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BCP-MAPF – branch-and-cut-and-price for multi-agent path finding

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BCP-MAPF
========

BCP-MAPF is superseded by a [new version](https://github.com/ed-lam/bcp2-mapf).

BCP-MAPF is an implementation of a branch-and-cut-and-price algorithm for the multi-agent path finding problem. It is described in the paper:

[Branch-and-Cut-and-Price for Multi-Agent Path Finding](https://ed-lam.com/papers/bcpmapf2022.pdf). Edward Lam, Pierre Le Bodic, Daniel Harabor and Peter J. Stuckey. Computers & Operations Research, vol. 144, pp. 105809. 2022.

Please cite this article if you use this code for the multi-agent path finding problem or as a template for other branch-and-cut-and-price codes.

License
-------

BCP-MAPF is released under the GPL version 3. See LICENSE.txt for further details.

Dependencies
------------

BCP-MAPF is implemented in C++17 and is compiled using CMake, so you will need a recent compiler and a recent version of CMake. It is tested to run on Mac and Linux. It has not been tested on Windows.

BCP-MAPF uses SCIP 9.2.0 for branch-and-bound. Source code to SCIP is available free (as in 🍺) for academic use. BCP-MAPF calls Gurobi or CPLEX for solving the linear relaxation. [Gurobi](https://www.gurobi.com/downloads/end-user-license-agreement-academic/) and [CPLEX](https://community.ibm.com/community/user/datascience/blogs/xavier-nodet1/2020/07/09/cplex-free-for-students) are commercial software but provide free binaries under an academic license. BCP-MAPF is tested with Gurobi 10.0.1 and CPLEX 20.1.0.

Compiling
---------

1. Download the source code to BCP-MAPF by cloning this repository and all its submodules.
```
git clone --recurse-submodules https://github.com/ed-lam/bcp-mapf.git
cd bcp-mapf
```

2. Download the [SCIP Optimization Suite 9.2.0](https://www.scipopt.org/index.php#download) into the `bcp-mapf` directory and extract it. You should find the subdirectory `scipoptsuite-9.2.0/scip/src`.
```
tar xf scipoptsuite-9.2.0.tgz
```

3. Compile BCP-MAPF.

1. If using Gurobi, locate its directory, which should contain the `include` and `lib` subdirectories. Copy the main directory into the command below.
    ```
    cmake -DGUROBI_DIR={PATH TO GUROBI DIRECTORY} -B build/ .
    cmake --build build/ --parallel
    ```

2. If using CPLEX, locate the subdirectory `cplex` and copy this directory into the command below.
    ```
    cmake -DCPLEX_DIR={PATH TO cplex SUBDIRECTORY} -B build/ .
    cmake --build build/ --parallel
    ```

Usage
-----

After compiling, run BCP-MAPF with:
```
./build/bcp-mapf {PATH TO INSTANCE}
```

You can also set a time limit in seconds:
```
./build/bcp-mapf —-time-limit={TIME LIMIT} {PATH TO INSTANCE}
```

BCP-MAPF can be run as a bounded suboptimal algorithm by setting an optimality gap. For example, enter `0.1` for a 10% optimality gap.
```
./build/bcp-mapf —-gap-limit={OPTIMALITY GAP} {PATH TO INSTANCE}
```

The [Moving AI benchmarks](https://movingai.com/benchmarks/mapf.html) can be found in the `instances/movingai` directory. Most scenarios have a total of 1000 agents. You can specify how many of the first N agents to run. For example, you can run an instance with only the first 50 agents:
```
./build/bcp-mapf --time-limit=30 --agent-limit=50 instances/movingai/Berlin_1_256-random-1.scen
```

The optimal solution (or feasible solution if a time limit or gap limit is reached) will be saved into the `outputs` directory.

Contributing
------------

We welcome code contributions and scientific discussion subject to [Monash University’s equal opportunity and harassment policies](https://www.monash.edu/about/diversity-inclusion/staff/equal-opportunity).

Authors
-------

BCP-MAPF is invented by Edward Lam with assistance from Pierre Le Bodic, Daniel Harabor and Peter J. Stuckey. Edward can be reached at [ed-lam.com](https://ed-lam.com).

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