Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems
Abstract The Newton-Raphson-Based Optimizer (NRBO), a new metaheuristic algorithm, is
suggested and developed in this paper. The NRBO is inspired by Newton-Raphson's …
suggested and developed in this paper. The NRBO is inspired by Newton-Raphson's …
Designing an adaptive and deep learning based control framework for modular production systems
In today's rapidly changing production landscape with increasingly complex manufacturing
processes and shortening product life cycles, a company's competitiveness depends on its …
processes and shortening product life cycles, a company's competitiveness depends on its …
A Hierarchical Multi-Action Deep Reinforcement Learning Method for Dynamic Distributed Job-Shop Scheduling Problem With Job Arrivals
The Distributed Job-shop Scheduling Problem (DJSP) is a significant issue in both
academic and industrial fields. In real-world production, uncertain disturbances such as job …
academic and industrial fields. In real-world production, uncertain disturbances such as job …
Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation
Y Li, X Li, L Gao, Z Lu - Robotics and Computer-Integrated Manufacturing, 2025 - Elsevier
Reconfigurable manufacturing system is considered as a promising next-generation
manufacturing paradigm. However, limited equipment and complex product processes add …
manufacturing paradigm. However, limited equipment and complex product processes add …
Reinforcement Learning for Two-Stage Permutation Flow Shop Scheduling—A Real-World Application in Household Appliance Production
Solving production scheduling problems is a difficult and indispensable task for
manufacturers with a push-oriented planning approach. In this study, we tackle a novel …
manufacturers with a push-oriented planning approach. In this study, we tackle a novel …
Robustness prediction in dynamic production processes—a new surrogate measure based on regression machine learning
F Grumbach, A Müller, P Reusch, S Trojahn - Processes, 2023 - mdpi.com
This feasibility study utilized regression models to predict makespan robustness in dynamic
production processes with uncertain processing times. Previous methods for robustness …
production processes with uncertain processing times. Previous methods for robustness …
A Reinforcement Learning Approach to Robust Scheduling of Permutation Flow Shop
T Zhou, L Luo, S Ji, Y He - Biomimetics, 2023 - mdpi.com
The permutation flow shop scheduling problem (PFSP) stands as a classic conundrum
within the realm of combinatorial optimization, serving as a prevalent organizational …
within the realm of combinatorial optimization, serving as a prevalent organizational …
Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution
Manufacturing systems develop toward cloud-edge collaboration where manufacturing and
computation are tightly coupled. Under this circumstance, large-scale hybrid tasks that …
computation are tightly coupled. Under this circumstance, large-scale hybrid tasks that …
An improved memetic algorithm for distributed hybrid flow shop scheduling problem with operation inspection and reprocessing
Y Zheng, N Peng, H Qi, G Gong… - Measurement and …, 2024 - journals.sagepub.com
The classical distributed hybrid flow shop scheduling problem (DHFSP) only considers static
production settings while ignores operation inspection and reprocessing. However, in the …
production settings while ignores operation inspection and reprocessing. However, in the …
[PDF][PDF] Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning. Processes 2023, 11, 1267
F Grumbach, A Müller, P Reusch… - … mit Techniken des …, 2024 - researchgate.net
This feasibility study utilized regression models to predict makespan robustness in dynamic
production processes with uncertain processing times. Previous methods for robustness …
production processes with uncertain processing times. Previous methods for robustness …