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A simultaneous batching and sequencing problem for injection molding process: an industrial case study.

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Title: A simultaneous batching and sequencing problem for injection molding process: an industrial case study.
Authors: Behiri, W.1 (AUTHOR) walid.behiri@gmail.com, Belmokhtar-Berraf, S.1 (AUTHOR), Chu, C.1 (AUTHOR), La Palombara, N.1 (AUTHOR), Sali, M.2 (AUTHOR)
Source: International Journal of Production Research. Apr2026, Vol. 64 Issue 8, p2996-3019. 24p.
Subjects: Injection molding, Batch processing, Inventory control, Scheduling, Production planning, Mathematical optimization, Metaheuristic algorithms
Abstract: This study focuses on simultaneous batching and sequencing within a real-life injection molding process. This is an operational-level production planning problem with a fine temporal granularity, as the period is very short, causing setups to overlap over several periods. However, it takes into account demand satisfaction as in classical DLSPs (discrete lot-sizing and scheduling problems). In addition, batch sequencing must be determined by considering sequence-dependent setup times and inserted idle times. Several practical constraints, including minimum coverage stock, inventory capacity, and minimum batch size, are taken into account. The objective is to minimise backorders and coverage stock shortages while maximising throughput. A lexicographic approach is used to address this multi-criteria feature. An initial formulation based on sequencing batching models (BSM) is proposed, but it can only solve very small-size instances. Therefore, an alternative time-indexed machine-states-based model (TSM) is proposed. This latter model shows strong ability to handle small- and medium-size instances. An experimental study analyses the impact of key parameters and classifies instances into scenario types based on hardness levels. A sensitivity analysis highlights limitations in the partner company's decision making and suggests improvements. Finally, an ant colony optimisation metaheuristic is proposed to efficiently handle large-scale, real-world instances. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: A simultaneous batching and sequencing problem for injection molding process: an industrial case study.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Apr2026, Vol. 64 Issue 8, p2996-3019. 24p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Injection+molding%22">Injection molding</searchLink><br /><searchLink fieldCode="DE" term="%22Batch+processing%22">Batch processing</searchLink><br /><searchLink fieldCode="DE" term="%22Inventory+control%22">Inventory control</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Production+planning%22">Production planning</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study focuses on simultaneous batching and sequencing within a real-life injection molding process. This is an operational-level production planning problem with a fine temporal granularity, as the period is very short, causing setups to overlap over several periods. However, it takes into account demand satisfaction as in classical DLSPs (discrete lot-sizing and scheduling problems). In addition, batch sequencing must be determined by considering sequence-dependent setup times and inserted idle times. Several practical constraints, including minimum coverage stock, inventory capacity, and minimum batch size, are taken into account. The objective is to minimise backorders and coverage stock shortages while maximising throughput. A lexicographic approach is used to address this multi-criteria feature. An initial formulation based on sequencing batching models (BSM) is proposed, but it can only solve very small-size instances. Therefore, an alternative time-indexed machine-states-based model (TSM) is proposed. This latter model shows strong ability to handle small- and medium-size instances. An experimental study analyses the impact of key parameters and classifies instances into scenario types based on hardness levels. A sensitivity analysis highlights limitations in the partner company's decision making and suggests improvements. Finally, an ant colony optimisation metaheuristic is proposed to efficiently handle large-scale, real-world instances. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207543.2025.2585173
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        Text: English
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        Type: general
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      – SubjectFull: Inventory control
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      – SubjectFull: Production planning
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      – SubjectFull: Mathematical optimization
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      – SubjectFull: Metaheuristic algorithms
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              Text: Apr2026
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