WSC 2003 Final Abstracts

A Hybrid Approach to Manufacturing Enterprise Simulation
Luis Rabelo and Magdi Helal (University of Central Florida), Young-Jun Son (University of Arizona), Albert Jones and Jason Min (National Institute of Standards & Technology) and Abhijit Deshmukh (University of Massachusetts)

Abstract:
Manufacturing enterprise decisions can be classified into four groups: business decisions, design decisions, engineering decisions, and production decisions. Numerous physical and software simulation techniques have been used to evaluate specific decisions by predicting their impact on the system as measured by one or more performance measures. In this paper, we focus on production decisions, where discrete-event simulation models perform that evaluation. We argue that such an evaluation is limited in time and scope, and does not capture the potential impact of these decisions on the whole enterprise. We propose integrating these discrete-event models with system dynamic models and we show the potential benefits of such an integration using an example of semiconductor enterprise.

Deterministic and Stochastic Dynamic Modeling of Continuous Manufacturing Systems Using Analogies to Electrical Systems
Bashar H. Sader and Carl D. Sorensen (Brigham Young University)

Abstract:
A dynamic system model of continuous manufacturing systems has been developed based on analogies with electrical systems. This model has the capability to model both deterministic and stochastic systems. The model provides physically meaningful governing equations to describe both the steady state and transient responses of continuous manufacturing systems. For stochastic solutions, the model is not limited to any specific probabilistic distribution. The model is demonstrated by application to a representative continuous manufacturing line for both deterministic and stochastic cases. The results of the stochastic case are compared to those from a discrete event simulation tool using a paired t-test at the 95% confidence level. For some results, the difference is statistically insignificant. For others, there is a statistically significant difference. However, in both cases the percentage difference is within a reasonable range.

Data Driven Design and Simulation System based on XML
Guixiu Qiao, Frank Riddick, and Charles McLean (National Institute of Standards & Technology)

Abstract:
Implementing a highly flexible manufacturing approach, like mass customization manufacturing, demands an integrated design and simulation system. This system must be able to cope with difficult issues such as a high level of product variety, uncertainty in the product demand forecast, and the reconfiguration of manufacturing resources to support the introduction and integration of new manufacturing capabilities. In this paper, a data-driven design and simulation system to support flexible manufacturing is presented. A neutral model of shop information, based on the eXtensible Markup Language, is used to describe the important information about the manufacturing facilities and processes, to configure simulation models and to exchange data between simulation and other manufacturing applications. When demand changes, the simulation model can be quickly modified to perform analysis according to the new demand. Manufacturing capabilities and production processes can be adjusted, layout reconfigured, and resources reassigned according to the analysis results.

Automobile Manufacturing Supply Chain Simulation in the GRIDS Environment
Gary Tan and Na Zhao (National University of Singapore) and Simon J.E. Taylor (Brunel University)

Abstract:
A Supply Chain is the series of activities that an organization uses to deliver value to its customers. In today's competitive environment, the globalization of markets has rapidly substituted the traditional integrated business. The competitive success of an organization no longer depends only on its own efforts, but relies on the efficiency of the entire supply chain. Therefore, building an effective supply chain is fast becoming paramount in today's marketplace. Distributed Supply Chain (DSC) Simulation has been identified as one of the best means to test and analyze the performance of supply chains. The Generic Runtime Infrastructure for Distributed Simulation (GRIDS) is a middleware that supports the reuse and interoperation of DSC simulations. This paper reports the experience on employing GRIDS to support the distributed collaboration of an automobile manufacture supply chain simulation. Several advantages of GRIDS are also discussed here which make it an ideal middleware for DSC simulations.

EPOCHS: Integrated Commercial Off-the-Shelf Software for Agent-Based Electric Power and Communication Simulation
Kenneth M. Hopkinson and Kenneth P. Birman (Cornell University), Renan Giovanini and Denis V. Coury (University of São Paulo) and Xiaoru Wang and James S. Thorp (Cornell University)

Abstract:
This paper reports on the development of the Electric Power and Communication Synchronizing Simulator (EPOCHS), a distributed simulation environment. Existing electric power simulation tools accurately model power systems of the past, which were controlled as large regional power pools without significant communication elements. However, as power systems increasingly turn to protection and control systems that make use of computer networks, these simulators are less and less capable of predicting the likely behavior of the resulting power grids. Similarly, the tools used to evaluate new communication protocols and systems have been developed without attention to the roles they might play in power scenarios. EPOCHS utilizes multiple research and commercial off-the-shelf (COTS) systems to bridge the gap. EPOCHS is also notable for allowing users to transparently encapsulate complex system behavior that bridges multiple domains through the use of a simple agent-based framework.

Simulation of Distributed Manufacturing Enterprises: A New Approach
Sameh M. Saad, Terrence Perera, and Ruwan Wickramarachchi (Sheffield Hallam University)

Abstract:
The globalization of markets and world-wide competition forces manufacturing enterprises to enter into alliances leading to the creation of distributed manufacturing enterprises. Before forming a partnership it is essential to evaluate viability of proposed enterprise as well as how a company’s operations are affected by the proposed virtual enterprise. Distributed simulation provides an attractive tool to make decisions on such situations. However, due to its complexity and high cost distributed simulation failed to gain a wide acceptance from industrial users. This paper presents a new approach for distributed manufacturing simulation (DMS) including a formal methodology for DMS and, implementation approach using current commercial simulation software, employing widely available and cost effective technologies. The main objective of this work is to promote the use of distributed simulation particularly in distributed manufacturing by making it fast to develop and less complicated for implementation.

A Simulation Test Bed for Production and Supply Chain Modeling
S. T. Enns and Pattita Suwanruji (University of Calgary)

Abstract:
Production systems and supply chains are difficult to model at the level of detail required to understand factors affecting the behavior of material flow. This is particularly true when use of centralized planning systems, such as MRP or DRP, is of interest. Therefore a test bed, comprised of a planning module and a simulator module, has been developed. This test bed is designed to be simple, transparent and flexible. It supports research as well as training. The planning module uses a spreadsheet-based interface and logic embedded in extensive VBA macros. The simulator module is made up of a generic ARENA program that requires no direct modeling inputs when scenarios are changed. Dynamic communication between the modules is facilitated using VBA. Transient and steady-state behavior can be observed under diverse conditions. Production systems or supply chains using MRP/DRP, re-order points or Kanban systems can be compared.

Benchmarking of a Stochastic Production Planning Model in a Simulation Testbed
German Riaño (Universidad de los Andes Bogota), Szu Hui Ng (National University of Singapore), Richard Serfozo and Steven Hackman (Georgia Institute of Technology) and Lai Peng Chan and Peter Lendermann (Singapore Institute of Mfg. Technology)

Abstract:
A major problem in production planning is to determine when to release products into production to meet forecasted requirements. Recently, Riano et al. (2002) proposed the Stochastic Production Planning (SPP) model for a multi-period, multi-product system, where the lead time to produce a product may be random. The model determines release times for the products that ensure the requirements in each time period are met with desired probabilities at a minimum cost. This paper describes how an advanced planning model like SPP can be integrated with discrete event simulation models to make the simulations more realistic and informative. This paper also compares the performance of the SPP model with the classical MRP (materials requirements planning) model, and with a stochastic variation of the MRP model in a simulation study. The costs associated with the production plans from SPP are about 10% less than the costs from the other two models.

Comparison of Bottleneck Detection Methods for AGV Systems
Christoph Roser, Masaru Nakano, and Minoru Tanaka (Toyota Central Research & Development Labs)

Abstract:
The performance of a manufacturing or logistic system is determined by its constraints. Therefore, in order to improve the performance, it is necessary to improve the constraints, also known as the bottlenecks. Finding the bottlenecks, however, is not easy. This paper compares the two most common bottleneck detection methods, based on the utilization and the waiting time, with the shifting bottleneck detection method developed by us, for AGV systems. We find that the two conventional methods have many shortcomings compared to the shifting bottleneck detection method. In the example presented here, conventional methods are either unable to detect the bottleneck at all or detect the bottleneck incorrectly. The shifting bottleneck detection method not only finds the bottlenecks but also determines the magnitude of the primary and secondary bottlenecks.

Reducing Human Error in Simulation in General Motors
Demet Wood (General Motors) and Echo A. Harger (Production Modeling Corporation)

Abstract:
This paper focuses on the steps taken to minimize human error in simulation modeling in General Motors. While errors are costly and undesirable in any field, they are especially harmful in simulation which has been struggling to gain acceptance in the business world for a long time. The solution discussed in this paper can be summarized as “enter the data once and use the best tool for the job.”

Paint Line Color Change Reduction in Automobile Assembly through Simulation
Yong-Hee Han, Chen Zhou, Bert Bras, Leon McGinnis, Carol Carmichael, and P.J. Newcomb (Georgia Institute of Technology)

Abstract:
The painting process is an important part of the entire automobile manufacturing system. Changing color in the painting process is expensive because of the wasted paint and solvent during color change. By intelligently selecting cars toward downstream operations at the places where conveyors converge or diverge, we can reduce the number of such color changes without additional hardware investment. Discrete Event Simulation is a tool of choice in analyzing these issues in order to develop an effective and efficient selection algorithm to ensure the system throughput. The concepts and methods presented here are also applicable to other discrete event manufacturing processes where setup reduction is pursued.

Using Empirical Evidence of Variations in Worker Performance to Extend the Capabilities of Discrete Event Simulations in Manufacturing
Tim Baines, Linda Hadfield, and Steve Mason (Cranfield University) and John Ladbrook (Ford Motor Company)

Abstract:
Discrete Event Simulation of manufacturing systems has become widely accepted as an important tool to aid the design of such systems. Often, however, it is applied by practitioners in a manner which largely ignores an important element of industry; namely, the workforce. Workers are usually represented as simple resources, often with deterministic performance values. This approach ignores the potentially large effect that human performance variation can have on a system. A long-term data collection exercise is described with the aim of quantifying the performance variation of workers in a typical automotive assembly plant. The data are presented in a histogram form which is immediately usable in simulations to improve the accuracy of design assessment. The results show levels of skewness and range which are far larger than anticipated by current researchers and practitioners in the field.

Generic Case Studies for Manufacturing Simulation Applications
Charles McLean and Guodong Shao (National Institute of Standards & Technology)

Abstract:
Manufacturing managers typically commission simulation case studies to support their decision-making processes. These studies are used to evaluate alternative solutions to manufacturing problems in areas such as plant layout, scheduling, capacity planning, capital equipment acquisition, inventory management, and supply chain planning. Procedures for performing case studies vary from organization to organization, and situation to situation. It is possible that two different simulation analysts faced with the same manufacturing problem would perform their case studies differently, obtain different results, and reach different conclusions. The authors contend that standardization of the case study methodology and development of generic case studies would increase the likelihood that the simulation process will be deterministic, i.e., produce repeatable results. This paper presents background on case studies and makes recommendations concerning the advancement of manufacturing simulation case study methods and practices.

Simulation Modeling for Quality and Productivity in Steel Cord Manufacturing
Can H. Turkseven (Purdue University) and Gürdal Ertek (Sabanci University)

Abstract:
We describe the application of simulation modeling to estimate and improve quality and productivity performance of a steel cord manufacturing system. We describe the typical steel cord manufacturing plant, emphasize its distinguishing characteristics, identify various production settings and discuss applicability of simulation as a management decision support tool. Besides presenting the general structure of the developed simulation model, we focus on wire fractures, which can be an important source of system disruption.

NIST XML Simulation Interface Specification at Boeing: A Case Study
Roberto F. Lu (The Boeing Company) and Guixiu Qiao and Charles McLean (National Institute of Standards & Technology)

Abstract:
Efficient and consistent simulation data management is indispensable and a challenging problem to be solved in modeling manufacturing and business processes. An extensible markup language (XML) based simulation interface specification is being developed by the National Institute of Standards and Technology (NIST). The proposed NIST document contains a prototype generic simulation data specification, which is an endeavor to fill a void in exchanging reusable simulation data. A case study was performed at Boeing Commercial Airplanes (BCA), utilizing the NIST XML simulation interface specification. Entity classes in this case study simulation model contain asynchronous servers, multi-input-output buffers, bidirectional cranes, labors (manpower), processes, and machines on different shifts. This model can be executed from a batch control language document, that is derived from the proposed NIST XML-based simulation specification. This case study illustrates a feasible method for using the XML-based NIST specifications in a discrete event simulation model of a manufacturing process.

Buffer Allocation Model based on a Single Simulation
Christoph Roser, Masaru Nakano, and Minoru Tanaka (Toyota Central Research & Development Labs)

Abstract:
Allocating buffers in manufacturing systems is one of easiest ways to improve the throughput of the system, as changes can be implemented quickly and the initial cost of the change is low. Yet, while an increase in the buffer size usually increases the throughput, it often also increases the work in progress and the makespan, therefore increasing the inventory and the time to the customer. Subsequently, the trade off between the throughput, the work in progress, and the makespan are of significant research interest. This paper describes a general prediction model of these performance measures for different buffer size increases based on only a single simulation. A fully automated implementation of the simulation analysis and prediction model for manufacturing systems of any size and complexity is available. The method can be used for flow shops, job shops, and serial or parallel systems.

Shared Resource Capacity Analysis in Biotech Manufacturing
Prasad V. Saraph (Bayer HealthCare)

Abstract:
Simulation is a relatively new tool for business process analysis in the Biotech industry. This paper discusses an application of discrete event simulation in analyzing the capacity needs of a shared resource in the manufacturing facility at Bayer Corporation’s Berkeley site. The SIGMA® simulation model was used to analyze the workload patterns, run different workload scenarios, taking into consideration uncertainty and variability, and provide recommendation on a capacity increase plan. This analysis also demonstrated benefits of certain operational scheduling policies. The analysis outcome was used to determine capital investments for 2002. The paper illustrates the power of simulation tools in providing quick and robust analysis with solutions to planning problems.

Behavior of an Order Release Mechanism in a Make-to-Order Manufacturing System with Selected Order Acceptance
Amitava Nandi (Nortel Networks) and Paul Rogers (University of Calgary)

Abstract:
The value of holding orders in a pre-shop pool, prior to their release to the factory floor, is a somewhat controversial topic. This is especially true for make-to-order manufacturing systems, where, if capacity is fixed and exogenous due dates are inflexible, having orders wait in a pre-shop pool may cause the overall due date performance of the system to deteriorate. In such circumstances, selective rejection of orders offers an alternative approach to dealing with surges in demand whilst maintaining acceptable due date performance. This paper reports on the behavior of such a make-to-order manufacturing system under a control policy involving both an order release component and an order acceptance/rejection component.

A Simulation-Optimization Approach Using Genetic Search for Supplier Selection
Hongwei Ding, Lyès Benyoucef, and Xiaolan Xie (INRIA-Lorraine (The French National Institute for Research in Computer Science & Control))

Abstract:
The paper presents a simulation-optimization approach using genetic algorithm to the supplier selection problem. The problem consists in selecting a portfolio of suppliers from a set of pre-selected candidates. The supplier selection is a multi-criteria problem which includes both qualitative and quantitative criteria. In order to select the best suppliers it is crucial to make a trade off between these tangible and intangible criteria, some of which may be contradictory. The proposed approach uses discrete-event simulation for performance evaluation of a supplier portfolio and a genetic algorithm for optimum portfolio identification based on performance indices estimated by the simulation. Numerical results on a real-life case study are presented.

Simulation based Optimization for Supply Chain Configuration Design
Tu Hoang Truong and Farhad Azadivar (University of Massachusetts, Dartmouth)

Abstract:
The design of a supply chain network as an integrated system with several tiers of suppliers is a difficult task. It consists of making strategic decisions on the facility location, stocking location, production policy, production capacity, distribution and transportation modes. This research develops a hybrid optimization approach to address the Supply Chain Configuration Design problem. The new approach combines simulation, mixed integer programming and genetic algorithm. The genetic algorithm provides a mechanism to optimize qualitative and policy variables. The mixed integer programming model reduces computing efforts by manipulating quantitative variables. Finally simulation is used to evaluate performances of each supply chain configuration with non-linear, complex relationships and under more realistic assumptions. The approach is designed to be robust and could handle the large scale of the real world problems.

Incorporating Fuzzy Logic Admission Control in Simulation Models
Qisheng Le (The Louisiana State University) and Gerald M. Knapp (Louisiana State University)

Abstract:
Admission Control is of great interest in computer, communication and production network applications. As systems become more complex or need to make decision based on multiple objectives or require to satisfy several constraints, Fuzzy Logic Control (FLC) is a natural tool to handle admission problems. This paper discusses the framework of a tool developed for easily integrating fuzzy logic admission control into Arena simulation models. The tool is implemented as a "drop-in" model block that performs admission control. The block can be configured to perform simple or priority-based admission, as well as multiple queue selection. The FLC "rules" are set through a design-time user interface and stored in an MS Access database. Because multiple FLC blocks may be included in a model, almost any possible admission scenario (multiple entry points, parallel lines, etc) may be modeled. A case study is used to demonstrate the developed framework.

Designing Reusable Simulation Modules for Electronics Manufacturing Systems
Phani S. Mukkamala, Jeffrey S. Smith, and Jorge F. Valenzuela (Auburn University)

Abstract:
Developing simulation models for related problems in the same domain is generally a repetitive process. Such simulation models are similar in many aspects and have only minor differences. Modeling efforts can be reduced to a great extent through the development of domain specific modules or templates that encapsulate the domain-specific logic and hide many of the modeling details. This paper describes the development of such a domain-specific template for electronics assembly. In particular, the template focuses on the automated assembly of printed circuit boards. The template encompasses the complexity of the target domain and simplifies the model-building process. While the paper focuses on a language-neutral description of the template, specific experience with Arena is described.

Information Structure to Support Discrete Event Simulation in Manufacturing Systems
Björn Johansson, Joacim Johnsson, and Anders Kinnander (Chalmers University of Technology)

Abstract:
Discrete Event Simulation (DES) is ranked among the top three tools for management support. However, it lags in becoming the successful tool in the industry that many experts have predicted. In this paper, sixteen projects accomplished in the area of DES have been analyzed in order to find the reasons for this delay. Most important is the lack of reliable manufacturing data in companies. This is due to inadequate practices within the organization, thus forcing users to build simulation models with estimated data. The paper also answers other questions as to why DES is an underutilized decision tool. DES is an information-intensive tool for decision-making, but has weak support concerning working procedures within organizations. Continuous generation of manufacturing data at all levels has to be supported by the working procedure in order to increase the use of DES as an everyday tool. How to improve this situation also is discussed.

A Neutral Information Model for Simulating Machine Shop Operations
Y. Tina Lee (National Institute of Standards & Technology), Charles McLean (National Institute of Standards & Technology (NIST)) and Guodong Shao (National Institute of Standards & Technology)

Abstract:
Small machine shops typically do not have the resources to develop custom simulations of their operations or data translators to import their data from other manufacturing software applications. This paper presents an overview of an information model currently under development at the National Institute of Standards and Technology (NIST) to address this problem. The model provides neutral data interfaces for integrating machine shop software applications with simulation. The information model provides mechanisms for describing data about organizations, calendars, work, resources, schedules, parts, process plans, and layouts within a machine shop environment. The model has been developed using the Unified Modeling Language (UML) and the Extensible Markup Language (XML).

Distributed Supply Chain Simulation Using a Generic Job Running Framework
Haifeng Xi, Heng Cao, Leonard Berman, and David Jensen (IBM T.J. Watson Research Center)

Abstract:
For supply chain performance simulation that involves aggregating results from multiple runs of the same underlying model, simulation iterations can be distributed to networked computing resources to achieve significant speedup. This paper presents a generic distributed job running framework that facilitates such high performance supply chain simulation. We first introduce a supply chain modeling and simulation tool developed by IBM Research, and summarize the strategy to enhance it. A closer look is then taken at a generic job running framework we designed and how it was used to bring the distributed simulation capability to the tool. After reviewing an ongoing effort to integrate the new tool with the IBM MathGrid environment, we conclude the paper with a brief discussion of our future work.

A Simulation-Based Tool for Inventory Analysis in a Server Computer Manufacturing Environment
Heng Cao, Feng Cheng, Haifeng Xi, Markus Ettl, and Stephen Buckley (IBM T.J. Watson Research) and Carlos Rodriguez (IBM Enterprise Server Group)

Abstract:
In this paper, we describe a simulation-based inventory management tool developed for the IBM Enterprise Server Group. Through the Web interface of the tool, an inventory manager is able to visualize Days of Supply (DOS) levels – current and projected, and to carry out what-if scenario analysis to identify potential opportunities for improvement. The highly complicated server manufacturing environment poses simulation modeling challenges such as two-stage fabrication/fulfillment process, multi-echelon bills-of-materials, complex server box configurations, part tests with random yields, stochastic lead times and so on. In the following sections, we will introduce the common characteristics of the server manufacturing environment, present the overall architecture of our tool, and describe the simulation model design and how we addressed those challenges. At the end of the paper, we will show some results collected from the tool and point out our future research directions.

A Bayesian Framework for Modeling Demand in Supply Chain Simulation Experiments
David F. Muñoz (Instituto Tecnológico Autónomo de México)

Abstract:
In order to postpone production planning based on information obtained close to the time of sale, decision support systems for supply chain management often include demand forecasts based on little historical data and/or subjective information. Particularly, when simulation models for analyzing decisions related to safety inventories, lot sizing or lead times are used, it is convenient to model (daily) demand by considering historical data, as well as information (often subjective) of the near future. This article presents an approach for modeling a random input (e.g., demand) in simulation experiments. Under this approach, the family of distributions proposed for modeling demand should include two types of parameters: the ones that capture information of historical data and the ones that depend on the particular scenario that is to be simulated. The approach is extended to the case where uncertainty on the appropriate family of distributions is present.