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.Herrmann, Jeffrey W.Subject: search.filters.subject.algorithms

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Now showing 1 - 7 of 7
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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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    A Genetic Algorithm for a Minimax Network Design Problem
    (1999) Herrmann, Jeffrey W.; ISR
    This paper considers the problem of designing a network to transport material from sources of supply to sites where demand occurs. However, the demand at each site is uncertain. We formulate the problem as a robust discrete optimization problem. The minimax objective is to find a robust solution that has the best worst-case performance over a set of possible scenarios. However, this is a difficult optimization problem. This paper describes a two-space genetic algorithm that is a general technique to solve such minimax optimization problems. This algorithm maintains two populations. The first population represents solutions. The second population represents scenarios. An individual in one population is evaluated with respect to the individuals in the other population. The populations evolve simultaneously, and they converge to a robust solution and a worst-case scenario. Experimental results show that the two-space genetic algorithm can find robust solutions to the minimax network design problem. Since robust discrete optimization problems occur in many areas, the algorithm will have a wide variety of applications.
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    Evaluating Sheet Metal Nesting Decisions
    (1998) Herrmann, Jeffrey W.; Delalio, David R.; ISR
    This paper describes models that estimate the cost and time of sheet metal punching when nesting (batching) orders. These models help decision-makers plan production and evaluate the impact of changing the nesting policy. In addition, we use them to formulate a nesting optimization problem. Finally, we use the models to evaluate the sensitivity of the nesting policy to manufacturing parameters. We conclude that dynamic nesting can reduce the capacity requirements, material requirements, and cost of sheet metal punching.
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    A Genetic Algorithm for Minimax Optimization
    (1997) Herrmann, Jeffrey W.; ISR
    This paper describes a two-space genetic algorithm that finds solutions to minimax optimization problems. The genetic algorithm maintains two populations and searches both simultaneously. Each individual is evaluated with respect to the individuals in the other population. Preliminary experimental results confirm that the algorithm can find good solutions to minimax optimization problems.
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    Minimization of Acquisition and Operational Costs in Horizontal Material Handling System Design
    (1995) Herrmann, Jeffrey W.; Ioannou, George; Minis, Ioannis; Proth, J.M.; ISR
    This paper considers the problem of minimizing the fixed cost of acquiring material handling transporters and the operational cost of material transfer in a manufacturing system. This decision problem arises during manufacturing facility design, and is modeled using an integer programming formulation. Two efficient heuristics are developed to solve it. Computational complexity, worst-case performance analysis, and extensive computational tests are provided for both heuristics. The results indicate that the proposed methods are well suited for large-scale manufacturing applications.
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    On Parallel-Machine Scheduling with Operator-Constrained Setups
    (1994) Herrmann, Jeffrey W.; Lee, Chung-Yee; ISR
    The processing of a task on a machine often requires an operator to setup the job. In this paper we consider the problem of scheduling a finite set of jobs on a number of identical parallel machines. Each job has a setup that must be performed by an operator, who can perform only one setup at a time. We examine the problems of minimizing the schedule makespan. Out results include complexity proofs, special cases that can be solved in polynomial time, lower bounds, and approximation algorithms with error bounds.
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    Design of Material Flow Networks in Manufacturing Facilities
    (1994) Herrmann, Jeffrey W.; Ioannou, George; Minis, Ioannis; Nagi, R.; Proth, J.M.; ISR
    In this paper we consider the design of material handling flow paths in a discrete parts manufacturing facility. A fixed-charge capacitated network design model is presented and two efficient heuristics are proposed to determine near-optimal solutions to the resulting NP- hard problem. The heuristics are tested against an implicit enumeration scheme used to obtain optimal solutions for small examples. For more realistic cases, the solutions of the heuristics are compared to lower bounds obtained by either the linear programming relaxation of the mixed integer program, or an iterative dual ascent algorithm. The results obtained indicate that the heuristics provide good solutions in reasonable time on the average. The proposed methodology is applied to design the flow paths of an existing manufacturing facility. The role of the flow path network problem in the integrated shop design is also discussed.