In this module, you will be introduced to network models, a foundational class of optimization models used extensively in business analytics, operations, and decision science. Many real-world business problems, such as supply chain design, transportation planning, project scheduling, and information flow, can be naturally represented as networks consisting of nodes and arcs. Understanding how to model and analyze these structures is a critical skill for any analytics professional. You will learn how to formulate and solve common network problems using Excel and Solver, with a focus on translating business contexts into clear, structured models. By the end of this module, you will be able to recognize when a business problem can be framed as a network model, build the corresponding Excel model efficiently, and use Solver to generate and interpret optimal solutions. These skills will prepare you for more advanced optimization techniques later in the course and provide immediately applicable tools for real-world analytics tasks.

Many real-world business decisions involve choices that are fundamentally discrete: whether to open a facility, produce a product, or assign a resource. In these settings, traditional linear programming models are often insufficient because decision variables must take on whole-number or yes–no values. Integer Programming (IP) provides the analytical framework needed to model and solve these types of decisions rigorously. In this module, you will learn how to formulate and solve integer programming models using Microsoft Excel and Solver. Building on your prior experience with linear optimization, you will see how integer and binary decision variables allow you to capture operational realities such as indivisible production quantities, fixed setup costs, and coverage requirements. Through practical, business-focused examples, the module focuses on three core applications. You will begin with production planning models that incorporate integer decisions to ensure feasible and implementable production schedules. You will then study fixed cost manufacturing problems, where binary variables are used to model setup decisions and economies of scale. Finally, you will explore set covering models, a powerful class of integer programs used to determine the minimum-cost selection of options needed to meet coverage requirements, such as facility placement or service availability. By the end of this module, you will be able to translate complex business decisions into integer programming formulations, implement them in Excel, and interpret Solver output to support data-driven managerial decisions.

This module introduces nonlinear programming as a modeling framework for solving optimization problems in which the objective function and/or constraints are nonlinear. Students will explore how nonlinear relationships arise in business applications such as pricing, revenue management, portfolio allocation, and resource utilization, and how these relationships influence both solution methods and managerial insight. Particular attention is given to issues of local versus global optima and the implications these have for decision-making. The module emphasizes practical implementation using Excel Solver, with a focus on the GRG Nonlinear algorithm. Students will learn how to formulate nonlinear models in Excel, configure Solver appropriately, interpret Solver output, and diagnose common modeling and convergence issues. Through applied examples and exercises, students will analyze sensitivity to key assumptions and assess the robustness and limitations of solutions obtained via GRG Nonlinear, preparing them to apply nonlinear optimization effectively in real-world business settings.

This module extends and deepens the concepts and techniques introduced in Nonlinear Programming I, moving from single-objective nonlinear optimization models to richer, more realistic decision-making frameworks. Building on your understanding of nonlinear objective functions, constraints, and the use of Excel Solver, this module emphasizes applications where multiple objectives, risk–return trade-offs, and structured data relationships play a central role. A primary focus of the module is portfolio optimization, where nonlinear programming is used to minimize portfolio variance subject to return and allocation constraints. You will implement these models in Excel Solver, making use of matrix functions to compute variances. This reinforces both the mathematical structure of quadratic optimization problems and their practical implementation in a widely used analytics tool. The module then broadens the scope of nonlinear optimization to include goal programming and multi-objective decision making. You will examine situations in which competing objectives cannot be optimized simultaneously, introducing the concepts of Pareto optimality, trade-off curves, and efficient frontiers. You will explore how changes in priorities and constraints affect optimal solutions, providing insight into managerial and financial decision contexts where compromise and balance are essential.

This final assessment serves as a capstone for Business Analytics II, bringing together the core analytical tools and decision-making frameworks developed throughout the course. Students will analyze a realistic business case with competing objectives, requiring them to formulate, solve, and interpret optimization models using Excel Solver, including nonlinear and multi-goal programming approaches. The assessment emphasizes the complete analytics workflow: translating a business problem into a quantitative model, evaluating trade-offs and uncertainty through sensitivity or scenario analysis, and interpreting results in managerial terms. Students must justify assumptions, explain model limitations, and recommend a defensible course of action aligned with organizational priorities. Overall, the final assessment evaluates both technical proficiency and the ability to communicate analytic insights clearly, reflecting how advanced business analytics is applied in real-world decision-making contexts.