Analysis of polling systems
On the optimal control of two queues with server setup times and its analysis
SIAM Journal on Computing
Efficient visit frequencies for polling tables: minimization of waiting cost
Queueing Systems: Theory and Applications
Queueing Systems: Theory and Applications - Polling models
Efficient visit orders for polling systems
Performance Evaluation
Heuristic scheduling of parallel heterogeneous queues with set-ups
Management Science
Queueing analysis of polling models: progress in 1990-1994
Frontiers in queueing
A Practical Scheduling Method for Multiclass Production Systems with Setups
Management Science
Mathematics of Operations Research
Sequencing and Routing in Multiclass Queueing Networks Part I: Feedback Regulation
SIAM Journal on Control and Optimization
Polling systems in heavy traffic: Exhaustiveness of service policies
Queueing Systems: Theory and Applications
Sequencing and Routing in Multiclass Queueing Networks Part II: Workload Relaxations
SIAM Journal on Control and Optimization
A Fluid Heuristic for Minimizing Makespan in Job Shops
Operations Research
LIMIT THEOREMS FOR POLLING MODELS WITH INCREASING SETUPS
Probability in the Engineering and Informational Sciences
Stabilizing Queueing Networks with Setups
Mathematics of Operations Research
Online make-to-order joint replenishment model: primal dual competitive algorithms
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
A Marginal Productivity Index Rule for Scheduling Multiclass Queues with Setups
Network Control and Optimization
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This paper considers a multiproduct, single-server production system where both setup times and costs are incurred whenever the server changes product. The system is make-to-order with a per unit backlogging cost. The objective is to minimize the long-run average cost per unit time. Using a fluid model, we provide a closed-form lower bound on system performance. This bound is also shown to provide a lower bound for stochastic systems when scheduling is local or static, but is only an approximation when scheduling is global or dynamic. The fluid bound suggests both local and global scheduling heuristics, which are tested for the stochastic system via a simulation study.