Results 111 to 120 of about 7,634,020 (164)

Editorial: Dynamic Scheduling Problems. [PDF]

J Sched, 2020
Agnetis A, Gawiejnowicz S, Lin BM, Mosheiov G.   +3 more
europepmc   +1 more source

The Research on a Collaborative Management Model for Multi-Source Heterogeneous Data Based on OPC Communication. [PDF]

Sensors (Basel)
Tian J, Shang C, Ren T, Li Z, Zhang E, Yang J, He M.   +6 more
europepmc   +1 more source

A New Frequency Analysis Operator for Population Improvement in Genetic Algorithms to Solve the Job Shop Scheduling Problem. [PDF]

Sensors (Basel), 2022
Viana MS, Contreras RC, Morandin Junior O.   +2 more
europepmc   +1 more source

Quality of experience-aware application deployment in fog computing environments using machine learning. [PDF]

PeerJ Comput Sci
Jenifer P, Angela Jennifa Sujana J.
europepmc   +1 more source

EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments. [PDF]

Sci Rep
Khan A, Ullah F, Shah D, Khan MH, Ali S, Tahir M.   +5 more
europepmc   +1 more source

Inverse scheduling method for aircraft flat-tail assembly production based on improved genetic algorithm. [PDF]

Sci Rep
Li T, Hua M, Wang J, Qin W.
europepmc   +1 more source

A polyhedral approach to single-machine scheduling problems. [PDF]

Savelsbergh, M.W.P., Akker, J.M. van den, Hoesel, C.P.M. van   +2 more
core  

EACCO: Optimizing the Computation and Communication in Resource-Constrained IoT Devices for Energy-Efficient Swarm Robotics. [PDF]

Sensors (Basel)
Ijaz A, Haghbayan H, Nigussie E, Malik A, Plosila J.   +4 more
europepmc   +1 more source

Scalable hybrid framework for real time and non real time task scheduling in fog computing using federated reinforcement learning and PSO GA. [PDF]

Sci Rep
Liu F, Liu Z, Liu X, Zhou H.
europepmc   +1 more source

Decentralized adaptive task allocation for dynamic multi-agent systems. [PDF]

Sci Rep
Tarasova E, Erofeeva V, Granichin O, Chernikov K.   +3 more
europepmc   +1 more source