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relay-scheduler

An Answer Set Programming domain for scheduling relay events. Originally created for our Light Rail Relay 2021 event.

  • Schedules from scratch
  • ...or give a sketch and have the solver fill in the rest
  • Customize race format (e.g. number of runners per leg, minimum distance)
  • Configurable optimization for total duration or matching runner preferences for distance, pace, or end exchange. Use multiple objectives lexicographically
  • Consumes legs specified as GPX files

Usage

Get a working installation of Clingo >=5.5. Potassco's Anaconda channel makes this easy, or you can make a virtual env and install from requirements.txt

For Apple Silicon Macs, use Homebrew and ensure you install cffi in the correct version of Python, e.g. python3.12 -m pip install cffi.

Specify your problem matching the format used in lrr202X/lrr.lp.

Now:

./solve.py lrr2023

Solutions will stream into a timestamped folder in solutions/. By default, all optimal solutions are saved.

Use --help to see additional options.

Note that the solver will process float terms by converting them to a fixed precision (two decimal places, by default).

To view a solution, use

    ./print_schedule.py solutions/<run>/solution.json

Formatting Legs

A leg is a GPX file with a single track. The file is named StartExchangeID-EndExchangeID.gpx. The <name> tag should contain Start Exchange Name to End Exchange Name, and a <desc> tag with a summary of the leg.

Formatting Participants

The participant file is a TSV with the following columns:

  • Name
  • Pace ("MM:SS", min/mi)
  • Distance (mi)
  • PreferredEndExchange exchange name, (optional)
  • Leader whether participant wants to lead (optional, yes/no)

Loading participants from TSV is purely to make it easier to copy and paste from a spreadsheet; you can provide participant/1 and preference facts manually if you prefer.

Debugging and Extending

Running solve.py will output facts.lpx into the domain folder so you can check how any TSV/GPX specified facts were loaded.

In contrast with the facts output, the ground program has rules and simplifications applied. Inspecting the fully ground facts (solve with --save-ground-facts) can help you catch missing facts and bugged rules.

solve.py is basically equivalent to clingo --outf=0 --out-atomf=%s. scheduling-domain.lp domain/*.lp domain/facts.lpx, so you can further debug using clingo-specific options. --text will output the full ground program (including expanded optimization directives).

You can use print_schedule.py to view a schedule table directly from raw clingo output. Call clingo with clingo --outf=0 --out-atomf=%s. scheduling-domain.lp domain/*.lp domain/facts.lpx > solutions.txt (note the important dot delimiter argument). Then run print_schedule.py solutions.txt to view the schedule.