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Villa Maria College, Buffalo ​hello I'm ed Hughes the product manager for SAS award I'll be talking about modeling the operations of complex systems with SAS simulation studio a key component of SAS Oh are that provides a graphical environment for discrete event simulation modeling and analysis in this first session will cover a discrete event simulation and begin to get to know SAS simulation studio in the second session we'll explore SAS simulation studio more extensively through a sample project first let's see what the screen event simulation is and what it offers discrete event simulation is used for systems in which the state of the system changes not continuously but only when discrete separable events occur examples of events would be an arrival a departure starting or ending a task a person starting or ending a lunch break a piece of equipment going offline and so on fortunately this qualification doesn't do much to diminish the applicability of discrete event simulation to real-world systems since many systems have this quality and some examples include supply chains with goods moving around from supplier to factory to distribution center to retail sales call centers where callers move through a series of contacts to relay their questions or concerns emergency rooms in which patients arrive and interact with clerical staff medical personnel and medical facilities and retail stores in which customers interact with sales staff and cashiers now all of these instances involve individuals or objects moving through a workflow pattern in a system however the presence of one individual in a system can affect others there's interaction busy phone lines at a call center a crowded waiting room an emergency room and long lines a checkout in a retail store are examples of this interaction interaction complicates the process of using analytic methods like queueing theory for example to compute the performance of the system another complication is the presence of multiple sources of random variation like times between arrivals times needed for service and other examples if you want to try to determine how a system will behave under different scenarios different operating conditions different configurations it's really not possible to create a functional relationship between these inputs and the behavior of the system captured by key performance indicators instead we use simulation we play out and track the behavior of the system for varying scenarios sometimes the factors defining the scenarios of things we can control like how many cashiers to have on duty on a Thursday afternoon and sometimes there conditions out of our control like how many patients come to an emergency room on a Friday evening discrete event simulation enables you to build a model of the system that supports creating all these scenarios and playing what-if with them studying how the system responds now as we saw briefly earlier discrete event simulation has many different applications to real-world systems here's a more extensive but not exhaustive list in manufacturing there are several possibilities experimenting with different methods of scheduling production different inventory policies and various staffing plans service operations make up a large part of simulation application since in these settings customers arrive and are served which is a classic paradigm for simulation modeling healthcare is in a very real sense and extension of service operations but it's one where the stakes are much higher for all concerned all the more reason to use simulation modeling to help ensure that things run smoothly in computer and telecommunications networks information and data places loads on systems and it's a good idea to study how systems react to these loads before they're experienced in the real world pharmaceutical applications are of great interest currently the individuals moving through the system here are new compounds or drugs that need to go through development or testing stages and each stage involves personnel facilities or other resources military applications are numerous and can involve the movement of equipment and personnel in many ways they're similar to supply chain models and in insurance and public policy you're dealing with sequential processing of claims applications and cases and critical often quite limited resources are tied up accordingly and it makes sense to plan ahead and explore how the system load will evolve and can be managed now let's talk about some more reasons why discrete event simulation is a good approach beyond letting you play what-if with complex systems performance under different circumstances the alternative to discrete event simulation is observing the real world in real time and possibly needing to build and implement it first now the most obvious advantage for discrete event simulation is your ability to try out more scenarios configurations and conditions you don't have to build it and you don't have to wait for the right set of conditions to arise before you start gathering data now clearly discrete event simulation is the lowercost option there's an even greater consideration here though and that's risk any system has an obligation either formal or informal to function effectively there's a need for the real world system to work well and that means that whatever changes you make to it cannot impair its functioning by very much and that rules out trying out extreme scenarios under direct observation but it does nothing to limit what you do with simulation since you're not working in the real


world there look at a particular sensitive case medical care in some scenarios patient care and patient outcomes might be severely compromised and you never want these outcomes to happen you'd want to simulate them first and then take steps to ensure they never really occur now beyond limiting cost and risk discrete event simulation saves time and too many time savings equate to cost savings no matter what you do it takes 60 minutes to gather one hours worth of data by direct observation of the system that doesn't even account for the time waste and in transitioning from one configuration to another or time spent waiting for the right set of operating conditions to occur so you can gather relevant data in contrast discrete event simulation enables you to create operations conditions and configurations instantly as needed and enables you to gather hours days weeks or months of data sometimes in only seconds of real time part of the reason goes back to the fact that the systems being studied only changed state when discrete events occur that means it only makes sense to observe the system when those events are occurring in between nothing changes and so that means the computer simulation can jump from event to event to event and not linger over all that time that occurs between events when nothing really of interest is happening and the second part is even simpler computer simulation model can run much more quickly than at the real time rate and finally the screen event simulation lets you look at system performance in more detail than real time observation can possibly permit you can know everything that's happening and the entire detailed state of the system at any chosen time you can play back a time period and add more tracking you can slow down time by controlling animation speed you can focus on the smallest minut details and none of this is practical under real-world observation with the screen event simulation you can simply gather more data better data more quickly more easily and with less cost and risk now here's a quick screenshot of SAS simulation Studios graphical interface at the upper left is the project Explorer listing all the open projects and their contents the active project corresponds to the large window in the interface which is the project window it shows the active project is called call center a the simulation model is shown in the model window here inside the project window is also an experiment window here at the lower left of the project window each project one created opens a project window containing one model window and one experiment window you can add more of each if you like a notice that the simulation model and the model window is made up of a number of connected blocks you build a model by dragging and dropping blocks from the block templates on the left side of the interface there are five templates in all and currently and by default this standard template is shown this contains the elementary blocks that you need to build just about any simulation model the other templates focus on other areas such as data and display capabilities and more advanced capabilities of says simulation studio the experiment window helps configure the simulation model and controls how it's run at a minimum controlling the start time the end time and the number of replications for the simulation runs you can also define factors that supply key parameters to the model and responses that track elements of model performance which equates to system performance for this call center model we've defined factors for phone lines and operators along with various responses tracking hang-ups completed calls and busy signals in the top row the table factors are highlighted in yellow and the responses are highlighted in pink I notice there are several rows in the experiment when corresponding to several different combinations of factor values each one of these is called a design point as each design point is run the values generated for responses are displayed in that design points row in this case each design point has run five times indicated by the value in the replicates column that's the column between the the yellow and the pink columns so each row summarizes the responses from five replicates and you can expand the row to see the detailed responses by replicate and all that individual data is stored as well for further analysis at the upper left corner of the interface are the menus and the toolbar the menus are fairly self-explanatory file template run analyze tools and help the ones you'll use most often or file and run and maybe even just file the run menu is probably the most used and much of its functionality is also present in the buttons of the toolbar so I typically just use the toolbar rather than using the run menu because the the functionality is more immediately available left to right the buttons listed there are run augment pause stop and animate most of those functions are clear from the names animate simply turns animation on and off everyone knows what run pauses meen augment is worth a little bit of attention it's used to run parts of an experiment selectively if after you make an initial run you've gone back and added design points or added more replications to some existing design points augment runs just those additions but it does so in a way that the results are identical to running the entire experiment over again either leaves things make sure that the results are completely consistent as we've seen briefly and we'll explore more extensively in session to SAS simulation studio provides a graphical interface for building running and analyzing discrete-event simulation models it's straightforward you drag and drop blocks from templates to a model window configure them and then connect them to create a model of the workflow in a system SAS simulation studio includes the ability to represent resources both as static model elements as blocks that are visited and also as mobile objects that move through the model and we'll see an example of this in session two it's a very powerful feature SAS


simulation studio is a part of Sasso R and as such it can integrate with SAS for source data and analysis of results it integrates with jump for the same purposes and can also integrate with jump for automated experimental design creating those design points we saw in the interface and input analysis which is fitting distributions to historical data that you have on file so that the variation in that data can be represented in a simulation model thus a simulation studio is a java application and it's supported on Windows it's a client application which which means that it's intended to run on desktop PCs it's included with SAS awara on Windows and it's also available as SAS simulation studio for jump and add-on to jump SAS simulation studio 12.1 the current release works with jump 10 now in a simulation model of a systems workflow entity is the term for objects that are used to represent the things or individuals which could be insurance claims customers patients that flow through the system now as we blocks that are linked to each other by links constitute much of a model each block has ports read ports to permit the flow of entities between blocks those are typically on the sides of the blocks and blue ports on the top and bottom to allow the flow of data into and out of the blocks an external data SAS data sets or jump tables can be used as SAS simulation studio models either sampled directly or simulated with probability distributions that are determined by input analysis using jump resources are often central to simulation models and often the resource is the main reason why the entities are moving through the model while the entities are in the system in the first place they're seeking some form of assistance service or aid from the resources treatment from a medical professional processing of a claim completion of a sale or in some other form if the resources are limited they're going to act as controls on the flow of entities massage simulation studio includes the ability to model resources as a special class of entities that can flow through the model just like regular entities instead of visiting a static resource block entities can cease resource entities when they need them carry them through the model and then release them when they're no longer needed and in many cases this method can produce a more realistic model more realistic depiction of the role of resources in a system and additionally resource entities can have their availability scheduled which enables you to model things like periodic breaks planned maintenance and random failures of key resources in a system finally as covered earlier an experiment represented in an experiment window controls how a simulation model is run how the model is initialized and how the multiple runs of each version of the model are conducted this concludes session one in session two we'll pick up with an elementary example and a more advanced example to dig more deeply into house size simulation studio operates thank you United States Merchant Marine Academy, Kings Point.

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