Data is crucial for the mathematical optimization process to work. However, it is about more than just having data; proper data management and the right format can affect how quickly the data is analyzed.
Let’s explore the key aspects of data and how having the proper data management, including ensuring your data is in a “tidy” format (where each variable is a column, each observation is a row, and each value has its own cell), can help you and your optimization team bring solutions to the table faster. This organized and consistent data structure is crucial for efficient analysis and modeling within mathematical optimization.
Core Steps: Define the problem & setting objectives.
Data will help define the scope of the problem, so consider this: What set of data and variables are needed to optimize your operations? For example, schedule optimization of your production line would need variables like machine capacity, labor availability, and raw material stock levels are necessary to provide you with the best possible solution.
Setting an objective is just as crucial as the amount of data you manage. Knowing what direction to go gives you a guide to the optimization process. Some of the objectives to think about are minimizing costs, maximizing profits, and improving efficiencies (such as equipment utilization). In many cases, improving one objective will result in improving another. In general, part of improving profits may be to reduce costs. It is important to align the objectives with the business goals.
Remember that some objectives may be at cross purposes with others. Some objectives may improve your while others can present new challenges in the business (think customer satisfaction vs increase in delivery costs). It is a good strategy to present a set of multiple possibilities so it can be decided which is most important for the business as a whole and to move forward.
Build: Parameters and Model Validation
In some instances, we need a series of parameters that can represent real-world values to get accurate results. Parameters like travel speed and fuel costs are essential parameters for transportation optimization. With realistic parameters, you can build a model that will adapt to your unique problems and optimization needs.
Data is crucial for assessing accuracy and validating the optimization model. If your data file includes historical data or real-world observations, it allows mathematical models to reflect a system with real-world situations being optimized.
Solve: Input data & evaluation.
For mathematical optimization models to give you the best possible results, we use data as input to find the best solution. This type of data can include operational data, historical trends, and forecast data. If you are optimizing inventory levels, you need data on lead times, storage costs, and demand patterns to feed into the algorithm, which will give you a unique solution to your unique problem.
Data is also used to evaluate the solution found by the algorithm. Evaluating the solution to the current problem allows us to measure the potential improvements this solution can bring to the organization and make more informed decisions.
Continuous improvement: Monitoring, adaptation, and beyond.
Once the optimized solution is presented, you will be able to see the valuable feedback and opportunities it presents. These results present a new opportunity to refine the model, adjust parameters, and improve the optimization process as your organization grows. The optimization model is an algorithm that can be refined and improved as quickly as new data and parameters change. It is not a stagnant model, and ensures that the solutions it provides you are relevant to your current situation.
Data changes, and so do your optimization needs. Allow for faster adaptation and improvement in your organization by updating the data into the optimization model.
Things to Consider: Data quality, volume, and complexity.
Your optimization model is as rich and accurate as the quality of data you input into it. Data accuracy and consistency are important for your organization but crucial for your optimization model. Poor data quality often leads to inaccurate models and solutions that can hurt and delay your organization in the optimization process.
Raw data needs to be cleaned, transformed, and prepared before it can be used for optimization models. This is related to data quality and its importance for your optimization models and organization as a whole. By reviewing raw data, you can identify missing values, outliers, and inconsistencies.
Another important factor to consider is the volume and complexity of your data. When you deal large volumes of data and complex data structures, computational challenges may arise. To handle these challenges efficient data management and processing techniques are critical.
Conclusion
Data is the lifeline of mathematical optimization solutions. It provides us with a base to inform us of the problem, the state of your business and guides the algorithm to evaluate your solution. Recognizing the value of data and being proactive about proper data management will get you a head start to the optimization process.
When you have the right systems in place, and data is being managed and updated promptly and correctly, mathematical optimization experts can start working on your model faster, providing you with the best solution in record time.
Are you ready to explore your options with mathematical optimization? Contact us or schedule a free consultation with our expert PhDs to get started.
Modaai has Operations Research PhDs with over 25 years of modeling and tuning experience. We can tackle your toughest problems. Whether you want us to take over your optimization project completely or supplement your team with training, model design, or model tuning, we can support you.