How much longer is an electric delivery fleet's route plan once the battery and the chargers are counted, and does a charge-aware search recover it?

research/infra-routing

Solomon RC208 (100 customers) with a battery: keeping the battery-blind routes and adding charging stops costs 28% more distance at 150 range units, a charge-aware search brings that to 5%; the battery is harmless above 200 units.

8 tests

What it found

The real Solomon RC208 VRPTW instance is solved battery-blind with PyVRP (4 routes, 778.93, equal to the published best-known distance as I recall it) and then given an electric fleet: range Q, full recharge at time g per unit, charging at the depot or at 2, 5 or 10 stations placed by k-means of the customers. Route-first, charge-second (the exact cheapest charging plan per route by a label-setting search, cutting routes that have none) costs 0 % extra distance at Q = 300, 0.4 % at 200, 28 % at 150, 36 % at 100 and 46 % at 75. An iterated local search whose moves are scored by the exact charging plan, 60 s from that start, cuts these to 4.9, 10.2 and 18.4 % (three seeds, best shown). Slower charging (g = 1) and fewer stations (2) leave 11 % and 12 % even after search; ten stations give 3.4 %. Not from a source: the battery range and recharge rate (swept, loosely anchored on one e-van’s range from memory), the station sites, full-charge-only recharging. The Schneider EVRPTW instances were unreachable, so this is RC208 with my own EV additions, one instance only; the search is short and seeds differ by up to 3 %. No tariff, so no time-of-use cost.

A figure from its own output

Figure produced by research/infra-routing: penalty — infra-routing/experiments/penalty.svg, written by the project's own experiment code.

Paper, code and results

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.