A Prediction-Based Recovery Strategy Under Demand Dynamics

Chen Peng (), Hongfeng Wang (), Yi Yang () and Yong Zhang ()
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Chen Peng: Shanghai University, School of Mechatronic Engineering and Automation
Hongfeng Wang: Northeastern University
Yi Yang: Shanghai University
Yong Zhang: China University of Mining and Technology, School of Information and Control Engineering

Chapter Chapter 6 in Modeling and Resilience Recovery for Disrupted Supply Chain, 2026, pp 121-137 from Springer

Abstract: Abstract To address long-term SC disruptions under COVID-19 with dynamic customer demand, this chapter proposes a prediction-based recovery strategy integrated with product change. A data-driven demand forecasting method with feedback errors is designed to predict future demand. A bi-objective mixed-integer programming (MIP) model is established for optimal supply portfolio selection, followed by goods allocation and order fulfillment strategies. A three-stage heuristic algorithm is developed to solve the integrated problem. A case study on Dongsheng Electronics verifies the strategy’s effectiveness: it reduces unit product cost and improves service level compared to the original method. Sensitivity analysis of product change cost further reveals its impact on SC performance.

Keywords: Prediction-based optimization; Supply chain recovery; Product change; Disruption mitigation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-95-4901-6_6

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DOI: 10.1007/978-981-95-4901-6_6

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