How do you manage linear programming data issues?

1 Data sources and formats

One of the first steps in LP modeling is to identify and collect the relevant data for your problem. Depending on the scope and context of your project, you may need to use different sources and formats of data, such as spreadsheets, databases, text files, or web services. You should always verify the quality and consistency of your data sources, and check for any errors, outliers, or missing values. You should also convert your data into a standard and compatible format that can be easily imported and processed by your LP software.

2 Data preprocessing and transformation

Once you have your data in a suitable format, you may need to perform some preprocessing and transformation operations to prepare it for LP modeling. For example, you may need to normalize, scale, or standardize your data to avoid numerical issues or improve the performance of your LP solver. You may also need to aggregate, filter, or join your data to create the necessary parameters, variables, and constraints for your LP model. You should always document and justify your data preprocessing and transformation steps, and test their impact on your LP results.

3 Data validation and verification

After you have built your LP model, you should always validate and verify your data and model assumptions. Validation means checking whether your data and model accurately represent the real-world situation and objectives that you are trying to optimize. Verification means checking whether your data and model are free of errors, inconsistencies, or logical flaws that could affect your LP solution. You can use various methods and tools to validate and verify your data and model, such as sensitivity analysis, scenario analysis, benchmarking, or debugging.

4 Data analysis and interpretation

Finally, you should always analyze and interpret your LP solution in relation to your data and model. Analysis means examining the optimal values, dual prices, reduced costs, and other information that your LP solver provides. Interpretation means explaining the meaning, implications, and limitations of your LP solution for your decision-making process. You should always report and communicate your data analysis and interpretation clearly and concisely, and acknowledge any uncertainties, trade-offs, or ethical issues that may arise from your LP solution.

5 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?