A Mixed Integer Linear Programming Model for Livestock Planning

Intelligent Agriculture

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A Mixed Integer Linear Programming Model for Livestock Planning

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https://doi.org/10.54963/ia.v1i2.1944
Volume 1 Issue 2 (2025)
Copyright (c) 2025 Joaquín Pablo Tempelsman, Javier Marenco
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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Intelligent Agriculture (IA) publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by Intelligent Agriculture (IA) agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source is properly cited, anyone may copy, redistribute, reuse and transform the content.

Authors

  • Joaquín Pablo Tempelsman

    Escuela de Negocios, Universidad Torcuato Di Tella, Buenos Aires C1428BCW, Argentina
  • Javier Marenco

    Escuela de Negocios, Universidad Torcuato Di Tella, Buenos Aires C1428BCW, Argentina

Received: 30 August 2025 | Revised: 23 October 2025 | Accepted: 26 October 2025 | Published Online: 14 November 2025

In this work, we propose a mixed integer linear programming formulation for the strategic planning of a livestock operation over a multi-period horizon. The model aims to optimize the overall revenue of the operation while explicitly accounting for the complex biological, operational, and economic constraints inherent to livestock breeding systems. These constraints include herd dynamics, breeding and replacement decisions, capacity limitations, resource availability, and time-dependent production factors. The proposed formulation provides a structured and transparent decision-support framework that captures the dependencies between planning periods and supports long-term strategic decision-making. To assess the effectiveness and robustness of the model, extensive computational experiments are conducted across multiple scenarios and parameter settings that reflect realistic operational conditions. The results are benchmarked against a contesting model that represents the heuristic-based decision rules currently applied by company managers. The numerical results demonstrate that the model consistently adapts to diverse scenarios, outperforming the existing heuristics in terms of revenue and resource utilization. Moreover, the solutions obtained are interpretable and aligned with managerial intuition, which facilitates their practical adoption. Overall, the proposed approach shows strong potential as a systematic tool to support and improve livestock operation planning and management decisions.

Keywords:

Mixed Integer Linear Programming Livestock Planning Decision Support Systems

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