Offered to students admitted to Year 1 in ALL
Major/Minor ALL
Course Type
Offer in 2018 - 2019 Y N
Course Code MATH3901
Date2019/05/26 04:02
Enquiry for Course Details
MATH3901 Operations research I (6 credits) Academic Year 2018
Offering Department Mathematics Quota ---
Course Co-ordinator Dr Z Qu, Mathematics < zhengqu@maths.hku.hk >
Teachers Involved (Dr Z Qu,Mathematics)
Course Objectives The objective is to provide a fundamental account of the basic results and techniques of Linear Programming (LP) and its related topics in operations research. There is an equal emphasis on all three aspects of understanding, algorithms and applications. The course serves, together with a course on network models, as essential concept and background for more advanced studies in operations research.
Course Contents & Topics - Linear Programming
- Duality Theory
- Sensitivity Analysis and Parametric Linear Programming
- Network Flow Problems
- Matrix Games
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 understand the fundamental concept and approach of linear programming appropriate to the further study of operations research
CLO 2 demonstrate knowledge and understanding of the underlying techniques of the simplex method and its extensions such as the dual simplex algorithm and the transportation simplex algorithm
CLO 3 understand and apply the theory of LP duality such as in sensitivity analysis, matrix games and network flow problems
Pre-requisites
(and Co-requisites and
Impermissible combinations)
Pass in MATH2014 or MATH2101 or MATH2102
Course Status with Related Major/Minor /Professional Core 2018 Major in Decision Analytics ( Disciplinary Elective )
2018 Major in Mathematics ( Disciplinary Elective )
2018 Minor in Mathematics ( Disciplinary Elective )
2018 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
2017 Major in Decision Analytics ( Disciplinary Elective )
2017 Major in Mathematics ( Disciplinary Elective )
2017 Major in Mathematics/Physics ( Disciplinary Elective )
2017 Minor in Mathematics ( Disciplinary Elective )
2017 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
2016 Major in Decision Analytics ( Disciplinary Elective )
2016 Major in Mathematics ( Disciplinary Elective )
2016 Major in Mathematics/Physics ( Disciplinary Elective )
2016 Minor in Mathematics ( Disciplinary Elective )
2016 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
2015 Major in Decision Analytics ( Disciplinary Elective )
2015 Major in Mathematics ( Disciplinary Elective )
2015 Major in Mathematics/Physics ( Disciplinary Elective )
2015 Minor in Mathematics ( Disciplinary Elective )
2015 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
2014 Major in Decision Analytics ( Disciplinary Elective )
2014 Major in Mathematics ( Disciplinary Elective )
2014 Major in Mathematics/Physics ( Disciplinary Elective )
2014 Minor in Mathematics ( Disciplinary Elective )
2014 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
Course to PLO Mapping 2018 Major in Decision Analytics < PLO 1,3,4 >
2018 Major in Mathematics < PLO 1,2,3 >
2017 Major in Decision Analytics < PLO 1,3,4 >
2017 Major in Mathematics < PLO 1,2,3 >
2017 Major in Mathematics/Physics < PLO 1,2,3 >
2016 Major in Decision Analytics < PLO 1,3,4 >
2016 Major in Mathematics < PLO 1,2,3 >
2016 Major in Mathematics/Physics < PLO 1,2,3 >
2015 Major in Decision Analytics < PLO 1,3,4 >
2015 Major in Mathematics < PLO 1,2,3 >
2015 Major in Mathematics/Physics < PLO 1,2,3 >
2014 Major in Decision Analytics < PLO 1,3,4 >
2014 Major in Mathematics < PLO 1,2,3 >
2014 Major in Mathematics/Physics < PLO 1,2,3 >
Offer in 2018 - 2019 Y        1st sem    Examination Dec     
Offer in 2019 - 2020 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate an excellent understanding of key concepts and ideas by being able to identify basic principles, appropriate theorems, algorithms and their applications through correctly analysing problems, clearly and elegantly presenting correct logical reasoning and argumentation and being able to carry out computations carefully and correctly, and to solve problems with some innovative approaches.
B Demonstrate a good understanding of key concepts and ideas by being able to identify basic principles, appropriate theorems, algorithms and their applications through correctly analysing problems, but with some minor inadequacies in arguments, identifying the appropriate theorems or their applications and presentation or with some minor computational errors.
C Demonstrate an acceptable understanding of key concepts and ideas by being able to identify basic principles, appropriate theorems, algorithms and their applications but with some inadequacies in applying the theorems through incorrectly analysing problems with poor argument and presentation or a number of minor computational errors.
D Demonstrate some understanding of key concepts and ideas by being able to identify basic principles, appropriate theorems, algorithms and their applications but with substantial inadequacies in applying the theorems through incorrectly analysing problems with poor argument or presentation or with substantial computational errors.
Fail Demonstrate poor and inadequate understanding by not being able to identify basic principles, appropriate theorems, algorithms or their applications, or not being able to complete or compute the solution.
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36
Tutorials 12
Reading / Self study 100
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)
Assessment Methods
to CLO Mapping
Assignments Coursework assessment 10 CLO 1,2,3
Examination 50 CLO 1,2,3
Test Two midterm tests 40 CLO 1,2,3
Required/recommended reading
and online materials
J.P. Ignizio and T.M. Cavalier: Linear Programming (Prentice-Hall International, 1994)
J.P. Ignizio: Goal Programming and Extensions (Lexington Books, 1976)
H.A. Taha: Operations Research (Prentice-Hall International, 7/e 2003)
P.R. Thie: An Introduction to Linear Programming and Game Theory (Wiley 2/e 1988)
W.L. Winston: Introduction to Mathematical Programming (Duxbury 4/e 2003)
Course Website http://moodle.hku.hk/
Additional Course Information Tutorial timetable:
http://hkumath.hku.hk/~math/Timetable/timetable1819_S1.pdf
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