STU11004 – Introduction to Management Science

Module Learning Outcomes

On successful completion of this Module, students will be able to:

• LO1: Identify two-variable linear programming problems, solve them using the graphical method or recognise when there is no solution, or an unbounded solution or if there are multiple solutions.
• LO2: Solve probability based problems using the laws of probability including the Partition law, Bayes’ law and the Expected Value of a random variable.
• LO3: Identify the three components (actions, states of nature and consequences) of a decision problem and use them to construct a decision table or decision tree.
• LO4: Solve a decision problem using the heuristics of max-mim, Hurwicz, regret and Laplace’s indifference.
• LO5: Solve a decision problem under uncertainty using the principle of maximising expected value or expected utility, and be able to compute the value of perfect and imperfect information in that problem.
• LO6: Be able to construct and solve a decision tree for decision problems involving a sequence of actions and states of nature.
• LO7: Identify the four feature of a time series.
• LO8: Make a prediction for the next value of a time series using moving average and exponential smoothing.

Semester 2

• L09: Explain why the value of money decreases as a function of how far in the future it will be available.
• L10: Compute: the simple and compound interest of any amount of money, the value of an annuity and the payment of an amortised loan.
• L11: Identify and solve problems using dynamic programming.
• L12: Compute the shortest spanning tree of a network and the shortest path between two points in a network.
• L13: Derive the maximal flow through a network.
• L14: Compute the optimal inventory policy for the classic formulation, and also with constant receipt and shortages.
• L15: Explain risk averse and risk prone behaviour, give examples of each and demonstrate that decreasing marginal worth leads to risk averse behaviour.
• L16: Calculate properties of a queueing system from information about number of servers, arrival rates and service rates.

Module Content

This module covers a range of subjects in management science at an introductory level. The objectives of the module are to give students an overview of the subject, to teach important basic techniques and introduce systematic thinking about problems. The first semester starts with graphical linear programming and moves on to cover applications of probability, decision analysis, and time series forecasting. The second semester introduces the value of money, the theory of queues, and develops ideas in the time value of money, classic network problems, inventory control, and basic transportation & allocation algorithms. The module will combine lectures and demonstrations of mathematical solutions to management science problems.

Teaching and learning Methods

This course will be delivered by both pre-recorded lectures/videos and live online lectures/videos.

Reassessment Details

In the supplemental examinations, assessment is by online time-limited examination only, accounting for 100% of the overall mark.

Contact Hours and Indicative Student Workload

An Introduction to Management Science by David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm and R. Kipp Martin. Cengage South-Western. 2011.

Introduction to Management Science (10th Edition) by Bernard W. Taylor. Prentice Hall. 2012.

Module Pre-requisites

Prerequisite modules:

Other/alternative non-module prerequisites:

None

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