Decision Modelling For Business Analytics MIS775 Exam Help

Non-Linear Programming Models

Nonlinear programming is a mathematical technique used for finding optimal solutions to optimize problems with nonlinear functions as the objective function or as some of the constraints.

The main types of Non-linear programming models are-

• Pricing model
• Advertising response and selection models
• Facility location models
• Portfolio optimization models

Linear Programming Models

Linear programming is a special case of mathematical programming. Linear programming is the challenge of maximizing or minimizing a linear function under linear constraints. Different types of Linear programming models are-

• Advertising models
• Employee scheduling models
• Aggregate planning models
• Blending models
• Production process models
• Financial models

Integer Linear Programming Models

Integer linear programming expresses the optimization of a linear function to a set of linear constraints over integer variables.

3 types of Integer Linear Programming Models are-

• Capital budgeting models
• Fixed cost models
• Cutting stock models

Networking Models

A network model is a database model which is designed to represent objects and their relationships. A unique feature of the network model is its schema, which is viewed as a graph where relationship types are arcs and object types are nodes.

A network model is comprised of nodes (locations, either origins or destinations) connected by arcs (arrows representing shipping routes between locations), and amounts (unit shipping costs) associated with the arcs and/or nodes.

Types of Networking models are-

• Random network
• Scale-free network
• Hierarchical network

Decision Analysis

Decision analysis is a method for making strategic business decisions that is systematic, quantitative, and visual. Decision analysis involves components of psychology, management practices, and economics, as well as a number of instruments.

Steps in Decision Analysis

1. Identify the decision alternatives (different strategies available to decision-maker)
2. Identify the states of nature (future chance outcomes which may occur – these are not under the control of the decision-maker)
3. Determine the payoff (consequence) for each combination of decision alternatives and possible outcomes
4. Assess the probabilities of the states of nature
5. Develop a decision tree
6. Select decision criteria for choosing the best decision alternative
7. Apply the criteria and make your decision

Types of Business Decision Models

Deterministic Decision Models

• Deterministic models are built on the assumption that all inputs to a model, apart from the decision variables, are fixed.

Stochastic Decision Models

• Some inputs to these models are stochastic, i.e. they exhibit random behavior
• Instead of making a single “best guess” for each of these random inputs, we
• incorporate uncertainty into these models via probability distributions
• This in turn produces random variation in model outputs, which we quantify in
• terms of probabilities and statistics
• This information is then used to guide our decision making