User Story - Virtual Tutor // Production and Supply Chain Management: Interactive Tool for Formulating Mathematical Models

[dias-altay]

Is your feature request related to a problem?

This user story is valid for Production and Supply Chain Management course

As Stefan, I want an interactive tool to formulate and implement optimization models, including defining variables, constraints, and objective functions, so that I can master mathematical modeling with guided feedback and intuitive interfaces.

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Description:

Stefan is studying Production and Supply Chain Management and needs to develop optimization models for real-world problems. He wants a tool that helps him define variables, constraints, and objective functions interactively. The tool should also provide feedback on his formulations and allow him to interpret results through an intuitive interface, such as Jupyter notebooks.

Describe the solution you'd like

Acceptance Criteria:

Model Formulation Assistance:

1. Interactive Model Building:

   • Stefan can define optimization problems step-by-step, including:
     • Variables and their bounds (e.g., decision variables for production quantities).
     • Constraints (e.g., capacity limits, demand requirements).
     • Objective functions (e.g., minimizing costs, maximizing profit).

2. Guided Prompts:

   • The tool provides guided prompts and templates to assist in building models.
   • Prompts are context-sensitive based on the type of problem Stefan is working on.

Feedback Mechanism:

1. Immediate Feedback:

   • After each step of model formulation, the tool validates inputs and provides feedback, such as:
     • Whether constraints are logically consistent.
     • Whether the objective function aligns with the problem statement.
   • Errors and potential improvements are highlighted with explanations.

2. Solution Validation:

   • The tool verifies whether the optimization solution satisfies all constraints and achieves the objective.

Intuitive Results Interpretation:

1. Visualization of Results:

   • The tool generates visual outputs (e.g., charts, graphs) to help Stefan interpret results.
   • Results include:
     • Values of decision variables.
     • Optimal objective function value.
     • Slack or surplus in constraints.

2. Integration with Jupyter Notebooks:

   • Stefan can export models and results to a Jupyter notebook for further analysis or reporting.

Progress Tracking:

1. Performance Metrics:
   • Stefan can track his progress through:
     • Number of models successfully completed.
     • Error rates and areas needing improvement.

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User Experience:

1. User-Friendly Interface:

   • The tool has a clean and intuitive interface with clear instructions at each step.
   • Error messages and feedback are non-disruptive and easy to understand.

2. Accessibility and Compatibility:

   • The tool works seamlessly on both desktop and mobile devices.
   • It integrates with existing tools like Jupyter notebooks without additional setup.

3. Customizability:

   • Stefan can select problem types (e.g., transportation, scheduling, inventory management) to customize the tool’s prompts and guidance.

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Technical Requirements:

1. Optimization Engine:

   • The tool integrates with robust optimization libraries to solve mathematical models efficiently.

2. Error Detection:

   • Automatic detection and flagging of errors in model formulation (e.g., undefined variables, infeasible constraints).

3. Scalability and Performance:

   • The system supports multiple simultaneous users with smooth operation.
   • Models are processed and visualized with minimal delay.

4. Integration:

   • The tool integrates seamlessly with existing e-learning platforms and provides export options for completed models.

Describe alternatives you've considered

No response

Additional context

Definition of Done (DoD):

• The interactive tool for formulating mathematical models has been implemented, and all acceptance criteria are met.
• The model-building process, including variable definition, constraints, and objective functions, works as intended.
• Feedback and validation mechanisms provide accurate and actionable insights.
• Visualization of results and Jupyter notebook integration function correctly.
• The tool has been successfully tested on multiple devices and with various optimization problems.
• Accessibility requirements are met, ensuring usability for all students.
• Documentation for the tool, including user guides and integration steps, is complete.
• The product owner and QA team have approved the feature.