Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
AI-enabled predictive scheduling for minimizing downtime in robotic distribution systems. |
| Συγγραφείς: |
Bandyopadhyay, Anupam1 (AUTHOR) b.anupam23@gmail.com, Gupta, Ashish2 (AUTHOR) |
| Πηγή: |
International Journal of Advanced Manufacturing Technology. Sep2026, p1-17. 17p. |
| Θεματικοί όροι: |
*Scheduling, *System downtime, *Boosting algorithms, *Particle swarm optimization, *Automated materials handling, *Random forest algorithms, *Industry 4.0 |
| Περίληψη: |
The modern industrial automation needs intelligent coordination of Robotic Distribution Systems (RDS) to reduce downtime and maximize the use of resources. This paper suggests an AI-based predictive scheduling model to combine a hybrid model of Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and ensemble-based downtime predictions. The framework is a hybrid which incorporates predictive intelligence and adaptive scheduling to reach dynamic optimization in robotic task allocation. The down time events are predicted with an impressive accuracy of 98.30% in a hybrid ensemble of Random Forest, XGboost, LightGBM, and Long Short-Term Memory (LSTM) networks, and the GA-PSO scheduler further enhances the efficiency of the tasks, throughput, and resource use. Simulation outcomes show that there is a great deal of performance improvements which include the following; downtime is cut down by 6.00 to 1.00, throughput improved by 5.5% and robot utilization improved by 5.3% over base models. The hybrid method suggested is better than the classical methods like LSTM, LightGBM, XGBoost, and Random Forest in predictive accuracy and time schedule parameters. The flexibility and resilience of the system attest to its possible usage in Industry 4.0 applications that need proactive maintenance and never-ending functioning. In general, the suggested model helps to fix the gap between predictive maintenance and intelligent scheduling and make real-time decisions based on the data to build resilient robotic distribution networks. [ABSTRACT FROM AUTHOR] |
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