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Modeling Technologies: AI, Mathematical Optimization, and Simulation

, | July 5, 2024 | By

Ai and Maths models, SimWell

Modeling technology can be complex, yet fascinating—especially when different methods solve customer problems. But what are your options? Today, let’s demystify three critical technologies: artificial intelligence (AI), mathematical optimization, and simulation—including discrete event simulation (DES) and agent-based modeling (ABM).

AI takes precedence for the public these days, convincing newcomers that it’s superior for modeling problems. But in reality, AI, optimization, and simulation shine for different types of challenges. Let’s explore the benefits and use cases for these common modeling and simulation tools and how you can choose based on your needs.

Artificial Intelligence (AI)

AI involves creating algorithms and computational systems to perform tasks that typically require human intelligence. These tasks include learning, reasoning, and self-correction 

But in practical terms, AI helps interpret complex data, predict trends, and automate decision-making.

AI Technology Applications

AI technologies vary in complexity. Machine learning models can make predictions based on historical data, while neural networks are capable of image recognition, natural language processing, and more. These technologies excel in environments where large amounts of data are available, patterns need to be identified, or decisions need to be made quickly.

Example: An organization that manually inputs thousands of invoices in an ERP every year can train an AI algorithm to recognize invoice information to automatically input into the system.

But in operations and logistics for manufacturing, warehousing, supply chain networks, or mines, AI is restricted because of the limited data those systems generate. The world is constantly changing, so data from last month may already be outdated. Plus, you must also think about the future state—increased demand, added automation, and new products—and no past data exists for this.

Mathematical Optimization

Mathematical optimization is a branch of applied mathematics used to find the best solution from a set of available alternatives under a given set of constraints. This is crucial in logistics and operations where resources are limited and need to be allocated efficiently.

Optimization models need clear definitions for three key elements:

  • An objective for what to maximize or minimize, such as profit, cost, time, or distance
  • Constraints that must be adhered to, like budget limits or resource capacities
  • Decision variables that include all choices you can control

Mathematical Optimization Applications

This method is extremely powerful for planning and scheduling, helping businesses make strategic decisions aligned with their goals. SimWell’s MohOpt Route Optimization Engine is a prime example, alongside others that include production scheduling, HR scheduling, and train scheduling.

Mathematical optimization is ideal for short time horizons, like optimizing today’s schedule. The method considers a deterministic world without looking at variability. But as Mike Tyson observed, “Everyone has a plan until they get punched in the face.” The world is not deterministic—process times vary, equipment breaks down, and traffic slows travel. You must be agile and ready to reoptimize on the fly to take full advantage of mathematical optimization.


Simulation creates virtual representations of real-world processes or systems. It enables organizations to analyze the impact of different strategies in a risk-free environment. 

Simulation Applications

Modeling and simulation are ideal for a range of use cases, such as CAPEX investments or system debottlenecking. If you need to visualize complex systems with variability, what-if scenarios, or longer time horizons, simulation can support you. Two primary tactics are used:

Discrete Event Simulation (DES) models systems where the operation occurs as a sequence of discrete events in time. Each event marks a change in the system state.

Example: In a manufacturing plant, events could include material arrival, machine cycle completion, or product departure.

Agent-Based Modeling (ABM) models agent dynamics—autonomous entities such as vehicles, customers, or employees—and their interactions within a defined system. It’s useful in logistics for studying complex decision-making processes and operational interactions.

Example: You can model forklifts in a warehouse with both DES and ABM. If the forklifts operate with simple FIFO rules, you can opt for DES. However, if they’re intelligent agents making complex decisions, ABM is ideal.

Two supply chain professionals reviewing a simulation model

Choose the Right Tool for Your Modeling and Simulation Needs

Modeling and simulation technologies do not compete with one another but rather are suited to different problems. AI works for predicting patterns from large datasets or automating decision-making, while mathematical optimization helps allocate resources with constraints and objectives in mind, and simulation helps to analyze system behavior risk-free.

But what’s really beneficial is that you can combine modeling and simulation techniques to solve complex problems. Using a holistic approach allows decision-makers to visualize the impacts of their choices in a simulated environment before implementation, reducing risks in real-world execution. By leveraging the strengths of each technology, logistics and operations can achieve precision and adaptability to become more responsive.

SimWell provides tailored solutions using these technologies, helping clients improve operational efficiency, reduce costs, or enhance decision-making—and gain a competitive edge. The secret is to understand what aligns with your business challenges. Check out an example of how AI and simulation can work together to solve advanced problems.

Not sure where to start? We'll help guide you through the noise. Get in touch to explore how modeling and simulation technologies can help your business.


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