I worked under the guidance of Professor Benoit Legat of MIT for a Multi-objective optimization problem for the whole set of UN countries to reduce the cost and pollution of oil transfer; transition towards United Nations SDG Number 7.

 

I used Mixed Integer Linear Programming techniques and the HiGHS Optimizer as part of the JuMP module in Julia Programming Interface. My research highlights a method to assist policy making for oil imports for United Nations’ member states to reduce their cost (including shipment) and pollution (including carbon dioxide and sulphur dioxide emissions), and assists with the transition towards United Nations Sustainable Development Goal Number 7, of being able to have a world with cleaner and affordable energy.

 

This utilises Mixed Integer Linear Programming to solve a global optimal cost for oil transportation to ensure energy security, whilst reducing the cost and pollution to the environment. Techniques such as Multiple Linear Regression, Branch and Bound, and the use of Primal and Dual values are used to solve this optimization problem. Future work can be done to enhance solutions to this problem, consisting of certain aspects of Game Theory, such as the Nash equilibrium and shared utility to ensure that the difference between each cost of each country is minimal. This provides a solution which policy-makers will be amenable to implementing.​

 

Impact:

 

This development framework can be utilised by the United Nations to help and reduce carbon emissions and pollution, alongside cost. By implementing a game theory approach, it can also help to bolster SDG 17: partnership for the goals​

 

Recognition:

 

This paper has been published in the SSRN Energy Journal and on Researchgate and has been submitted to the International Journal for High School Research for publication