Multi-objective non-linear cold storage capacity model for optimizing sustainability in bimodal climate regions

Cold storage is crucial for ensuring food security and optimizing supply chain efficiency, particularly in tropical regions with seasonal demand fluctuations. Existing optimization models often focus solely on economic and environmental aspects, neglecting a holistic sustainability approach and the uncertainty of seasonal demand fluctuations. This study develops a multi-objective non-linear programming (MO-NLP) model that optimizes economic profit, carbon emissions, and food security, incorporating sustainability weights (λ1, λ2, λ3) to accommodate different priorities. The model considers stochastic demand and land area, budget, and electricity constraints. The results show that the model effectively balances profitability, environmental impact, and food security. In the standard scenario, the optimal cold storage area is 256 m², resulting in a profit of USD 742,368, carbon costs of USD 122,579, and a probability of 0.9962 for food security. Sensitivity analysis indicates that increasing land availability improves performance but eventually reaches a saturation point, while seasonal demand fluctuations have a significant impact on decisions. A higher food security weight (λ3) stabilizes supply but reduces profitability, whereas profit dominance (λ1) boosts income but compromises environmental and social aspects. This study provides an optimization-based tool for industry stakeholders and policymakers. Integrating sustainability into a single framework provides an adaptive and efficient approach to cold storage planning in the face of economic and environmental uncertainties.

Abstract licence: CC BY-NC-SA

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