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All Journal International Journal of Enterprise Modelling
Kelle Snyder Han
Breda University of Applied Sciences, Netherland
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Optimizing robust routing and production planning in stochastic supply chains: Addressing uncertainty of timing and demand for enhanced resilience and efficiency Kelle Snyder Han; Kouvelis Geovany Ortizan
International Journal of Enterprise Modelling Vol. 16 No. 2 (2022): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (441.417 KB) | DOI: 10.35335/emod.v16i2.62

Abstract

Unpredictable timing and demand changes can greatly impair supply chain performance and resilience. Optimizing robust routing and production planning in stochastic supply chains improves efficiency and adaptability. Addressing timing and demand uncertainty improves resilience and efficiency. Supply chain management research emphasizes stochastic factors and resilient optimization. This research introduces a mathematical model that accounts for stochastic demand, transportation costs, holding costs, production capabilities, and lead times. The formulation minimizes cost while meeting uncertain demand and capacity constraints. Numerical examples demonstrate the model's use. Due to restrictions, the numerical example results are not supplied, but expected outputs include optimal routing and production plans, total cost minimization, sensitivity analysis, and insights into uncertainty. Comparisons with baseline situations can show how the proposed strategy improves resilience and efficiency. Supply chains may become more resilient, flexible, and efficient by optimizing routing and production planning in uncertainty. This research introduces stochastic components and resilient optimization methods to supply chain management. To improve the proposed approach in real-world supply chains, further research can examine improved algorithms, real-time data integration, and practical implementation strategies.
Co-Authors Kouvelis Geovany Ortizan