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<p>Production planning in make-to-order foundries involves a complex trade-off between meeting customer orders on time, optimizing resource utilization, and controlling costs. These foundries face challenges due to high demand variability and capacity constraints, often resulting in production delays. To address these issues, we propose service levels and capacity utilization constraints for a mixed integer linear programming lot-sizing problem formulation. These constraints prioritize on-time delivery, production throughput, and furnace capacity utilization. Computational experiments conducted across different scenarios highlight important trade-offs: prioritizing service levels improves order fulfillment but raises costs, whereas increasing production capacity does not necessarily result in greater productivity. Additionally, we analyze the impact of these constraints on key performance indicators such as lead time, throughput, and work-in-process, providing practical insights for improving production planning in foundries.</p>

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Using mathematical modeling to meet service levels in production planning in market foundries

  • 期刊论文

  • 发表日期：

    2025年7月12日

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  • Giovanna Abreu Alves,
  • Clarissa Fullin Barco,
  • Roberto Tavares,
  • Victor Claudio Bento Camargo

摘要

Production planning in make-to-order foundries involves a complex trade-off between meeting customer orders on time, optimizing resource utilization, and controlling costs. These foundries face challenges due to high demand variability and capacity constraints, often resulting in production delays. To address these issues, we propose service levels and capacity utilization constraints for a mixed integer linear programming lot-sizing problem formulation. These constraints prioritize on-time delivery, production throughput, and furnace capacity utilization. Computational experiments conducted across different scenarios highlight important trade-offs: prioritizing service levels improves order fulfillment but raises costs, whereas increasing production capacity does not necessarily result in greater productivity. Additionally, we analyze the impact of these constraints on key performance indicators such as lead time, throughput, and work-in-process, providing practical insights for improving production planning in foundries.