Waterloo Startup Uses Algorithms To Make Logistics Suck Less

Citius Solutions
Credit: Citius Solutions

It was the Christmas season of 2015, and Jamal Mehdi – now COO at Citius Solutions – was a student working part-time at one of the largest courier companies in the world. Jamal was helping drivers deliver the packages at each stop they had to make when he noticed that every morning, before setting out to make their deliveries, the drivers would spend up to 45 minutes at the back of their trucks. No funny business, they were just spending time marking all the packages they had to deliver on a physical map and then eyeballing the best route to go about delivering all 120+ of them. He looked at that and thought, “Okay, there’s GOT to be a better way to do this.”

Hence began months of research that Jamal and fellow physics student, Maarij, embarked on, during which they learnt about the storied and incredibly complex history of the Travelling Salesman Problem (TSP). It quickly became clear why even multi-billion-dollar courier companies were resorting to pen and paper for this problem: it was VERY difficult to solve. A verifiable proof to the TSP had eluded mathematicians for over a century, and the Clay Mathematics Institute still has a standing offering of $1m to anyone who can solve it.

Citius co-founders. From left to right: Maarij Rehman, Kevin Burt and Jamal Mehdi.

However, mathematical proofs (especially those involving P vs NP) are hard. Jamal, Maarij, and Kevin – who joined the team in March 2016 – are not mathematicians; they are entrepreneurs. More importantly, they are from the University of Waterloo, one of the only universities in Canada with a dedicated Mathematics faculty, where the world’s leading experts in combinatorics and optimization taught.

With unfettered access to this incredible breadth of research and guidance from people who (literally) wrote the book on the TSP, a solution began to emerge. Perhaps not a solution that met the requirements of mathematical rigour, but one that was far more practical and applicable to the real world. A solution that people would pay money for.

Today, the goal at Citius is simple: provide a centralized solution to the problem of last-mile inefficiencies that includes sorting packages into trucks, calculating efficient routes for each truck so they can make their deliveries within the constraints they have, and monitoring road conditions in real-time, so that drivers and dispatchers have more visibility and are able to avoid delays due to traffic and construction.

Table 1: Case studies run on real sample data from (A) a local shuttle service, and (B), a multinational transportation company.

Citius still draws from the endless well of academic research and combines cutting edge algorithms into a proprietary process that – even at its current early stage of development – has shown significant results in test metrics (Table 1). These results have not gone unnoticed by titans in this industry: multi-billion dollar companies who immediately saw the value of improving their operational efficiency in one of their biggest pain areas.

Of course, finding a significant problem and building a cool solution are only the first steps: the real challenge is figuring out how to build a business around it. Here is where the founders truly hit gold: they found themselves in an environment that not only encouraged startup culture but celebrated it to an extent that rivalled their Southern neighbours at Silicon Valley. The Toronto-Waterloo corridor is the best place in Canada to start a business.

With the first paying clients already under their belt, the coming months look busy for the three founders: the agenda for the summer involves finishing, testing, and releasing the first version of the product to market, as well as participating in the 2017 Next36 cohort and raising their first round of pre-seed funding. Fortunately, it won’t be just the three of them for long – they’re also looking to expand their dev team!

This post is part of our contributor series. It is written and published independently of TNW.

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