Course Introductory Methods of Planning Analysis - CRP 5250 Semester Spring 2015 M/W 10:10 AM – 12:05 PM @ Sibley Hall room 101 Lectures 1. Tuesday 4:00 PM – 6:00 PM‚ Location: Sibley Hall‚ 3rd floor Lab (Woosung) 1. Monday 12:30 PM – 2:00 PM‚ Location: Sibley Hall‚ Room 313 (Arash) 2. Wednesday 2:30 PM – 4:00 PM‚ Location: Sibley Hall‚ Room B-10 (Woosung) 3. Thursday 4:00 PM – 5:30 PM‚ Location: Sibley Hall‚ Room B-10 (Rachel) 4. Monday 4:00 PM – 5:30 PM‚ Location: Sibley Hall‚ Room B-10 (Rachel)
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the complicated problem of managerial decisionmaking. • Utilizes a computerized model. • To represent actual decision-making under conditions of uncertainty for evaluating alternative courses of action based upon facts and assumptions. MONTE CARLO TECHNIQUE STEPS: 1. Setting up a probability distribution for variables to be analyzed. 2. Building a cumulative probability distribution for each random variable. 3. Generate random numbers . 4. Conduct the simulation experiment by means
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historical return based on the randomly selected month. 2. Repeat the process 71 more times to generate random monthly returns for the next 6 years. Use the simulated returns to calculate the ending portfolio values. 3. Make sure that Random Values (Monte Carlo) under Setting is selected. This would allow random numbers to be generated every time the spreadsheet is refreshed. 2. Simulate 1000 iterations of the two strategies over the six-year period. Create a histogram of the final fund values. Based
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includes modeling‚ simulation and optimization of construction methods used in breakwater Methodology In this project suitable simulation techniques are used to model the construction of a breakwater and capture the uncertainties in the construction methods. Optimization is done with the help of software like EZstrobe which use Monte Carlo simulation to simulate the activities and suggest improvements. Application of Heuristic methods and Genetic algorithm to arrive at improvements is suggested.
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Department of Finance Fisher College of Business The Ohio State University Prof. George Pinteris Handout on Crystal Ball This handout supplements the lecture notes on Monte Carlo simulation techniques. In this handout‚ I will discuss how to use Crystal Ball to fit a distribution to historical data and how to produce tornado and sensitivity charts that allow the analyst to evaluate the impact of the model’s driver(s) on the model’s variable(s) of interest (such as firm value or NPV in the
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achieved fame for the magnitude of his design efforts across a variety of industries. He was recognized for this by Time magazine and featured on its cover on October 31‚ 1949. • Born: November 5‚ 1893‚ Paris‚ France • Died: July 14‚ 1986‚ Monte Carlo‚ Monaco • Education: University of Paris - Loewy studied electrical engineering at the University of Paris‚ graduating in 1910. • Books: The Locomotive‚ Never Leave Well Enough Alone‚ Industrial Design • Spouse: Viola Erickson Loewy (m
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Computer Simulation * A software program that runs on any computer that attempts to simulate some phenomenon based on a scientist’s conceptual and mathematical understanding of the given phenomenon. * The scientist’s conceptual understanding is reduced to an algorithmic or mathematical logic‚ which is then programmed in one of many programming languages (Fortran‚ C‚ C++‚ etc.) and compiled to produce a binary code that runs on a computer. * Have become a useful part of modeling
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Analysis (Page 1 of 2) Course: QNT 5040 LEARNING OUTCOME/S:(see syllabus) Date:02/10/2014 PURPOSE: To facilitate effective decision making under uncertain conditions by quantifying risk. Name of Student: Stephanie Preston VALIDITY: Best practices in Monte Carlo simulation. Name of Faculty: Professor Yurova COMPANION DOCUMENTS: Assignment and format instructions‚ Case Earning maximum points in each box in ‘PROFICIENT’ column and / or points in columns to the right of ‘PROFICIENT’ meets standard. <<<<<<<<<<
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MOUNT. Computerworld‚ 36(6)‚ 1. Heck‚ M. (1993‚ February 1). High-end project managers: coordinate enterprisewide projects with desktop flexibility. InfoWorld‚ 15(5)‚ 59+. Lanza‚ R. B. (2003). Getting to realistic estimates and project plans: A Monte Carlo approach. Information Strategy The Executives Journal‚ 19(4)‚ 26. Larson‚ E. W.‚ & Gray‚ C. F. (2011). Project Management The Managerial Process. New York: McGraw-Hill/Irwin. Prince‚ J. (2007). Best Practices for LAN Security Projects. Business
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LB5103 GLOBAL DESTINATION COMPETITIVENESS DESTINATION AUDIT PRESENTATION DESTINATION COMPETITIVENESS MODEL IN MONACO Lecturer: Dr.Jenny Panchal |NAME |STUDENT ID | |Shabeer Grewal |12803017 | |Huang Tianmi |12491112 | |Shao Zhe (Scarlett) |12875213 | |Yang Fan (Fany)
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