As an example, consider a problem that requires you to optimize a rail or truck fleet. Factors such as travel schedules, loading and unloading times, delivery time restrictions, and terminal point capacities make it difficult to approach with a spreadsheet. A vehicle’s availability at a particular location on a particular date at a particular time depends on a sequence of preceding events ‐ and answering the question of where to send the vehicle when it is idle requires us to analyze future event sequences.ġ0 AnyLogic 6 in Three Days Formulas that are good for expressing static dependencies between variables typically don't do well in describing systems with dynamic behavior. It’s why we use another modeling technology ‐ simulation modeling ‐ to analyze dynamic systems. In most cases, it’s impossible to obtain the right formulas, much less put together a mental model of such a system. Non‐intuitive influences between variablesĪll above combined with uncertainty and large number of parameters However, there's also a large class of problems where the analytic (formula‐ based) solution doesn’t exist or it is very hard to find. This class includes dynamic systems that feature: The input and output values linked by chains of formulas and – in more complex models – scripts. Various add‐ons allow you to perform parameter variation, Monte Carlo, or optimization experiments. Computers are extensively used for modeling, and they provide us with a flexible virtual world where we can easily create anything imaginable. Of course, there are many different types of computer models, from spreadsheets that allow anyone to model expenses to simulation modeling tools that help users explore dynamic systems such as consumer markets and battlefields.Īnalytical vs. simulation modeling If you could ask a major organization’s strategic planning, sales forecasting, logistics, marketing, or project management teams about their preferred modeling tools and technologies, you'd quickly find Microsoft Excel is the most popular modeling software. Excel has obvious advantages: you can find it on any office computer and it is very easy to use. It’s also extensible: you can add scripts to your formulas as the spreadsheet logic becomes more sophisticated.Īnalytical model (Excel spreadsheet) The technology behind spreadsheet‐based modeling is simple: you enter the model inputs in some cells and you view the outputs in others. Decisions such as what to say to your child, what to eat for breakfast, who to vote for, or where to take your girlfriend are all based on mental models. Types of models There are many types of models, including the mental models we each use to understand how things work in the real world: friends, family, colleagues, car drivers, town where you live, things that you buy, economy, sports, politics, or your own body. The whole modeling thing is actually about finding the way from the problem to its solution through a risk‐free world where we are allowed to make mistakes, undo things, go back in time, and start over again. After we have built the model – and sometimes even as we build the model – we start to explore and understand the original system's structure and behavior, test how the system will behave under a variety of conditions, play and compare scenarios, and optimize. After we find our solution, we then can map it to the real world. This is still more an art than a science. The world of models The real worldĨ AnyLogic 6 in Three Days Modeling The model‐building phases ‐ mapping the real world to the world of models, choosing the abstraction level, and choosing the modeling language are less formalized than the process of using models to solve problems. The model is always less complex than the original system. This process assumes abstraction: we omit the details we think are irrelevant and we keep those we think are important. If this is so, we leave the real world and go to the world of models as shown in the figure below. We build a model of a real system: its representation in a modeling language. Modeling is one of the ways to solve real‐world problems. In many cases, we can’t afford to find the right solutions by experimenting with real objects: building, destroying, making changes may be too expensive, dangerous, or just impossible. Andrei Borshchev and partially available on AnyLogic website. Modeling and simulation modeling This chapter is from "The Big Book of Simulation Modeling: Featuring AnyLogic" being currently written by Dr. AnyLogic 6 in Three Days A quick course in simulation modeling
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