Estimating the time-of-arrival is a common problem in a wide range of settings, e.g. in logistics. This post will show a distribution-based approach that enables us to get more insights about arrival times and how we could use this information for decision-making in the logistics industry. By Jonas Laake.
In times of Google maps the estimation of expected times of arrival has become a common thing in everyday lives. Point estimators give us a single time of arrival and we assume or at least hope, that this estimated time will be as accurate as possible.
For these use-cases more information is required to answer questions like, in which time window will our cargo arrive with a n-percent probability (quantiles), how sure are we that the goods will arrive at that day +/- n-days and so on. To answer that kind of questions we need a time-of-arrival distribution rather than a point estimator.
The article main bits:
- A little introduction to distributions
- Routing considerations and notation
- Deriving the building blocks
- Edges (Direct leg model)
- Nodes (Transfer model)
- Assembling the parts
- Use case scenarios
- Possible issues: Talking about data
Source: https://www.inovex.de/blog/time-of-arrival-distributions/
The framework discussed here is the base of an advanced analysis setup for time-of-arrival analysis. It can provide useful insights in the logistic setups of different kinds of companies. By leveraging or restricting assumptions and inserting business knowledge into models and routers it can be customized to serve the individual needs. Good read!
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