Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Fu, Michael C.Subject: search.filters.subject.algorithms

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    Comparing Gradient Estimation Methods Applied to Stochastic Manufacturing Systems
    (2000) Mellacheruvu, Praveen V.; Fu, Michael C.; Herrmann, Jeffrey W.; ISR
    This paper compares two gradient estimation methods that can be usedfor estimating the sensitivities of output metrics with respectto the input parameters of a stochastic manufacturing system.A brief description of the methods used currently is followedby a description of the two methods: the finite difference methodand the simultaneous perturbation method. While the finitedifference method has been in use for a long time, simultaneousperturbation is a relatively new method which has beenapplied with stochastic approximation for optimizationwith good results. The methods described are used to analyzea stochastic manufacturing system and estimate gradients.The results are compared to the gradients calculated fromanalytical queueing system models.

    These gradient methods are of significant use in complex manufacturingsystems like semiconductor manufacturing systems where we havea large number of input parameters which affect the average total cycle time.These gradient estimation methods can estimate the impact thatthese input parameters have and identify theparameters that have the maximum impact on system performance.

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    Simulation-Based Algorithms for Average Cost Markov Decision Processes
    (1999) He, Ying; Fu, Michael C.; Marcus, Steven I.; Fu, Michael C.; Marcus, Steven I.; ISR
    In this paper, we give a summary of recent development of simulation-based algorithmsfor average cost MDP problems, which are different from those for discounted cost problems or shortest pathproblems. We introduce both simulation-based policy iteration algorithms and simulation-based value iterationalgorithms for average cost problems, and give the pros and cons of each algorithm.
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    Gradient Estimation of Two-Stage Continuous Transfer Lines Subject to Operation-Dependent Failures
    (1998) Fu, Michael C.; Xie, Xiaolan; ISR
    This paper addresses the gradient estimation of transfer linescomprising two machines separated by a buffer of finite capacity. A continuous flow model is considered, where machines are subject tooperation-dependent failures, i.e., a machine cannot fail when it is idle. Both repair times and failure times may be general, i.e., they need not be exponentially distributed.The system is hybrid in the sense that it hasboth continuous dynamics, as a result of continuous material flow, and discrete events: failures and repairs. The purpose of this paper is to estimate the gradient of the throughput rate with respect to the buffer capacity. Both IPA estimators and SPA estimators are derived. Simulation results show that IPA estimators do not work, contradicting the common belief that IPA always works for continuous flow models.
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    A Lower Bounding Result for the Optimal Policy in an Adaptive Staffing Problem
    (1998) Assad, Arjang A.; Fu, Michael C.; Yoo, Jae-sin; ISR
    We derive a lower bound for the staffing levels required to meet a projected load in a retail service facility. We model the queueing system as a Markovian process with non-homogeneous Poisson arrivals. Motivated by an application from the postal services, we assume that the arrival rate is piecewise constant over the time horizon and retain such transient effects as build- up in the system. The optimal staffing decision is formulated as a multiperiod dynamic programming problem where staff is allocated to each time period to minimize the total costs over the horizon. The main result is the derivation of a lower bound on the staffing requirements that is computed by decoupling successive time periods.