Skip to main content

A flexible implementation of the adaptive large neighbourhood search (ALNS) algorithm.

Project description

PyPI version Build Status codecov Codacy Badge

This package offers a general, well-documented and tested implementation of the adaptive large neighbourhood search (ALNS) meta-heuristic, based on the description given in Pisinger and Ropke (2010). It may be installed in the usual way as,

pip install alns

How to use

The alns package exposes two classes, ALNS and State. The first may be used to run the ALNS algorithm, the second may be subclassed to store a solution state - all it requires is to define an objective member function.

The ALNS algorithm must be supplied with an acceptance criterion, to determine the acceptance of a new solution state at each iteration. An overview of common acceptance criteria is given in Santini et al. (2018). Several have already been implemented for you, in alns.criteria,

  • HillClimbing. The simplest acceptance criterion, hill-climbing solely accepts solutions improving the objective value.
  • RecordToRecordTravel. This criterion only accepts solutions when the improvement meets some updating threshold.
  • SimulatedAnnealing. This criterion accepts solutions when the scaled probability is bigger than some random number, using an updating temperature.

Each acceptance criterion inherits from AcceptanceCriterion, which may be used to write your own.

Examples

The examples/ directory features some example notebooks showcasing how the ALNS library may be used. Of particular interest are,

  • The travelling salesman problem (TSP), here. We solve an instance of 131 cities to within 2.1% of optimality, using simple destroy and repair heuristics with a post-processing step.
  • The cutting-stock problem (CSP), here. We solve an instance with 180 beams over 165 distinct sizes to within 1.35% of optimality in only a very limited number of iterations.

References

  • Pisinger, D., and Ropke, S. (2010). Large Neighborhood Search. In M. Gendreau (Ed.), Handbook of Metaheuristics (2 ed., pp. 399-420). Springer.
  • Santini, A., Ropke, S. & Hvattum, L.M. (2018). A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic. Journal of Heuristics 24 (5): 783-815.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for alns, version 1.2.2
Filename, size File type Python version Upload date Hashes
Filename, size alns-1.2.2-py3-none-any.whl (27.7 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size alns-1.2.2.tar.gz (17.9 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page