MN3032 Management science methods
Students may bring into the examination hall their own hand held electronic calculator. If calculators are used they must satisfy the requirements listed in Section 4, Assessment for the programme, of the Detailed Regulations.
Graph paper will be provided.
Prerequisites (applies to degree students only)
ST104A Statistics 1 andeither MT105A Mathematics 1 or MT1174 Calculus.
The topics dealt with in this course are:
Problem structuring and problem structuring methods: problem structuring methods such as JOURNEY (Jointly Understanding, Reflecting, and NEgotiating strategY) making, Soft Systems Methodology and Strategic Choice.
Network analysis: planning and control of projects via the critical path; float (slack) times, cost/time tradeoff, uncertain activity completion times and resource considerations.
Inventory control: problems that arise in the management of inventory (stock); Economic Order Quantity, Economic Batch Quantity, quantity discounts, probabilistic demand, Materials Requirements Planning, Just-in-Time, Optimised Production Technology and supply chain issues.
Mathematical programming: formulation: the representation of decision problems using linear models with a single objective which is to be optimised; the formulation of both linear programs and integer programs.
Linear programming: solution: the solution of linear programs; the numeric solution of two variable linear programs, sensitivity analysis and robustness.
Data envelopment analysis: assessing the relative efficiency of decision making units in organisations; input/output definitions, basic efficiency calculations, reference sets, target setting and value judgements.
Multicriteria decision making: approaches to decision problems that involve multiple objectives; analytic hierarchy process which considers the problem of making a choice, in the presence of complete information, from a finite set of discrete alternatives; goal programming which considers, via linear programming, multicriteria decision problems where the constraints are 'soft'.
Decision making under uncertainty: approaches to decision problems where chance (probability) plays a key role; payoff tables; decision trees; utilities and expected value of perfect information.
Markov processes: approaches used in modelling situations that evolve in a stochastic (probabilistic) fashion though time; systems involving both non-absorbing and absorbing states.
Queueing theory and simulation: the representation and analysis of complex stochastic systems where queueing is a common occurrence; M/M/1 queue; discrete event simulation.