Wallace E. Carroll School of Management

BOSTON COLLEGE WALLACE E. CARROLL SCHOOL OF MANAGEMENT MD604 MANAGEMENT SCIENCE

Fall, 2004 David R. McKenna Office hours: M, W 10:30-11:45AM and TH 3:00 – 4:30PM Fulton Hall 226A Other times by appointment. (617) 552-0472 Email: [email protected], webpage www2.bc.edu/~mckenna

TEXT: Management Science, Anderson, Sweeney and Williams, eleventh edition, South Western Publishing, 2005.

This course is an introduction to today's scientific methods and principles that are used to assist management in defining and solving decision problems. The objectives of this course are to have each student acquire the ability to understand and apply the models and methodologies of the scientific approach to management as useful tools for formulating, solving, and analyzing decision problems and to understand the assumptions and limitations inherent in the quantitative models of management science.

Corporations and industries are increasingly competitive. Firms are always searching for a competitive advantage and now recognize the importance of quantitative methods, such as statistics, optimization models, simulation, forecasting techniques, and decision theory, in maintaining competitiveness. Understanding and applying management science techniques will provide a clearer perspective for planning, organizing and evaluating decision situations. Moreover, the scientific method allows objectivity in the approach to problem analysis and management decisions.

It is my opinion that some fundamental knowledge of theory is required for understanding and applying concepts and models. So, there will be an occasional discussion of theory to assist your understanding of principles. Yet, it may be that the most effective means for acquiring basic comprehension is to solve problems; usually this is how most people learn. Therefore, the course will stress problem solving and decision making, not theory. You will need to use a personal computer equipped with an optimization code (Excel Solver or Lotus 1-2- 3 Solver capability.)

Since the text is self-contained it does not appear that lectures that merely repeat these materials are especially useful. Consequently, we will devote class time to providing alternate examples of decision situations, presenting some of the theory (the text is weak here), clarifying and interpreting the text and discussing formulation and solutions to decision problems. There are multiple strategies for learning, but to do well in a quantitative course you must give regular and significant attention toward solving the assigned problems. You should read the material twice, before and after we discuss the material in class, and then do the assigned problems. Pay particular attention to the examples in the text. You may want to work through these examples yourself with pencil and paper handy.

Another goal of the course is to help you become more self-sufficient concerning your learning needs so that you sever the dependency on being coached by an instructor and in the future your boss. My intention is that this classroom will be a friendly learning environment. Be patient with yourself and work; do not worry!

Journal Articles and Reports: You will read and report on two quantitatively oriented articles (theory and/or application). Each of the two reports will not exceed two machine-generated 8.5 X 11 pages. Each report will include one paragraph summarizing the article followed by your interpretation and critique concerning the applicability and limitations of the article's content in the business environment. These reports are due on or before November 23, 2004. Late reports will not be accepted.

Course grades. Your grade will be determined by your performance on the mid-term exam (25%), case analyses (20%), article reports (10%), participation (10%) and the final exam (35%). Both examinations will be open book and notes. In fact you are allowed to bring and use any non-human resource during the exams. TENTATIVE SCHEDULE Date Topic(s) Assignments & Homework Problems

Sept. 7 Ch. 1 Introduction; Ch. 2 Introduction to Ch. 1: 8-11, 14-16 Linear Programming - Graphical Method Ch. 2: 11 - 13, 21, 22, 24, 37, 43 45, 46, 48, 49, 53

Sept. 14 Ch. 2 LP Graphical Method Continued Ch. 3 LP Sensitivity Analysis and Computer Solution Ch 3: 5, 7, 8, 10 - 12, 16, 17, 20-23, 31, 33, 34 Ch. 4 LP Applications (Omit section 4.5) Ch 4: 2-7, 13, 16, 21, 24, 25

Sept. 21 Ch. 4 LP Applications Continued; Ch. 5: 8, 10, 17, 18, 23 - 25 Ch. 5 Simplex Method.

Sept. 28 Ch. 6 Simplex based Sensitivity Analysis Ch. 6: 6, 7, 9, 10, 16, 18, 20 Ch. 7 Transportation, Assignment models Ch. 7: 4, 6, 7, 8, 12-14, 21, 23, 24, and Transshipment Models (Omit sections 7.5 and 7.6) 26, 29

Oct. 5 Ch. 7 Continued; Ch. 8 Integer Programming

Oct. 12 Ch. 8 Integer Programming continued

Oct. 19 Ch. 8 Integer Programming continued; Ch. 9 Networks Ch 8: 4, 5, 7, 8, 10 - 12, 14, 15, 21

Oct. 26 Mid-term Examination (Chapters 1 through 8)

Nov. 2 Ch. 9 Networks continued; Ch. 9: 4, 7, 9, 12, 13, 17, 18, 20

Nov. 9 Ch. 10 Project Scheduling; Ch. 14 Decision Analysis Ch. 10: 7, 11, 12, 15, 17, 18, 20 Ch. 14: 3, 4, 6 - 8, 11, 12, 17, 22 - 25, 28, 33, 34

Nov. 16 Ch. 14 Decision Analysis Continued

Nov. 23 Ch. 15 Multicriteria Decision Problems Ch. 15: 1, 2, 5-7, 10, 15, 18, 25

Nov. 30 Ch. 15 continued. Ch. 12 Waiting Line Models Ch. 12: 1-4, 10, 16, 17, 22, 23, 26, 29, 31, 34

Dec. 7 Ch. 12 continued

Dec. 11 & 12 Study days

Dec. 14 Comprehensive Final Examination at 4:30PM