SAS Simulation Studio is a component of SAS/OR software that provides an interactive, graphical environment for building, running, and analyzing discrete-event simulation models. In a broader sense, it is also an integral part of the SAS analytic platform. This paper illustrates how SAS Simulation Studio enables you to tackle discrete-event simulation challenges.
An oil company has a set of wells and a set of well operators. Each well has an established amount of time required for servicing. For a given planning horizon, the company wants to determine which operator should perform service on which wells, on which days, and in which order, with the goal of minimizing service time plus travel time. A frequency constraint for each well restricts the number of days between visits. The solution approach that is presented in this paper uses several features in the OPTMODEL procedure in SAS/OR software. A simple idea and a small change in the code reduced the run time from one hour to one minute.
This paper provides guidelines and best practices in change management that the SAS Advanced Analytics Division uses with customers when it implements prescriptive analytics solutions (provided by SAS/OR software).
This presentation demonstrates OPTMODEL’s power and versatility in building and solving optimization models, noting the significant improvements resulting from recently added features. Emphasizes integration with SAS data, analytic, and reporting capabilities, largely focusing on case studies drawn from our successful work with customers across a broad range of industries.
This presentation demonstrates how you can use the OPTMODEL procedure's power and versatility to build and solve optimization models in a variety of settings. Includes a survey of recent consulting projects, highlighting our transportation optimization work with Boston Public Schools.
Classical and practical examples demonstrate OPTMODEL's power and versatility in building and solving optimization models in a variety of settings. Includes a survey of recent consulting projects, highlighting our transportation optimization work with Boston Public Schools.
This presentation explores practical applications of building and solving mathematical optimization models with PROC OPTMODEL, and also describes how optimization methods are being used to improve predictive machine learning models.
This paper uses various techniques for finding the k best solutions to the linear assignment problem in order to illustrate several features recently added to the OPTMODEL procedure in SAS/OR software.
This paper begins with a look at both optimization modeling and discrete-event simulation modeling, and explores how they can most effectively work together to create additional analytic value. It then considers two examples of a combined optimization and simulation approach and discusses the resulting benefits.
This paper demonstrates how to build business rules and then optimize the rule parameters to maximize the effectiveness of those rules.
This paper looks at some discrete-event simulation modeling needs that arise in specific settings and some that have broader applicability, and it considers the ways in which SAS Simulation Studio modeling can meet those needs.
This paper describes the use of the OPTMODEL and OPTLSO procedures on the SAS High-Performance Analytics infrastructure together with the FCMP procedure to model and solve this highly nonlinear optimization problem.
This paper illustrates PROC OPTMODEL’s power and versatility in building and solving optimization models and describes the significant improvements that result from PROC OPTMODEL’s many new features.
For other SAS/OR technical papers, visit the Statistics and Operations Research Focus Area.