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I'm trying to solve the following problem:

  • A delivery man has N packages to deliver, already sorted.
  • He starts the route at start_time, he works until end_time.
  • I have historical data of his delivery schedules.
  • There's no geographic information between each drop point.

How can I predict the delivery times for each package?

I've tried the following approaches, which don't seem to solve this appropriately:

  1. Machine learning: perfect for classifying the input into classes, which doesn't apply for this scenario. I even did the ML Coursera course in 2 weeks, just to see if any topics were suitable.
  2. Kalman filter: or other DSP-related algorithms focus too much on real-time, short-spanned predictions, which is not the case. No way of leveraging historical data.
  3. Markov chains: great for guessing the present state, not so much for a future value.
  4. Mathematical model: I could come up with a mathematical model (gaussian curves?) which denotes the time it takes between package delivery, depending on hour of day. Based on that, predict the delivery times.

Which topics should I focus on?

A few notes on the historical data:

  • I have everything from start times, end times, and delivery times for each package. I also have all the GPS data for those routes, if that helps.
  • The drop-off places should be considered random, so the delivery times look pretty random themselves.
  • There's a slow-down on lunch time.
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    What does the historical data look like? – Dan Pichelman Oct 7 '15 at 14:45
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    @DanPichelman check edited answer – jlhonora Oct 7 '15 at 14:51
  • (aside - there is some discussion in Software Engineering Chat (The Whiteboard) about this if you want to poke in there) – user40980 Oct 7 '15 at 14:54
  • Clarify your input(s) and expected output(s) very precisely pls. – NoChance Oct 7 '15 at 22:00

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