How can you select and validate assumptions, parameters, and objectives for optimization models?

1 Step 1: Identify the problem and the scope

The first step is to clearly define the problem that you want to solve with optimization, and the scope of the model that you will use. You should ask yourself questions such as: What are the main goals and challenges of the problem? What are the relevant variables and constraints that affect the problem? What are the inputs and outputs of the model? How detailed and realistic do you want the model to be? How will you measure and compare the performance of different solutions?

2 Step 2: Select the assumptions and parameters

The second step is to select the assumptions and parameters that will simplify and quantify the problem and the model. Assumptions are statements that reduce the complexity and uncertainty of the problem by making some simplifications or generalizations. For example, you might assume that the demand for a product is constant, or that the processing time of a task follows a certain distribution. Parameters are numerical values that specify the characteristics and relationships of the variables and constraints in the model. For example, you might specify the cost, capacity, or efficiency of a resource, or the weight, priority, or preference of an objective.

3 Step 3: Validate the assumptions and parameters

The third step is to validate the assumptions and parameters that you have selected, to ensure that they are reasonable, consistent, and accurate. You should check that the assumptions are not too restrictive or unrealistic, and that they do not contradict each other or the facts of the problem. You should also check that the parameters are based on reliable data sources, and that they reflect the current or expected conditions of the problem. You can use various methods to validate your assumptions and parameters, such as sensitivity analysis, scenario analysis, or expert judgment.

4 Step 4: Define the objectives and criteria

The fourth step is to define the objectives and criteria that will guide the optimization process and the evaluation of the solutions. Objectives are statements that describe what you want to achieve or optimize with the model, such as minimizing costs, maximizing profits, or satisfying customers. Criteria are standards or rules that define how you will measure and compare the objectives and the solutions, such as weights, ranks, or thresholds. You should define your objectives and criteria clearly, explicitly, and comprehensively, and make sure that they are aligned with the problem and the scope of the model.

5 Step 5: Review and refine the model

The final step is to review and refine the model based on the assumptions, parameters, objectives, and criteria that you have selected and validated. You should test and debug the model to ensure that it works correctly and efficiently, and that it produces feasible and optimal solutions. You should also analyze and interpret the results of the model, and check whether they meet your expectations and requirements. If not, you should revise and improve the model by modifying or adding assumptions, parameters, objectives, or criteria, until you find the best possible solution for the problem.

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?