What are the key factors to consider when formulating OR problems for decision-making?

Objective: Clearly define the problem's goal or objective that you aim to optimize or minimize. Constraints: Identify all relevant constraints or limitations that must be satisfied. Decision Variables: Define the decision variables that you can control or manipulate. Data and Assumptions: Ensure data accuracy and clearly state any assumptions made. Model Selection: Choose an appropriate OR technique or method to address the problem effectively. Sensitivity Analysis: Assess the model's sensitivity to changes in parameters or assumptions. Practicality: Ensure that the formulated problem aligns with the real-world context and is solvable within available resources and time.

When formulating OR problems, consider key factors like defining the problem clearly, identifying decision variables, considering constraints, specifying objectives and measures of success, and analyzing data. These factors help provide clarity, determine variables, address limitations, establish goals, and utilize relevant data for effective decision-making.

In my perspective, the factors to consider can be summarized as: Problem Definition; Data Availability and Quality; Consult with experts/Stakeholder Engagement; Constraints and Assumptions; Decision-Making Criteria and Implementation Feasibility. By considering these key factors during the formulation of OR problems, decision-makers can develop comprehensive and effective solutions that address complex decision-making challenges and yield optimal results for the organization.

When formulating a problem for a decision-maker, several key factors must be considered. The problem statement should be clear, relevant, and within a defined scope. It should be measurable, involve relevant stakeholders, and rely on available data. Assess the problem's impact and priority, taking into account constraints and alternative solutions. Evaluate risks, ethical considerations, and whether it's a short-term or long-term issue. Consider how the problem will be communicated and its feasibility. Establish a feedback mechanism for ongoing monitoring and adjustments. Careful consideration of these factors leads to a well-structured problem statement that facilitates informed decision-making.

The key factors to consider in formulating OR problems include the followings:to define the problem clearly and precisely,also to identify the main goal,relevant variables,the constraints,and the criteria for evaluating the solution.

In OR, a clear understanding of the objective, variables, constraints, and evaluation criteria is crucial for accurate problem definition. To solve a problem effectively: 1. Identify the main goal. 2. Determine relevant variables. 3. Establish constraints. 4. Consider scope and scale. 5. Assess available data and resources. 6. Ensure the problem is rooted in real-world conditions.

The paired extended decision analysis fill the gap of decisions games' consideration in OR problems. Indeed, the commonly made distinctions between decision analysis and game theory is that the first focuses on one decision maker facing an incertain environnent, whereas the second study the strategic interaction between decision makers. However, many sequential decision making process like investing through investment funds are two or more stage games in which decision analysis deal with strategic interactions. Such process call to foresee both playoff or utility to optimize and the equilibrium strategy profile to achieve.

In operations research (OR), not all problems necessarily have an explicit objective function. While many optimization problems in OR are framed to maximize/minimize an objective function, there are situations where the objective might not be explicitly stated or where the problem itself does not involve optimization. In certain use cases, the goal is to find a solution that satisfies a set of constraints without necessarily optimizing a specific objective function. This is common in constraint programming, which emphasizes finding feasible solutions. Some problems, like network flow problems, may involve finding a feasible solution that satisfies certain flow conservation constraints without necessarily optimizing an objective function.

The objective function in OR is a mathematical expression that defines the goal of the problem you're trying to solve. It incorporates the decision variables - the elements you can control - to calculate either a maximum value or a minimum value. The objective function is what you optimize to make the best decision based on the problem's requirements. For instance, it can assist in determining the most profitable or time-efficient solution. Applying this formula allows you to discover the optimal outcome for your specific scenario.

In OR, constraints are the rules that limit options. They can be compared to boundaries that determine what can and cannot be done, such as rules, regulations, time, or resources. These limits are expressed as mathematical equations that use decision variables and fixed values. When setting up constraints, it is crucial to ensure they align with your overall objective and are feasible. They should also be clearly defined so that they do not make finding a solution impossible.

Sensitivity analysis in OR evaluates the impact of parameter and assumption changes on optimal solutions. It uncovers key factors affecting the solution, enhancing the formulation's resilience. This analysis assists in scenario planning, risk assessment, and decision refinement.

When concluding OR problems, it is crucial to communicate the findings to stakeholders in a clear and concise manner. Summarize the OR problem, optimal solution, and sensitivity analysis briefly. Provide detailed explanations of assumptions, limitations, and outcomes, and suggest actionable steps. Use formats such as reports and visuals to enhance clarity and strengthen your arguments.