Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Prediction-Based Recovery Strategy Under Demand Dynamics

  • 章节

  • 发表日期：

    2026年

查看全文
  • Chen Peng,
  • Hongfeng Wang,
  • Yi Yang,
  • Yong Zhang

摘要

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.