Search Results - "Reinforcement learning"

Shields for Safe Reinforcement Learning.

Authors: Könighofer, Bettina Bloem, Roderick Jansen, Nils et al.

Source: Communications of the ACM. Nov2025, Vol. 68 Issue 11, p80-90. 11p.

Subject Terms: *ARTIFICIAL intelligence, REINFORCEMENT learning, SAFETY, DRONE aircraft, DEEP reinforcement learning

Facility layout optimisation of an intelligent manufacturing unit based on queueing network and deep reinforcement learning.

Authors: Li, Huan Zhang, Hui-yu Liao, Yong et al.

Source: International Journal of Production Research. Oct2025, p1-24. 24p. 15 Illustrations.

Subject Terms: *QUEUEING networks, *RESOURCE management, *INDUSTRIAL efficiency, *INDUSTRY 4.0, *DECISION making, *MARKOV processes, *PLANT layout, REINFORCEMENT learning

Bayesian Design Principles for Frequentist Sequential Learning.

Authors: Xu, Yunbei Zeevi, Assaf

Source: Journal of the ACM. Oct2025, Vol. 72 Issue 5, p1-65. 65p.

Subject Terms: *BAYESIAN analysis, *MATHEMATICAL optimization, MULTI-armed bandit problem (Probability theory), REINFORCEMENT learning, MACHINE learning

Robust seru production optimisation under uncertain worker processing times.

Authors: Wen, Muyang Zhang, Yuli Hu, Linyuan et al.

Source: International Journal of Production Research. Oct2025, Vol. 63 Issue 20, p7687-7727. 41p.

Subject Terms: *FLEXIBLE manufacturing systems, *ANALYSIS of variance, *LABOR time, ROBUST optimization, REINFORCEMENT learning, CONSTRAINT satisfaction, EVOLUTIONARY computation, OPTIMIZATION algorithms

Dynamic scheduling for flexible job-shop with reconfigurable manufacturing cells considering dynamic job arrivals based on deep reinforcement learning.

Authors: Zheng, Liang Chen, Xiaodi Zhuang, Cunbo et al.

Source: International Journal of Production Research. Oct2025, Vol. 63 Issue 20, p7427-7459. 33p.

Subject Terms: *SCHEDULING, *MANUFACTURING cells, REINFORCEMENT learning

Rediscovering Reinforcement Learning.

Authors: Barto, Andrew G.¹ (AUTHOR) barto@cs.umass.edu

Source: Communications of the ACM. Dec2025, Vol. 68 Issue 12, p98-102. 5p.

Subject Terms: *TECHNOLOGICAL progress, *GOVERNMENT aid to research, REINFORCEMENT learning

Company/Entity: UNITED States. Air Force. Office of Scientific Research

Mechanism driven robotic compliant disassembly strategy for disassembling peg-holes under different maintenance conditions.

Authors: Liu, Jiayi Zhang, Xiaolong Xu, Wenjun et al.

Source: International Journal of Production Research. Sep2025, p1-19. 19p. 10 Illustrations.

Subject Terms: *REMANUFACTURING, REINFORCEMENT learning, MECHANICAL models, CONDITION-based maintenance

Dynamic flexible job shop scheduling problem considering multiple types of dynamic events.

Authors: Yi, Wenchao Deng, Qing Wang, Boyu et al.

Source: International Journal of Production Research. Aug2025, p1-18. 18p. 4 Illustrations.

Subject Terms: *SCHEDULING, *PRODUCTION scheduling, *MANUFACTURING industries, REINFORCEMENT learning, COMPUTATIONAL complexity, TARDINESS

Digital twin-driven deep reinforcement learning for real-time optimisation in dynamic AGV systems.

Authors: Lee, Donggun Kang, Yong-Shin Noh, Sang Do

Source: International Journal of Production Research. Aug2025, p1-19. 19p. 9 Illustrations.

Subject Terms: *REAL-time computing, *LOGISTICS, DIGITAL twin, REINFORCEMENT learning, ROBOTIC path planning, DYNAMICAL systems, AUTOMATED guided vehicle systems

Inventory replenishment and fulfilment decisions for an omnichannel retailer: a reinforcement learning-based method.

Authors: Kolyaei, Maryam Zhang, Lele Blom, Michelle

Source: International Journal of Production Research. Aug2025, p1-22. 22p. 5 Illustrations.

Subject Terms: *INVENTORY control, *RETAIL industry, *MARKOV processes, *ECONOMIC demand, *PROFIT maximization, REINFORCEMENT learning

A hierarchical reinforcement learning approach for real-time berth allocation and quay crane scheduling.

Authors: Jo, Seongbae Moon, Ilkyeong

Source: International Journal of Production Research. Aug2025, p1-26. 26p. 15 Illustrations.

Subject Terms: *HARBOR management, *RESOURCE allocation, REINFORCEMENT learning, SUSTAINABILITY, CRANES (Machinery), CARBON emissions, CONTAINER terminals

Actor-critic driven deep reinforcement learning for optimising agri-food supply chain.

Authors: Shukla, Aditya Kakde, Shubham Tanaji Mitra, Rony et al.

Source: International Journal of Production Research. Jul2025, p1-20. 20p. 9 Illustrations.

Subject Terms: *DECISION making, *RESOURCE allocation, *SUGAR industry, REINFORCEMENT learning, FOOD production

The beer game bullwhip effect mitigation: a deep reinforcement learning approach.

Authors: Rozhkov, Maxim Alyamovskaya, Nataliya Zakhodiakin, Gleb

Source: International Journal of Production Research. Sep2025, Vol. 63 Issue 18, p6630-6647. 18p.

Subject Terms: *SUPPLY chains, *SUPPLY chain disruptions, *INVENTORY control, REINFORCEMENT learning, DEMAND forecasting, REWARD (Psychology)

A novel hybrid intelligent scheduling: integrating human feedback into reinforcement learning for adaptive preference objectives.

Authors: Ding, Chen Qiao, Fei Wang, Dongyuan et al.

Source: International Journal of Production Research. Aug2025, Vol. 63 Issue 16, p6037-6055. 19p.

Subject Terms: *SCHEDULING, *AEROSPACE industries, *AUTOMATED planning & scheduling, REINFORCEMENT learning, INDIVIDUALS' preferences, MULTI-objective optimization

Flexible material handling system for multi-load autonomous mobile robots in manufacturing environments: a hierarchical reinforcement learning approach.

Authors: Park, Keonwoo Jo, Seongbae Shin, Youngchul et al.

Source: International Journal of Production Research. Aug2025, Vol. 63 Issue 15, p5671-5691. 21p.

Subject Terms: *AUTOMATED materials handling, *PRODUCTION scheduling, *MANUFACTURING industries, *PROCESS optimization, REINFORCEMENT learning, MOBILE robots, GRAPH neural networks, ROBOTIC path planning

Deep reinforcement learning for AgPd-based multimetallic nanoclusters: Accelerating global minimum discovery in high-entropy alloy systems.

Authors: Mubeen, Malik Ahmed Chen, Fuyi

Source: Journal of Chemical Physics. 11/7/2025, Vol. 163 Issue 17, p1-15. 15p.

Subject Terms: *DEEP reinforcement learning, *POTENTIAL energy, *GENETIC algorithms, *HIGH-entropy alloys, *THERMAL stability, *MOLECULAR dynamics

Improving efficiency of steel converter facility: a digital twin and reinforcement learning approach.

Authors: Kumar, Amit Kumar, Ramesh

Source: International Journal of Production Research. Apr2025, p1-16. 16p. 11 Illustrations.

Subject Terms: DEEP reinforcement learning, REINFORCEMENT learning, DISCRETE event simulation, DIGITAL twin, LIQUID iron

An end-to-end decentralised scheduling framework based on deep reinforcement learning for dynamic distributed heterogeneous flowshop scheduling.

Authors: Li, Haoran Gao, Liang Fan, Qingsong et al.

Source: International Journal of Production Research. Jun2025, Vol. 63 Issue 12, p4368-4388. 21p.

Subject Terms: REINFORCEMENT learning, DEEP reinforcement learning, PARTIALLY observable Markov decision processes, FEATURE extraction, AUTODIDACTICISM

A reinforcement learning-based approach for solving multi-agent job shop scheduling problem.

Authors: Dong, Zhuoran Ren, Tao Qi, Fang et al.

Source: International Journal of Production Research. May2025, Vol. 63 Issue 10, p3512-3537. 26p.

Subject Terms: *PRODUCTION scheduling, *BEES algorithm, *PRODUCTION planning, REINFORCEMENT learning, DEEP reinforcement learning

A scalable Deep Q-Learning approach for hot stamping process under dynamic control environment.

Authors: Nievas, Nuria Pagès-Bernaus, Adela Abio, Albert et al.

Source: International Journal of Production Research. Apr2025, Vol. 63 Issue 8, p2831-2852. 22p.

Subject Terms: *ARTIFICIAL intelligence, *MANUFACTURING processes, DEEP reinforcement learning, REINFORCEMENT learning, FOIL stamping