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SIMULATION-DRIVEN OPTIMIZATION FOR PRODUCTION PLANNING IN EQUIPMENT MANUFACTURING.

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Title: SIMULATION-DRIVEN OPTIMIZATION FOR PRODUCTION PLANNING IN EQUIPMENT MANUFACTURING.
Authors: Chen, N.1, Li, H. X.2 lihanxiong@xauat.edu.cn, Ma, J.1, Lv, N.1
Source: International Journal of Simulation Modelling (IJSIMM). Jun2026, Vol. 25 Issue 2, p318-329. 12p.
Subjects: Production planning, Discrete event simulation, Scheduling, Manufacturing industry equipment, Dynamic simulation, Predictive control systems, Production scheduling
Abstract: Equipment manufacturing is characterized by multi-variety, small-batch production and frequent dynamic disturbances. Conventional production simulation is mainly used for offline validation and is rarely integrated with production planning, which limits its applicability in dynamic environments. This study develops a simulation-driven closed-loop rolling optimization framework based on a multi-agent discrete event simulation model. The framework integrates rolling horizon control, real-time interaction between simulation and optimization, and disturbance-triggered rescheduling. A coupling mechanism between the simulation clock and optimization process is established to support continuous plan adjustment. Simulation experiments in a real manufacturing workshop show that the proposed approach reduces order tardiness and makespan while maintaining stable equipment utilization under dynamic disturbances, compared with offline simulation-based optimization and conventional scheduling methods. The results indicate that production simulation can be integrated into planning decisions and used for dynamic adjustment in complex manufacturing environments. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Simulation Modelling (IJSIMM) is the property of DAAAM International 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.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 194217262
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PubType: Academic Journal
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  Data: SIMULATION-DRIVEN OPTIMIZATION FOR PRODUCTION PLANNING IN EQUIPMENT MANUFACTURING.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+N%2E%22">Chen, N.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+H%2E+X%2E%22">Li, H. X.</searchLink><relatesTo>2</relatesTo><i> lihanxiong@xauat.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+J%2E%22">Ma, J.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lv%2C+N%2E%22">Lv, N.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Simulation+Modelling+%28IJSIMM%29%22">International Journal of Simulation Modelling (IJSIMM)</searchLink>. Jun2026, Vol. 25 Issue 2, p318-329. 12p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Production+planning%22">Production planning</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+event+simulation%22">Discrete event simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+industry+equipment%22">Manufacturing industry equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+simulation%22">Dynamic simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink>
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  Label: Abstract
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  Data: Equipment manufacturing is characterized by multi-variety, small-batch production and frequent dynamic disturbances. Conventional production simulation is mainly used for offline validation and is rarely integrated with production planning, which limits its applicability in dynamic environments. This study develops a simulation-driven closed-loop rolling optimization framework based on a multi-agent discrete event simulation model. The framework integrates rolling horizon control, real-time interaction between simulation and optimization, and disturbance-triggered rescheduling. A coupling mechanism between the simulation clock and optimization process is established to support continuous plan adjustment. Simulation experiments in a real manufacturing workshop show that the proposed approach reduces order tardiness and makespan while maintaining stable equipment utilization under dynamic disturbances, compared with offline simulation-based optimization and conventional scheduling methods. The results indicate that production simulation can be integrated into planning decisions and used for dynamic adjustment in complex manufacturing environments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Simulation Modelling (IJSIMM) is the property of DAAAM International 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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      – Type: doi
        Value: 10.2507/IJSIMM25-2-CO7
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 318
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      – SubjectFull: Production planning
        Type: general
      – SubjectFull: Discrete event simulation
        Type: general
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Manufacturing industry equipment
        Type: general
      – SubjectFull: Dynamic simulation
        Type: general
      – SubjectFull: Predictive control systems
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      – SubjectFull: Production scheduling
        Type: general
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      – TitleFull: SIMULATION-DRIVEN OPTIMIZATION FOR PRODUCTION PLANNING IN EQUIPMENT MANUFACTURING.
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            NameFull: Chen, N.
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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