Currently there are 3 billion people in lockdown and the economy about to stagnate so the world needs urgently solutions in the face of the current crisis, as well as to build resilience post-pandemic, to avoid the collapse governments and society need to reshape, repurpose and rebalance the current assets in order to optimise them and use them to boost the wave that will drive out this pandemic in a reasonable safety way.
What it does
This is a platform that is a seed in the short term that will help to rebalance medical assets and goods using genetic and recursive algorithms to connect producers with consumers through movers, solving this pressing problems, in this way we are enabled to focus on repurposing to help companies and society to reuse and adapt the current assets to fulfil the urgent needs and finally the last stage is to help oganisations, business and supply chains to reshape industries for the post-pandemic world using machine and deep learning algorithms to visualise the new economy,
How I built it
The platform has 3 stages (Short, Medium and Long Term) and 3 levels (Countries, Regions and Local Communities) using existing information the platform is fed, in the first stage and level the proposed solution is about how we can create or rebalance existing supply chains at countries level in order to satisfy the demands due to the current crisis to connect suppliers, consumers and transporters, and potentially leverage capabilities for suppliers towards new/critical focus areas as required. At a regions level, its about how to connect and enable the supply process for specific entities like hospitals with suppliers in some specific regions and roll this up to a nations-level view. At stage 3 and local level its about how to enable local business networks to connect with end-consumers to provide medical or daily supplies or provisions, such as convenience stores, pharmacies, groceries stores, etc with individual consumers through phone app(s) that connect local business and consumers communities inside a radius of <5 Kms.
We have grouped the actors in this chain into four broad groups:
- Shortage - or those in need (hospitals in our scenario), and what they need right now
- Makers - those who can produce/manufacture certain products
- Surplus - those who have products they can spare (hospitals or makers) - and last but not the least -
- Movers - those who can help move items from one location to another
We do have a proposed end-state view of the solution architecture - we could always evolve this as we go through subsequent stages.
Challenges I ran into
There are two challenges we face today:
- There doesnt seem to exist a single consolidated view of this form - and
- How do we best connect those in need with those who can fulfill the need.
For the second challenge, we looked at options and ended up selected Genetic Algorithms as a way of finding the most optimum solution given a list of shortages and surplus/makers. We have built an implementation of this algorithm for our specific example context. (Pls note that the data here is simulated, but the core solution would work once we get the data in place.)
Accomplishments that I'm proud of
We came top 20 in the Swedish Hackathon for Covid 19 among 540 odd submissions.
What I learned
Governments more than ever are willing to do whatever is needed to protect citizens, business and communities, act quickly in the following 2 months will make a huge impact, this is a challenge with no precedent in our modern history
What's next for Optimising Supply Chains - Reshaping/Repurposing/Rebalancing
Adapting and customising Phase 1 at Satage 1 Level 1 in the next 6 weeks, extending its capabilities to fulfil the crisis needs during the next 3-6 months.
Try It out
algorithms, angular-for-the-ui, css3, google-maps, html5, java, jgap-(java-genetic-algorithms-implementation), php, recursive, spring-boot