Operations Research
Management Science
Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Stochastic approximation and optimization of random systems
Stochastic approximation and optimization of random systems
Numerical methods for stochastic control problems in continuous time
Numerical methods for stochastic control problems in continuous time
Queueing simulation in heavy traffic
Mathematics of Operations Research
Dynamic scheduling of a multiclass fluid network
Operations Research
Efficient simulation of multiclass queueing networks
Proceedings of the 29th conference on Winter simulation
A New Algorithm for State-Constrained Separated Continuous Linear Programs
SIAM Journal on Control and Optimization
New linear program performance bounds for queueing networks
Journal of Optimization Theory and Applications - Special issue in honor of Yu-Chi Ho
The O.D. E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
SIAM Journal on Control and Optimization
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Sequencing and Routing in Multiclass Queueing Networks Part I: Feedback Regulation
SIAM Journal on Control and Optimization
Queueing Systems: Theory and Applications
State space collapse with application to heavy traffic limits for multiclass queueing networks
Queueing Systems: Theory and Applications
Dynamic scheduling in multiclass queueing networks: Stability under discrete-review policies
Queueing Systems: Theory and Applications
Value iteration and optimization of multiclass queueing networks
Queueing Systems: Theory and Applications
Sequencing and Routing in Multiclass Queueing Networks Part II: Workload Relaxations
SIAM Journal on Control and Optimization
Approximating Martingales for Variance Reduction in Markov Process Simulation
Mathematics of Operations Research
In Search of Sensitivity in Network Optimization
Queueing Systems: Theory and Applications
WSC '04 Proceedings of the 36th conference on Winter simulation
ODE methods for Markov chain stability with applications to MCMC
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Exponential bounds and stopping rules for MCMC and general Markov chains
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Mathematics of Operations Research
Non-linear control variates for regenerative steady-state simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A switching rule for plastics identification in electronics recycling
International Journal of Computer Integrated Manufacturing
Positive harris recurrence and diffusion scale analysis of a push pull queueing network
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
A Push---Pull Network with Infinite Supply of Work
Queueing Systems: Theory and Applications
Coding and control for communication networks
Queueing Systems: Theory and Applications
A fluid approach to large volume job shop scheduling
Journal of Scheduling
Stability of multi-class queueing networks with infinite virtual queues
Queueing Systems: Theory and Applications
Hi-index | 0.00 |
This paper concerns modeling and policy synthesis for regulation of multiclass queueing networks. A 2-parameter network model is introduced to allow independent modeling of variability and mean processing-rates, while maintaining simplicity of the model. Policy synthesis is based on consideration of more tractable workload models, and then translating a policy from this abstraction to the discrete network of interest. Translation is made possible through the use of safety-stocks that maintain feasibility of workload trajectories. This is a well-known approach in the queueing theory literature, and may be viewed as a generic approach to avoid deadlock in a discrete-event dynamical system. Simulation is used to evaluate a given policy, and to tune safety-stock levels. These simulations are accelerated through a variance reduction technique that incorporates stochastic approximation to tune the variance reduction. The search for appropriate safety-stock levels is coordinated through a cutting plane algorithm. Both the policy synthesis and the simulation acceleration rely heavily on the development of approximations to the value function through fluid model considerations.