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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 00207543 |
| DOI: | 10.1080/00207543.2025.2585173 |
