Introduction: Ensuring food security and managing perishable products—particularly in the strategic poultry industry, which supplies the bulk of societal protein—are critical logistical challenges. Poultry products are highly sensitive to temperature variations, and thermal deviations during storage and transportation lead to extensive waste and financial losses. On the other hand, traditional linear distribution approaches can no longer meet sustainable development requirements and waste management needs in this industry. The primary objective of this research is to develop a multi-objective mixed-integer linear programming (MILP) mathematical model to design a sustainable and smart closed-loop supply chain network for the poultry industry. The model simultaneously aims to minimize total system costs (including storage, transportation, procurement, product spoilage damages, and fixed costs) and reduce environmental emissions under Internet of Things (IoT) technology through real-time cold chain monitoring.

Methods: This study formulates waste and the forward and reverse chain flows into a comprehensive, multi-echelon, multi-product, and multi-period mathematical programming model. The proposed model was initially solved and evaluated for small-scale instances using the exact augmented epsilon-constraint method in GAMS. Given the multi-objective and NP-hard nature of the problem at large scales, two multi-objective metaheuristic algorithms were developed in Python: Multi-Objective Particle Swarm Optimization (MOPSO) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The key parameters of both algorithms were meticulously tuned and optimized using the Taguchi experimental design method and Signal-to-Noise (S/N) ratio analysis. Furthermore, to generalize the findings and evaluate the statistical significance of the differences between the algorithms' performance, the non-parametric Wilcoxon signed-rank test was applied across 10 large-scale problem instances.

Results and discussion: The numerical results demonstrated that as the network dimensions expand, both cost and emission objective function values exhibit an upward trend; however, the growth rate of emissions is more sensitive to problem scale than costs. In comparing the metaheuristic algorithms, output analyses and performance metrics indicated that NSGA-II performs better in the Mean Ideal Distance (MID) metric, offering superior convergence toward the true Pareto front. Conversely, MOPSO proved significantly superior in metrics measuring solution diversity and distribution, including the Number of Pareto Solutions (NPS) and the Spacing Metric (SM), while recording much lower computational time (CPU Time). According to the Wilcoxon test results, the P-values for all these metrics were less than 0.05, statistically confirming a significant performance difference between two multi-objective meta-heuristic algorithms.

Conclusion: The findings of this research emphasize that integrating Internet of Things (IoT) technology and real-time temperature monitoring minimizes the risk and cost of product spoilage, providing a dynamic framework to balance economic interests and environmental imperatives in the poultry industry. The closed-loop supply chain approach and the recycling of waste into secondary inputs not only reduce environmental damage but also enhance the economic viability of the network. Given MOPSO's high computational speed and its ability to generate diverse options along the Pareto front, this metaheuristic tool enables managers to accelerate strategic decision-making (such as dynamic facility location and product allocation), thereby maximizing supply chain efficiency and agility in real-world, dynamic environments. Given MOPSO's high computational speed and its ability to generate diverse options along the Pareto front, this metaheuristic tool enables managers to accelerate strategic decision-making (such as dynamic facility location and product allocation), thereby maximizing supply chain efficiency and agility in real-world, dynamic environments.