Enquiry for Course Details
MATH4902 Operations research II (6 credits) Academic Year 2025
Offering Department Mathematics Quota ---
Course Co-ordinator Prof G Han, Mathematics < ghan@maths.hku.hk >
Teachers Involved
Course Objectives The objective is to provide a fundamental account of the basic results and techniques of dynamic programming (DP), Markov decision processes (MDP), Queueing Theory (QT) and simulation in operations research. There is emphasis on aspects of algorithms as well as applications. The course serves, together with courses on linear programming and network models, to provide essential optimization concept and algorithms for more advanced studies in operations research.
Course Contents & Topics - Dynamic programming (deterministic/stochastic)
- Markov decision process (discounted/average costs)
- Queueing Theory
- Simulation
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 understand the terminology and nomenclature appropriate to dynamic programming, Markov decision process, queueing theory and simulation
CLO 2 explain the typical techniques employed in dynamic programming, Markov decision process, queueing theory and simulation
CLO 3 demonstrate the knowledge on algorithms for a variety of problems in operations research
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in MATH2101, MATH2211 and MATH3603.
Course Status with Related Major/Minor /Professional Core 2025 Major in Mathematics ( Disciplinary Elective )
2025 Major in Mathematics (Intensive) ( Disciplinary Elective )
2025 Minor in Mathematics ( Disciplinary Elective )
2025 Minor in Operations Research & Mathematical Programming ( Disciplinary Elective )
2024 Major in Mathematics ( Disciplinary Elective )
2024 Major in Mathematics (Intensive) ( Disciplinary Elective )
2024 Minor in Mathematics ( Disciplinary Elective )
2024 Minor in Operations Research & Mathematical Programming ( Disciplinary Elective )
2023 Major in Mathematics ( Disciplinary Elective )
2023 Major in Mathematics (Intensive) ( Disciplinary Elective )
2023 Minor in Mathematics ( Disciplinary Elective )
2023 Minor in Operations Research & Mathematical Programming ( Disciplinary Elective )
2022 Major in Mathematics ( Disciplinary Elective )
2022 Major in Mathematics (Intensive) ( Disciplinary Elective )
2022 Minor in Mathematics ( Disciplinary Elective )
2022 Minor in Operations Research & Mathematical Programming ( Disciplinary Elective )
2021 Major in Mathematics ( Disciplinary Elective )
2021 Major in Mathematics (Intensive) ( Disciplinary Elective )
2021 Minor in Mathematics ( Disciplinary Elective )
2021 Minor in Operations Research & Mathematical Programming ( Disciplinary Elective )
Course to PLO Mapping 2025 Major in Mathematics < PLO 1,2,3 >
2025 Major in Mathematics (Intensive) < PLO 1,2,3 >
2024 Major in Mathematics < PLO 1,2,3 >
2024 Major in Mathematics (Intensive) < PLO 1,2,3 >
2023 Major in Mathematics < PLO 1,2,3 >
2023 Major in Mathematics (Intensive) < PLO 1,2,3 >
2022 Major in Mathematics < PLO 1,2,3 >
2022 Major in Mathematics (Intensive) < PLO 1,2,3 >
2021 Major in Mathematics < PLO 1,2,3 >
2021 Major in Mathematics (Intensive) < PLO 1,2,3 >
Offer in 2025 - 2026 Y        2nd sem    Examination May     
Offer in 2026 - 2027 N
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.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Examination 50.0 1,2,3
Test 50.0 1,2,3
Required/recommended reading
and online materials		 S. Dreyfus and A. Law: The Art and Theory of Dynamic Programming (Academic Press, 1977)
P. Thie: Markov Decision Processes (COMAP, Inc. 1983)
S. M. Ross: Introduction to Probability Models (Academic Press, 2007, 9th ed.)
Course Website http://moodle.hku.hk/
Additional Course Information


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