Simulation-Driven Optimization for Dynamic Production Planning in Cyber-Physical Smart Factories

DOI

10.2991/978-94-6239-750-7_13How to use a DOI?

Keywords

Smart Factory; Dynamic Production Planning; Simulation-Driven Optimization; Industry 4.0; Adaptive Scheduling; Digital Twin; Discrete Event Simulation; Intelligent Manufacturing; Cyber-Physical Systems (CPS); Metaheuristic Algorithms; Industrial Internet of Things (IIoT)

Abstract

The increasing integration of cyber-physical systems (CPS), Industrial Internet of Things (IIoT), and real-time analytics has transformed conventional manufacturing environments into intelligent, data-driven smart factories. However, dynamic production planning in such environments remains a complex multi-objective problem characterized by stochastic demand, machine variability, resource constraints, and real-time disturbances. This research proposes a simulation-driven optimization framework for dynamic production planning in cyber-physical smart factories. The framework integrates discrete-event simulation, digital twin modeling, and metaheuristic optimization to enable adaptive decision-making under uncertainty. A closed-loop architecture is developed wherein real-time shop-floor data continuously update simulation models, allowing predictive evaluation of alternative production schedules. Multi-objective optimization algorithms are employed to simultaneously minimize makespan, operational cost, and energy consumption while maximizing resource utilization and system robustness. The proposed methodology is validated through experimental scenarios reflecting high-mix, low-volume manufacturing conditions. Results demonstrate significant improvements in scheduling flexibility, resilience to disruptions, and overall production efficiency compared to static planning approaches. The study contributes a scalable and computationally efficient decision-support framework that aligns with Industry 4.0 paradigms, enhancing operational intelligence and strategic responsiveness in next-generation smart manufacturing ecosystems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Anand Mohan Dwivedi
AU  - Anurag Singh
AU  - Deepak Agarwal
PY  - 2026
DA  - 2026/08/31
TI  - Simulation-Driven Optimization for Dynamic Production Planning in Cyber-Physical Smart Factories
BT  - Proceedings of the International Conference on Advanced Design, Manufacturing, and Sustainable Energy Systems (ICADMSES 2026)
PB  - Atlantis Press
SP  - 174
EP  - 184
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6239-750-7_13
DO  - 10.2991/978-94-6239-750-7_13
ID  - Dwivedi2026
ER  -