This repository contains the source code for the scheduling problem with energy states and costs studied in [Benedikt2020a]. The data (datasets, experiments and results) are available in data repository.

You need the following

• .NET Core (>= 2.1)
• Python (>= 3.7)
• IBM CP Optimizer (>= 12.9)
• Gurobi (= 8.1)
• docplex (>= 2.10)

The code is known to work on Fedora 29 and Debian Stretch operating systems. Also make sure that environment variable GUROBI_HOME exists and it points to the installation directory, .e.g., on GNU/Linux

>> echo $GUROBI_HOME
/home/modosist/opt/gurobi810/linux64

The repository contains one C# solution with the following projects

• Shared - library with common code shared among the rest of the projects, e.g., it contains the source codes of the solvers.
• DatasetGenerators - generators of instance dataset from the given prescription.
• Experiments - runs the specified solvers on a dataset.
• SolverCli - command line interface for solving one instance with a specified solver.
• Tests - project containing tests of the code.

The following sections explains the solutions projects in detail.

The repository provides a few run configurations for Jetbrains Rider IDE. To use them with the data, either modify the paths in run configurations or create a symlink to the data repository in the root directory of the C# solution

ln -s DATA_REPOSITORY_PATH data

This project contains the code of the solvers which is shared among all the remaining projects. The solvers are written in different programming languages (C#, Python). For solvers written in C# and Python, nothing needs to be done as they are compiled when the projects are built.

To compile and run the project from the command line (passing no arguments will print the help message)

>> dotnet run -c Release -p Iirc.EnergyStatesAndCostsScheduling.DatasetGenerators -- DATA_ROOT_PATH PRESCRIPTION_FILE

where

• DATA_ROOT_PATH is the path to root directory with data (see the data repository for description of the filesystem structure).
• PRESCRIPTION_FILE is the filename (including suffix) of the prescription file that describes how the instances are to be generated. The prescription files are in JSON format, see class Prescription in Iirc.EnergyStatesAndCostsScheduling.DatasetGenerators/Prescription.cs for the description of the files content (Prescription class is used for deserializing the JSON files).

The command will create a new dataset directory with a name of the prescription file (without the suffix) in {DATA_ROOT_PATH}/datasets and fills it with the generated instances.

The generated instance files are in JSON format, see class JsonInstance in Iirc.EnergyStatesAndCostsScheduling.Shared/Input/Readers/InputReader.cs for the description of the files content (JsonInstance class is used for deserializing the JSON files).

To compile and run the project from the command line (passing no arguments will print the help message)

>> dotnet run -c Release -p Iirc.EnergyStatesAndCostsScheduling.Experiments -- DATA_ROOT_PATH PRESCRIPTION_FILE

where

• DATA_ROOT_PATH is the path to root directory with data (see the data repository for description of the filesystem structure).
• PRESCRIPTION_FILE is the filename of the prescription file that describes the experimental setup, e.g., the solvers to test. The prescription files are in JSON format, see class Prescription in Iirc.EnergyStatesAndCostsScheduling.Experiments/Prescription.cs for the description of the files content (Prescription class is used for deserializing the JSON files).

The command will create a new result directory with a name of the prescription file (without the suffix) and fills it with the results. The location of each result has the following format

{DATA_ROOT_PATH}/results/{prescriptionFilename}/{datasetName}/{solverId}/{instanceFilename}.json

where

• {prescriptionFilename} is the file name of the experimental setup prescription.
• {datasetName} is the dataset name as specified in the prescription.
• {solverId} is the solver id as specified in the prescription.
• {instanceFilename} is the file name of the instance for which a result is generated.

The generated result files are in JSON format, see class Result in Iirc.EnergyStatesAndCostsScheduling.Shared/Output/Result.cs for the description of the files content (Result class is used for deserializing the JSON files).

The experiment is generating the results on the fly. By default, if the experiment is interrupted before it is completed, restarting the experiment will resume from the previous point, i.e., it will keep the previously generated results. If flag --from-scratch is passed, the previously generated results are deleted and the experiment starts from scratch.

By default, only one instance is being solved at a time. The number of instances to solve in parallel can be specified using option --num-threads.

To compile and run the project from the command line (passing no arguments will print the help message)

>> dotnet run -c Release -p Iirc.EnergyStatesAndCostsScheduling.SolverCli -- CONFIG_PATH INSTANCE_PATH

where

• CONFIG_PATH is the path to configuration file. The configuration files are in JSON format, see class Config in Iirc.EnergyStatesAndCostsScheduling.SolverCli/Config.cs for the description of the files content (Config class is used for deserializing the JSON files).
• INSTANCE_PATH is the path to the instance file.

The command will run the given instance on the specified solver within the configuration and prints the solving information to the standard output.

The tests can be run by

>> dotnet test -c Debug

Debug configuration is recommended for enabling asserts and code checks.

MIT license

Please see file AUTHORS.txt for the list of authors.

[Aghelinejad2017a] MohammadMohsen Aghelinejad, Yassine Ouazene & Alice Yalaoui (2018) Production scheduling optimisation with machine state and time-dependent energy costs, International Journal of Production Research, 56:16, 5558-5575, DOI: 10.1080/00207543.2017.1414969

[Benedikt2020a] Ondřej Benedikt, István Módos & Zdeněk Hanzálek (2020) Power of Pre-Processing: Production Scheduling with Variable Energy Pricing and Power-Saving States, CPAIOR2020

TODO