20.04.15

[Delivery Optimization] Routing, priority and efficiency

We are working with https://207inc.jp. We have been using clustering to optimize delivery packages. So far, I’ve been using approximate values of distance, but I’d like to include more detailed pathfinding.

It is not a machine that actually makes the delivery, but a human being, so an interface that is easy for a human being to use is important. Here, I want to calculate the routes so that I can make multiple route “suggestions” to deliveries to make it easier to introduce parking, breaks, and experiences while calculating the routes so that deliveries are comfortable.

Last time we decided on an area from clustering and distributed the timed and untimed parcels across multiple time zones to mix the untimed parcels into the timed parcels so that a no-brainer delivery plan could be made.

This time, we did the routing inside the final clustering and organizing of time zones, and expressed it as a mobile web app. For routing, I used a different library than D-Wave this time.

Eventually the delivery will be delivered to mobile so that it’s more user friendly.

With D-Wave and mobile apps and libraries, we’re on track to optimize our deliveries. In the future, we will demonstrate this in actual delivery. For routing, we used vcopt from vigentte&clarity.



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