16 courses with the subject OPR, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
OPR 320Linear Models for Decision Making4.0
Applies modeling and mathematical techniques to complex decision problems in business, with a focus on deterministic systems. Covers linear programming, integer programming, goal programming and networks.
Applies modeling and mathematical techniques to complex decision problems, with a focus on nonlinearity and uncertainty in the business environment. Covers nonlinear programming, dynamic programming, queuing theory, Markov Processes, decision analysis and simulation.
This course will cover the basics of analytical modeling, optimization, and simulation as tools for decision-making in the public sector. The students will analyze cases illustrating the powerful impact of using these tools in cities across the country. Of particular focus will be the implementability of these tools and their recommendations in the real-world. Moreover, a city, especially one as big as Philadelphia, is a complex and dynamic environment, so we will investigate how to address some of the resulting challenges in our analyses. Specifically, we will address scenarios involving the improvement of existing operations, optimal resource allocation and distribution, and measuring and improving the quality and efficiency of service delivery.
Quantitative finance can be grouped into the following categories: (1) valuation of financial instruments by the use of risk-neutral probability distributions; (2) financial planning using real-life probability distributions. This course focuses on financial planning. There are two key ideas: the first is to model decision making and planning as a mathematical optimization problem with variables, an objective function, and constraints. The second is to model uncertainty using the tools of probability theory. This is an introductory course: we focus on building models and use standard spreadsheet software to find solutions.
OPR 601Managerial Decision Models and Simulation3.0
Introduces students to the basic modeling tools and techniques for making managerial decisions in a complex and dynamic business environment. Topics include linear, discrete, and nonlinear optimization, multicriteria decision making, decision analysis under uncertainty, and simulation.
Covers theory and applications of linear programming, including the simplex method, sensitivity analysis and duality, formulation and solution of transportation and network optimization problems. Extensions include game theory, quadratic programming, financial optimization, and emerging solution techniques such as interior-point methods.
This course covers modeling and solving optimization problems under uncertainty. Topics will include stochastic processes, queueing systems and dynamic programming.
This course covers algorithms and software development for nonlinear programming, integer programming, and global optimization. Special emphasis is placed on solution methods for constrained and unconstrained nonlinear optimization, a survey of methods for integer linear and nonlinear optimization, and search techniques for global optimization.
This course will cover the basics of analytical modeling, optimization, and simulation as tools for decision-making in the public sector. The students will analyze cases illustrating the powerful impact of using these tools in cities across the country. Of particular focus will be the implementability of these tools and their recommendations in the real-world. Moreover, a city, especially one as big as Philadelphia, is a complex and dynamic environment, so we will investigate how to address some of the resulting challenges in our analyses. Specifically, we will address scenarios involving the improvement of existing operations, optimal resource allocation and distribution, and measuring and improving the quality and efficiency of service delivery.
This course focuses on quantitative methods for financial planning such as optimal investment strategy, currency conversion, portfolio optimization, etc. Topics include fundamental concepts in (quantitative) finance, convexity theory, general theory of linear programming (duality, Farkas’ Theorem on linear inequalities, von Neumann’s Theorem on two-person zero-sum game), basics of probability and stochastic optimization models in finance. Furthermore, some recent advances in the theory of risk measurement, such as VaR (Value-at-Risk), CVaR (Conditional Value-at-Risk), and their multivariate counterpart; MVaR and MCVaR, etc., are also covered.
Covers theory and applications of linear programming, including the simplex method, sensitivity analysis and duality, formulation and solution of transportation, and network optimization problems. Extensions include integer programming, quadratic programming, and emerging solution techniques such as interior-point methods.
This course covers modeling and solving optimization problems under uncertainty. Topics will include stochastic optimization, queueing systems, and dynamic programming.
This course covers algorithms and software development for nonlinear programming, integer programming, and global optimization. Special emphasis is placed on solution methods for constrained and unconstrained nonlinear optimization, a survey of methods for integer linear and nonlinear optimization, and search techniques for global optimization.
