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<p>This study introduces a novel multi-factory production scheduling model designed to address challenges in coordinating production and delivery across multiple jobs and factories with diverse due dates. Motivated by the need to minimize total costs while ensuring on-time delivery, the model integrates three key innovations: (1) simultaneous processing of multiple jobs within a batch, (2) application of diverse batching techniques, and (3) coordination of production and delivery batches— aspects not extensively addressed in previous studies. Formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem and linearized to a Mixed-Integer Linear Programming (MILP) model for computational efficiency, the approach demonstrates up to 99% reduction in runtime and 92% fewer explored nodes compared to MINLP, without compromising solution quality. Sensitivity analysis reveals that tardiness penalties have the strongest influence on scheduling decisions, followed by holding and transportation costs. The model’s robustness across scenarios highlights its practical applicability, particularly in industries such as spare parts production, where multiple factories and partners must coordinate tightly to meet strict due dates. Overall, this model provides a decision-support tool to optimize batching strategies, balance costs, and improve operational performance in dynamic manufacturing environments.</p>

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Integrated production and delivery batching for simultaneous multi-Job scheduling in multi-factory environments: modeling, linearization, and optimization

  • 期刊论文

  • 发表日期：

    2025年11月29日

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  • Sinta Rahmawati,
  • Nur Aini Masruroh,
  • Achmad Pratama Rifai

摘要

This study introduces a novel multi-factory production scheduling model designed to address challenges in coordinating production and delivery across multiple jobs and factories with diverse due dates. Motivated by the need to minimize total costs while ensuring on-time delivery, the model integrates three key innovations: (1) simultaneous processing of multiple jobs within a batch, (2) application of diverse batching techniques, and (3) coordination of production and delivery batches— aspects not extensively addressed in previous studies. Formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem and linearized to a Mixed-Integer Linear Programming (MILP) model for computational efficiency, the approach demonstrates up to 99% reduction in runtime and 92% fewer explored nodes compared to MINLP, without compromising solution quality. Sensitivity analysis reveals that tardiness penalties have the strongest influence on scheduling decisions, followed by holding and transportation costs. The model’s robustness across scenarios highlights its practical applicability, particularly in industries such as spare parts production, where multiple factories and partners must coordinate tightly to meet strict due dates. Overall, this model provides a decision-support tool to optimize batching strategies, balance costs, and improve operational performance in dynamic manufacturing environments.