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.Systems Integration Methodology

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Now showing 1 - 9 of 9
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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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    Reducing Manufacturing Cycle Time during Product Design
    (1999) Herrmann, Jeffrey W.; Chincholkar, Mandar; ISR
    This paper describes an approach that can reduce manufacturing cycle time during product design. Design for production (DFP) determines how manufacturing a new product design affects the performance of the manufacturing system. This includes design guidelines, capacity analysis, and estimating manufacturing cycle times. Performing these tasks early in the product development process can reduce product development time. Previous researchers have developed various DFP methods for different problem settings. This paper discusses the relevant literature and classifies these methods. The paper presents a systematic DFP approach and a manufacturing system model that can be used to estimate the manufacturing cycle time of a new product. This approach gives feedback that can be used to eliminate cycle time problems. This paper focuses on products that are produced in one facility. We present an example that illustrates the approach and discuss a more general approach for other multiple-facility settings.
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    Using Neural Networks to Generate Design Similarity Measures
    (1999) Balasubramanian, Sundar; Herrmann, Jeffrey W.; Herrmann, Jeffrey W.; ISR
    This paper describes a neural network-based design similarity measure for a variant fixture planning approach. The goal is to retrieve, for a new product design, a useful fixture from a given set of existing designs and their fixtures. However, since calculating each fixture feasibility and then determining the necessary modifications for infeasible fixtures would require too much effort, the approach searches quickly for the most promising fixtures. The proposed approach uses a design similarity measure to find existing designs that are likely to have useful fixtures. The use of neural networks to generate design similarity measures is explored.This paper describes the back-propagation algorithm for network learning and highlights some of the implementation details involved. The neural network-based design similarity measure is compared against other measures that are based on a single design attribute.
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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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    An Internet-Based Work Instructions System
    (1998) Herrmann, Jeffrey W.; Lin, Edward; Minis, Ioannis; ISR
    The Black & Decker factory in Easton, Maryland, uses parallel, off-line assembly lines to produce multiple models in small, infrequent production runs. The University of Maryland and Black & Decker have implemented an Internet-based work instructions system that supports parallel, off-line assembly. Black & Decker personnel create and update easy-to-read paperless work instructions, and each assembly station automatically retrieves the correct paperless work instructions and displays them.
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    A Generative Approach for Design Evaluation and Partner Selection for Agile Manufacturing
    (1996) Minis, Ioannis; Herrmann, Jeffrey W.; Lam, Giang; ISR
    An agile manufacturing firm forms partnerships with other manufacturers as necessary to design and manufacture a product quickly in response to a market opportunity. In order to form a successful partnership, the firm needs to create a superior design and select the partners that best fit the partnership's scope. In this paper we consider the intrinsic relationship between design evaluation and partner selection. The paper presents a generative approach that a design team can use to obtain feedback about a new product embodiment based on high- level process plans and on the manufacturing capabilities and performance of potential partners. Using this information, the design team can improve their design and identify the potential partners that best fit its manufacturing requirements. The primary application of this work is to certain types of mechanical and electronic products.
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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.