Crop Planting Layout Solver

A Constraint Programming solver in Python designed to address the Crop Planting Layout Problem (CPLP). The project models crops spatial allocation using modern CP-SAT formulations.

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Problem Overview

The Crop Planting Layout Problem involves assigning agricultural crops to clusters of species across discrete planning horizons while respecting spatial constraints:

• Spatial Allocation: Preventing overlaps of clusters.
• Demand Satisfaction: Meeting the expected units of species.
• Objective: Maximizing beneficial interactions between species.

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Instance format

The instances must be a .dat file like this one:

K=6;
M=198;
H=3;
a=[[0, 1, 0], 
[1, 0, 0], 
[0, 0, 0]]; 
o = [ 2, 1, 1 ];
c_min = [ 32, 33, 1 ];
c_max = [ 33, 33, 33 ];
d = [ 16, 165, 1 ];

where

• K: Number of rows
• M: Total number of cells
• H: Number of species
• a: Score matrix
• o: Number of cells required for one unit of species
• c_min: Minimum cluster length
• c_max: Maximum cluster length
• d: Demand of units of species

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Tech Stack & Tools

• Language: Python 3.10+
• Solver Engine: Google OR-Tools (CP-SAT)
• Data Handling: Pandas
• Visualization: Matplotlib

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Repository Structure

.
├── utils/
├── README.md
├── analyze_benchmarks.py
├── main.py
├── requirements.txt
└── run_benchmarks.py

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Getting Started

1. Clone the Repository

git clone https://github.com/davidecavone/Crop-Planting-Layout-Solver.git

2. Environment Setup

Create a clean virtual environment and install the dependencies:

python -m venv venv
# Linux / macOS:
source venv/bin/activate
# Windows:
# .\venv\Scripts\activate

pip install -r requirements.txt

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Main script usage

python main.py <instance> [-h] [--mode {hard,soft}] [--allelopathy-threshold INT]
                          [--time-limit INT] [--workers INT] [--export-plots]

where

• instance: Instance path
• --mode: Adjacency constraint: 'hard' or 'soft' (default: hard)
• --allelopathy-threshold: Below this allelopathy threshold two species are considered incompatibles (default: -100)
• --time-limit: Solver Time Limit (default: 60)
• --workers: Number of parallel threads used by the CP-SAT solver (default: 4)
• --export-plots: Exports found solution as a PNG image (default: False)

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Computational campaign usage

In the project path, do

cd computational_campaign
python run.py <instance> [-h] [--allelopathy-threshold INT] [--export-results] [--export-plots]

where

• instance: Instance path
• --allelopathy-threshold: Below this allelopathy threshold two species are considered incompatibles (default: -100)
• --export-results: Exports solutions benchmarks in results folder as CSV file
• --export-plots: Exports found solutions as a PNG image (default: False) Campaign time limits, pool size and configurations can be adjusted in the config.py file.

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Screenshots

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References

If you use this work or reference the underlying model and tools, please cite the corresponding sources:

Tommaso Adamo, L. Colizzi, G. Dimauro, Emanuela Guerriero, and D. Pareo, "Crop planting layout optimization in sustainable agriculture: A constraint programming approach," Computers and Electronics in Agriculture, vol. 224, p. 109162, 2024. https://doi.org/10.1016/j.compag.2024.109162

Philippe Laborie, Jérôme Rogerie, Paul Shaw, and Petr Vilím, "IBM ILOG CP Optimizer for scheduling," Constraints, vol. 23, no. 2, pp. 210–250, 2018. https://doi.org/10.1007/s10601-018-9281-x

Philippe Laborie, Jérôme Rogerie, Paul Shaw, and Petr Vilím, "Interval-Based Language for Modeling Scheduling Problems: An Extension to Constraint Programming," in Algebraic Modeling Systems: Modeling and Solving Real World Optimization Problems, Applied Optimization, vol. 104, J. Kallrath, Ed., Springer, pp. 111–143, 2012. https://doi.org/10.1007/978-3-642-23592-4_6

Filippo Focacci, Andrea Lodi, and Michela Milano, "Mathematical Programming Techniques in Constraint Programming: A Short Overview," Journal of Heuristics, vol. 8, no. 1, pp. 7–17, 2002.

K. A. Bybee-Finley and M. R. Ryan, "Advancing intercropping research and practices in industrialized agricultural landscapes," Agriculture, vol. 8, no. 6, p. 80, 2018.

Laurent Perron and Frédéric Didier, "Google OR-Tools CP-SAT Solver," 2024. https://developers.google.com/optimization/cp/cp_solver

IBM Corporation, "IBM ILOG CP Optimizer User's Manual," Version 22.1.1, 2023.

Stuart Russell and Peter Norvig, "Constraint Satisfaction Problems," in Artificial Intelligence: A Modern Approach, 4th ed., ch. 5, Pearson, 2020.

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License

Distributed under the MIT License. See LICENSE for more details.