Dynamically optimal policies for stochastic scheduling subject to preemptive-repeat machine breakdowns.
Xiaoqiang CaiXianyi WuXian ZhouPublished in: IEEE Trans Autom. Sci. Eng. (2005)
Keyphrases
- optimal policy
- scheduling problem
- flowshop
- scheduling algorithm
- long run average cost
- state dependent
- control policies
- markov decision processes
- parallel machines
- decision problems
- state space
- finite horizon
- dynamic programming
- single machine
- stochastic inventory control
- infinite horizon
- reinforcement learning
- finite state
- processing times
- serial inventory systems
- multistage
- sufficient conditions
- setup times
- dynamic programming algorithms
- average reward reinforcement learning
- long run
- periodic review
- lot size
- policy iteration
- initial state
- average cost
- production planning
- np hard
- sample path
- demand distributions
- average reward
- lost sales
- markov decision problems
- monte carlo
- partially observable markov decision processes
- fixed point
- markov decision process
- semi markov decision processes
- steady state
- multi agent
- machine learning