How can you incorporate quality control into OR problem formulations?

1 QC in OR objectives

The first step of OR problem formulation is to state the objectives of the project, which are the desired outcomes or goals that you want to achieve. To incorporate QC into this step, you should make sure that your objectives are clear, specific, measurable, achievable, relevant, and time-bound. These criteria are also known as SMART objectives, and they help you to avoid ambiguity, confusion, or unrealistic expectations. By setting SMART objectives, you can also define the indicators and metrics that you will use to measure and evaluate your OR performance and results.

2 QC in OR constraints

The second step of OR problem formulation is to identify the constraints of the project, which are the limitations or restrictions that affect the feasibility or optimality of the solutions. To incorporate QC into this step, you should make sure that your constraints are realistic, consistent, and comprehensive. You should also verify the sources and validity of the data and information that you use to define the constraints. By doing so, you can avoid errors, inconsistencies, or gaps that might compromise your OR models or solutions.

3 QC in OR variables

The third step of OR problem formulation is to determine the variables of the project, which are the factors or elements that can be changed or controlled to influence the outcomes. To incorporate QC into this step, you should make sure that your variables are relevant, representative, and rational. You should also check the accuracy and reliability of the data and information that you use to assign values or ranges to the variables. By doing so, you can avoid bias, distortion, or outliers that might affect your OR analysis or optimization.

4 QC in OR assumptions

The fourth step of OR problem formulation is to state the assumptions of the project, which are the conditions or premises that you accept as true or valid without proof or verification. To incorporate QC into this step, you should make sure that your assumptions are reasonable, logical, and transparent. You should also justify and document the basis and implications of your assumptions, and test their sensitivity and robustness. By doing so, you can avoid fallacies, contradictions, or uncertainties that might undermine your OR models or solutions.

5 QC in OR models

The fifth step of OR problem formulation is to develop the models of the project, which are the mathematical or logical representations of the problem that capture the relationships between the objectives, constraints, variables, and assumptions. To incorporate QC into this step, you should make sure that your models are valid, reliable, and accurate. You should also verify and validate your models using various methods, such as data analysis, simulation, sensitivity analysis, or scenario analysis. By doing so, you can ensure that your models reflect the reality and complexity of the problem, and that they can generate feasible and optimal solutions.

6 QC in OR solutions

The final step of OR problem formulation is to obtain the solutions of the project, which are the optimal or best values or decisions that satisfy the objectives and constraints of the problem. To incorporate QC into this step, you should make sure that your solutions are effective, efficient, and ethical. You should also evaluate and compare your solutions using various criteria, such as cost, benefit, risk, or impact. By doing so, you can ensure that your solutions are aligned with your OR goals and expectations, and that they can be implemented and communicated successfully.