| ANALYSIS OF ELECTRONICS
ASSEMBLY OPERATIONS: LONGBOW HELLFIRE MISSILE POWER SUPPLY |
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| Kurt G.
Springfield John D. Hall TASC, Inc. 700 Boulevard South, Suite 201 Huntsville, Alabama 35802, U.S.A. |
Gregg W.
Bell Northrop Grumman Corporation Land Combat Systems - Huntsville 915 Explorer Boulevard Huntsville, Alabama 35806, U.S.A. | |
| ABSTRACT | ||
| This paper describes the use of discrete event simulation and design of experiments to analyze electronics assembly operations. A study was performed to determine if proposed changes to electronics assembly operations could achieve higher production throughput. This work supported the U.S. Army's Longbow HELLFIRE Missile program. The design of experiment used a modified orthogonal array containing both two and three-level factors. The authors describe the use of factor level average analysis to analyze experimental data. The Army used study results to assess risks in the program while the manufacturer gained information needed to improve the efficiency of its operations. | ||
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| DESIGN AND EVALUATION OF
A SELECTIVE ASSEMBLY STATION FOR HIGH PRECISION SCROLL COMPRESSOR SHELLS
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|---|---|---|
| Arne
Thesen Akachai Jantayavichit Department of Industrial Engineering University of Wisconsin-Madison 1513 University Ave, Madison, WI 53706, U.S.A. |
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| ABSTRACT | ||
| Certain automotive parts call for assemblies to be produced to tolerances that cannot be economically reached using standard high volume machining practices. Shims are used instead. We show that the required precision may be reached by using selective assembly. An efficient selective assembly system is proposed. Simulation is used to evaluate the performance of this system, and configurations capable of tolerance improvements of up to 1/20 are suggested. | ||
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| Integrating
Discrete-Event Simulation with Statistical Process Control Charts for
Transitions in a Manufacturing Environment
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| Harriet Black
Nembhard Ming-Shu Kao Gino Lim Department of Industrial Engineering University of Wisconsin-Madison Madison, WI 53706, U.S.A. |
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| ABSTRACT | ||
| We present a model that integrates real-time process control charting with simulation modeling to illustrate the effects and benefits of SPC charts for quality improvement efforts. The integrated model is particularly significant in addressing transition issues arising from changes in the input material. A case study based on a medical manufacturing industry process is used to illustrate the approach. | ||
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| COMPARISON OF
DISPATCHING RULES FOR SEMICONDUTOR MANUFACTURING USING LARGE FACILITY
MODELS |
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| Manfred
Mittler IBM Global Services - Industrial Sector Decision Technology 0817/7103-47 D-70548 Stuttgart, GERMANY |
Alexander K.
Schoemig Infineon Technologies AG Operational Excellence P.O. Box 10 09 44 D-93009 Regensburg, GERMANY | |
| ABSTRACT | ||
| In this paper, we present a comparison of five dispatching rules that aim to reduce the mean and the variance of cycle times. The performance of the dispatch rules is evaluated using simulation results for two large semiconductor wafer fabrication facilities. The results show that which dispatch rule achieves the best results depends on the fab, on the load of the fab and on the product. | ||
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| EVALUATION OF CLUSTER
TOOL THROUGHPUT FOR THIN FILM HEAD PRODUCTION
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|---|---|---|
| Eric J.
Koehler Timbur M. Wulf Alvin C. Bruska Wafer Systems Engineering Seagate Technology One Disc Drive Bloomington, MN 55435, U.S.A. |
Marvin S.
Seppanen Production Systems of Winona, MN 2225 Garvin Heights Road Winona, MN 55987, U.S.A. | |
| ABSTRACT | ||
| This paper describes the application of simulation for analyzing cluster tool cycle times and cluster tool capacity planning. The objective of this project was to develop a flexible and expandable tool for rapidly calculating tool cycle times for a multiple step process through alternative tool configurations. The calculated process cycle times are then used to calculate equipment tool set requirements against product demand. The Seagate Industrial Engineering group utilized a simulation based cluster tool model developed to predict cluster tool cycle times and analyze cluster tool capacity across multiple tools and compare with results from static probability based model predictions. | ||
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| DETERMINING OPTIMAL
LOT-SIZE FOR A SEMICONDUCTOR BACK-END FACTORY
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| Juergen
Potoradi Gerald Winz Infineon Technologies Asia Pacific 168 Kallang Way SINGAPORE 349253 |
Lee Weng
Kam Infineon Technologies (Integrated Circuit) Sdn Bhd Free Trade Zone, Batu Berendam Melaka, MALAYSIA | |
| ABSTRACT | ||
| Modeling analysts are using a
methodology that applies queuing theory logistics laws and simulation to
factory performance analysis. These methods are being applied at
semiconductor back-end factories, where a major focus is on achieving
capacity increases with minimal equipment additions. This paper describes this technical methodology and investigates an optimum lot-size for back-end factories based upon given throughput and cycle time targets. The analysis provides a recommended lot-size of 6800 for the overall production area, allowing the factory to maximize throughput while still meeting overall factory cycle time goals. The model indicates a potential 14% increase in throughput by selecting the optimal lot-size. | ||
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| OPTIMIZATION OF CYCLE
TIME & UTILIZATION IN SEMICONDUCTOR TEST MANUFACTURING USING
SIMULATION BASED, ON-LINE NEAR-REAL-TIME SCHEDULING SYSTEM
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| Appa Iyer
Sivakumar Gintic Institute of Manufacturing Technology Nanyang Technological University 71 Nanyang Drive, 638075, SINGAPORE |
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| ABSTRACT | ||
| A discrete event simulation based "on-line near-real-time" dynamic scheduling and optimization system has been conceptualized, designed, and developed to optimize cycle time and asset utilization in the complex manufacturing environment of semiconductor test manufacturing. Our approach includes the application of rules and optimization algorithm, using multiple variables as an integral part of discrete event simulation of the manufacturing operation and auto simulation model generation at a desired frequency. The system has been implemented at a semiconductor back-end site. The impact of the system includes the achievement of world class cycle time, improved machine utilization, reduction in the time that planners and manufacturing personnel spend on scheduling, and more predictable and highly repeatable manufacturing performance. In addition it enables managers and senior planners to carry out "what if" analysis to plan for future. | ||
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| Use of Dynamic
Simulation to Analyze Storage and Retrieval Strategies
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|---|---|---|
| Mark A.
Kosfeld Intel Corporation Building C11-110 6505 West Chandler Boulevard Chandler, Arizona 85226-3324, U.S.A. |
Timothy D.
Quinn Intel Corporation Building CH3-84 5000 West Chandler Boulevard Chandler, Arizona 85226-3324, U.S.A | |
| ABSTRACT | ||
| In the second half of 1998, shipment
volumes at one of Intel's warehouses had increased beyond the storage and
retrieval capabilities of the facility. An engineering improvement team
began studying changes to the Warehouse Management System (WMS) that would
increase throughput. From observation it was unclear what WMS code changes
would actually improve throughput, and nearly impossible to predict the
amount of improvement that would be realized in the facility. To solve
these issues, the algorithms for storing product, releasing orders, and
routing vehicles were first analyzed in a dynamic simulation model.
Strategies that showed a significant increase in throughput were
recommended for coding into the WMS software. Using a simulation model not
only allowed the strategies to be prioritized, but also predicted the
performance of each strategy. The equipment and physical layout of the facility were comprehended in the simulation model. The storage area consisted of twelve aisles, each 112 bins long and 16 bins high. Product was stored in boxes, which were retrieved and stored by operators driving Stockpicker vehicles. Since both the storage and retrieval of material were entirely controlled by the WMS, it was imperative that a logical routing decision for each Stockpicker vehicle be made. The initial storage and retrieval strategies were first coded in the simulation model to ensure that the model outputs were valid. Then, numerous storage and retrieval strategies were coded and analyzed to determine which ones would increase throughput. The final simulation results showed that throughput could be increased by 110% per day by simply improving the WMS storage and retrieval strategies. No additional vehicles or headcount were required which resulted in a significant annual cost savings. | ||
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| A SIMULATION MODEL TO
STUDY THE DYNAMICS IN A SERVICE-ORIENTED SUPPLY CHAIN
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|---|---|---|
| Edward G.
Anderson Management Department CBA 4.202 The University of Texas at Austin Austin, Texas 78712 |
Douglas J.
Morrice MSIS Department CBA 5.202 The University of Texas at Austin Austin, Texas 78712 | |
| ABSTRACT | ||
| In this paper, we investigate the dynamic behavior of a simple service-oriented supply chain in the presence of non-stationary demand using simulation. The supply chain contains four stages in series. Each stage holds no finished goods inventory. Rather, the order backlog can only be managed by adjusting capacity. These conditions reflect the reality of many service (and custom manufacturing) supply chains. The simulation model is used to compare various capacity management strategies. Measures of performance include application completion rate, backlog levels, and total cumulative costs. | ||
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| Increasing the Power and
Value of Manufacturing Simulation via Collaboration with Other Analytical
Tools: A Panel Discussion |
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|---|---|---|
| Onur M.
Ülgen Production Modeling Corporation and Industrial & Manufacturing Systems Department University of Michigan Dearborn, MI 48128, USA |
John
Shore Production Modeling Corporation Three Parklane Boulevard, Suite 1006W Dearborn, MI 48126, USA |
Gene
Coffman Ford Motor Company Advanced Manufacturing Technology Development 24500 Glendale Avenue, Redford, MI 48239, USA |
| David Sly Engineering Animation, Inc. VP Factory Products 2321 North Loop Drive Ames, Iowa 50010, USA |
Matt Rohrer AutoSimulstions 655 Medical Drive Bountiful, Utah 84010, USA |
Demet Wood General Motors NA Quality, Reliability & Comp. Oper. Impl. 31 E Judson St., 2nd Floor Pontiac, MI 48342, USA |
| ABSTRACT | ||
| The objective of this panel session is to describe how and when should manufacturing simulation practitioners add to the value of projects by interfacing simulation analyses with other analyses such as optimization, layout/material flow, scheduling, robotic, and queuing. The panelists will discuss how each analytical tool adds value to the discrete-event manufacturing simulation, when in the life cycle of a project it should be brought in, what are the main advantages and disadvantages of bringing in the additional tools, managing and selling collaborative analyses projects, and training requirements for collaborative analyses. | ||
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| ANCILLARY EFFECTS OF
SIMULATION |
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|---|---|---|
| Matt
Hickie MOS 12 Die Manufacturing Motorola 1300 North Alma School Road, Mail Drop CH 305 Chandler, AZ, 85224, U.S.A. |
John W. Fowler,
Ph.D. Industrial Engineering Arizona State University Tempe, AZ, 85287-5906, U.S.A. | |
| ABSTRACT | ||
| Simulation can often be one of the first modeling tools implemented at a manufacturing site. When this occurs, much effort must be used to get current manufacturing data into the simulation model. The amount of time and data needed to get the simulation running to an acceptable validation level and to maintain that validation level over time, should lead to an effort to automate the loading of factory data into simulation. If this automation effort is efficient and comprehensive, it can become the cornerstone of a system that benefits manufacturing from more than just simulation analysis. The other benefits range from the development of a simple times theoretical analysis of the line to the complex development of an infinite capacity planning system. This paper will discuss a real world example of the extra benefits received from implementing simulation at a semiconductor manufacturing plant. | ||
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| VALIDATING A
MANUFACTURING PARADIGM: A SYSTEM DYNAMICS MODELING APPROACH
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|---|---|---|
| Richard A.
Reid Elsa L. Koljonen Anderson Schools of Management University of New Mexico Albuquerque, NM 87131, U.S.A. |
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| ABSTRACT | ||
| Logic tools from the Theory of Constraints (TOC) provide the ability to descriptively characterize the entity relationships responsible for a typical, although somewhat chaotic, manufacturing environment. Basically through one-to-one mappings, System Dynamics (SD) models are created from the TOC logic diagrams. Insights gained from exercising the SD models are used to establish a new managerial conceptual framework. This structure guides managers through the continuous improvement process relative to addressing either a physical, policy, or paradigm constraint in their production system. | ||
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| SIMULATION AS A TOOL FOR
CONTINUOUS PROCESS IMPROVEMENT |
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|---|---|---|
| Mel
Adams Paul Componation Hank Czarnecki Bernard J. Schroer University of Alabama in Huntsville Huntsville, AL 35899, U.S.A. |
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| ABSTRACT | ||
| Simulation offers a powerful tool to support the continuous improvement process. This paper presents a description of the tools of lean manufacturing, the steps in the continuous improvement process and two case studies where simulation was used in the continuous improvement. | ||
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| A COMPARISON OF THE
EXPONENTIAL AND THE HYPEREXPONENTIAL DISTRIBUTIONS FOR MODELING MOVE
REQUESTS IN A SEMICONDUCTOR FAB |
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| Siroos
Sokhan-Sanj Gabriel Gaxiola PRI Automation, Inc. Automation Planning and Design 1250 S. Clearview Ave. #104 Mesa, Arizona 85208, USA |
Gerald T. Mackulak,
Ph.D. Fredrik B. Malmgren Department of Industrial Engineering Arizona State University Tempe, Arizona 85287-5906, USA | |
| ABSTRACT | ||
| Variability in any manufacturing process negatively impacts performance since it leads to system disruption. Semiconductor manufacturing, with its characteristic reentrant flow, typically experiences extreme variability. The Automated Material Handling System (AMHS) in a semiconductor fab is subject to this variability and yet must still complete deliveries within a specified time limit. When designing the AMHS the variability used in the simulation model will have a direct impact on the equipment set selected. Sizing a system based on the average case scenario creates a system incapable of meeting the extreme conditions often encountered in reality. The challenge for the modeler of a semiconductor fab is to accurately represent this variability. This paper discusses how the hyperexponential distribution more accurately represents the variability in semiconductor fabs than the typically used exponential distribution. | ||
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| REDUCING MODEL CREATION
CYCLE TIME BY AUTOMATED CONVERSION OF A CAD AMHS LAYOUT DESIGN
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| Igor
Paprotny Automation Planning and Design Group PRI Automation, Inc. Mesa, Arizona 85208, USA |
Wendy
Zhao Software Division PRI Automation, Inc. Billerica, MA 01821. USA |
Gerald Mackulak,
Ph.D. Department. of Industrial Engineering Arizona State University Tempe, Arizona 85287-5906, USA |
| ABSTRACT | ||
| Simulation is a popular tool for accurately estimating the performance of an automated material handling system (AMHS). Accuracy of the model is normally dependent on a detailed description of the AMHS physical system components and their coordinate positions. In this paper, a methodology is defined for automatically inputting the physical system components used to describe an AMHS within a simulation language. The method is based on data extraction from a CAD layout file of the system. Automatically generating the physical system components reduces simulation model building time and increases model accuracy. | ||
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| INCREASING FIRST PASS
ACCURACY OF AMHS SIMULATION OUTPUT USING LEGACY DATA
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|---|---|---|
| Scott
Wu John Rayter Igor Paprotny Automation Planning and Design Group PRI Automation, Inc. Mesa, Arizona 85208, USA |
Gerald Mackulak,
Ph.D. Joakim Yngve Department Of Industrial Engineering Arizona State University Tempe, Arizona 85287-5906, USA | |
| ABSTRACT | ||
| The operating characteristics of wafer
production facilities are extremely dynamic, driven by short product life
cycles, rapid equipment obsolescence and recurring layout expansion. These
factors also have an impact on the design of the Automated Material
Handling System (AMHS). The AMHS must be able to react and accept change
as rapidly as the production process dictates. The AMHS design engineer
faces a significant challenge in that modeling efforts must be proactive
and anticipate the long-term requirements of a given
facility. There are, of course, several methods available for addressing this issue. This paper will point out the limitations to these methods when applied to AMHS modeling and propose an alternative. Specifically, behavioral trends from historical data can be exploited when appropriate. Simulation results from legacy designs may prove to be an efficient indicator of the validity of new designs. | ||
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| DEVELOPMENT OF A
SIMULATION MODEL FOR AN ARMY CHEMICAL MUNITION DISPOSAL FACILITY
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| Michael A.
Berger Jiuyi Hua Paul T. Otis Katrina S. Werpetinski Mitretek Systems, Inc. 7525 Colshire Drive McLean, VA, 22102-7400, U.S.A. |
Vincent F.
Johnston U.S. Army Program Manager for Chemical Demilitarization Attn: SFAE-CD-CO-O Aberdeen Proving Ground, MD 21010-5401, U.S.A. | |
| ABSTRACT | ||
| The U.S. Army is in the process of disposing of its stockpile of obsolete chemical weapons. A simulation model has been developed to help identify facility operational strategies that may increase the number of munitions or the quantity of chemical agent processed over an extended period of time. It is also used to assess the potential effects of proposed plant modifications and alternative process configurations on plant performance, schedule, and operating costs prior to their implementation. A new customized graphical user interface to the simulation model has been developed to overcome software limitations and enhance the model system. This allows more rapid and complete assessments by a variety of users at different facilities. | ||
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| Interface Driven
Domain-Independent Modeling Architecture for "Soft-Commissioning" and
"Reality in the Loop" |
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| Franz
Auinger Markus Vorderwinkler Georg Buchtela PROFACTOR Produktionsforschungs GmbH Wehrgrabengasse 1-5, A-4400 Steyr, Austria |
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| ABSTRACT | ||
| As industrial manufacturing and automation systems grow in complexity, there is a need for control software engineering support. Soft-Commissioning and Reality in the Loop (RIL) are two novel approaches which allow coupling simulation models to real world entities and allow the analyst to pre-commission and test the behavior of a system, before it is completely built in reality. To be flexible and fast in building up a simulation model fulfilling the requirements of Soft-Commissioning and RIL there is a need for a component-based modeling architecture. In this paper we define the characteristic requirements, and derive an architecture out of them, which is discussed from different aspects. Finally we briefly present a simple example. | ||
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| SIMULATION OPTIMIZATION
WITH THE LINEAR MOVE AND EXCHANGE MOVE OPTIMIZATION ALGORITHM
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|---|---|---|
| Marcos Ribeiro Pereira
Barretto Leonardo Chwif Mechatronics Lab University of São Paulo Av. Prof. Mello Moraes 2231 Sao Paulo, 05508-900, BRAZIL |
Tillal Eldabi Ray J. Paul Centre For Applied Simulation Modelling Department Of Information Systems And Computing Brunel University Uxbridge, Middlesex, UB8 3PH, U.K. | |
| ABSTRACT | ||
| The Linear Move and Exchange Move Optimization (LEO) is an algorithm based on a simulated annealing algorithm (SA), a relatively recent algorithm for solving hard combinatorial optimization problems. The LEO algorithm was successfully applied to a facility layout problem, a scheduling problem and a line balancing problem. In this paper we will try to apply the LEO algorithm to the problem of optimizing a manufacturing simulation model, based on a Steelworks Plant. This paper also demonstrates the effectiveness and versatility of this algorithm. We compare the search effort of this algorithm with a Genetic Algorithm (GA) implementation of the same problem. | ||
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