Parallelized cooperative GRASP-ILS hybrid OP solver
Some initial pointers for reproducing the experiments with parallelized GRASP-ILS hybrid on various OP variants:
- Cython
- mpi4py
- MPI
Extract, then run pyxify.sh and follow the instructions printed.
OP and GOP test data:
http://josilber.scripts.mit.edu/gop.zip
TOPTW, MCTOPTW and TDOP benchmarks are available here:
http://www.mech.kuleuven.be/en/cib/op/
Run 2000 iterations, 10 repeated tests on OP instance in file 1rat99.gop with GRILS-T
mpirun python ./loader.py op 1rat99.gop 10 2000 logfile
Use op_i as the first parameter to loader.py to run GRILS-I and op_dpr to run distributed strategy GRASP-PR.
Run 2000 iterations, 10 repeated tests on GOP instance in file 4china27.gop (k=4) with GRILS-T
mpirun python ./loader.py gop 4china27.gop 10 2000 4 logfile
Use gop_i to run GRILS-I and gop_dpr to run distributed strategy GRASP-PR.
Run 2000 iterations, 10 repeated tests on TOPTW instance in file r201.txt (m=2) with GRILS-T
mpirun python ./loader.py toptw r201.txt 10 2000 2 logfile
Use toptw_i to run GRILS-I and toptw_dpr to run distributed strategy GRASP-PR.
Run 2000 iterations, 10 repeated tests on MCTOPTW instance in file MCTOPMTW-2-rc106.txt with GRILS-T
mpirun python ./loader.py mctoptw MCTOPMTW-2-rc106.txt 10 2000 logfile
Use mctoptw_i to run GRILS-I and mctoptw_dpr to run distributed strategy GRASP-PR.
Run 10000 iterations, 10 repeated tests on TDOP instance in file p1.1.a.txt with GRILS-T. arc_cat_1.txt contains the arc cateories
mpirun python ./loader.py tdop p1.1.a.txt arc_cat_1.txt 10 10000 logfile
Use tdop_i to run GRILS-I and tdop_dpr to run distributed strategy GRASP-PR.
In these examples, the number of workers is pre-configured, hence no parameters to mpirun.
GRILS-I runs faster and GRASP-PR runs significantly slower that GRILS-T (number of iterations < 1000 is usually appropriate, except with very small instances).
Outdated runtests.sh and test.bash are included as they can be helpful in providing a starting point for automated tests. Both will require fixing and tweaking to match the current version of the Python scripts and the local implementation of MPI.
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you may download and use this code for non-commercial purposes, including reproducing test results as part of academic peer review process. The right to use does not extend to redistributing the code in original or modified form.