The broiler chicken industry plays a crucial role in food security and employment generation; however, it faces challenges such as increasing demand, mortality losses, and appropriate breed selection. This study aims to develop an integrated mathematical model for analyzing and optimizing the chicken meat supply chain by simultaneously addressing breed selection, production planning, logistics management, and loss reduction. The proposed model is formulated as a mixed-integer linear programming problem that maximizes the total profit of the supply chain by considering revenues from chicken meat and poultry manure, as well as all procurement, production, and distribution costs. The model includes constraints related to supply availability, production and distribution capacities, demand satisfaction, and the limitation of raising only one breed per farm. The model is solved using GAMS based on data obtained from reliable sources. The results demonstrate effective resource allocation, optimal breed selection, and improved logistics network design. Sensitivity analysis further confirms the robustness and reliability of the proposed model.
Introduction
The broiler industry plays a crucial role in ensuring food security and creating employment. However, this industry faces several structural challenges, including demand fluctuations, rising input costs, inefficient loss management, and suboptimal breed selection. Previous studies have primarily focused on optimizing individual segments such as production, logistics, or financial performance, while limited attention has been given to integrated models that simultaneously address strategic and tactical decisions across the entire supply chain. This research gap highlights the need for a comprehensive decision-making framework capable of capturing interactions among different levels of the broiler supply chain. Accordingly, the main objective of this study is to design and present an integrated mathematical model that simultaneously optimizes breed selection, production planning, logistics management, and loss reduction within a four-tier supply chain.
Research Background
In recent years, extensive research has been conducted on broiler supply chain optimization. Some studies have focused on allocation and scheduling problems using mixed-integer linear programming (MILP) models, while others have examined the impacts of business policies or uncertainty management practices. Additional research has explored the integration of production and financial decisions, as well as supply chain modeling during disruptive events such as the COVID-19 pandemic. Despite these efforts, a comprehensive review of the literature reveals that most existing studies address the problem in an isolated and one-dimensional manner, often neglecting optimal breed selection and mortality management. In some cases, simplifying assumptions—such as allowing the simultaneous breeding of multiple breeds—have been adopted, which do not reflect operational realities. This study seeks to bridge this gap by explicitly integrating breed selection and loss management into a unified optimization framework.
Method
This study proposes an integrated mathematical model for optimizing the chicken meat supply chain. The system under consideration consists of a four-level supply chain including suppliers (day-old chicks, feed, and vaccines), breeding farms, slaughterhouses, and distribution centers (markets). The proposed model is formulated as a mixed-integer linear programming (MILP) problem with the objective of maximizing total network profit by accounting for revenues from chicken meat and poultry manure sales, as well as all supply, production, and distribution costs. Biological characteristics of different chicken breeds—such as feed conversion ratio, rearing period, mortality rate, and final weight—are incorporated as model parameters. The constraints include supply, production, and distribution capacity limitations, demand satisfaction requirements, and the restriction of raising only one breed per farm. The study is conducted in two main stages: model formulation and model implementation. In the first stage, the mathematical model is developed by defining key decision variables, including breed selection, supplier assignment, capacity allocation, and production and distribution planning. In the second stage, the model is implemented and solved using GAMS software with the CPLEX solver. The required data are obtained from a combination of industry reports, expert opinions, and market data. A realistic numerical example involving two chick suppliers, three feed suppliers, three vaccine suppliers, four farms, three slaughterhouses, and six distribution centers is designed to validate the model. After obtaining the optimal solution, a comprehensive sensitivity analysis is performed on key parameters such as meat selling price, input costs, mortality rates, and transportation costs to assess the stability and reliability of the model under varying market conditions.
Discussion and Results
Solving the model using realistic data demonstrates its capability to generate operationally optimal solutions. The key findings are summarized as follows:

Optimal breed selection: The final solution selects only the Ross and Kap breeds, while the Aryan breed is excluded. This outcome reflects the superior performance of these breeds in terms of biological parameters (e.g., mortality rate and feed conversion ratio) and their compatibility with the cost and capacity structure of the network.
Resource allocation: Among the four hypothetical farms, only two are optimally activated. This result is attributed to their comparative advantages in terms of proximity to slaughterhouses, effective capacity utilization, and reduced logistics costs.
Sensitivity analysis: The sensitivity analysis confirms the rational and stable behavior of the model. The selling price of chicken meat and the purchase price of chicks are identified as the most influential parameters affecting profit. Additionally, the results indicate that increases in mortality rates have a nonlinear and substantial negative impact on profitability, underscoring the importance of investing in effective loss reduction strategies. Optimal breed combination: In the final solution, only the Ross and Kap breeds were selected and the Aryan breed was eliminated. This selection indicated the superiority of these two breeds in terms of the combination of technical parameters (such as mortality rate and conversion factor) and coordination with the cost and capacity structure of the network.
Resource allocation: Out of the four hypothetical farms, only two farms were optimally activated, the reason for which can be found in the comparative advantage of these farms in terms of proximity to slaughterhouses, effective capacity and reduced logistics costs.
Sensitivity analysis: The results of the sensitivity analysis confirmed the rational and stable behavior of the model. Specifically, the selling price of meat and the price of chicken were identified as the most influential parameters on increasing and decreasing profits, respectively. The model also showed that increasing casualty rates have a nonlinear and significant effect on decreasing profits, which highlights the need to invest in casualty reduction strategies.

Conclusion
This study developed an integrated modeling framework for optimizing the broiler supply chain by endogenously incorporating breed selection and mortality management into the decision-making process. The results demonstrate that simultaneously considering biological, economic, and logistical factors enhances supply chain integration and performance. The proposed model serves as an effective decision-support tool for poultry industry managers and policymakers, enabling optimal resource allocation and strategic planning to maximize profitability and efficiency in competitive environments. The primary limitation of the study lies in the difficulty of accessing accurate and confidential data from production units, which was addressed through the use of composite and approximate data. Therefore, the results should be interpreted with this limitation in mind. The model can also be applied as an analytical tool to estimate equilibrium chicken meat prices under scenarios involving changes or removal of government support policies, such as preferential exchange rates for livestock inputs. By evaluating alternative input pricing scenarios, the model helps identify prices that maintain producer profitability without imposing excessive economic pressure on consumers. At a macro level, the application of such models can assist policymakers in designing targeted support strategies to improve productivity, enhance profitability, and promote sustainability in the broiler supply chain under volatile market conditions. Future research may extend the model by incorporating uncertainty-based approaches, such as stochastic or fuzzy programming, to better address demand and price fluctuations. Additionally, developing a multi-objective version of the model that accounts for environmental (e.g., carbon footprint reduction) and social sustainability criteria represents a promising direction for further research.