A recurrent reinforcement learning strategy for optimal scheduling of partially observable job-shop and flow-shop batch chemical plants under uncertainty.
Daniel Rangel-MartinezLuis A. Ricardez-SandovalPublished in: Comput. Chem. Eng. (2024)
Keyphrases
- learning strategies
- job shop
- optimal scheduling
- scheduling problem
- partially observable
- flowshop
- reinforcement learning
- partial observability
- open shop
- state space
- markov decision processes
- single machine
- job shop scheduling
- decision problems
- dynamical systems
- np hard
- online learning
- tabu search
- processing times
- active learning
- job shop scheduling problem
- belief state
- infinite horizon
- parallel machines
- reward function
- scheduling algorithm
- special case
- production scheduling
- planning domains
- resource constrained
- learning process
- optimal policy
- training set
- graphical models
- neural network