MAT314 COURSE COMPACT
COLLEGE:
COLLEGE OF SCIENCE AND ENGINEERING
DEPARTMENT:
PHYSICAL DEPARTMENT
PROGRAMME:
INDUSTRIAL MATHEMATICS
COURSE CODE:
MAT 314
COURSE STATUS:
COMPULSORY
UNITS:
2
COURSE TITTLE: INTRODUCTION TO OPERATIONS RESEARCH
COURSE LECTURER Dr Oluranti Adenrele Adedayo ( Bsc, Msc ,,M.ed PhD )
Adedayo.Oluranti@lmu.edu.ng
OFFICE LOCATION
Old College Building,room 233
CONSULTATION HOURS: 9am to 4pm working days
SEMESTER: OMEGA
TIME OF LECTURE: Two hours per week for 15 weeks ( 30hours) .Wednesday 10am-12am
LOCATION: E 303 Old College Building Landmark University
COURSE CONTENTS
Phases of Operations Research study. Classifications of Operations Research models. Linear
,dynamic and Integer programming. Decision theory .Inventory models .Critical path analysis
and project control
COURSE DESCRIPTION
The course gives a general introductory approach to critical issues in Operations Research.
Some operations research models are included in the course contents and practical
problems solved .
COURSE JUSTIFICATION:
The course MAT 314 will prepare students for further studies in operations research since
it is just an introductory course .It will also help to develop students’ appreciation of use of
statistics in the manufacturing industry.
COURSE OBJECTIVES
At the end of the course the students should be able to
1.
2.
3.
4.
5.
6.
Understand the history of Operations research
Identify some important Operation research models
Solve various optimization problems using Operations Research models
Appreciate the importance of Operations research in decision-making
Solve practical problems on Operations Research models
Use appropriate software to solve problems in Operations Research
COURSE REQUIREMENTS
1 Possession of computers with installed OR software essential.
2. Attendance at lectures and practicals compulsory
3 Minimum of 75% attendance at lectures required in order to pass the course
METHOD OF GRADING
S/N
1
2
3
GRADING
TEST
ASSIGNMENT
FINAL EXAMINATION
SCORE(%)
20
10
70
C. METHOD OF LECTURE DELIVERY/ TEACHING AID COURSE OUTLINES
1.Interactive Lecture Method using relevant teaching aids.
2.Peer tutoring with group assignment
3.Discovery method using relevant materials from the Internet
4. Practicals on use of Operation Research software packages
TUTORIALS
Group and individualized assignments given during tutorials on theory and practice of topics
taught in class. Peer-tutoring encouraged during tutorials. Tutorials on take-home practical
assignments to be done every week
LECTURE CONTENTS ON WEEKLY BASIS
WEEKS 1-2
General introduction to the course. History of Operations Research(OR); uses and
methodology of OR;Modelling in OR.steps taken in modeling.classification of models
OBJECTIVES:
To introduce students to the origin and development of OR
To explain how models are classified
WEEKS 3-4
Linear programming model: uses ,requirements, assumptions, components and limitation.
Formulation of models and optimization techniques for maximization and minimization
problems .Simplex method.
OBJECTIVES
Students will be able to:
1. State the characteristics of each of the models.
2. Solve optimization linear programming models using graphical, algebraic and simplex
methods
3. To solve practical problems with the model using manual computations and software
packages
WEEKS 5-6
Transportation model: model formulation, developing initial feasible solution using North
West corner ,Least Cost and Vogel’s approximation methods, use of Modified Distribution
method
OBJECTIVES
Students will be able to:
1. Formulate a transportation model using given information
2. Carry out necessary computations on transportation model using various methods
WEEK 7-8
Assignment model: linear programming method; complete enumeration method
;Hungarian method ,Branch and Bound method. special types of assignment problems
OBJECTIVES:
Students will be able to:
1. Solve assignment problems using various methods
2. Solve problems with unequal rows and columns
3. Use computer software to solve assignment models
WEEK 9
Mid-semester test
WEEK 10
Decision analysis :phases, structure and types of decision environment .Maximax , maximin,
Hurwicz ,minimax regret and Laplace criteria. Concept of Dominance. Perfect information,
decision trees Bayes decision rule
OBJECTIVES:
Students will be able to:
1.
2.
3.
4.
5.
Describe the structure of a decision problem
State the various types of decision environments
Solve decision analysis problems using any of the stated criteria
Make appropriate use of decision trees
Solve practical problems using Bayes decision rule
WEEK 11
Inventory models; advantages and disadvantages of inventory ,EOQ and EBQ models, JIT
inventory models
OBJECTIVES
Students will be able to:
1. State the advantages and limitations of the inventory models
2. Use various inventory models to solve practical problems
WEEKS 12-13
Network analysis: Definition of terms used in network analysis, characteristics of project and
project manager, construction of networks, Critical Path Method (CPM) and Project
Evaluation and Review Technique (PERT)
OBJECTIVES:
Students will be able to;
1.
2.
3.
4.
5.
6.
Define terms used in Network Analysis
State the qualities desired in a good project
Construct networks form given information
Use the CPM to solve practical problems on projects
Solve problems using PERT
Use appropriate OR software to solve problems on Network Analysis
WEEK 14
Revision
WEEK 15
Examinations
READING LIST
1. Operations Research in Decision Analysis and Production Management by Adedayo et
al
2 Operations Research: Applications and Algorithms by Wayne,l
3 Introduction to Operations Research by Hillier, F.S
4 Operations Research: Deterministic Optimization Models by Murty,K.G