{"title":"Impact of production run length on supply chain performance","authors":"D. J. Parsons, Robin J. Clark, Kevin L. Payette","doi":"10.1109/WSC.2004.1371442","DOIUrl":"https://doi.org/10.1109/WSC.2004.1371442","url":null,"abstract":"This paper documents an experiment designed to show the value of simulation in understanding the relationship between production run lengths and overall supply chain performance. Current production practices and supply chain policies of an existing company provided the starting point for the experiment. The experiment consisted of two deployment scenarios and a range of run length multipliers that vary the company's actual run length rules. Minimum cost run lengths were determined for twelve combinations of cost assumptions for changeovers and inventories.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128538528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Srimathveeravalli, N. Subramanian, T. Kesavadas
{"title":"A scenario generation tool for DDF simulation testbeds","authors":"G. Srimathveeravalli, N. Subramanian, T. Kesavadas","doi":"10.1109/WSC.2004.1371519","DOIUrl":"https://doi.org/10.1109/WSC.2004.1371519","url":null,"abstract":"An interactive tool has been developed for visualizing and creating scaled battlefield based scenarios for use in a simulation testbed to develop and test distributed data fusion and ad-hoc networking algorithms. This paper discus the design requirements and implementation issues for developing such a tool. Two main design goals were to enable design of complex scenarios in an intuitive and easy fashion, and provide a complete set of decision support utilities. The tool, called SceneGen, supports 3D visualization for creating scenarios and overcoming challenges including geospatial (GIS and Terrain) data management, entity information management and waypoint/path specification. The scenario generator includes a number of sensor and target models and provides database support to manage different fusion and network algorithms. This tool was implemented and tested successfully with several sample scenarios.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129067325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A template-based conceptual modeling infrastructure for simulation of physical security systems","authors":"A. Guru, P. Savory","doi":"10.1109/WSC.2004.1371401","DOIUrl":"https://doi.org/10.1109/WSC.2004.1371401","url":null,"abstract":"Although simulation is one of the most innovative and cost-effective tools for modeling and analyzing a system, simulation studies often fail to provide any useful results. One reason is attributed to the fact that model formulation depends on the skills of the analyst. This paper describes a research to develop a conceptual modeling infrastructure to assist a simulation analyst in specifying components for studying physical security systems. The modeling framework has been programmed as an Internet-based Web application. Using the application, the successful development and implementation of a physical security simulation model will be aided by a defined scientific methodology rather than simply the skills of the analyst. Further the modeling framework is simulation language independent, thus allowing for a top-down or bottom-up approach to developing the conceptual model. This offers support for an object-oriented modeling design.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129354537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using dynamic multiresolution modelling to analyze large material flow systems","authors":"W. Dangelmaier, B. Mueck","doi":"10.1109/WSC.2004.1371522","DOIUrl":"https://doi.org/10.1109/WSC.2004.1371522","url":null,"abstract":"The interactive, simulation-aided analysis of material flow systems is often done with the help of virtual reality. If a user wants to influence the simulation run, the simulation and the visualization have to be computed simultaneously. Large, detailed simulation models, which can not be calculated fast enough, can not be analyzed interactively. This paper presents a method which only computes those parts of simulation models that are visited by the user. If a user shifts its attention, the area being simulated in detail is updated. Since the major part of the simulation is not detailed, the necessary calculating complexity is reduced. On the basis of models allowing a multiresolution simulation methods are introduced which regulate the level of detail based on the evaluation of the user's attention. Changing the levels of details leads to the exchange of models with different level of details.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130519367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Building credible input models","authors":"L. Leemis","doi":"10.1109/WSC.2004.1371299","DOIUrl":"https://doi.org/10.1109/WSC.2004.1371299","url":null,"abstract":"Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling. The general question considered here is how to model an element (e.g., arrival process, service times) in a discrete-event simulation given a data set collected on the element of interest. For brevity, it is assumed that data is available on the aspect of the simulation of interest. It is also assumed that raw data is available, as opposed to censored data, grouped data, or summary statistics. This example-driven tutorial examines introductory techniques for input modeling. Most simulation texts (e.g., Law and Kelton 2000, Fishman 2001) have a broader treatment of input modeling than presented here. Nelson and Yamnitsky (1998) survey advanced techniques.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124367207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhancing simulation education with a virtual presentation tool","authors":"Davis He, P. Banerjee","doi":"10.1109/WSC.2004.1371524","DOIUrl":"https://doi.org/10.1109/WSC.2004.1371524","url":null,"abstract":"This paper describes a research plan to address the undergraduate learning challenges encountered when teaching an introductory simulation course in industrial engineering programs. It outlines the implementation tasks of the research methodology, an evaluation plan, and one direction for future work.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124190518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Franzese, Luiz Otávio Abdenur, R. Botter, Arnoldo R. Cano, D. W. Starks
{"title":"Simulating the Panama Canal: present and future","authors":"L. Franzese, Luiz Otávio Abdenur, R. Botter, Arnoldo R. Cano, D. W. Starks","doi":"10.5555/1161734.1162075","DOIUrl":"https://doi.org/10.5555/1161734.1162075","url":null,"abstract":"This paper presents the methodologies and preliminary results of the project to develop a simulation model of the Panama Canal, one of the most famous waterway and locks system of the world. The case is based on the project conducted by Rockwell Software and Paragon Consulting Solutions, helping Panama Canal Authority design a strategic planning tool, based on Arena simulation software.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126254596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adaptive control variates","authors":"Sujin Kim, S. Henderson","doi":"10.1109/WSC.2004.1371369","DOIUrl":"https://doi.org/10.1109/WSC.2004.1371369","url":null,"abstract":"Adaptive Monte Carlo methods are specialized Monte Carlo simulation techniques where the methods are adaptively tuned as the simulation progresses. The primary focus of such techniques has been in adaptively tuning importance sampling distributions to reduce the variance of an estimator. We instead focus on adaptive control variate schemes, developing asymptotic theory for the performance of two adaptive control variate estimators. The first estimator is based on a stochastic approximation scheme for identifying the optimal choice of control variate. It is easily implemented, but its performance is sensitive to certain tuning parameters, the selection of which is nontrivial. The second estimator uses a sample average approximation approach. It has the advantage that it does not require any tuning parameters, but it can be computationally expensive and requires the availability of nonlinear optimization software.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126217212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using simulated data in support of research on regression analysis","authors":"C. M. Hill, L. Malone","doi":"10.5555/1161734.1161910","DOIUrl":"https://doi.org/10.5555/1161734.1161910","url":null,"abstract":"Using simulated data to develop and study diagnostic tools for data analysis is very beneficial. The user can gain insight about what happens when assumptions are violated since the true model is known. However, care must be taken to be sure that the simulated data is a reasonable representation of what one would usually expect in the real world. This paper discusses the construction of simulated data sets and provides specific examples using linear and logistic regression analysis. It also addresses the execution of simulation based data studies following data construction.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125485304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Numerical accuracy issues in using Excel for simulation studies","authors":"K. Keeling, R. Pavur","doi":"10.1109/WSC.2004.1371492","DOIUrl":"https://doi.org/10.1109/WSC.2004.1371492","url":null,"abstract":"Many researchers use Excel to perform simulations, but with each upgrade to Excel - Excel 97, Excel 2000, Excel XP, and Excel 2003 - numerical accuracy problems have been noted. In the latest version, Excel 2003, some substantial changes have been made to its algorithms as noted on its Web site. This paper discusses generating random numbers in Excel - including uniform, normal, and Poisson variates. In addition, the study assesses how Excel's accuracy stacks up to other statistical software by using the NIST Statistical Reference Datasets tests as certified benchmarks of numerical accuracy. This paper will reveal that Excel 2003 still has room for improvement.","PeriodicalId":257599,"journal":{"name":"Proceedings of the 2004 Winter Simulation Conference, 2004.","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130278942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}