COB 291 Introduction to Management Science learning objectives

Upon completing COB 291, students should be able to do the following.

1. Essential remedial algebra skills
• Graph linear equations and inequalities in two variables.
• Solve linear equations in one variable for the unknown.
• Solve linear equations in multiple variables for any variable.
• Solve two linear equations in two unknowns simultaneously.
• Explain what it means to say that a point is 'on the graph' of a relation, or a solution to a relation.
• Evaluate algebraic expressions when given numerical values for its variables
• Check reasonableness of answer by inspection and backsubstitution
• Demonstrate basic knowledge of summation notation, factorials, the exponential function exp(x), and laws of exponents.


2. Linear Programming
• Identify constraints, objective, and decision variables in English (problem types include product mix, cost minimization, multiperiod planning/multistage production, blending, scheduling, and network).
• Represent these items as measurable quantities, mathematical expressions and relations.
• Represent and solve an LP in two decision variables graphically.
• Explain concepts of unboundedness, unbounded feasible region, infeasiblity, infeasible solution, binding constraint, nonbinding constraint, redundant constraint, feasible solution, and optimal solution.
• Explain the concepts of slack and shadow price.
• Solve LPs using Excel (includes entering the model, generating reports, and interpreting them).
• Identify right-hand-side ranges, objective function coefficient ranges, slack variable values, and shadow prices on software report.
• Interpret right-hand-side ranges for limited resource and quota constraints.
• Interpret objective function coefficient ranges on decision variables.


3. Decision Analysis and Probability
• Explain basic probability concepts (joint, marginal, conditional probability, complementary events, and expected value).
• Use contingency tables and apply Bayes' Theorem.
• Construct decision trees, with or without research branches.
• Evaluate (roll back) decision trees.
• State optimal strategy and expected payoff.
• Compute and interpret the Expected Value of Perfect Information and the Expected Value of Sample Information.


4. Queuing Models
• Explain key queuing concepts (queue, system, steady-state, transient state, Poisson distribution, negative exponential distribution, mean arrival rate, mean service rate, mean service time, variance in service time, channel).
• Identify values of model parameters in a word problem.
• Identify the appropriate queuing model from a word problem.
• Compute and interpret the operating characteristics of queuing system (average waiting time in the system and in the queue, average number of customers in the system and in the queue, and the probability of n customers in the system).
• Perform simple economic analysis of waiting lines, factoring in channel, server, and waiting costs.


5. Discrete event simulation
• Construct relative frequency distributions from frequency distribution data.
• Construct process generators from relative frequency distributions.
• Use discrete process generators with random numbers.
• Construct a table to keep track of the events in a simple simulation.
• Conduct a simple simulation.
• Explain the advantages and disadvantages of simulation modeling


6. Forecasting
• Mechanically apply formulae for simple moving average, weighted moving average, simple exponential smoothing, and linear regression.
• Explain key concepts these methods (meaning of parameters and weights).
• Compute Mean Squared Error (MSE) and Mean Absolute Deviation (MAD) for a forecast.
• Explain the significance of MSE and MAD.
• Select an appropriate forecasting method for a particular situation
• Explain behavior of methods in (a) (with respect to anticipating or following a trend and capturing a seasonal component).
• Demonstrate a rudimentary understanding of the trend and seasonal components of a forecast.
• Demonstrate a rudimentary understanding of qualitative forecasting techniques.