A review of challenges and solution approaches in smart manufacturing: a view on scheduling in the Industry 4.0 era

Hassan Esfahani; Mohammad Mahdi Nasiri; Fariborz Jolai; Frank Werner; Hassan Esfahani; Mohammad Mahdi Nasiri; Fariborz Jolai; Frank Werner

doi:10.3934/jimo.2026090

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 5: 2451-2478. doi: 10.3934/jimo.2026090

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A review of challenges and solution approaches in smart manufacturing: a view on scheduling in the Industry 4.0 era

1.
Department of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran
2.
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
3.
Faculty of Mathematics, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

Received: 22 December 2025 Revised: 22 March 2026 Accepted: 08 April 2026 Published: 27 April 2026
90-02, 90B35

In this systematic literature review, we addressed the critical gap in smart manufacturing scheduling research by developing an integrated framework that bridges problem evolution characteristics with solution method suitability. First, we identified that Industry 4.0 technologies fundamentally transform production scheduling through three defining characteristics: Distributed decision-making, Dynamic adaptability, and Flexible operations, collectively termed the DDF paradigm. Second, employing a PRISMA-guided methodology, we analyzed 44 problem-solving studies published between 2016 and 2025 to systematically evaluate existing solution approaches. Our analysis revealed that only 16% of the reviewed studies simultaneously address all three DDF characteristics, highlighting a significant research gap. Furthermore, we demonstrated distinct performance trade-offs among three major methodological families: mathematical programming (including optimal control), simulation and multi-agent systems, and heuristic/metaheuristic algorithms. Mathematical programming, particularly optimal program control, offers rigorous handling of structural dynamics but faces scalability challenges. Simulation and multi-agent systems provide high real-time suitability for decentralized environments, while metaheuristics remain the most frequently applied approach due to their flexibility in complex search spaces. Based on these findings, we proposed a comprehensive research roadmap that extends the DDF framework to incorporate emerging Industry 5.0 requirements for resilience, sustainability, and human-centricity. This review provides practitioners with actionable guidance for method selection and offers researchers a structured agenda for advancing toward more integrated, adaptive, and human-centric scheduling systems in the era of intelligent manufacturing.
- scheduling,
- smart manufacturing,
- industrial management,
- Industry 4.0,
- systematic review
Citation: Hassan Esfahani, Mohammad Mahdi Nasiri, Fariborz Jolai, Frank Werner. A review of challenges and solution approaches in smart manufacturing: a view on scheduling in the Industry 4.0 era[J]. Journal of Industrial and Management Optimization, 2026, 22(5): 2451-2478. doi: 10.3934/jimo.2026090

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Abstract

In this systematic literature review, we addressed the critical gap in smart manufacturing scheduling research by developing an integrated framework that bridges problem evolution characteristics with solution method suitability. First, we identified that Industry 4.0 technologies fundamentally transform production scheduling through three defining characteristics: Distributed decision-making, Dynamic adaptability, and Flexible operations, collectively termed the DDF paradigm. Second, employing a PRISMA-guided methodology, we analyzed 44 problem-solving studies published between 2016 and 2025 to systematically evaluate existing solution approaches. Our analysis revealed that only 16% of the reviewed studies simultaneously address all three DDF characteristics, highlighting a significant research gap. Furthermore, we demonstrated distinct performance trade-offs among three major methodological families: mathematical programming (including optimal control), simulation and multi-agent systems, and heuristic/metaheuristic algorithms. Mathematical programming, particularly optimal program control, offers rigorous handling of structural dynamics but faces scalability challenges. Simulation and multi-agent systems provide high real-time suitability for decentralized environments, while metaheuristics remain the most frequently applied approach due to their flexibility in complex search spaces. Based on these findings, we proposed a comprehensive research roadmap that extends the DDF framework to incorporate emerging Industry 5.0 requirements for resilience, sustainability, and human-centricity. This review provides practitioners with actionable guidance for method selection and offers researchers a structured agenda for advancing toward more integrated, adaptive, and human-centric scheduling systems in the era of intelligent manufacturing.

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