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Multi-process scheduling with integrated order acceptance and preventive maintenance.

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Title: Multi-process scheduling with integrated order acceptance and preventive maintenance.
Authors: Piri, Salman1 (AUTHOR) piri.salman@gmail.com, Manafi, Bahman2 (AUTHOR) Bahman.Manafi@Gmail.com, Gholizadeh, Hadi3 (AUTHOR) hadi.gholi-zadeh.1@ulaval.ca
Source: International Journal of Advanced Manufacturing Technology. Jun2026, Vol. 144 Issue 7/8, p5789-5829. 41p.
Subjects: Multi-objective optimization, Plant maintenance, Stochastic models, Production planning, Production management (Manufacturing), Robust optimization, Order management systems
Abstract: This study develops a robust and resilient multi-objective decision-support framework for the integrated order acceptance, production planning, scheduling, and preventive maintenance (OAPPS-PM) problem under uncertainty. While most existing models address these decisions separately or only partially integrate them, this research proposes a unified Multi-Objective Mixed-Integer Nonlinear Programming (MOMINLP) formulation that concurrently minimizes total cost and delivery delay while maximizing order fulfillment. The model captures real-world manufacturing complexities, including machine availability, material lead times, task precedence, and preventive maintenance scheduling. Unlike conventional robust optimization approaches that rely on fixed uncertainty sets, a scenario-based adaptive uncertainty modeling scheme is introduced, allowing dynamic representation of stochastic variations in processing times, lead times, and equipment reliability. Risk aversion and infeasibility penalties are incorporated to flexibly control trade-offs between robustness and performance. To address computational challenges, two complementary solution strategies are developed, the Enhanced Augmented ε-Constraint (EAE-ε) method to generate a well-balanced Pareto frontier and a Hybrid Adaptive Large Neighborhood Search with Scenario Grading and Surrogate Evaluation (HALNS-SGSE) that utilizes XGBoost surrogate models for rapid scenario evaluation for large-scale instances. Application to a real-world battery manufacturing case demonstrates that HALNS-SGSE achieves cost reductions of up to 7.3%, delay reductions of 11.6%, and order fulfillment improvements of 6.2% compared to baseline heuristics, while maintaining average infeasibility below 0.05 across 30 stochastic scenarios. The proposed framework thus provides an effective and generalizable tool for resilient decision-making in uncertain production environments. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature 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: 194545568
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Multi-process scheduling with integrated order acceptance and preventive maintenance.
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  Data: <searchLink fieldCode="AR" term="%22Piri%2C+Salman%22">Piri, Salman</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> piri.salman@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Manafi%2C+Bahman%22">Manafi, Bahman</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> Bahman.Manafi@Gmail.com</i><br /><searchLink fieldCode="AR" term="%22Gholizadeh%2C+Hadi%22">Gholizadeh, Hadi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> hadi.gholi-zadeh.1@ulaval.ca</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. Jun2026, Vol. 144 Issue 7/8, p5789-5829. 41p.
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  Data: <searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+maintenance%22">Plant maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br /><searchLink fieldCode="DE" term="%22Production+planning%22">Production planning</searchLink><br /><searchLink fieldCode="DE" term="%22Production+management+%28Manufacturing%29%22">Production management (Manufacturing)</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+optimization%22">Robust optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Order+management+systems%22">Order management systems</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This study develops a robust and resilient multi-objective decision-support framework for the integrated order acceptance, production planning, scheduling, and preventive maintenance (OAPPS-PM) problem under uncertainty. While most existing models address these decisions separately or only partially integrate them, this research proposes a unified Multi-Objective Mixed-Integer Nonlinear Programming (MOMINLP) formulation that concurrently minimizes total cost and delivery delay while maximizing order fulfillment. The model captures real-world manufacturing complexities, including machine availability, material lead times, task precedence, and preventive maintenance scheduling. Unlike conventional robust optimization approaches that rely on fixed uncertainty sets, a scenario-based adaptive uncertainty modeling scheme is introduced, allowing dynamic representation of stochastic variations in processing times, lead times, and equipment reliability. Risk aversion and infeasibility penalties are incorporated to flexibly control trade-offs between robustness and performance. To address computational challenges, two complementary solution strategies are developed, the Enhanced Augmented ε-Constraint (EAE-ε) method to generate a well-balanced Pareto frontier and a Hybrid Adaptive Large Neighborhood Search with Scenario Grading and Surrogate Evaluation (HALNS-SGSE) that utilizes XGBoost surrogate models for rapid scenario evaluation for large-scale instances. Application to a real-world battery manufacturing case demonstrates that HALNS-SGSE achieves cost reductions of up to 7.3%, delay reductions of 11.6%, and order fulfillment improvements of 6.2% compared to baseline heuristics, while maintaining average infeasibility below 0.05 across 30 stochastic scenarios. The proposed framework thus provides an effective and generalizable tool for resilient decision-making in uncertain production environments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature 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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RecordInfo BibRecord:
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        Value: 10.1007/s00170-026-18178-3
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      – Code: eng
        Text: English
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        PageCount: 41
        StartPage: 5789
    Subjects:
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Plant maintenance
        Type: general
      – SubjectFull: Stochastic models
        Type: general
      – SubjectFull: Production planning
        Type: general
      – SubjectFull: Production management (Manufacturing)
        Type: general
      – SubjectFull: Robust optimization
        Type: general
      – SubjectFull: Order management systems
        Type: general
    Titles:
      – TitleFull: Multi-process scheduling with integrated order acceptance and preventive maintenance.
        Type: main
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            NameFull: Piri, Salman
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            NameFull: Manafi, Bahman
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            NameFull: Gholizadeh, Hadi
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 7/8
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            – TitleFull: International Journal of Advanced Manufacturing Technology
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