The University of Texas at Austin Spring 2011 Department of Civil Engineering CE 395R.6 SYLLABUS CE 395R.6 Unique No. 15805 Quantitative Methods for Project Analysis INSTRUCTOR: Dr. Stephen R. Thomas ECJ 5.428 471-7862 ECJ 232-3007 CII or email: sthomas@mail.utexas.edu MEETINGS: Lecture: 11:00-12:30, T,TH Room ECJ 5.416 OFFICE HOURS: T,TH 9:00-10:00 and 12:30-1:00 or by appointment COURSE OVERVIEW: The complexities of today s projects and the competitive nature of the engineering and construction industry make an understanding of the various quantitative methods for project analysis and decision making essential for effective project management. The success of many projects is often dependent upon proper analysis of project data and the effective and timely allocation of resources in a dynamic environment of shifting priorities. An understanding of techniques for analyzing metrics is fundamental to understanding project performance. This course offers graduate engineering students an opportunity to explore many of these methods and their applicability to the project environment. Techniques for analyzing project data will typically be presented using metrics developed by the Construction Industry Institute (CII). In addition, quantitative methods for analyzing and solving practical, every day problems such as bid selection, capital budgeting, allocation of resources, equipment replacement analysis, and the optimization of capital structure will be explored. An introduction to optimization model development will permit students to gain experience with deterministic models and Monte Carlo simulations will be introduced as a technique for addressing uncertainty in model assumptions. Emphasis will be placed on using Microsoft Excel and Excel add-ins which are reasonably available to students. Excel add-ins such as StatTools, Decision Tools (@Risk, Evolver, and RiskOptimizer ), and Oracle Crystal Ball will be used to expand the analytical power of the spreadsheet. The course is divided into 3 modules to address the topics: data analysis, deterministic models, and simulations. Major data analysis topics include descriptive statistics and bivariate and multivariate relationships including regression and ANOVA. Deterministic modeling covers the basics of optimization modeling and the solution of these problems using the spreadsheet add-in, Evolver. The simulations module provides a
background on computer simulations followed by the development, solution, and interpretation of actual models using @Risk and Oracle Crystal Ball. COURSE PREREQUISITE: The only prerequisite for this course is graduate standing in the Cockrell School of Engineering. TEXT: Data Analysis and Decision Making, 4th Edition (includes Printed Access Card), S. Albright, Wayne Winston, and Christopher Zappe, South-Western Cengage Learning, Mason, OH., 2011. Includes access to Decision Tools and Statistic Tools Suite. ISBN -10: 0538476125. COMPUTER & SOFTWARE: Microsoft Excel, StatTools and Decision Tools risk analysis and modeling software (provided with text), will be required to support course work. Oracle Crystal Ball will be offered as an alternative to @Risk and RiskOptimizer. Separate statistical analysis packages may be used, but are not required. COURSE WEB PAGE: Blackboard at UT LESSON SCHEDULE: A schedule of topics to be covered is attached. The schedule is subject to modification as the term progresses. GRADING: Midterm 25% Individual Participation 10% Group Exercises 15% Group Report 25% Final Exam 25% 100% COURSE LETTER GRADES: A 93-100 A- 90-92 B+ 87-89 B 83-86 B- 80-82 C+ 77-79 C 73-76 C- 70-72 D+ 67-69 D 63-66 D- 60-62 F 0-59 2
COURSE/INSTRUCTOR EVALUATION: A course/instructor evaluation will be conducted in class during the last scheduled lecture. Any suggestions you have on improving the course, however, are welcome throughout the term. ADDITIONAL REFERENCES: A Complete Guide to PivotTables, A Visual Approach, Paul Cornell, Apress, 2005. Applied Regression, An Introduction, Michael S. Lewis-Beck, Sage Publications, Beverly Hills, 1980. Applied Regression Analysis for Business and Economics, 3 rd Ed., Terry Dielman, Duxbury Press, Pacific Grove, CA., 2001. Applied Risk Analysis, Johnathan Mun, John Wiley & Sons, Inc., Hoboken, NJ. 2004. Applied Systems Analysis, Engineering Planning and Technology Management, Richard de Neufville, McGraw-Hill, Inc., New York, NY, 1990. Correlation and Regression, Application for Industrial Organizational Psychology and Management, Philip Bobko, Second Edition, Sage Publications, Thousand Oaks, CA. 2001. Data Analysis, Regression, and Forecasting, Arthur Schleifer, Jr. and David E. Bell, Course Technology, Inc., Cambridge, MA. 1995. Financial Models Using Simulation and Optimization, Wayne Winston, Palisade Corporation, Newfield, NY, 1998. Introduction to Simulation and Risk Analysis, J.E. Evans and D.L. Olson, Prentice-Hall, Upper Saddle River, NJ, 1998. Making Hard Decisions, An Introduction to Decision Analysis, Robert T. Clemen, Second Edition, Duxbury Press, Belmont, CA, 1996. Operations Research, Applications and Algorithms, Wayne L. Winston, Third Edition, Duxbury Press, Belmont, CA., 1993. Practical Management Science, Spreadsheet Modeling and Applications, Wayne L. Winston, S. Christian Albright, Duxbury Press, New York, N.Y., 1997. Practical Risk Assessment for Project Management, Stephen Grey, John Wiley & Sons, New York, N.Y., 1995. Quantitative Methods for Business, 9 th Ed., David Anderson, Dennis Sweeney, Thomas Williams, South-Western College Publishing, St. Paul, M.N., 2004. Risk Analysis in Project Management, John Raftery, E&FN SPON, London, UK., 1994. Simulation Using ProModel, Charles Harrel, Biman Ghosh, and Royce Bowden, McGraw- Hill, New York, N.Y., 2000. The New Statistical Analysis of Data, T.W. Anderson and Jeremy D. Finn, Springer Publishing, New York, NY, 1997. Simulation Modeling and Analysis, 3 rd Edition, Averill M. Law and W. David Kelton, McGraw Hill, New York, NY, 2000. Spreadsheet Modeling & Decision Analysis, 5th Edition, Cliff T. Ragsdale, Thomson South- Western, Mason, OH., 2007. 3
POLICIES: 1. Scholastic Dishonesty Policy: Students who violate University rules on scholastic dishonesty are subject to disciplinary penalties, including the possibility of failure in the course and/or dismissal from the University. Since such dishonesty harms the individual, all students, and the integrity of the University, policies on scholastic dishonesty will be strictly enforced. For further information, visit the Student Judicial Services web site http://deanofstudents.utexas.edu/sjs/. 2. Graded Individual Homework Assignments: a. Graded individual homework assignments may be periodically assigned and if so will count as part of the individual participation grade. b. Students will have a minimum of one week to complete the assignments. c. Homework is due at the beginning of the class period on the due date, unless otherwise instructed. d. Homework will not be accepted for credit or graded if submitted late unless prior coordination has been made with the instructor. e. Although homework assignments are individual graded events, collaboration is permitted to the extent that it contributes to the learning process as further explained. Students are permitted to collaborate with other students if necessary to achieve a general understanding of the material. Such collaboration may include discussion and interpretation of the problem statement and general technique(s) of solution. These discussions should not include review and or discussion of the actual solution or discussions of specific approaches to solving the assigned problem. Copying of another student s homework is not permitted and will be treated as unauthorized collaboration and scholastic dishonesty. 3. Graded Group Exercises: a. All students will participate in periodic graded group assignments requiring application of numerous quantitative methods presented in class. b. In addition, a group report will be required of each group documenting as a case study the application of an optimization model or simulation model within the engineering and construction industry. Additional details will be provided in class. 4. Exams: a. The midterm exam, March 8, 2011, will be closed book, closed notes. b. Students must complete all exams to pass the course. In accordance with University regulations, students who miss examinations will receive grades of zero. Make-up exams will be offered only in the event of an extreme, verified emergency and must be coordinated prior to the scheduled exam. c. The final exam will be administered as a take-home exam and will be due at 5:00 PM May 12, 2011. d. All assigned material is subject to examination whether or not it is covered in class. 5. Class Participation: The classroom experience is enhanced by the collective contributions of all class members. Class attendance therefore is expected; if you 4
find that you must miss a class, you should notify the instructor prior to the class to be missed. 6. Students with Disabilities: "The University of Texas at Austin provides, upon request, appropriate academic adjustments for qualified students with disabilities. Any student with a documented disability (physical or cognitive) who requires academic accommodations should contact the Services for Students with Disabilities area of the Division of Diversity and Community Engagement at 471-6259 as soon as possible to request an official letter outlining authorized accommodations. For more information, contact that office at 471-6259, Video Phone 232-2937, or the School of Engineering Director of Students with Disabilities at 471-4321." 7. Important Dates: From the 1st through the 4th class day, graduate students can drop a course via the web and receive a refund. During the 5th through 12th class day, graduate students must initiate drops in the department that offers the course and receive a refund. After the 12th class day, no refund is given. No class can be added after the 12th class day. From the 13th through the 20th class day, an automatic Q is assigned with approval from the Graduate Advisor and the Graduate Dean. From the 21st class day through the last class day, graduate students can drop a class with permission from the instructor, Graduate Advisor, and the Graduate Dean. Students with 20-hr/week GRA/TA appointment or a fellowship may not drop below 9 hours. 8. Web-based, password-protected class sites will be associated with all academic courses taught at the University. Syllabi, handouts, assignments and other resources are types of information that may be available within these sites. Site activities could include exchanging e-mail, engaging in class discussions and chats, and exchanging files. In addition, electronic class rosters will be a component of the sites. Students who do not want their names included in these electronic class rosters must restrict their directory information in the Office of the Registrar, Main Building, Room 1. For information on restricting directory information, see the General Information Catalog or go to: http://www.utexas.edu/student/registrar/catalogs/gi05-06/app/appc09.html. 5
LESSON SCHEDULE Spring 2011 (Unique No. 15805) Week Date Topic Reference 1 Jan 18, 20 Introduction of Basic Modeling Concepts, Introduction to Optimization Modeling and Simulations with Spreadsheets. Introduction & Review of Basic Statistical Concepts, Describing & Summarizing Numerical Data Describing & Summarizing Categorical Data 2 Jan 25, 27 Introduction of Group Term Project Data Mining and Pivot Tables Probability & Probability Distributions 3 Feb 1, 3 Sampling & Sampling Distributions Confidence Intervals & Intro to Hypothesis Testing Test Statistics & Hypothesis Testing - Difference Between Means 4 Feb 8, 10 Regression Analysis Complications with Regression Analysis 5 Feb 15, 17 Multiple Regression & Hypothesis Testing ANOVA Group Term Project Pre-brief (Selection & Brief) 6 Feb 22, 24 ANOVA Post Hoc Analysis Modeling & Intro to Optimization Modeling Text Preface Chap 1, 2, 3 Chap 3, 4, 5, Chap 7, 8, 9 Chap 10, 11 Chap 10, 11 Chap 13 7 Mar 1, 3 Optimization Modeling with Spreadsheets Chap 13, 14 8 Mar 8, 10 Midterm Exam Optimization Modeling Applications Chap 14 9 Mar 15, 17 Spring Break 10 Mar 22, 24 Problems & Issues with Optimization Models Introduction to Spreadsheet Simulations Chap 15 11 Mar 29, 31 Simulation Applications & Techniques Chap 16 12 Apr 5, 7 Analyzing Simulation Results Combining Simulation with Optimization Models 13 Apr 12, 14 Group Report Presentations Optimization Group Report Presentations Optimization 14 Apr 19, 21 Combining Simulation with Optimization Models Analytic Hierarchy Process (AHP) 15 Apr 26, 28 Group Report Presentations - Simulation Group Report Presentations - Simulation 16 May 3, May 5 AHP with Expert Choice Ethics in Data Analysis/ Modeling/Course Wrap-up May 12 Final Exam 2:00PM - 5:00PM 6