NATIONAL UNIVERSITY OF SINGAPORE
School of Business
Department of Decision Sciences
DSC2003 Management Science
Lecturer
:
A/P Chou Fee Seng (Course Coordinator)
Session
:
Semester II, 2007/08
Prerequisites
Although there are no formal prerequisites, this module assumes prior knowledge of
the following probability concepts: expected value, variance, conditional probability,
Bayes’s Rule, Normal distribution, and Poisson distribution.
Students must ensure that they are adequately prepared for this module.
Course Objectives
Management science makes use of analytical methods to distil intelligence for
leaders' decision-making. Thus, this module is concerned with modelling and
problem solving, and shall find applications in fields like finance, economics,
operations management, logistics, and engineering.
As an introductory module, we strive for breadth, giving an overview of several
practical approaches, as well as sufficient depth, so as to provide a substantial feel
for the discipline and a good foundation for further reading. Topics will include linear
programming (including spreadsheet solution & sensitivity analysis), integer
programming, network flow models, project management, decision analysis, and
queuing models.
Course Outline
1.
Modelling business problems
2.
Linear Programming
formulation (including transportation model, multiperiod inventory
models), graphical analysis, using the Solve, graphical sensitivity
analysis of objective function coefficients and RHS constants, shadow
prices, interpreting the Solver sensitivity report
3.
Integer Programming
LP vs ILP, LP relaxation, rounded solutions, binary variables, fixed
charge problem, transhipment problem, assignment problem, Solver
solution
4.
Network Flow Models
shortest route problem, minimum spanning tree problem, maximal flow
problem
5.
Project Management
activity list, critical path, PERT, CPM, project crashing
6.
Decision Analysis
Part (a): decision making criteria (maximax, maximin, minimax regret,
Hurwicz, Laplace), decision trees, expected value of perfect
information, sequential decision trees, expected value of sample
information.
Part (b): utility theory, St Petersburg paradox, certainty equivalent,
risk premium
7.
Queueing Analysis
exponential distribution; basic M/M/1 model, operating characteristics,
steady-state results, Little’s Law, finite queue M/M/1/c, multiple server
queue M/M/s, M/G/1 queue, simple economic analysis of queueing
systems
Main Text
B. Taylor: Introduction to Management Science, 9th edition, Prentice Hall
Reference Texts
Render, Stair and Hanna: Quantitative Analysis for Management, 9th edition,
Prentice-Hall, 2006
Anderson, Sweeney & Williams: An Introduction to Management Science, 11th
edition, Thomson, 2005
Moore & Weatherford : Decision Modeling with Microsoft Excel, 6th edition, PrenticeHall, 2001
Other Information

Weekly lectures of 2 hours each, and weekly tutorial sessions of 1 hour each.

Although the course has a quantitative flavour, the mathematical
requirements are quite modest. What is important is that you should be
prepared to think analytically. For topics such as decision analysis and
(especially) queueing analysis, you will need to be familiar with probabilistic
concepts from a standard introductory course in probability or statistics.

There are no formal computer programming requirements. However, basic
proficiency with Microsoft Excel will be assumed.