O
Olivier Grunder
Researcher at Universite de technologie de Belfort-Montbeliard
Publications - 78
Citations - 1738
Olivier Grunder is an academic researcher from Universite de technologie de Belfort-Montbeliard. The author has contributed to research in topics: Job shop scheduling & Supply chain. The author has an hindex of 16, co-authored 73 publications receiving 1178 citations. Previous affiliations of Olivier Grunder include University of Burgundy.
Papers
More filters
Journal ArticleDOI
Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm
TL;DR: In this article, a two-layer decomposition technique and a hybrid model based on fast ensemble empirical mode decomposition (FEEMD), VMD and back propagation neural network optimized by firefly algorithm are proposed.
Journal ArticleDOI
A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine
TL;DR: The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy.
Journal ArticleDOI
Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction
TL;DR: A novel hybrid model based on variational mode decomposition, phase space reconstruction and wavelet neural network optimized by genetic algorithm for multi-step ahead wind speed forecasting that outperforms all other comparison models.
Journal ArticleDOI
A hybrid genetic algorithm for a home health care routing problem with time window and fuzzy demand
TL;DR: The Vehicle Routing Scheduling problem as it applies to HHC companies is studied, and a hybrid genetic algorithm integrated with stochastic simulation methods to solve the proposed model is proposed.
Journal ArticleDOI
A memetic algorithm for a home health care routing and scheduling problem
TL;DR: The memetic algorithm is efficient whether the problem is studied with hard or soft time window and synchronization constraints, various caregivers qualification or several home health care offices.