mariabazotte / saa_endog

This is a repository with the implementation of the SAA Method for solving the network design and facility protection problem with endogenous uncertainty.

saa-endog-opt

This project contains the code for the paper ``The Sample Average Approximation Method for Solving Two-Stage Stochastic Programs with Endogenous Uncertainty''. It contains the two-stage Network Design and Facility Protection Problem (NDFPP), introduced in this paper.

Instances

Folder data/ contains the data files needed for experiments in the paper. The folder data/generator contains the file usa_network_generator.py to create new network instances as explained in the paper. The instances are generated based on the Basic US Cities dataset available at https://simplemaps.com/data/us-cities (folder data/generator/data_usa/). To generate a network file, execute:

python usa_network_generator.py -minpop X -nbfacilities X

where:

• -minpop 3000 # Threshold population to select cities from the southeastern region of the United States.
• -nbfacilities 4 # Number of facilities of the instance.

The network file is created at the directory data/usa/. The directory data/ also contains the parameters file, which contains:

• nbProtectionLevelsFacilities=4 # Number of protection levels for the facilities
• facilitiesImpact=0.3 # Impact of other facilities (for binomial, normal and std. normalization)
• penaltyUnmetDemand=10 # Unit penalty for unmet demand
• objectiveOrConstriantWithInstallCost=0 # 0: Cost in budget constraint, 1: Cost is in the objective
• noDisrUncertain=0 # 0: no-disruption event is not uncertain, 1: no-disruption event is uncertain

Main Code

Folder scr/ contains the source/main code. To run the main code, execute:

make 
./exe -parameters

Where we have the following parameters:

Mandatory parameters:

• -paramfile ../data/params.txt # Path to parameters file
• -networkfile ../data/usa/15nodes4facilities.txt # Path to network file
• -solver 0 # 0: SAA, 1: Det. Equiv. (Referred here as Linearization), 2: Det. Equiv. (Refererred as Linearization-LShaped), 3: EV and EEV problems
• -distribution # 0: Discrete Selection (NDFPP-Selection), 1: Binomial (NDFPP-Binomial), 2: Normal (NDFPP-Normal), 3: Std. Normalization (NDFPP-Discrete)

Instance parameters:

• -nb_capa_levels 2 # Number of capacity levels of facilities for discrete distributions (for Normal is 0)
• -percentage_budget 0.5 # Percentage of maximal budget for the budget constraint
• -edge_cost_mult 10 # Unit cost for opening one unit of edge

Running parameters:

• -timelimit 3600 # Time limit for optimization
• -nbthreads 1 # Number of threads for gurobi
• -seed 0 # Seed (data generation - cost of facility protection)
• -verbose 0 # 0 -> nothing, 1-> moderated, 2-> intensive (log files)

Lshaped parameters:

• -precision 0.0001 # Precision to stop optimization
• -ithotstart 100 # Max nb of iterations for hot start
• -papadakos 1 # 0-> no papadakos cuts, 1-> include papadakos cuts
• -callback 0 # 0-> normal LShaped (multiple trees), 1-> "Benders and cut"
• -typecut 0 # 0-> cuts only when a integer solution is found, 1-> cuts for fractional solutions also included
• -validinequalityEV # 0-> do not use EV valid inequality, 1: use EV valid inequality

SAA parameters:

• -nbproblemsSAA 50 # Number of problems for SAA
• -nbscenariosSAA 750 # Number of scenarios for each SAA problem
• -nbvalidatescenariosSAA 150000 # Number of scenarios for the validation problem of the SAA
• -validinequalitySAA # 0-> do not use valid inequality for SAA problem, 1-> use valid inequality (only for Binomial and Std. Nomalization)

Results

The folder scripts contains the sbatch files for replicating the results of the paper. Specifically, the files job_linearization.sh, job_saa.sh, and job_expected.sh contain the commands for the paper's results.

The results are saved in a repository such as:

• results
  • compiled
  • saa
    • discretechoice
    • binomial
    • stdnormalization
    • normal
  • expected
    • discretechoice
    • binomial
    • stdnormalization
    • normal
  • linearization:
    • discretechoice

Unifying paper results

The file unify_results.py unifies the results shown in the paper. After running the sbatch files, execute:

python unify_results.py

You can modify this file to see the results of other parameters. The unified results are in the folder results/compiled.

Extra covariance test

Update the make file by changing main_command.o with main_updcorrelation.o. Include the folder results/correlation. Execute:

make
./exe -parameters

Where we have the following parameters:

Mandatory parameters:

• -paramfile ../data/params.txt # Path to parameters file
• -networkfile ../data/usa/15nodes4facilities.txt # Path to network file
• -nbsamples 20 # Number of samples
• -sizesamples 1000 # Number of scenarios for each sample
• -d 0 # Distribution: 0: Discrete Selection (NDFPP-Selection), 1: Binomial (NDFPP-Binomial), 2: Normal (NDFPP-Nornal), 3: Std. Normalization (NDFPP-Discrete)
• -nbsolutions 20 # Number of first-stage solutions to evaluate.

By running this code, we obtain the matrix of covariance of the SAA estimates for different first-stage solutions using the same sequence of uniform random numbers (Common Random Numbers). Execute the sbatch file job_covariance_test.sh (in folder scripts/) for examples.