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
alns package exposes two classes,
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
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/ directory features some example notebooks showcasing how the ALNS library may be used. Of particular interest are,
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