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<p>This study presents a fuzzy multi-objective Mixed-Integer Linear Programming (MILP) model to optimize the chicken meat supply chain (CMSC). The model aims to maximize total profit, enhance service level, and minimize food loss and waste while accounting for uncertainty in demand and cost parameters. The proposed framework considers a three-echelon cold-chain network integrating poultry farms, processing and distribution centers, and retail nodes. Optimization was conducted using a solver-based computational framework to ensure both transparency and practical applicability. The results demonstrate the model’s ability to achieve high profitability, efficient service fulfillment, and minimal food waste generation. Sensitivity analysis reveals that selling price, demand, and procurement cost are the most influential factors affecting profitability. In contrast, other cost parameters, such as holding, disposal, and transportation costs, have relatively minor impacts, with profit variations remaining within ± 0.3&#xa0;billion IDR from the baseline scenario. Finally, the proposed model serves as a robust decision-support tool for guiding strategic and operational decisions to improve profitability, service reliability, and sustainability in poultry cold-chain systems under uncertain market conditions.</p> Graphical Abstract <p></p>

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A Sustainable Chicken Meat Supply Chain Optimization Model Incorporating Fuzzy Demand and Food Waste Reduction

  • 期刊论文

  • 发表日期：

    2026年4月18日

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  • Novrianty Rizky,
  • Satrio Yunandaru,
  • Muhammad Al Ghifari Setiawan,
  • Agus Mansur,
  • Ivan Darma Wangsa

摘要

This study presents a fuzzy multi-objective Mixed-Integer Linear Programming (MILP) model to optimize the chicken meat supply chain (CMSC). The model aims to maximize total profit, enhance service level, and minimize food loss and waste while accounting for uncertainty in demand and cost parameters. The proposed framework considers a three-echelon cold-chain network integrating poultry farms, processing and distribution centers, and retail nodes. Optimization was conducted using a solver-based computational framework to ensure both transparency and practical applicability. The results demonstrate the model’s ability to achieve high profitability, efficient service fulfillment, and minimal food waste generation. Sensitivity analysis reveals that selling price, demand, and procurement cost are the most influential factors affecting profitability. In contrast, other cost parameters, such as holding, disposal, and transportation costs, have relatively minor impacts, with profit variations remaining within ± 0.3 billion IDR from the baseline scenario. Finally, the proposed model serves as a robust decision-support tool for guiding strategic and operational decisions to improve profitability, service reliability, and sustainability in poultry cold-chain systems under uncertain market conditions.

Graphical Abstract