Arcflow models for the Skiving Stock Problem (SSP)

Here, you can find the reference implementation of the arcflow-based models for the SSP introduced in [1].

A basic example looks like this:

import ssplib

inst = 10, [6, 5, 4, 3], [1, 1, 2, 4]
print(inst)

model = ssplib.arcflow.build(inst)
model.optimize()
print(model.objVal)

solution = ssplib.arcflow.extract_solution(inst, model)
print(solution)

Installation

The file environment.yml contains a description of all required packages. You can create a clean conda environment from this file using

conda env create

and activate it using

conda activate grb

Afterwards, use

conda develop .

to setup a link to the ssplib package such that it can be loaded easily.

Usage

Example scripts can be found in the examples directory.

For instance, start

python examples/benchmark_datasets.py -m arcflow ../ssp-data/data/C1/Scholl_3_HARD.dat -o scholl3_reflect.log --relax

to compute solve the LP relaxation of the instances in Scholl_3_HARD.dat, where ../ssp-data is the location of the ssp-data directory which contains the data from here.

References

[1] Martinovic, J., Delorme, M., Iori, M., Scheithauer, G., & Strasdat, N. (2020). Improved flow-based formulations for the skiving stock problem. Computers & Operations Research, 113, 104770.

Some example instances can be found here: https://github.com/wotzlaff/ssp-data.