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What are the best ways to ensure scalability of your OR simulation models?

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Operations research (OR) is a discipline that applies mathematical and analytical methods to solve complex problems in various domains, such as engineering, management, logistics, and health care. OR simulation models are tools that mimic the behavior of real-world systems and scenarios under different conditions and assumptions. They can help you test hypotheses, evaluate alternatives, optimize performance, and support decision making.

However, OR simulation models can also become very large, complex, and computationally intensive, especially when they involve many variables, parameters, interactions, and uncertainties. How can you ensure that your OR simulation models are scalable, meaning that they can handle increasing amounts of data, complexity, and demand without compromising accuracy, efficiency, and usability? Here are some best practices to follow.

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Ahmed Raecky Baihaqy

Associate Consultant at Altha Consulting

1 Choose the right simulation method

There are different types of OR simulation methods, such as discrete-event simulation, system dynamics, agent-based simulation, and Monte Carlo simulation. Each method has its own advantages and disadvantages, depending on the nature and purpose of your problem. You should choose the simulation method that best suits your problem characteristics, such as the level of detail, the time horizon, the stochasticity, and the feedback loops. For example, discrete-event simulation is good for modeling dynamic and discrete systems with sequential events and queues, while system dynamics is good for modeling continuous and aggregate systems with feedback loops and delays.

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Ahmed Raecky Baihaqy

Associate Consultant at Altha Consulting
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It is highly suggested to identify whether the system we observe should be modelled using discrete or continous apporach. System such as production, inventory, and service maybe more appropriate to model using discrete-event simulation approach under some considerations and circomstances. Usually DES is used to model in a micro to meso scale. However, once the causal loop is involved and we are interested in observing the change in state variable over time, hence exploting system dynamics is a very wise choice. When we are interested in observing a meso to macro system, system dynamics comes very useful.

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2 Simplify and modularize your model

One way to improve the scalability of your OR simulation model is to simplify and modularize it. Simplification means reducing the unnecessary complexity and detail of your model, such as eliminating irrelevant variables, parameters, or processes, or aggregating them into higher-level entities. Modularization means dividing your model into smaller and independent components or submodels, such as functions, classes, or modules, that can be easily reused, modified, or integrated. Simplification and modularization can help you reduce the computational burden, improve the readability and maintainability, and facilitate the verification and validation of your model.

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Ahmed Raecky Baihaqy

Associate Consultant at Altha Consulting
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When building simulation model from the system we are observing, it is highly necessary to determing the scope of our system by setting several limitations and assumptions. However, we need to be careful when determining the limitations and assumptions. When there are a lot of limititations and assumptions we set into the model, it is very concerning since we may over simplify both the model and problem. Hence, the model we build may be very far from the actual system and it's worrying since there is higher chance the model we build is not valid and not able to represent the real system.

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3 Use parallel and distributed computing

Another way to improve the scalability of your OR simulation model is to use parallel and distributed computing. Parallel computing means using multiple processors or cores to execute your model simultaneously, while distributed computing means using multiple machines or nodes to execute your model in a network. Parallel and distributed computing can help you speed up the execution, increase the capacity, and enhance the reliability of your model. However, they also require careful design and coordination of your model, such as partitioning, communication, synchronization, and load balancing. You should also consider the trade-offs between the benefits and the costs of parallel and distributed computing, such as the hardware, software, and network requirements.

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Ahmed Raecky Baihaqy

Associate Consultant at Altha Consulting
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This is actually true. Running simulation model may require more sophisticated processor especially when the model we build is very complex and require a great number of replication to run. More sophisticated processor should have higher performance. Thus, it will most likely be able to run a complex simulation model with relatively short duration.

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4 Incorporate data management and analytics

A third way to improve the scalability of your OR simulation model is to incorporate data management and analytics. Data management means organizing, storing, and accessing the data that your model needs or generates, such as input data, output data, or intermediate data. Data analytics means processing, analyzing, and visualizing the data to extract meaningful insights, such as trends, patterns, or anomalies. Data management and analytics can help you improve the quality, efficiency, and usability of your model. However, they also require appropriate tools and techniques, such as databases, data warehouses, data lakes, or data pipelines, to handle the large and diverse data sets that your model may produce or consume.

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5 Update and refine your model

A final way to improve the scalability of your OR simulation model is to update and refine it. Update means adjusting your model to reflect the changes in the real-world system or scenario, such as new data, parameters, or assumptions. Refine means improving your model to enhance its accuracy, validity, or robustness, such as adding more detail, complexity, or uncertainty. Update and refine can help you keep your model relevant, reliable, and responsive. However, they also require continuous monitoring and evaluation of your model, such as using performance indicators, sensitivity analysis, or scenario analysis, to measure and compare its results and impacts.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best ways to ensure scalability of your OR simulation models?

1

2

3

4

5

6

1 Choose the right simulation method

2 Simplify and modularize your model

3 Use parallel and distributed computing

4 Incorporate data management and analytics

5 Update and refine your model

6 Here’s what else to consider

Operations Research

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What are the best ways to ensure scalability of your OR simulation models?

1

2

3

4

5

6

1 Choose the right simulation method

2 Simplify and modularize your model

3 Use parallel and distributed computing

4 Incorporate data management and analytics

5 Update and refine your model

6 Here’s what else to consider

Operations Research

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