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<p>Climate variability increasingly disrupts production-inventory systems by intensifying demand uncertainty, accelerating deterioration, and elevating shortage risks. This study develops a climate-responsive two-phase economic production quantity framework in which inventory dynamics are governed by stochastic differential equations. Three progressively enriched decision settings are analyzed: diffusion-driven uncertainty, diffusion with climate-induced shocks and emergency logistics, and proactive resilience design through packaging, hardening, and multi-sourcing. Risk exposure is controlled using chance constraints on service reliability and Conditional Value-at-Risk to penalize extreme outcomes. Closed-form moment expressions enable efficient evaluation of cycle costs and risks, allowing joint optimization of operational and resilience decisions. Numerical results show that resilience design consistently outperforms reactive strategies, reducing average total cost by approximately <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(2.2\%\)</EquationSource> </InlineEquation>, lowering waste by nearly <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(10\%\)</EquationSource> </InlineEquation>, and decreasing shortage risk by about <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(5.5\%\)</EquationSource> </InlineEquation>. Emergency logistics alone provides negligible improvement under baseline conditions. Sensitivity analysis under intensified climate stress confirms the robustness of resilience-oriented policies. Overall, the results demonstrate that proactive, design-based resilience investments yield superior economic and risk performance for climate-sensitive food, pharmaceutical, and agro-industrial supply chains.</p>

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A stochastic framework for climate-responsive inventory optimization with resilience design under uncertainty

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Prabal Das,
  • Nabendu Sen

摘要

Climate variability increasingly disrupts production-inventory systems by intensifying demand uncertainty, accelerating deterioration, and elevating shortage risks. This study develops a climate-responsive two-phase economic production quantity framework in which inventory dynamics are governed by stochastic differential equations. Three progressively enriched decision settings are analyzed: diffusion-driven uncertainty, diffusion with climate-induced shocks and emergency logistics, and proactive resilience design through packaging, hardening, and multi-sourcing. Risk exposure is controlled using chance constraints on service reliability and Conditional Value-at-Risk to penalize extreme outcomes. Closed-form moment expressions enable efficient evaluation of cycle costs and risks, allowing joint optimization of operational and resilience decisions. Numerical results show that resilience design consistently outperforms reactive strategies, reducing average total cost by approximately \(2.2\%\) , lowering waste by nearly \(10\%\) , and decreasing shortage risk by about \(5.5\%\) . Emergency logistics alone provides negligible improvement under baseline conditions. Sensitivity analysis under intensified climate stress confirms the robustness of resilience-oriented policies. Overall, the results demonstrate that proactive, design-based resilience investments yield superior economic and risk performance for climate-sensitive food, pharmaceutical, and agro-industrial supply chains.