Decision modelling for business analytics The MIS775 subject is taught to students pursuing a Master's degree at Deakin Business School in Australia. This subject is designed to provide students with an opportunity to upgrade their knowledge and understanding to build complex decision models and use advanced quantitative as well as network modelling techniques to develop practical solutions to
The subject Decision Modelling for Business Analytics (MIS 775) includes a wide range of concepts such as sensitivity analysis, linear programming applications, nonlinear programming models, integer linear programming models, network modelling, decision analysis, building stochastic decision models, simulation modelling, quantitative risk analysis, and many more.
There is no denying that decision modelling for business analytics is a complex subject, including various technical terms, that requires a tremendous amount of time and dedication from students to study for their online exams. However, not all students can devote long hours and, as a result, seek online exam help services.
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Nonlinear programming is a mathematical technique for finding optimal solutions to optimisation problems with nonlinear functions as the objective function or some of the constraints.
The main types of nonlinear programming models are:
Advertising response and selection models
Facility location models
Portfolio optimisation models
Linear programming aka mathematical programming. Linear programming is the challenge of maximising or minimising a linear function under linear constraints. Linear programming makes use of mathematical or graphical techniques to use limited resources.
For a problem with various constraints, linear programming offers the best-in-class solutions.
Employee scheduling models
Aggregate planning models
Production process models
Integer linear programming expresses the optimisation of a linear function to a set of linear constraints over integer variables. The variables are integer values, and the objective function and equations are linear.
Integer programming is a powerful problem-solving tool used in economics and operations research. Assigning an integer value to each variable and thus optimising the linear equations is the principle of integer linear programming models.
Capital budgeting models
Cutting stock models
A network is a database model designed to represent objects and their relationships. Schema is one of the unique features of networking models.
A network model consists of nodes (locations, either origins or destinations) connected by arcs (arrows representing shipping routes between places) and amounts (unit shipping costs) associated with the arches and nodes.
Decision analysis is a very intricate method. This method is used for making strategic business decisions. These decisons can often be classified in 3 categories, that is systematic, quantitative, and visual. Decision analysis involves components of psychology, management practices, economics, and several instruments.
1. Identify the decision alternatives (different strategies available to the decision-maker).
2. Identify the states of nature (future chance outcomes that may occur; these are not under the decision-maker's control).
3. Determine the payoff (consequence) for each combination of decision alternatives and possible products.
4. Assess the probabilities of the states of nature.
5. Develop a decision tree.
6. Taught that selecting decision criteria is crucial for choosing the best decision alternative.
7. Apply the requirements and make your decision.
Decision modelling is a structured process used in organisations to predict the outcome of specific scenarios. This outcome offers commendable and precious insights for the business owners to make decisions for the betterment of the firm.
When all inputs to a model, apart from decision variables, are fixed, then that model is called a deterministic model.
Exhibiting random behaviour is the prominent character trait of inputs in this model. Instead of making a single "best guess" for each random information, we incorporate uncertainty into these models via probability distributions.
That, 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.
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