<p>The Multi-Swarm Particle Swarm Optimization (MPSO) algorithm was employed to solve a Multi-Objective Linear Programming (MOLP) model developed to enhance sustainability in the cold supply chain of a major milk-processing industrial plant in Kerala, South India. The proposed framework determines the optimal quantity of raw milk to be procured from both local and external suppliers, ensuring a balance between economic efficiency, environmental stability, and sustainable resource utilization. The study aims to simultaneously minimize total cost, carbon emissions, and effluent water generated during milk processing. A normalised weighted-sum scalarization technique was adopted to address the multi-objective nature of the problem. The optimization algorithm, implemented in Python using the PyCharm environment, reveals that deviations beyond the optimal procurement and processing levels lead to increased costs and elevated environmental impacts. The sensitivity analysis results collectively indicate that environmental indicators, particularly effluent water generation, are more strongly affected by simultaneous changes in operational parameters than economic outcomes. These findings highlight the significance of local supplier engagement, efficient manufacturing practices, and emission reduction strategies as essential contributors to sustainable decision-making in the dairy supply chain. Model validation was performed by comparing the optimized outputs with the actual performance of the industrial plant, and a comparative study with other PSO variants confirms that MPSO provides stable and reliable performance even under varying milk-demand conditions.</p>