Lahore University of Management Sciences. DISC 322 Optimization Methods in Management Science Fall Semester 2017

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DISC 322 Optimization Methods in Management Science Fall Semester 2017 Instructor Kamran Rashid Room No. SDSB 421 Office Hours TBD Email kamran@lums.edu.

1 DISC 322 Optimization Methods in Management Science Fall Semester 2017 Instructor Kamran Rashid Room No. SDSB 421 Office Hours TBD Telephone X8020 TAs & Office Hours TBA Course URL (if any) suraj.lums.edu.pk COURSE BASICS Credit Hours 3 Lecture(s)/Lab(s) Nbr of Lec(s) Per Week 2 Duration 75 minutes Tutorial (per week) Nbr of Lec(s) Per Week Duration COURSE DESCRIPTION This is a core course for undergraduate students majoring in Management Science. It is designed to provide students with a sound conceptual understanding of the role that management science plays in the decision making process. The course intends to build a strong theoretical foundation in the area of optimization that would not only help students formulate optimization problems more effectively, but also benefit students in their research in graduate studies. It is an advanced level course in developing mathematical models and understanding their application to management problems. The emphasis is on models and techniques that are widely used in all industries and functional areas, including operations, supply chain, finance, HR, and marketing. Specific topics covered in this course include: techniques such as Linear programming (Simplex method, Tabular form, Big M method, Two Phase method, Revised Simplex, Duality theory), Binary Programming, Integer Programming (Branch & Bound algorithm, Branch & Cut algorithm), Goal programming, Multi objective optimization, Non linear programming, Meta heuristics, (Tabu search, Genetic Algorithm, Simulated Annealing). COURSE PREREQUISITE(S) DISC 212 Introduction to Management Science COURSE OBJECTIVE(S) The course has following primary objectives: 1. Introduce students to the concept of model driven decision making in business 2. Familiarize students to the theoretical foundation of Mathematical Programming 3. Learn key techniques and topics in Advanced Optimization Methods. Introduce the area of Integer Programming including solution methods and heuristics. 4. Develop student s appreciation of role of mathematical models in environmental and social sustainability of businesses 5. Develop student s ability to critically analyze a business problem, design and apply appropriate decision support tools and interpret the results generated from the tools COURSE LEARNING OUTCOMES Upon successful completion of the course, students should be able to: 1. Discuss advanced modeling techniques in the field of Mathematical Programming. 2. Implement these techniques using optimization tools 3. Setup and solve a range of optimization problems (Linear Programming, Goal Programming Multi Objective LP, Integer Programming, Non linear programing, etc.) by correctly recognizing constraints, decision variables and objective(s) 4. Effectively communicate their problem solving approach, selected tool(s), results, limitations and implications to support the decision maker

2 UNDERGRADUATE PROGRAM LEARNING GOALS & OBJECTIVES General Learning Goals & Objectives Goal 1 Effective Written and Oral Communication Objective: Students will demonstrate effective writing and oral communication skills Goal 2 Ethical Understanding and Reasoning Objective: Students will demonstrate that they are able to identify and address ethical issues in an organizational context. Goal 3 Analytical Thinking and Problem Solving Skills Objective: Students will demonstrate that they are able to identify key problems and generate viable solutions. Goal 4 Application of Information Technology Objective: Students will demonstrate that they are able to use current technologies in business and management context. Goal 5 Teamwork in Diverse and Multicultural Environments Objective: Students will demonstrate that they are able to work effectively in diverse environments. Goal 6 Understanding Organizational Ecosystems Objective: Students will demonstrate that they have an understanding of Economic, Political, Regulatory, Legal, Technological, and Social environment of organizations. Major Specific Learning Goals & Objectives Goal 7 (a) Discipline Specific Knowledge and Understanding Objective: Students will demonstrate knowledge of key business disciplines and how they interact including application to real world situations. (including subject knowledge) Goal 7 (b) Understanding the science behind the decision making process (for MGS Majors) Objective: Students will demonstrate ability to analyze a business problem, design and apply appropriate decision support tools, interpret results and make meaningful recommendations to support the decision maker PROGRAM LEARNING GOALS AND OBJECTIVES Goal 1 Effective Written and Oral Communication Goal 2 Ethical Understanding and Reasoning Goal 3 Analytical Thinking and Problem Solving Skills Goal 4 Application of Information Technology Goal 5 Teamwork in Diverse and Multicultural Environments Goal 6 Understanding Organizational Ecosystems Goal 7 (a) Discipline Specific Knowledge and Understanding (Subject Knowledge) Goal 7 (b) Understanding the science behind the decision making process COURSE LEARNING OBJECTIVES throughout the course CLO 7 throughout the course CLO 2 6 Students will learn to design and implement decision support tools primarily in Excel CLO 2 throughout the course CLO 1 6 COURSE ASSESSMENT ITEM Only written skills* can be assessed in Quizzes, Mid Term and Final, Project presentations will evaluate oral skills Assignment Quizzes, Mid Term, Final Exam and Quizzes, Mid Term, Final Exam and Quizzes, Mid Term and Final Exam Quizzes, Mid Term and Final Exam

3 TEXTBOOK(S)/SUPPLEMENTARY READINGS Introduction to Operations Research (10 th Edition) Frederick S. Hillier, Gerald J. Lieberman Spreadsheet Modeling and Decision Analysis Cliff T. Ragsdale Operations Research: Applications and Algorithms Wayne L. Winston GRADING BREAKUP Assignment(s): 10% Quiz(s): 20% Project: 15% Midterm Examination: 25% Final Examination: 30% EXAMINATION DETAIL Yes/No: Combine/ Separate: Midterm Duration: Exam Preferred Date: Exam Specifications: Yes/No: Combine/ Separate: Final Exam Duration: Exam Specifications: YES Separate 60 minutes Closed Books/Closed Notes/No help sheet YES Separate 120 minutes Closed Books/Closed Notes/No help sheet COURSE OVERVIEW LECTURE TOPICS RECOMMENDED READINGS SESSION OBJECTIVES 1 Course Introduction Overview, discussion on Course Outline 2 Basic of Linear Programming 3 Introduction to Simplex Method 4 Simplex Method for LP in standard form Chapters 1,2,3 DISC 212 Basics of Linear Algebra 5 Tabular Simplex Method 6 Simplex Method; Adaptations to non standard forms Different types of OR problems, Basic concepts in Linear Programming, rehearsal of first three lectures of DISC 212, Four properties of linear problems, Basic concepts behind SM, Canonical vs Standard form of an LP, Properties of Standard form, Basic vs Non Basic variables Algebra of SM, SM in tabular form, examples with Max objective function Unbounded problem, Multiple solutions, Tie breaking, Degeneracy, cycling, Solving Min problems, handling variables that are unrestricted in sign Big M method, Two Phase Method 7 Simplex Method; Adaptations to non standard forms Big M method, Two Phase Method 8 Sensitivity Analysis,5 Sensitivity analysis with Simplex Tableau and Solver output, Allowable increase/decrease 9 Sensitivity Analysis,5 Reduced costs, Shadow prices 10 Revised Simplex Chapter 5 Formulating LP in terms of vectors and matrices, Comparing Simplex Method with Revised Simplex

4 11 12 Revised Simplex Revised Simplex Lahore University of Management Sciences Chapter 5 Chapter 5 13 Duality Theory Chapter 6 14 Duality and Sensitivity Chapter 6 15 Mid term Exam 16 Goal Programming 17 Goal Programming 18 Multiple Objective Linear Program (MOLP) 19 Weighted MOLP 20 Binary Optimization Models Binary Programming Integer Programs (IP) Branch and Bound Algorithm Branch and Cut Algorithm Non Linear programs Evolutionary Algorithms / Heuristics Evolutionary Algorithms / Heuristics 28 Review Chapter 13 Chapter 14 Chapter 14 Solving LP using Revised Simplex Performing sensitivity analysis using Revised Simplex Understanding mathematical theory behind the Dual LP problem, Formulating a Dual LP Performing sensitivity analysis using Duality theory Introduction to Goal programming using goal constraints in an LP Handling LP problems with more than one objectives Solving MOLP problems, MiniMax Theorem for handling MOLP weights Understanding complexity of problems having integer restrictions for the variables Use of Binary variables in modeling linear programs Formulating and solving an Integer program Using Branch and Bound algorithm to solve IP Using Branch and Cut techniques to prepare IP problem for Branch and Bound algorithm Fundamentals of Non linear programming Genetic Algorithm, Simulated Annealing, Tabu Search Genetic Algorithm, Simulated Annealing, Tabu Search CLASS POLICY ATTENDANCE 1. Attendance in classes is highly recommended, but not mandatory 2. However, if you do decide to join, you are not allowed to leave the class before its completion without the permission of the instructor QUIZZES 1. Expect a short quiz at the start of almost every class. Consider this as an announcement for all quizzes

5 2. In case you miss a quiz, you may submit a Missed Quiz petition. However, only one missed quiz petition is allowed in usual circumstances 3. We will be dropping at least two quizzes from the final grade. The final number of dropped quizzes depends on the class s negotiation skills. However, this dropped quiz policy will only be applicable for students who have missed less than three quizzes during the semester ASSIGNMNETS 1. There will be a total of 2 3 assignments. 2. You may work in teams of maximum of two persons for your assignments 3. Detailed instructions will be provided for each assignment CELL PHONES AND SIMILAR DEVICES 1. Use of cell phones and similar devices is not permissible in the class sessions 2. Your phones should not be visible or heard during the sessions ACADEMIC INTEGRITY AND CODE OF CONDUCT We expect absolute integrity from you in all academic undertakings, and also seek your assistance in collectively upholding the standards of academic integrity at LUMS

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017 Instructor Syed Zahid Ali Room No. 247 Economics Wing First Floor Office Hours Email szahid@lums.edu.pk Telephone Ext. 8074 Secretary/TA TA Office Hours Course URL (if any) Suraj.lums.edu.pk FINN 321 Econometrics