Online Linear Programming tutor

4 weeks ago

Al Taawun, United Arab Emirates TeacherOn Full time

Work Package 1:
Problem Selection

Identify a real-world problem that can be effectively addressed using linear programming (LP) or integer programming (IP) optimization techniques.

Focus on problems with enough complexity to demonstrate your understanding of these methods, but ensure they remain solvable within the project timeframe.

Consider areas like supply chain optimization, staff scheduling, or investment portfolio planning. You may find inspiration from the lecture and the asynchronous workshop examples, but your model should exhibit greater complexity.
You could use tools, such as ChatGPT, Google Bard or other generative tools, to explore various topics and angles.

Be sure to record these sessions, as screenshots or summaries will need to be included in the appendix of your final report.

Once a topic is selected, provide a clear definition and background for readers unfamiliar with the specific concepts or industry terminology involved.


Work Package 2:
Data Collection and Preparation
Gather relevant data that will help in formulating the parameters of your LP/IP problem defined in Work Package 1. You are encouraged to utilize data-sharing platforms like to save time and access a broad range of datasets. Clearly explain the process of data collection, including sources and methods

Optimization involves planning for future scenarios. Explain how predictive methods are used on the data you collected to estimate the model parameters.

You could simply use the mean value of your data as an estimate for future trends; alternatively, you might consider using techniques such as linear regression, moving averages, among others.

While advanced predictive techniques may be utilized, the complexity and depth of the predictive method are not critical for the final grade.


Work Package 3:
Mathematical Modeling

Clearly list and explain the decision variables in your model. These represent the choices you'll be optimizing.

Formulate the problem you defined in Work Package 1 as a LP/IP model, including: Decision variables (what choices are being made), Objective function (what is being optimized), Constraints (limitations or requirements).

Clearly explain the logic behind the objective function and the constraints.

Work Package 4:
Implementation and Solution

Write Python code using PuLP to define the variables, objective, and constraints from the model you defined in Work Package 3.

Solve the optimization model and carefully interpret the optimal solution obtained, i.e., translating mathematical results back into the context of the real-world problem.


Work Package 5:
Data Visualization and Interpretation

Create clear and informative visual representations of:

Model parameters:
The data that feeds into your optimization model.

Optimization results:
The solutions generated by your model.
Provide a clear explanation for each visualization, justifying why it effectively communicates the relevant information.

Work Package 6:
Reflection

Choose one of the following reflection tasks that most interests you:
Task - Limitations and Future Development. Critically analyze your optimization model.

How well does it capture the complexities of the real-world problem? Identify potential shortcomings and areas where the model could be refined or expanded for greater accuracy.

Task - Skill analysis and future development.

Find a relevant job posting on LinkedIn (or a similar platform) that emphasizes analytics and data visualization skills within a business setting.

Reflect on how your skills developed in this course match the job requirements.

Identify any gaps and suggest further learning or development opportunities to bridge them and make you a strong candidate for similar roles.


Final Deliverable:


A detailed report that narrates the development process of your optimisation model, capturing the essence of each work package with supporting evidence and justifications.

A template on how to organise your report is avilable on LEARN.

The dataset:

Level:
Expert

Gender Preference:
None

Meeting options:
Available online - via skype etc.

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