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Analytics Magazine

Simulation Software Survery: Simulated worlds

November/December 2015

Driven by questions, fueled by thought and realized by simulation

James J. SwainBy James J. Swain

Our world and that of our clients is a complex place and fraught with uncertainty. The rewards for innovation and improved efficiency are immense if they can be realized, but there is risk as well: Can the innovations be implemented amid the connections that tie our system to our partners and customers? To gain knowledge, we turn to analysis and to experimentation. Yet how can experiments be run when the world will not stand still to be questioned?

For more than a half century, simulation has provided a major and constantly evolving tool to stand in proxy to the world, to provide a test bed for ideas and a basis for both experimentation and understanding. During that time, modeling has gone from an exotic tool that was limited to a few research centers to widespread, almost ubiquitous use. Simulation-based gaming has become a major presence in entertainment, and the size of that market has propelled graphic displays and the hardware that makes it routinely available. It is surely no longer necessary to explain what simulation is or convince clients to accept its results; they (or their children) are already familiar with the world or deeply immersed within it.

Computer-based simulation is an immense field that spans models that explore the collisions of galaxies, the flow of turbulence over a turbine blade, or examines the interactions among subatomic particles. Our focus in this survey is restricted to the realm of discrete-event simulation models that are particularly suited to the operation of parts, patients, vehicles and such in widely divergent fields of manufacturing, healthcare and other services settings, logistics, transportation or military operations to name a few areas of application.

This 10th biennial simulation survey provides a snapshot of a vital and robust area of analysis software. The vendors document both the growth and sophistication of their tools. Of perhaps greater interest is to examine the case studies and the white papers that many provide, demonstrating how the tools were successfully applied and the benefits that were obtained from the effort.

Airport Simulation
Airport example: Simulation provides a tool to stand in proxy to the real world.

Why is Simulation So Successful?

Simulation has grown as a tool because it provides us with a cost-effective and immensely powerful tool for exploration that is limited only by imagination. It is a tool that is only possible because of the computer, and its continued growth is made possible by maturation in diverse fields including modeling, analysis, computing capacity and speed, and programming tools.

Beside the growth in simulation tools there has been a steady growth in the body of knowledge about simulation and its application. Data is more accessible and more readily incorporated into models, thus increasing the level of detail that can be represented. And, of course, simulation tools incorporate the lessons of the experience in the field. Meanwhile, enabling technologies, such as random number generation, have improved over the decades. The state of the art in random number generation provides virtually unlimited, high-quality random numbers.

Another reason that simulation is so successful is that experimentation with the real world is rarely feasible, and there are economic and practical limitations to range and amount of experimentation that is possible. The statistician George E. P. Box once observed that “all models are wrong, but some are useful,” and this applies to simulation models as it did with statistical approximations. Simulation has been successful because it is possible to build simulation models that are sufficiently valid for an analysis to be useful.

Advantages of Simulation

One of the primary advantages of simulation is that it is a constructive tool. Simulation models attempt to reproduce the states and trajectories of the actual system, using transformations, rules or procedures that the real system would use. This has many advantages. First, it means that model building is based upon a description of the process that is already available. Moreover, many modeling languages have evolved constructs such as servers or workstations that fit many situations, or have evolved specialized instances applicable to particular fields such as manufacturing or healthcare, with both logic and animation built in.

Constructive models are certainly easier to understand and to explain. While a queueing formula may provide a prediction of the mean waiting time in a queue, its derivation is opaque to most clients and its application is further limited. Of course, the simulation model provides an estimate, but in many cases increases in computing power and speed reduce the delay in obtaining accurate estimates, and the price is generally well worth the gain in understandability and range of application.

In statistical modeling, a mechanistic model is one that arises from the theory of the process, often as a solution to a set of differential equations, as is the case in chemical models. Box observed that mechanistic models had several advantages over empirical regression models. Such models, he noted, contributed to our knowledge of the phenomenon, provided a better basis for extrapolation, and tended to require fewer parameters (“parsimony”) while providing a better estimate of the output. Simulation models, by their emulation of the process being simulated, seem to be a type of mechanistic model. This is critical if simulation models are going to be used as the basis of experimentation or optimization where a degree of extrapolation is necessary.

Of course, a critical advantage of constructive models is that they generate the states of the system as they evolve, driving animation and allowing a detailed analysis, including detailed statistical observations about the system. The former provides a powerful starting point for both validation and user credence. Providing a visually accurate portrayal of the system in question gives increased confidence in any analysis derived from the model. It can also be used to give snapshots about specific examples of typical or worst case behavior that can be examined in detail.

Animation can also be useful as a tool to facilitate communication and understanding among team members, who often come from different disciplines and backgrounds.

Many of the tools in this survey provide libraries of images that allow the realistic portrayal of equipment, parts or people to make the visualization accurate. Most now provide 3-D animation, which can be examined from different viewpoints and at various levels of detail. Here simulation benefits from the great strides made in the gaming industry for representation and perhaps the growth of graphics processors on home computers for gamers.

Improvements in programming have allowed some models to incorporate schedules, to link to scheduling algorithms or to run historical inputs (data-driven simulation). With some it is possible to make decisions based upon the state of the system, as the real system might do. For automation systems, some provide emulation of the control systems that are to be used to validate their operation in the field.

Simulation, Analytics & Optimization

It has long been appreciated that the constructive approach allows a more detailed analysis of the system statistics. Whereas the queueing formula may be limited to a prediction of the mean, in the simulation model it is possible to observe the variability of the response, not to mention relations among different statistics. This is just the beginning of the possibilities. Increasingly it has been noted that analytics and simulation might be good partners. Simulation can provide detailed data that can now be stored and accessed for insights using the tools of analytics, both within simulation replications and between different scenarios or cases. The 2015 Winter Simulation Conference has this connection as its theme: “Exploring Big Data through Simulation.”

Once a valid and credible model has been built, it can be used for experimentation and optimization. In the former case, many of the simulation tools have the capability of generating experimental results automatically. They are sometimes called scenario generators or experiments. That is, the user can specify the parameters values to be run and the complete set of replications can be performed automatically, with the results available as a group. The results can then be compared graphically and statistically. In other cases, the results can be exported for a more detailed analysis using statistical software.

Optimization is an important use of simulation. Optimizers such as OptQuest usually use heuristic approaches (e.g., Tabu search) that seem to work well where assumptions about the simulation responses are more limited. Optimization is used when the possible scenarios are too numerous to compare directly, so an algorithm is used to search among the parameter values to obtain improved responses.

Several products have the ability to perform sensitivity analysis on a model scenario given uncertainty in the model parameters. This can be used to determine the parameters that the responses are most sensitive to.

A final reason for simulation success is that the number of people trained in simulation has steadily increased. The Rockwell Software (Arena) website notes that 25,000 students take courses in the Arena software annually. Many of the simulation software in the survey provide low-cost student versions of their software and have textbooks that can be used for an introduction to their software. All industrial engineering programs include simulation in their curriculum, and simulation is also offered in many business programs as well.

The Survey

This survey is the 10th biennial survey of simulation software for discrete-event systems simulation and related products (Swain, 2013 [1]). All product information has been provided by the vendors. Products that run on personal computers to perform discrete-event simulation have been emphasized, since these are the most suitable for usage in management science and operations research. Simulation products whose primary capability is continuous simulation (systems of differential equations observed in physical systems) or training (e.g., aircraft simulators) are omitted here.

There are 55 products listed in the survey, taken from 31 vendors who submitted for the survey, once again surpassing the last survey. The range and variety of these products continues to grow, reflecting the robustness of the products and the increasing sophistication of the users. The information elicited in the survey from the vendors is intended to provide a general gauge of the product’s capability, special features and usage. This survey includes information about experimental run control (e.g., experimental design and automated scenario run capabilities) and special viewing features, including the ability to produce animations or demonstrations that can run independent of the simulation software itself. A separate listing gives contact information for all of the vendors whose products are in the survey. This survey is available online (http://www.orms-today.org/surveys/Simulation/Simulation.html) and will include vendors who missed the publishing deadline. Of course, most of the vendors provide their own websites with further details about their products. Many of the vendors also have active users groups that share experience in the specialized use of the software and are provided with special access to training and program updates.

There are a number of technical and professional organizations and conferences devoted to the application and methodology of simulation. The INFORMS publications Management Science, Operations Research and Interfaces publish articles on simulation. The INFORMS Simulation Society sponsors simulation sessions at the national INFORMS meeting and makes awards for both the best simulation publication and recognition of service in the area, including the Lifetime Achievement Award for service to the area of simulation. For further information about the Simulation Society, visit: https://www.informs.org/Community/Simulation-Society/. This site also contains links to many vendors of simulation products and sources of information about simulation, simulation education and references about simulation.

The Society for Modeling and Simulation International (www.scs.org), also devoted to all aspects of simulation, holds conferences in the spring, summer and fall that cover all aspects of simulation practice and theory. The AlaSim International Conference is a relatively new conference hosted in Huntsville, Ala., with a focus on DoD and other government applications of simulation. The next AlaSim conference is scheduled for May 2016.

The INFORMS Simulation Society and the Society for Modeling and Simulation are both sponsors of the annual Winter Simulation Conference. This year’s conference will be held Dec. 6-9 in Huntington Beach, Calif. As in past years the conference will be held together with the Modeling and Analysis of Semiconductor Manufacturing (MASM) conference. Further information and registration information is available from the site www.wintersim.org. This site also links to the complete contents of the Proceedings of the Winter Simulation Conference from 1968 to 2012 for ready access to research and applications of simulation.


James J. Swain (swainjj@uah.edu) is a professor in the ISEEM department at the University of Alabama in Huntsville. He is a member of INFORMS.

 

REFERENCES

  1. Swain, J. J., “Discrete Event Simulation Software Tools: A better reality,” OR/MS Today, Vol. 40, No. 5, October 2013, pp. 48–59.

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