Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Optimality of zero-inventory policies for unreliable manufacturing systems
Operations Research
Simulation optimization of (s,S) inventory systems
WSC '92 Proceedings of the 24th conference on Winter simulation
Journal of Optimization Theory and Applications
Journal of Optimization Theory and Applications
Monotonicity of optimal flow control for failure-prone production systems
Journal of Optimization Theory and Applications
Sample-path optimization of convex stochastic performance functions
Mathematical Programming: Series A and B
Sample-path solution of stochastic variational inequalities, with applications to option pricing
WSC '96 Proceedings of the 28th conference on Winter simulation
Analysis of sample-path optimization
Mathematics of Operations Research
A simulation-based approach to two-stage stochastic programming with recourse
Mathematical Programming: Series A and B
Optimization in simulation: a survey of recent results
WSC '87 Proceedings of the 19th conference on Winter simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Optimization Software Guide
Perturbation Analysis for Stochastic Fluid Queueing Systems
Discrete Event Dynamic Systems
Optimal and Hierarchical Controls in Dynamic Stochastic Manufacturing Systems: A Survey
Manufacturing & Service Operations Management
Perturbation Analysis of Multiclass Stochastic Fluid Models
Discrete Event Dynamic Systems
Make-to-stock systems with backorders: IPA gradients
WSC '04 Proceedings of the 36th conference on Winter simulation
Solving Stochastic Mathematical Programs with Complementarity Constraints Using Simulation
Mathematics of Operations Research
Minimizing makespan in a multiclass fluid network with parameter uncertainty
Probability in the Engineering and Informational Sciences
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A number of important problems in production and inventory control involve optimization of multiple threshold levels or hedging points. We address the problem of finding such levels in a stochastic system whose dynamics can be modelled using generalized semi-Markov processes (GSMP). The GSMP framework enables us to compute several performance measures and their sensitivities from a single simulation run for a general system with several states and fairly general state transitions. We then use a simulation-based optimization method, sample-path optimization, for finding optimal hedging points. We report numerical results for systems with more than twenty hedging points and service-level type probabilistic constraints. In these numerical studies, our method performed quite well on problems which are considered very difficult by current standards. Some applications falling into this framework include designing manufacturing flow controllers, using capacity options and subcontracting strategies, and coordinating production and marketing activities under demand uncertainty.