BA Module 4 Exam

Linear Programming

A general-purpose modeling methodology is applied to multiconstrained, multivariable problems when an optimal solution is sought. It is ideal for complex and large-scale problems where limited resources are being allocating advertising budgets to differing media, allocating human and technology resources to product production, and optimizing blends of mixing ingredients to minimize costs of food products.

Nonlinear Optimization

A large class of methodologies and algorithms is used to analyze and solve for optimal or near-optimal solutions when the behavior of the data is nonlinear. Examples include solving for optimized allocations of human, tech., and systems whose data appears to form a cost or profit function that is quadratic, cubic or nonlinear in some way.

Case Studies

A learning aid provides practical experience by offering real or hypothetical case studies of real-world applications of BA. For example, case studies can simulate the issues and challenges in an actual problem setting. This kind of simulation; can prep decision makers to anticipate and prepare for what has been predicted to occur by the predicted analytics step in the BA process. For example, a case study discussion on how to cope with organization growth might provide a useful decision-making environment for a firm whose analytics have predicted growth in the near future.

Decision Analysis

A set of methodologies, models, or principles is used to analyze and guide decision-making when mulitple choices fae the decision maker in differing decision environments (i.e uncertainty, risk, and certainty) Examples include selecting one from a set of computer systems, trucks, or site locations for a service facility.

Others Methodologies

Areas of operations research, decision sciences, and management science combine the applicaiton of mathematics, engineering, and computer science to offer a braod listing of prescriptive methodologies. These other methodologies include network modeling, project scheduling, dynamic programming, queing models, decision support systems, heuristics, artificial intelligence, expert systems, Markov processes, decision tree analysis, game theory, goal programming, nonlinear programming, and data envelopment analysis. There are virtually no application limitations of the collection of these methodologies

Simulation

Can be used in PA in situations where parameters are probabilistic, nonlinear or just too complex to use with other optimization models that require deterministic or linear behavior. For example, a bank might want to simulate the transactions they currently use to process a loan application to determine if changes in the process might reduce time and improve performance. The simulation model might be used to test alternative process scenarios.

Integer Programming

Same as LP, but it permits decision variables to be integer values. Examples include allocating stocks to portfolios, allocating personnel to jobs, and allocating types of crops to farm lands