Please enable JavaScript.

Coggle requires JavaScript to display documents.

Chapter 1: Methodology - Coggle Diagram

- - - - x: Number of days Mine X operates per week.
      - y: Number of days Mine Y operates per week.
    - - Ore production constraints:
        6x+1y≥12 (High-grade)
      - Maximum working days per week:
        x≤5, y≤5
      - Non-negativity constraints:
        x≥0, y≥0
    - - Minimize/ Maximize 180x+160y
- - - - Variables are continuous (can take fractional values).
      - There is a single objective to maximize or minimize.
      - Both the objective function and constraints are linear
  - - - The process involves converting a real-world scenario into a mathematical model.
        This is called formulating the model.
    - - Use quantitative methods (algorithms) to solve the model and get numeric solutions.
    - - Always involves optimization
    - - Once the numeric solution is obtained, it must be interpreted and applied back to the real-world scenario.
- - - - Input data is error-free.
      - The program is free of bugs and represents the model correctly.
      - Results are reasonable or understandable if surprising.
- - - - Clear identification of the client and problem owner.
      - Close and continuous communication between client and consultant.
      - Awareness of constraints the client faces (e.g., departmental boundaries, feasibility of changes).
    - - Lack of skills or resources within the organization.
      - Need for an independent perspective, external validation, or arbitration.
      - Creating a positive image or having a scapegoat if things go wrong.
    - - Help define the problem and respond to the client’s concerns.
      - Understand the technical, social, and cultural contexts of the problem.
  - - - Higher accuracy requires more time and resources.
      - Simplified models may be more accessible but less precise.
    - - More effort increases accuracy but requires higher costs.
  - - - Models may not fully represent real systems.
      - Multiple objectives may be difficult to quantify or agree upon.
      - Situations and data may change over time.
    - - Use sensitivity analysis to test the robustness of solutions.
      - Explore feasible options and present trade-offs to the client.
        (remains the best even if the problem chamges)
  - - - Feasible solutions that align with technological and cultural constraints.
      - Commitment from decision-makers with the power to implement.
    - - Regular and positive communication with the client to stay updated on problem changes.
      - Involve the client throughout the project to ensure buy-in.
      - Present solutions and their justifications in clear, plain language.
      - Simplify models to make them transparent and understandable.
      - Ensure recommended actions are within the client’s power to execute.