10452928
Detecting truncation point in steady-state simulation using chaos theory
In the steady-state simulation, it is important to identify initialization bias for the correct estimates of the simulation model under study. In this paper, the methods from chaos theory are applied to the determination of truncation points in the simulation data for controlling the initial bias. Two methods are proposed and evaluated based on their effectiveness for estimating the average waiting time in M/M/1(/spl infin/) queueing model.
14604625
Single run optimization using the reverse-simulation method
An efticient “Simulation Optimization” technique is developed to solve system design problems which can not be expressed in explicit analytical or mathematical models. In particular, we explore a new paradigm called the “Reverse-Simulation optimization method” which is quite Merent from current simulation optimization methods in the literature. This paper focuses on the method of on-line determination of steady-state, which is a very important issue in ReverseSimulation optimization, and the construction of a Reverse-Simulation algorithm with expert systems. The proposed algorithm finds the steady-state of a system and an optimal state. The algorithm employs the Lyapunov exponent of Chaos theory to determine both the steady-state and optimal state of a system.
46434197
Flow Control Strategy for Flexible Machining and Assembly System
Abstract In this paper an effective flow control strategy for a Flexible Machining and Assembly System is proposed using the concepts of operation-oriented and, combined push and pull control method. The proposed flow control mechanism could meet production demands with a minimum makespan while satisfying assigned due-dates and keeping a low volume of work-in-process at the same time. It also considers machine failures and rush jobs.
7927244
GM-WTA: An efficient workflow task allocation method in a distributed execution environment
A workflow is a collection of units of work called workflow tasks which cooperatively realize a business objective by utilizing system resources such as databases. The roles of workflow tasks are performed in the order driven by a computer representation of the workflow logic. During completing each task's role, the remote control transfers and the remote resource accesses may often occur in a distributed workflow system. Hence, the efficient distribution of workflow components, especially workflow tasks, is so effective as to improve the performance of workflow processing. If we can place adjacent workflow tasks as close as possible and locate workflow tasks near to the required resources, we can significantly reduce the overhead of workflow processing.In this paper, we propose an efficient workflow task allocation method in a distributed workflow system, which is based on the locality principle. The method that utilizes the concept of graph partitioning can improve the performanace of workflow processing by minimizing the remote processing costs incurred during workflow execution. In addition, we perform several experiments to evaluate our proposed method.
9799056
Fitting via alternative random-effect models
When there are two alternative random-effect models leading to the same marginal model, inferences from one model can be used for the other model. We illustrate how a likelihood method for fitting models with independent random effects can be applied to seemingly very different models with correlated random effects. We also discuss some merits of using these alternative models.
14282973
Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods
In this paper, we present a learning approach to the scenario template task of information extraction, where information filling one template could come from multiple sentences. When tested on the MUC-4 task, our learning approach achieves accuracy competitive to the best of the MUC-4 systems, which were all built with manually engineered rules. Our analysis reveals that our use of full parsing and state-of-the-art learning algorithms have contributed to the good performance. To our knowledge, this is the first research to have demonstrated that a learning approach to the full-scale information extraction task could achieve performance rivaling that of the knowledge engineering approach.
9259468
Classification of Multiple Cancer Types by Multicategory Support Vector Machines Using Gene Expression Data
MOTIVATION
High-density DNA microarray measures the activities of several thousand genes simultaneously and the gene expression profiles have been used for the cancer classification recently. This new approach promises to give better therapeutic measurements to cancer patients by diagnosing cancer types with improved accuracy. The Support Vector Machine (SVM) is one of the classification methods successfully applied to the cancer diagnosis problems. However, its optimal extension to more than two classes was not obvious, which might impose limitations in its application to multiple tumor types. We briefly introduce the Multicategory SVM, which is a recently proposed extension of the binary SVM, and apply it to multiclass cancer diagnosis problems.


RESULTS
Its applicability is demonstrated on the leukemia data (Golub et al., 1999) and the small round blue cell tumors of childhood data (Khan et al., 2001). Comparable classification accuracy shown in the applications and its flexibility render the MSVM a viable alternative to other classification methods.


SUPPLEMENTARY INFORMATION
http://www.stat.ohio-state.edu/~yklee/msvm.htm
5304492
Design specifications of a generic supply chain simulator
This paper describes a design specification for a generic, supply-chain-simulation system. The proposed simulation system is based on schedule-driven and stock-driven control methods to support the supply chain management. The simulation system includes three processing modes: business process flows, material process flows, and information process flows. The paper also discusses interface data requirements for the proposed supply-chain-simulation system.