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We consider the simulation of constrained optimization problem, the (s, S) inventory system with stochastic lead-time and a service level constraint. We allow the orders to cross in time, which makes the problem more complicated. Bashyam and Fu (1998) first present this problem and obtained the answer by using perturbation analysis. Angun, Gurken, Hertog and Kleijnen (2006) studied the same question...
Preference analysis is a class of important tasks in multi-criteria decision making. The classical rough set theory was generalized to deal with preference analysis by replacing equivalence relations with dominance relation. However, crisp preference relations can not reflect the fuzziness in criteria. In this paper, we introduce the logsig function to extract fuzzy preference relations from samples...
This paper addresses parameter estimation of superimposed signals jointly with their number within the Bayesian framework. We combine sparse Bayesian machine learning methods with the state of the art SAGE-based parameter estimation algorithm. Existing sparse Bayesian methods allow to assess model order through priors over model parameters, but do not consider models nonlinear in parameters. SAGE-based...
In this paper, we introduce a pricing model that ensures efficient resource allocation that provides guaranteed quality of service while maximizing profit in multiservice networks. Specifically, a dynamic allocation policy is examined that relies on online measurements while each service class operates under a probabilistic bound delay constraint. We present a rigorous analysis of the properties of...
Ordinal optimization has emerged as an efficient technique for simulation and optimization. Exponential convergence rates can be achieved in many cases. A good allocation of simulation samples across designs can further dramatically improve the efficiency of ordinal optimization by orders of magnitude. However, the allocation problem itself is a big challenge. Most existing methods offer approximations...
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