Ranking and Selection as Stochastic Control

Ranking and Selection as Stochastic Control

Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the associated Bellman equation. Using value function approximation, we derive an approximately optimal allocation policy. We show that this policy is not only computationally efficient but also possesses both one-step-ahe...

Parallel Ranking and Selection

The Winter Simulation Conference serves as the initial publication venue for many advances in ranking and selection (R&S), including the recently-developed R&S procedures that exploit high-performance parallel computing. We formulate a new stylized model for representing parallel R&S procedures, and we provide an overview of existing R&S procedures under the stylized model. We also discuss why ...

Feature Selection and Ranking

This chapter describes a method of feature selection and ranking based on human expert knowledge and training and testing of a neural network. Being computationally efficient, the method is less sensitive to round-off errors and noise in the data than the traditional methods of feature selection and ranking grounded on the sensitivity analysis. The method may lead to a significant reduction of ...

Ranking stochastic efficient DMUs based on reliability

Supplier selection using compromise ranking and outranking methods

In today’s highly competitive manufacturing environment, an effective supplier selection process is very important for the success of any business organization. Selection of the best supplier is always a difficult task for the purchasing manager. Suppliers have varied strengths and weaknesses which require careful assessment by the purchasing manager before selecting and ranking them. Any suppl...