensemble Task 2014: Preliminary Configuration of North American Rapid Refresh Ensemble (NARRE) Motivation As an expansion of computing resources for operations at EMC is becoming available
More informationDynamic Programming: Hidden Markov Models
University of Oslo : Department of Informatics Dynamic Programming: Hidden Markov Models Rebecca Dridan 16 October 2013 INF4820: Algorithms for AI and NLP Topics Recap n-grams Parts-of-speech Hidden Markov
More informationThe sample mean and sample variance are given by: x sample standard deviation Excel: STDEV(values)
Unless we have made a very large number of measurements, we don't have an accurate estimate of the mean or standard deviation of a data set. If we assume the values are normally distributed, we can estimate
More informationThere are three priority driven approaches that we will look at
Priority Driven Approaches There are three priority driven approaches that we will look at Earliest-Deadline-First (EDF) Least-Slack-Time-first (LST) Latest-Release-Time-first (LRT) 1 EDF Earliest deadline
More informationAnalytical Methods for Engineers
Unit 1: Analytical Methods for Engineers Unit code: A/601/1401 QCF level: 4 Credit value: 15 Aim This unit will provide the analytical knowledge and techniques needed to carry out a range of engineering
More informationPractical Applications of Probability in Aviation Decision Making
Practical Applications of Probability in Aviation Decision Making Haig 22 October 2014 Portfolio of TFM Decisions Playbook Reroutes Ground Stops Ground Delay Programs Airspace Flow Programs Arrival & Departure
More informationFuzzy Numerical Results Derived From Crashing CPM/PERT Networks of Padma Bridge in Bangladesh
Fuzzy Numerical Results Derived From Crashing CPM/PERT Networks of Padma Bridge in Bangladesh Md. Mijanoor Rahman 1, Mrinal Chandra Barman 2, Sanjay Kumar Saha 3 1 Assistant Professor, Department of Mathematics,
More informationA Dynamic Real-time Scheduling Algorithm for Reduced Energy Consumption
A Dynamic Real-time Scheduling Algorithm for Reduced Energy Consumption Rohini Krishnapura, Steve Goddard, Ala Qadi Computer Science & Engineering University of Nebraska Lincoln Lincoln, NE 68588-0115
More informationSYMBIOSIS CENTRE FOR DISTANCE LEARNING (SCDL) Subject: production and operations management
Sample Questions: Section I: Subjective Questions 1. What are the inputs required to plan a master production schedule? 2. What are the different operations schedule types based on time and applications?
More informationCLASSICAL PROBABILITY MODES OF CONVERGENCE AND INEQUALITIES
CLASSICAL PROBABILITY 2008 2. MODES OF CONVERGENCE AND INEQUALITIES JOHN MORIARTY In many interesting and important situations, the object of interest is influenced by many random factors. If we can construct
More information1. AN INTRODUCTION TO DESCRIPTIVE STATISTICS. No great deed, private or public, has ever been undertaken in a bliss of certainty.
CIVL 3103 Approximation and Uncertainty J.W. Hurley, R.W. Meier 1. AN INTRODUCTION TO DESCRIPTIVE STATISTICS No great deed, private or public, has ever been undertaken in a bliss of certainty. - Leon Wieseltier
More informationTotal No. of Questions : 10] [Total No. of Pages : 02. M.Sc. DEGREE EXAMINATION, DEC Second Year STATISTICS. Statistical Quality Control
(DMSTT21) Total No. of Questions : 10] [Total No. of Pages : 02 M.Sc. DEGREE EXAMINATION, DEC. 2016 Second Year STATISTICS Statistical Quality Control Time : 3 Hours Maximum Marks : 70 Answer any five
More informationActivity Predecessors Time (months) A - 2 B A 6 C A 4 D B 4 E C 2 F B,D 1 G D,E 2 H C 1. dummy0 C4 E2
AMS 341 (Spring, 2009) Exam 2 - Solution notes Estie Arkin Mean 68.56, median 70, high 99, low 13. 1. (12 points) A frazzled student is trying to plan all the work they must complete before graduating.
More informationStatistical Concepts. Constructing a Trend Plot
Module 1: Review of Basic Statistical Concepts 1.2 Plotting Data, Measures of Central Tendency and Dispersion, and Correlation Constructing a Trend Plot A trend plot graphs the data against a variable
More informationStatistics: Learning models from data
DS-GA 1002 Lecture notes 5 October 19, 2015 Statistics: Learning models from data Learning models from data that are assumed to be generated probabilistically from a certain unknown distribution is a crucial
More informationCS 237: Probability in Computing
CS 237: Probability in Computing Wayne Snyder Computer Science Department Boston University Lecture 13: Normal Distribution Exponential Distribution Recall that the Normal Distribution is given by an explicit
More informationJanusz Marecki Zvi Topol
Welcome Janusz Marecki Janusz Marecki Zvi Topol Janusz Marecki Zvi Topol Milind Tambe Solving MDPs with Continuous Time Why do I care about continuous time? 30 min At the airport 10:45 12:00 Start 10:15
More informationCh. 8 Math Preliminaries for Lossy Coding. 8.4 Info Theory Revisited
Ch. 8 Math Preliminaries for Lossy Coding 8.4 Info Theory Revisited 1 Info Theory Goals for Lossy Coding Again just as for the lossless case Info Theory provides: Basis for Algorithms & Bounds on Performance
More informationEfficient Workplan Management in Maintenance Tasks
Efficient Workplan Management in Maintenance Tasks Michel Wilson a Nico Roos b Bob Huisman c Cees Witteveen a a Delft University of Technology, Dept of Software Technology b Maastricht University, Dept
More informationUsing Method of Moments in Schedule Risk Analysis
Raymond P. Covert MCR, LLC rcovert@mcri.com BACKGROUND A program schedule is a critical tool of program management. Program schedules based on discrete estimates of time lack the necessary information
More informationTHE METHOD OF CONDITIONAL PROBABILITIES: DERANDOMIZING THE PROBABILISTIC METHOD
THE METHOD OF CONDITIONAL PROBABILITIES: DERANDOMIZING THE PROBABILISTIC METHOD JAMES ZHOU Abstract. We describe the probabilistic method as a nonconstructive way of proving the existence of combinatorial
More informationARE THE SEQUENCES OF BUS AND EARTHQUAKE ARRIVALS POISSON?
Application Example 6 (Exponential and Poisson distributions) ARE THE SEQUENCES OF BUS AND EARTHQUAKE ARRIVALS POISSON? The Poisson Process The Poisson process is the simplest random distribution of points
More informationExpectation of geometric distribution
Expectation of geometric distribution What is the probability that X is finite? Can now compute E(X): Σ k=1f X (k) = Σ k=1(1 p) k 1 p = pσ j=0(1 p) j = p 1 1 (1 p) = 1 E(X) = Σ k=1k (1 p) k 1 p = p [ Σ
More informationSome Statistics. V. Lindberg. May 16, 2007
Some Statistics V. Lindberg May 16, 2007 1 Go here for full details An excellent reference written by physicists with sample programs available is Data Reduction and Error Analysis for the Physical Sciences,
More information