The authors develop a two-timescale simultaneous perturbation stochastic approximation algorithm for simulation-based parameteroptimization over discrete sets. This algorithm is applicable in cases where the cost to ...
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The authors develop a two-timescale simultaneous perturbation stochastic approximation algorithm for simulation-based parameteroptimization over discrete sets. This algorithm is applicable in cases where the cost to be optimized is in itself the long-run average of certain cost functions whose noisy estimates are obtained via simulation. The authors present the convergence analysis of their algorithm. Next, they study applications of their algorithm to the problem of admission control in communication networks. They study this problem under two different experimental settings and consider appropriate continuous time queuing models in both settings. Their algorithm finds optimal threshold-type policies within suitable parameterized classes of these. They show results of several experiments for different network parameters and rejection cost. The authors also study the sensitivity of their algorithm with respect to its parameters and step sizes. The results obtained are along expected lines.
Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve thei...
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Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve their behavior. This paper focuses on experimentally investigating the role of a recent approach, the binary-coded local genetic algorithm (BLGA), as context-independent local search operator for three local search-based metaheuristics: random multi-start local search, iterated local search, and variable neighborhood search. These general-purpose models treat the objective function as a black box, allowing the search process to be context-independent. The results show that BLGA may provide an effective and efficient intensification, not only allowing these three metaheuristics to be enhanced, but also predicting successful applications in other local search-based algorithms. In addition, the empirical results reported here reveal relevant insights on the behavior of classical local search methods when they are performed as context-independent optimizers in these three well-known metaheuristics.
This paper establishes structural properties for the throughput of a large class of queueing networks with i.i.d. new-better-than-used service times. The main result obtained in this paper is applied to a wide range o...
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This paper establishes structural properties for the throughput of a large class of queueing networks with i.i.d. new-better-than-used service times. The main result obtained in this paper is applied to a wide range of networks, including tandems, cycles and fork-join networks with general blocking and starvation (as well as certain networks with splitting and merging of traffic streams), to deduce the concavity of their throughput as a function of system parameters, such as buffer and initial job configurations, and blocking and starvation parameters. These results have important implications for the optimal design and control of such queueing networks by providing exact solutions, reducing the search space over which optimization need be performed, or establishing the convergence of optimization algorithms. In order to obtain results for such disparate networks in a unified manner, we introduce the framework of constrained discrete event systems (CDES), which enables us to characterize any permutable and non-interruptive queueing network through its constraint set. The main result of this paper establishes comparison properties of the event occurrence processes of CDES as a function of the constraint sets, which are then translated into the above-mentioned concavity of the throughput as a function of system parameters in the context of queueing networks.
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