Fire department facilities in Belgium are in desperate need of renovation due to relentless budget cuts during the last decade. But time has changed and the current location is troubled by city center traffic jams or narrow streets. In other words, their current location is not ideal. The approach for our project was to find the best possible locations for new facilities, based on historical interventions and live traffic information for fire trucks. The goal was not to give just one answer, but to provide instead SAS^® Visual Analytics dashboards that allowed policy makers to play with some of the deciding factors and view the impact of cutting off or adding facilities on the estimated intervention time. For each option the optimal locations are given. SAS^® software offers many great products but the true power of SAS as a data mining tool is in using the full SAS suite. SAS^® Data Integration Studio was used for its parallel processing capabilities in extracting and parsing the traveling time via an online API to MapQuest. SAS/OR^® was used for the optimization of the model using a Lagrangian multiplier. SAS^® Enterprise Guide^® was used to combine and integrate the data preparation. SAS Visual Analytics was used to visualize the results and to serve as an interface to execute the model via a stored process.

Electrolux is one of the largest appliance manufacturers in the world. Electrolux North America sells more than 2,000 products to end consumers through 9,000 business customers. To grow and increase profitability under challenging market conditions, Electrolux partnered with SAS^® to implement an integrated platform for SAS^® for Demand-Driven Planning and Optimization and improve service levels to its customers. The process uses historical order data to create a statistical monthly forecast. The Electrolux team then reviews the statistical forecast in SAS^® Collaborative Planning Workbench, where they can add value based on their business insights and promotional information. This improved monthly forecast is broken down to the weekly level where it flows into SAS^® Inventory Optimization Workbench. SAS Inventory Optimization Workbench then computes weekly inventory targets to satisfy the forecasted demand at the desired service level. This presentation also covers how Electrolux implemented this project. Prior to the commencement of the project, Electrolux and the SAS team jointly worked to quantify the value of the project and set the right expectations with the executive team. A detailed timeline with regular updates helped provide visibility to all stake holders. Finally, a clear change management strategy was also developed to define the roles and responsibilities after the implementation of SAS for Demand-Driven Planning and Optimization.

Optimization models require continuous constraints to converge. However, some real-life problems are better described by models that incorporate discontinuous constraints. A common type of such discontinuous constraints becomes apparent when a regulation-mandated diversification requirement is implemented in an investment portfolio model. Generally stated, the requirement postulates that the aggregate of investments with individual weights exceeding certain threshold in the portfolio should not exceed some predefined total within the portfolio. This format of the diversification requirement can be defined by the rules of any specific portfolio construction methodology and is commonly imposed by the regulators. The paper discusses the impact of this type of discontinuous portfolio diversification constraint on the portfolio optimization model solution process, and develops a convergent approach. The latter includes a sequence of definite series of convergent non-linear optimization problems and is presented in the framework of the OPTMODEL procedure modeling environment. The approach discussed has been used in constructing investable equity indexes.

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In many discrete-event simulation projects, the chief goal is to investigate the performance of a system. You can use output data to better understand the operation of the real or planned system and to conduct various what-if analyses. But you can also use simulation for validation--specifically, to validate a solution found by an optimization model. In optimization modeling, you almost always need to make some simplifying assumptions about the details of the system you are modeling. These assumptions are especially important when the system includes random variation--for example, in the arrivals of individuals, their distinguishing characteristics, or the time needed to complete certain tasks. A common approach holds each random element at some nominal value (such as the mean of its observed values) and proceeds with the optimization. You can do better. In order to test an optimization model and its underlying assumptions, you can build a simulation model of the system that uses the optimal solution as an input and simulates the system's detailed behavior. The simulation model helps determine how well the optimal solution holds up when randomness and perhaps other logical complexities (which the optimization model might have ignored, summarized, or modeled only approximately) are accounted for. Simulation might confirm the optimization results or highlight areas of concern in the optimization model. This paper describes cases in which you can use simulation and optimization together in this manner and discusses the positive implications of this complementary analytic approach. For the reader, prior experience with optimization and simulation is helpful but not required.

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Because optimization models often do not capture some important real-world complications, a collection of optimal or near-optimal solutions can be useful for decision makers. 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. These features include the network solver, the constraint programming solver (which can produce multiple solutions), and the COFOR statement (which allows parallel execution of independent solver calls).

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