Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: MTH203
Module Title: Introduction to Operational Research
Module Level: Level 2
Module Credits: 5.00
Academic Year: 2019/20
Semester: SEM2
Originating Department: Mathematical Sciences
Pre-requisites: MTH104MTH113
   
Aims
The aims of this module are:

to make students appreciate the importance of mathematical methods to Management Sciences;

to introduce a range of models and techniques for solving problems under risk and uncertainty in Business, Industry, and Finance.

to train the students' ability to think logically and independently and to acquire the skills of problem solving.
Learning outcomes 
A. Establish a good understanding of the basic ideas of Operational Research.

B. Use mathematical methods to make acceptable (optimal) decisions in deterministic and probabilistic framework.

C. Understand the methods of simulation of uncertain systems.

D. Demonstrate competence in the mathematical formulation of real life problems.

E. Recognize the most appropriate solution technique for a given problem.

F. Build competence in drawing and analyzing decision trees, forecasting, analyzing standard queuing systems, simulating discrete events, building optimization models and developing solutions using classical methods.

G. Analyze the data available and interpret the results obtained using mathematical methods.

H. Show experience and enhancement of independent learning and problem design/solving skills.

Method of teaching and learning 
This module will be delivered by a combination of formal lectures and tutorials.
Syllabus 
1. Introduction (Operational Research as a mathematical discipline) and review of probability theory.

2. Decisions under risks and uncertainty, decision trees, Markov Decision Processes.

3. Time series and Forecasting. Constant level model, linear model and linear model with a seasonal effect.

4. Markovian Queuing Theory: general queueing model, M/M/1 queue, M/M/c queue.

5. Computer simulation. Standard Random Generators and discrete event simulation.

6. Introduction to linear programming. Simplex Method, Duality, and sensitivity analysis.

7. Classical deterministic optimization problems: assignment problem, the shortest path problem, the minimum spanning tree problem, the maximum flow problem, etc.

8. (If time permits) introduction to metaheuristics. Tabu search, simulated annealing and genetic algorithms.


Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 39    13      98  150 

Assessment

Sequence Method % of Final Mark

Module Catalogue generated from SITS CUT-OFF: 12/16/2019 7:38:58 AM