Introduction to Management Science

MSCI 2150

Course OutlineUniversity of Manitoba

I.H. Asper School of Business

Faculty of Management

Department of Supply Chain Management

A03 Fall 2018 4:00 pm - 5:15 pm TR Sept 5 - Dec 07, 2018, Drake 136

Instructor Name Dr S.S. Appadoo

Position Associate Professor, Head of Dept

E-mail ss.appadoo@umanitoba.ca

Office Location Room 630 Drake Building

Phone T (204)474-6870

Fax v (204)474-7545

Office Hours Will be announced in Class

Text Bernard W. Taylor III,

Introduction to Management Science Custom Edition for the University

of Manitoba. The custom edition prepared by S.S. Appadoo and Y. Gajpal.

Prentice Hall, ISBN : 9781323697603.

The University of Manitoba Bookstore carries a large selection of new and used textbooks

at competitive prices for every course offered at our University. We recommend that you

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log on to www.umanitoba.ca/bookstore to determine course book selection, availability

and pricing. The Fort Garry Bookstore is located in the heart of the Fort Garry Campus at

140 University Centre. For those people closer to the Health Sciences campus, the address

is 140 Brodie Centre, 727 McDermot. We suggest you print the course book list, confirm

with your professor, then proceed with your purchase.

Introduction to Management Science.

[Formerly 164.215] An Introduction to Management Science techniques and mod-

els. Topics include linear programming, distribution problems, decision theory and queuing

models. Prerequisites: (MATH 1310 (or 136.131) or MATH 1300 (or 136.130) or MATH

1301) or equivalent and MATH 1520 (or 136.152) or MATH 1500 (or 136.150) or equivalent.

Corequisites: STAT 1000 (or STAT 1001) or equivalent and COMP 1260 (or COMP 1261).

May not hold with the former 027.215.

[L’ancien164.215] Introduction aux techniques et modeles des sciences de la gestion. Les

sujets traits incluent la programmation lineaire, les problemes d’affectation et de transport,

la theorie de la decision, les files d’attente. Prealables: MATH 1310 (ou 136.131) ou MATH

1300 (ou 136.130) et MATH 1520 (ou 136.152), ou MATH 1500 (ou 136.150). Prealable ou

corequis: STAT 1000 (ou STAT 1001) et COMP 1260 (ou COMP 1261) ou consentement

du professeur. May not hold with former 027.215.

OBJECTIVES

(a) To introduce students to the subject of Management Science, and a variety of manage-

ment science models, methods and computational procedures that are helpful in solving

management problems in Finance, P.O.M., Accounting, M.I.S., Marketing, Operational

Research, Actuarial Science, etc. Emphasis is placed on models and their solutions.

The course offers you a broad spectrum of knowledge of the mechanics of management

science techniques and the types of problems to which these techniques are applied. The

ultimate test of a management scientist or a manager who uses management science

techniques is the ability to transfer textbook knowledge to the business world.

(b) To give students a good foundation in basic problem solving as a preparation for upper

level quantitative courses (Finance, Production/ Operations Management, Accounting,

M.I.S., Marketing, Operational Research, Supply Chain Management etc.).

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(c) Quantitative analysis is a scientific approach to decision making. The quantitative anal-

ysis approach includes defining the problem, developing a model, acquiring input data,

developing a solution, testing the solution, analyzing the results, and implementing the

results.

(d) To develop in students an appreciation of the management science approach to problem

formulation and solution, so important in the modern business and industrial world

with the increased use of computers. This course is designed to provide students with

a conceptual understanding of the role that management science plays in the decision-

making process. Quantitative methods will be discussed and there will be emphasis on

modeling, problem solving, and showing how quantitative approaches can be used in

decision making process.

(e) In the words of the authors. The objective of management science is to solve

the decision-making problems that confront and confound managers in both the public

and the private sector by developing mathematical models of those problems. These

models have traditionally been solved with various mathematical techniques, all of which

lend themselves to specific types of problems. Thus, management science as a field of

study has always been inherently mathematical in nature, and as a result sometimes

complex and rigorous. When I began writing the first edition of this book in 1979, my

main goal was to make these mathematical topics seem less complex and thus more

palatable to undergraduate business students. To achieve this goal I started out by

trying to provide simple, straightforward explanations of often difficult mathematical

topics. I tried to use lots of examples that demonstrated in detail the fundamental

mathematical steps of the modeling and solution techniques. Although in the last two

and a half decades the emphasis in management science has shifted away from strictly

mathematical to mostly computer solutions, my objective has not changed. I have

provided clear, concise explanations of the techniques used in management science to

model problems, and provided lots of examples of how to solve these models on the

computer, while still including some of the fundamental mathematics of the techniques.

The stuff of management science can seem abstract, and students sometimes have trou-

ble perceiving the usefulness of quantitative courses in general. I remember when I was

a student I could not foresee how I would use such mathematical topics (in addition to

a lot of the other things I learned in college) in any job after graduation. Part of the

problem is that the examples used in books often do not seem realistic. Unfortunately,

examples must be made simple to facilitate the learning process. Larger, more com-

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plex examples reflecting actual applications would be too complex to help the student

learn the modeling technique. The modeling techniques presented in this text are, in

fact, used extensively in the business world and their use is increasing rapidly because

of computer and information technology. Therefore, the chances of students using the

modeling techniques that they learn from this text in a future job are very great indeed.

Even if these techniques are not used on the job, the logical approach to problem solving

embodied in management science is valuable for all types of jobs in all types of orga-

nizations. Management science consists of more than just a collection of mathematical

modeling techniques; it embodies a philosophy of approaching a problem in a logical

manner, as does any science. Thus, this text not only teaches specific techniques but

also provides a very useful method for approaching problems.

My primary objective throughout all revisions of this text is readability. The modeling

techniques presented in each chapter are explained with straightforward examples that

avoid lengthy written explanations. These examples are organized in a logical step-by-

step fashion that the student can subsequently apply to the Problems at the end of each

chapter. I have tried to avoid complex mathematical notation and formulas wherever

possible. These various factors will, I hope, help make the material more interesting

and less intimidating to students.

Important Dates

Midterm Test Saturday, October 20, 2018, 11:30 am - 2:00 pm 35%

6 Quizzes In-class quizzes 5%

Voluntary Withdrawal Nov. 19, 2018 Fall (VW) Deadline

Final Examination Will be scheduled by student records 60%

In Class Quizzes

The in-class assignments are based on the lecture taught in that day. You need to finish

these assignments in the class. There is no fixed schedule for in-class assignments; hence,

your attendance is required to complete the in-class assignments. If you miss the class, you

will miss the in class assignment. However, I will give total of 6 in-class assignments and

select best 5 in-class assignments, hence, you can afford to miss maximum of one in-class

assignment

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[Mid-Term Test and the Final Exam will be Cumulative.

All examinations are closed-book - no notes or other memory aids are allowed.

]

Grade Conversion

The following table shows the tentative grade cut-offs:

Cumulative Marks Grade Performance

93 ≤ x ≤ 100 A+ Excellent87 ≤ x < 93 A Very Good80 ≤ x < 87 B+ Good71 ≤ x < 80 B Satisfactory65 ≤ x < 71 C+ Marginal60 ≤ x < 65 C Unsatisfactory50 ≤ x < 60 D Unsatisfactory

< 50 F Unsatisfactory

A Voluntary Withdrawal (VW) is the act of dropping a course following the end of

the registration revision period and prior to the Voluntary Withdrawal deadline. No refund

will be issued for a VW course; VW courses will be recorded on official transcripts and

student records; Courses cannot be withdrawn from after the VW deadline applicable to

that course has passed; Courses which are not dropped by this deadline will be assigned a

final grade.

Intended Learning Objectives:

After completing this course, students will be able to:

• Perform break even analysis to make managerial decision.

• Build a linear programming based mathematical model to capture the real life decision

making process.

• Solve linear programming model using graph method and using the Excel spread-

sheets.

• Perform sensitivity analysis to know the managerial interpretation of linear program-

ming model.

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• Model a wide variety of medium to large linear programming (LP) problem applied in

the various functional area of management such as marketing, production, Finance,

transportation etc.

• Perform network analysis, decision analysis for uncertain situation and waiting line

analysis to make managerial decision

Disability Policy Statement:

Any Student requesting academic accommodations based on a disability is required to

register with Disability Services each semester. Student with disability requiring accommo-

dations is encouraged to contact me after class or during office hours. All discussions will

remain confidential and upon request I will arrange accommodations to ensure full partic-

ipation. Additionally, students should contact Disability Services. All D.S. discussions are

confidential. If you meet their criteria, D.S. staff will notify me. I will work with them to en-

sure that examinations are delivered on time and picked up at the conclusion of the examina-

tion. Disability Services is located at 155 University Centre University of Manitoba.

Students are encouraged to visit or call Disability Services, in order to make an appoint-

ment with DS staff. Phone Number: (204) 474-6213 and Fax: (204) 261-7732. For further

information please visit http : //umanitoba.ca/student/resource/disability − services/

LECTURE SCHEDULE AND READINGASSIGNMENT.

• Chapter 2. Management Science, pp.3-34. The management science approach, Mod-

els of Cost, Revenue and Profit, Cost and Volume Models, Revenue and Volume

Models, Profit and Volume Models, BreakEven Analysis. In this chapter, the model

construction and solutions that constitute each management science technique will be

presented in detail and illustrated with examples. Components of Break-Even Analy-

sis are presented. The three components of break-even analysis are volume, cost, and

profit. Graphical illustration and profit analysis will be discussed.

• Chapter 3. An Introduction to Linear Programming pp.35-78, A Simple Maxi-

mization Problem, Problem Formulation, Graphical Solution Procedure, A Note on

Graphing Lines, Summary of the Graphical Solution Procedure, for Maximization

Problems, Slack Variables, Extreme Points and the Optimal Solution, vertex inspec-

tion method, A Simple Minimization Problem, Summary of the Graphical Solution

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Procedure, for Minimization Problems, Surplus Variables, Special Cases, Alternative

Optimal Solutions, Infeasibility, Unbounded, General Linear Programming Notation,

Solving Linear Programs with Excel. Linear programming models exhibit certain

common characteristics: An objective function to be maximized or minimized, A set

of constraints, Decision variables for measuring the level of activity, Linearity among

all constraint relationships and the objective function. The graphical approach is lim-

ited to models with only two decision variables. However, the analysis of the graphical

approach provides valuable insight into linear programming problems and their solu-

tions. In Chapter 4 we will show how linear programming solutions can be obtained

using computer programs such as Excel.

• Chapter 4. In this chapter pp.79-120 we will show how linear programming problems

can be solved using Excel computer software packages. We will also describe how to

use a computer solution result to experiment with a linear programming model to see

what effect parameter changes have on the optimal solution, referred to as sensitiv-

ity analysis. Students are also exposed to Excel formulation to linear programming

model. Sensitivity Analysis and Interpretation of Solution, Introduction to Sensitiv-

ity Analysis, Objective Function Coefficients Right-Hand Sides Sensitivity Analysis,

Computer Solution Interpretation of Computer Output, Cautionary Note on the In-

terpretation of shadow price Values, Limitations of Classical Sensitivity Analysis.

Problem Formulation and Computer Solution and Interpretation.

• Chapter 5 Linear Programming Applications pp.121-194 in Marketing, Finance, and

Operations Management, Marketing Applications, Media Selection, Marketing Re-

search, Financial Applications, Portfolio Selection, Operations Management Applica-

tions, Production Scheduling, Workforce Assignment, Blending Problems. Formulat-

ing a linear programming model from a written problem statement is often difficult.

The steps for model formulation described in this section are generally followed; how-

ever, the problem must first be defined (i.e., a problem statement or some similar

descriptive apparatus must be developed). The objective function and model con-

straints can be very complex, requiring much time and effort to develop. Simply

making sure that all the model constraints have been identified and no important

problem restrictions have been omitted is difficult. It is not uncommon for linear pro-

gramming models of real problems to encompass hundreds of functional relationships

and decision variables. What is possible is to provide the fundamentals of linear pro-

gramming model formulation and solution prerequisite to solving linear programming

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problems in actual practice.

Midterm Test Saturday Oct 20, 2018 11:30 A.M. - 2. 00 P.M.

Weight - 35%

Material covered: From Ch 2 to Ch 5.

Exam room: TBA

• Chapter 6. Distribution and Network Models pp.195-254, Transportation Problem,

Assignment Problem, Problem Variations, Transshipment Problem, Problem Varia-

tions, A General Linear Programming Model. In this chapter, three special types

of linear programming problems will be presented, the transportation problem, the

transshipment problem, and the assignment problem. As mentioned before they are

part of a larger class of linear programming problems, known as network flow prob-

lems. In the next chapter, we examine several additional examples of network flow

problems, including the shortest route problem, the minimal spanning tree problem,

and the maximal flow problem. Although they have different objectives, they share

the same general characteristics as the transportation and assignment problemsthat

is, the flow of items from sources to destinations.

• Chapter 7. Shortest-Route Problem pp.255-306, A General Linear Programming

Model, Minimal Spanning Tree, A Minimal Spanning Tree Algorithm. In this chapter,

we will examine a class of models referred to as network flow models. These included

the shortest route network and the minimal spanning tree network. These network

models are all concerned with the flow of an item (or items) through an arrangement

of paths (or routes). We will demonstrate solution approaches for each of the types

of network models. At times, it may have seemed tiresome to go through the various

steps of these solution methods, when the solutions could have more easily been found

by simply looking closely at the networks. However, as the sizes of networks increase,

intuitive solution by observation becomes more difficult, thus creating the need for

a solution procedure. Of course, as with the other techniques in this text, when a

network gets extremely large and complex, computerized solution becomes the best

approach.

• Chapter 8. Probability and Statistics pp.307-342. Students are advised to read this

chapter on their own.

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• Chapter 9. Decision analysis pp.343-406 has been widely used in practice. The

purpose of this chapter is to demonstrate the concepts and fundamentals of decision

making when uncertainty exists. Within this context, several decision-making criteria

will be presented. The maximax, maximin, minimax regret, equal likelihood, and

Hurwicz decision criteria are demonstrated for cases in which probabilities cannot be

attached to the occurrence of outcomes. The expected value criterion will be discussed

for cases in which probabilities can be assigned to the states of nature of a decision

situation. All the decision criteria presented in this chapter are demonstrated by ex-

amples; actual decision-making situations are usually more complex. Decision analysis

can be used to determine a recommended decision alternative or an optimal decision

strategy when a decision maker is faced with an uncertain future events. The goal

of decision analysis is to identify the best decision alternative or the optimal decision

strategy given information about the uncertain events and the possible consequences

or payoffs. The uncertain future events are called chance events and the outcomes of

the chance events are called states of nature.

• Chapter 10. In this chapter pp.407-448 we present a variety of waiting line models

that can help managers make better decisions concerning the operation of waiting

lines. For each model, we will present formulas that can be used to develop operating

characteristics or performance measures for the system being studied. The operating

characteristics include the following:

– Probability that no units are in the system.

– Average number of units in the waiting line.

– Average number of units in the system.

– Average time a unit spends in the waiting line.

– Average time a unit spends in the system.

– Probability that arriving units will have to wait for service.

However, with a little creativity, waiting line models can be applied to many different

situations such as telephone calls waiting for connections, mail orders waiting for

processing, machines waiting for repairs, manufacturing jobs waiting to be processed,

and money waiting to be spent or invested.

FFFFFinal Exam details: This exam is comprehensive.

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FFFFStudents are expected to study all course materials.

FFFF(Chapters 2, 3, 4, 5, 6, 7, 9, 10) for the final exam.

IMPORTANT NOTE:

Students are strongly advised to go through solved examples, Problems and Case Problems

in the textbook. Always adopt pen and paper for doing Management Science problems. You

will be deceiving yourself if you simply read the text. In order to draw the maximum benefit

out of this course, you should always, before going to the next lecture, go through the reading

assignment. This will create greater interest in the lecture.

The final grade will be determined based on the scores obtained on the closed-book test,

several graded quizzes and on the final exam. On all the written tests, you have to show all

your work on the paper you hand in. The grade will be based on the work shown, not on

what was intended or implied. A correct answer is not sufficient for full credit: missing or

poorly justified steps, excessively sloppy and disorganized work will result in a lower grade.

It is the Responsibility of all Students.

• FF Responsibility: You are responsible for obtaining notes and handouts

from any classes you miss.

• FF Attendance: It has been observed that your attendance in class has a

direct correlation with your final grade. Do make an attempt to attend all

classes.

• FF Exams: The Tests and Final will be closed book, no notes allowed.

• FF Students should familiarize themselves each year with the university’s

academic regulations and policy in general;

• FF Students should familiarize themselves with the regulations and poli-

cies applying specifically to their faculty, school, or program;

• FF Students should familiarize themselves with the specific graduation

requirements of the degree, diploma, or certificate they are seeking; and

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• FF To ensure that the courses they have selected are appropriate to their

programs.

Attendance Policy

Attendance will not be taken. However, students are expected to attend the classes. While

your absences will not directly influence your grades, you are responsible for all material

covered in class whether you attend classes regularly or not. Ultimately, the final grade

will depend on how much you have learned and not how often you came to class (although

the two are usually highly correlated since missing classes may impair your understanding

of the material). If you need my help and are unable to come to my office hours, don’t

hesitate to schedule an appointment to see me some other time. The best way to contact

me outside office hours is by e-mail. Feel free to interrupt me (as long as you don’t overdo

it) during class and to talk to me after class if you have questions. You are expected to be in

a professional business manner in asking questions and replying to questions from both the

instructor and other classmates. Late arrivals and walking in and out of the class disrupt

the flow of the class. Please avoid private conversations that may distract your classmates

and your instructor, and turn off your cellular phones.

You must come to my office prepared in the following sense: before posing a question,

make a serious effort to answer it yourself. In particular, if you have a question about an

exercise, make every effort to understand its formulation, the definitions and theorems that

are related to the exercise, as well as the employed notations. Most likely it will be enough

for you to read the corresponding paragraphs in your notes and/or the textbook. In short,

office hours will not be a substitute for lectures.

Exam Policy

No cellular telephones or other electronic communication devices are allowed during exam-

inations. Such items will be removed when discovered, and returned upon the completion

of the examination.

No make-up examinations will be given. If you miss a Mid-Term Examination for health

reasons you are expected to have your doctor complete a Medical Absenteeism Form and

submit it to your instructor.

If you miss the Final Examination you will be required to consult with the Student Advisors

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in the Undergraduate Program Office, Room 268 Drake Centre. If you meet the criteria and

qualify for a Deferred Final Examination, the Department of Supply Chain Management

will schedule another opportunity in the New Year for you to write your Final Examination

in the course.

If you miss a Mid-Term Examination for a reason other than illness, you are expected

to contact your instructor at your earliest possible opportunity and explain the circum-

stances surrounding your absence. Your instructor will require appropriate documentary

evidence to justify your absence. Then the matter will be referred to the Department Head

to ensure that all scenarios are dealt with in a uniform manner.

ADDITIONAL RESOURCES: Your textbook contains numerous Problems, Self Test

Exercises and Case Problems (voluntary or otherwise) at the conclusion of each chapter.

You are seriously encouraged to work through many of them in order to consolidate your

understanding and provide you with confidence when writing the examinations.

You will have Library Reserve Access to Practice Mid-Term Examinations and a Practice

Final Examination. These former examinations will make you aware of the examination

format and the scope of the material included in an examination in this course. You will re-

alize that you will be expected to solve a variety of analytical problems in a limited amount

of time and space.

The best way to succeed in this course is to read your textbook, practice solving problems,

complete homework prior to each class, and review the lecture notes with the examples

done in class. It is strongly advised that you start studying for this course well before the

examination date. Few students are able to learn all the required topics in the last week

before the examination. Limited knowledge of a few of the topics covered in the class may

not be sufficient to earn a passing grade in the course.

ACADEMIC INTEGRITY:

It is critical to the reputation of the Asper School of Business and of our degrees, that

everyone associated with our faculty behave with the highest academic integrity. As the

faculty that helps create business and government leaders, we have a special obligation to

ensure that our ethical standards are beyond reproach. Any dishonesty in our academic

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transactions violates this trust. The University of Manitoba General Calendar addresses

the issue of academic dishonesty under the heading Plagiarism and Cheating. We expect

you to familiarize yourself with the Academic Integrity guidelines. Students who violate

University standards of academic integrity are subject to disciplinary sanctions, including

failure in the course and suspension from the University. Since dishonesty in any form

harms the individual, other students, and the University, policies on academic integrity will

be strictly enforced.

Offenses: Cheating on any exam, quiz, work to be completed in class; theft, or attempted

theft of exam questions; possession of exam questions prior to the time for examination; or

use of a graphing calculator on a test shall all be offenses subject to appropriate penalties.

I expect you to exhibit integrity in all of your actions related to this course.

What does integrity mean? On exams, the meaning is clear: dont cheat, and

we all know what that means. In addition to homeworks, I will provide practice

midterms and a practice final. These are based on past exams and are meant

as a guide for how the exams may look like. They are not meant to tell you

what will be on the exam.

How to study for this class: We will cover a wide range of topics in this

class. The exams will require that you can solve analytical problems in a limited

amount of time. The best way to succeed in this class is to thoroughly practice

solving problems, doing the homeworks, reading the textbook, and reviewing the

lecture notes and examples given in class. I strongly advise you to start study-

ing for this class well before the exam date. Few people manage to learn all the

required topics in the last week before the exam. Limited knowledge of a few of

the covered topics may not be sufficient to pass the course.

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