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.
{"title":"A simulation model to study the dynamics in a service-oriented supply chain","authors":"E. Anderson, D. Morrice","doi":"10.1145/324138.324470","DOIUrl":"https://doi.org/10.1145/324138.324470","url":null,"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.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133801424","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}
Multitrajectory Simulation allows random events in a simulation to generate multiple trajectories, a technique called "splitting", with explicit management of the set of trajectories. The goal is to gain a better understanding of the possible outcome set of the simulation and scenario. This has been applied to a prototype combat simulation, "eaglet" which was designed to have similar, but simpler, representations of the features of the "Eagle" simulation used for Army analyses. The study compared the number of multitrajectory simulation trajectories with numbers of stochastic replications to experimentally determining the rate of convergence to a definitive outcome set. The definitive set was determined using very large numbers of replications to develop a plot of loss exchange ratio versus losses of one side. This was repeated with scenarios of from 40 to 320 units. While the multitrajectory technique gave superior results in general as expected, there were some anomalies, particularly in the smallest scenario, that illustrate limitations of the technique and the assessment method used. 1 BACKGROUND The goal of multitrajectory simulation is to explore the outcome space of a simulation, that is, the set of all possible outcomes, more systematically and less expensively (for a given quality of understanding) than can be achieved with conventional stochastic simulation. This may be considered a variance reduction technique, but the analysis goals may be formulated not only in terms of better estimates of statistical properties of the outcome set, e.g. a mean and variance for Measures of Effectiveness (MOEs), but also representative instances of extreme behavior or other "interesting" cases (Al-Hassan, Gilmer, and Sullivan 1997). The heart of the proposed method is to explicitly track each possible trajectory, as illustrated in Figure 1. When an event that would normally be stochastic occurs, instead of one outcome, multiple outcomes are generated, each constituting a trajectory having its own state. Because the trajectory bifurcates, this is also referred to as "splitting", with "cloning" of the state. In concept, such a multiple trajectory simulation is integrated with its support system in such a way that its use provides outcomes with probabilities associated with each, an accounting for the key events or circumstances leading to the differences, and some measure of confidence in these results.
多轨迹仿真允许模拟中的随机事件生成多个轨迹，这种技术称为“分裂”，并对轨迹集进行显式管理。目标是更好地理解模拟和场景的可能结果集。这已经被应用于一个原型战斗模拟，“小鹰”，它被设计成具有类似的，但更简单的，用于陆军分析的“鹰”模拟的特征表示。该研究将多轨迹模拟轨迹的数量与随机重复的数量进行了比较，以实验确定收敛到确定结果集的速度。最终的集合是通过大量的重复来确定的，以形成一个损失交换比与一侧损失的图。在40到320个单位的场景中重复了这一过程。虽然多轨迹技术总体上如预期的那样提供了更好的结果，但也存在一些异常情况，特别是在最小的场景中，这说明了该技术和所使用的评估方法的局限性。多轨迹模拟的目标是探索模拟的结果空间，即所有可能结果的集合，比传统的随机模拟更系统和更便宜(对于给定的理解质量)。这可以被认为是一种减少方差的技术，但分析目标不仅可以根据更好地估计结果集的统计特性来制定，例如有效性度量(MOEs)的平均值和方差，还可以根据极端行为的代表性实例或其他“有趣的”案例(Al-Hassan, Gilmer, and Sullivan, 1997)。所建议的方法的核心是显式地跟踪每个可能的轨迹，如图1所示。当一个通常是随机的事件发生时，会产生多个结果，而不是一个结果，每个结果都构成一个有自己状态的轨迹。因为轨迹分叉，这也被称为“分裂”，与状态的“克隆”。从概念上讲，这种多轨迹模拟与它的支持系统集成在一起，它的使用提供了与每个结果相关的概率，对导致差异的关键事件或情况的说明，以及对这些结果的一些信心度量。
{"title":"Multitrajectory simulation performance for varying scenario sizes","authors":"J. B. Gilmer, F. J. Sullivan","doi":"10.1145/324898.325018","DOIUrl":"https://doi.org/10.1145/324898.325018","url":null,"abstract":"Multitrajectory Simulation allows random events in a simulation to generate multiple trajectories, a technique called \"splitting\", with explicit management of the set of trajectories. The goal is to gain a better understanding of the possible outcome set of the simulation and scenario. This has been applied to a prototype combat simulation, \"eaglet\" which was designed to have similar, but simpler, representations of the features of the \"Eagle\" simulation used for Army analyses. The study compared the number of multitrajectory simulation trajectories with numbers of stochastic replications to experimentally determining the rate of convergence to a definitive outcome set. The definitive set was determined using very large numbers of replications to develop a plot of loss exchange ratio versus losses of one side. This was repeated with scenarios of from 40 to 320 units. While the multitrajectory technique gave superior results in general as expected, there were some anomalies, particularly in the smallest scenario, that illustrate limitations of the technique and the assessment method used. 1 BACKGROUND The goal of multitrajectory simulation is to explore the outcome space of a simulation, that is, the set of all possible outcomes, more systematically and less expensively (for a given quality of understanding) than can be achieved with conventional stochastic simulation. This may be considered a variance reduction technique, but the analysis goals may be formulated not only in terms of better estimates of statistical properties of the outcome set, e.g. a mean and variance for Measures of Effectiveness (MOEs), but also representative instances of extreme behavior or other \"interesting\" cases (Al-Hassan, Gilmer, and Sullivan 1997). The heart of the proposed method is to explicitly track each possible trajectory, as illustrated in Figure 1. When an event that would normally be stochastic occurs, instead of one outcome, multiple outcomes are generated, each constituting a trajectory having its own state. Because the trajectory bifurcates, this is also referred to as \"splitting\", with \"cloning\" of the state. In concept, such a multiple trajectory simulation is integrated with its support system in such a way that its use provides outcomes with probabilities associated with each, an accounting for the key events or circumstances leading to the differences, and some measure of confidence in these results.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114212728","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}
Simulation is an ideal tool for addressing wide ranging issues in health care delivery. These issues involve public policy, patient treatment procedures, capital expenditure requirements, and provider operating policies. This tutorial presents example applications in each of these areas. Modeling, experimentation, and other project issues are discussed. A summary of technical issues, as well as issues relating to the acceptance of the use of simulation in health care delivery, is presented.
{"title":"A tutorial on simulation in health care: applications issues","authors":"C. Standridge","doi":"10.1145/324138.324149","DOIUrl":"https://doi.org/10.1145/324138.324149","url":null,"abstract":"Simulation is an ideal tool for addressing wide ranging issues in health care delivery. These issues involve public policy, patient treatment procedures, capital expenditure requirements, and provider operating policies. This tutorial presents example applications in each of these areas. Modeling, experimentation, and other project issues are discussed. A summary of technical issues, as well as issues relating to the acceptance of the use of simulation in health care delivery, is presented.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129733382","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}
This paper presents an approach towards building a flexible modeling and simulation environment with database technologies. The main problem of defining complex systems by component based simulation systems is solved by a set of predefined micro-functions, similar to modern microprocessor architectures. The execution order and additional parameters are also stored in a database.
{"title":"Database oriented modeling with simulation microfunctions","authors":"Thomas Weidemann","doi":"10.1145/324138.324440","DOIUrl":"https://doi.org/10.1145/324138.324440","url":null,"abstract":"This paper presents an approach towards building a flexible modeling and simulation environment with database technologies. The main problem of defining complex systems by component based simulation systems is solved by a set of predefined micro-functions, similar to modern microprocessor architectures. The execution order and additional parameters are also stored in a database.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130727596","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}
Further develops some of the ideas set out previously by the author (1998 Winter Simulation Conf., pp. 653-59, 1998) for output analysis using Bayesian Markov-chain Monte-Carlo (MCMC) techniques, when a regression metamodel is to be fitted to simulation output. The particular situation addressed in the previous paper was where there is uncertainty about the number of parameters needed to specify a model. This arises because there may be uncertainty about the number of terms to be included in the regression model to be fitted. The statistically non-standard nature of the problem means that it requires special handling. In this paper, the author uses the derived chain method suggested in the previous paper. However, whereas in that paper the distribution of the response output of interest was assumed to be simply normal, it is typically the case, especially in the study of systems working near their capacity limit, that this distribution is skewed, and moreover the distribution has a support that is effectively bounded from below-i.e. the distribution has a threshold. We describe how the derived MCMC method might be applied in this situation and illustrate it with a numerical example involving the simulation of a computer PAD network.
{"title":"Regression metamodeling in simulation using Bayesian methods","authors":"R. Cheng","doi":"10.1145/324138.324236","DOIUrl":"https://doi.org/10.1145/324138.324236","url":null,"abstract":"Further develops some of the ideas set out previously by the author (1998 Winter Simulation Conf., pp. 653-59, 1998) for output analysis using Bayesian Markov-chain Monte-Carlo (MCMC) techniques, when a regression metamodel is to be fitted to simulation output. The particular situation addressed in the previous paper was where there is uncertainty about the number of parameters needed to specify a model. This arises because there may be uncertainty about the number of terms to be included in the regression model to be fitted. The statistically non-standard nature of the problem means that it requires special handling. In this paper, the author uses the derived chain method suggested in the previous paper. However, whereas in that paper the distribution of the response output of interest was assumed to be simply normal, it is typically the case, especially in the study of systems working near their capacity limit, that this distribution is skewed, and moreover the distribution has a support that is effectively bounded from below-i.e. the distribution has a threshold. We describe how the derived MCMC method might be applied in this situation and illustrate it with a numerical example involving the simulation of a computer PAD network.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123065278","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}
Onur M. Ülgen, J. Shore, G. Coffman, D. Sly, M. Rohrer, Demet C. Wood
The objective of this panel session is to describe how a when manufacturing simulation practitioners should add the value of projects by interfacing simulation analys with other analyses such as optimization, layout/mater flow, scheduling, robotic, and queuing. The panelists w discuss how each analytical tool adds value to the discr event manufacturing simulation, when in the life cycle of project it should be brought in, what are the ma advantages and disadvantages of bringing in the additio tools, managing and selling collaborative analyses proje and training requirements for collaborative analyses.
{"title":"Increasing the power and value of manufacturing simulation via collaboration with other analytical tools (panel session): a panel discussion","authors":"Onur M. Ülgen, J. Shore, G. Coffman, D. Sly, M. Rohrer, Demet C. Wood","doi":"10.1145/324138.324471","DOIUrl":"https://doi.org/10.1145/324138.324471","url":null,"abstract":"The objective of this panel session is to describe how a when manufacturing simulation practitioners should add the value of projects by interfacing simulation analys with other analyses such as optimization, layout/mater flow, scheduling, robotic, and queuing. The panelists w discuss how each analytical tool adds value to the discr event manufacturing simulation, when in the life cycle of project it should be brought in, what are the ma advantages and disadvantages of bringing in the additio tools, managing and selling collaborative analyses proje and training requirements for collaborative analyses.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123394210","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}
A unique approach is developed for evaluating personnel requirements of the National Imagery and Mapping Agency (NIMA). With this approach, new ways of measuring personnel availability are proposed and made available to ensure that NIMA remains ready to provide timely, relevant and accurate imagery, imagery intelligence and geospatial information in support of the national security objectives of the USA during the projected defense draw-down beyond the Year 2000. The development of this analysis methodology was established as an alternative approach to existing studies to determine appropriate hiring and attrition rates, under all categories, to maintain appropriate personnel levels of effectiveness in order to support existing and future mission requirements. The contribution of this research is a prescribed method for the strategic analyst to incorporate a personnel and cost simulation model, which can be used to project personnel requirements and evaluate workforce sustainment, at the least cost, through time. This allows various personnel managers to evaluate multiple resource strategies, present and future, maintaining near-perfect hiring/attrition policies to support a 9000+ NIMA workforce.
{"title":"“Personnel forecasting strategic workforce planning”: a proposed simulation cost modeling methodology","authors":"S. R. Parker, J. Marriott","doi":"10.1145/324898.325287","DOIUrl":"https://doi.org/10.1145/324898.325287","url":null,"abstract":"A unique approach is developed for evaluating personnel requirements of the National Imagery and Mapping Agency (NIMA). With this approach, new ways of measuring personnel availability are proposed and made available to ensure that NIMA remains ready to provide timely, relevant and accurate imagery, imagery intelligence and geospatial information in support of the national security objectives of the USA during the projected defense draw-down beyond the Year 2000. The development of this analysis methodology was established as an alternative approach to existing studies to determine appropriate hiring and attrition rates, under all categories, to maintain appropriate personnel levels of effectiveness in order to support existing and future mission requirements. The contribution of this research is a prescribed method for the strategic analyst to incorporate a personnel and cost simulation model, which can be used to project personnel requirements and evaluate workforce sustainment, at the least cost, through time. This allows various personnel managers to evaluate multiple resource strategies, present and future, maintaining near-perfect hiring/attrition policies to support a 9000+ NIMA workforce.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123341278","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}
We present CONLOAD (CONstant LOAD), a new lot release rule for wafer fabs. It was developed to overcome some performance problems of traditional lot release rules such as CONWIP or Workload Regulation during product mix changes. We show that CONLOAD outperforms CONWIP and Workload Regulation with respect to keeping the bottleneck utilization at a desired level and to provide a smooth evolution of the WIP.
{"title":"CONLOAD—a new lot release rule for semiconductor wafer fabs","authors":"O. Rose","doi":"10.1145/324138.324535","DOIUrl":"https://doi.org/10.1145/324138.324535","url":null,"abstract":"We present CONLOAD (CONstant LOAD), a new lot release rule for wafer fabs. It was developed to overcome some performance problems of traditional lot release rules such as CONWIP or Workload Regulation during product mix changes. We show that CONLOAD outperforms CONWIP and Workload Regulation with respect to keeping the bottleneck utilization at a desired level and to provide a smooth evolution of the WIP.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116702694","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}
Panelists respond to the question, " What does industry need from simulation vendors in Y2k and after? " The panelists include software vendors, simulation modelers from industry, simulation consulting, and academia.
{"title":"What does industry need from simulation vendors in Y2K and after? (panel discussion)","authors":"J. Banks","doi":"10.1145/324898.325313","DOIUrl":"https://doi.org/10.1145/324898.325313","url":null,"abstract":"Panelists respond to the question, \" What does industry need from simulation vendors in Y2k and after? \" The panelists include software vendors, simulation modelers from industry, simulation consulting, and academia.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121562757","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}
This paper describes how simulation was used for business case benefits and return on investment (ROI) projection for the procurement and rollout of a new call routing technology to 25 call centers. With investment costs of about 17 million dollars and annual operating costs of about 8 million for the new technology, we needed to determine if the technology would provide enough cost savings and cost avoidance (through reduced trunk costs, increased agent productivity, and ability to service more calls) to warrant its nationwide implementation. We constructed a model of the existing call center environment consisting of 25 call centers where calls were distributed to the sites based on a system of percentage allocation routing; for example, the telephone network provider directs calls to each site based on the number of agents scheduled. We then modeled the same call system dynamics and intricacies under the new call routing system where calls are distributed based on longest available agent. Subsequently, we conducted average day simulations with light and heavy volumes and other “what if” laboratory analyses and experiments to facilitate planning decisions required to be documented and substantiated in the business case.
{"title":"Simulation of the call center environment for comparing competing call routing technologies for business case ROI projection (case study)","authors":"K. Miller, V. Bapat","doi":"10.1145/324898.325366","DOIUrl":"https://doi.org/10.1145/324898.325366","url":null,"abstract":"This paper describes how simulation was used for business case benefits and return on investment (ROI) projection for the procurement and rollout of a new call routing technology to 25 call centers. With investment costs of about 17 million dollars and annual operating costs of about 8 million for the new technology, we needed to determine if the technology would provide enough cost savings and cost avoidance (through reduced trunk costs, increased agent productivity, and ability to service more calls) to warrant its nationwide implementation. We constructed a model of the existing call center environment consisting of 25 call centers where calls were distributed to the sites based on a system of percentage allocation routing; for example, the telephone network provider directs calls to each site based on the number of agents scheduled. We then modeled the same call system dynamics and intricacies under the new call routing system where calls are distributed based on longest available agent. Subsequently, we conducted average day simulations with light and heavy volumes and other “what if” laboratory analyses and experiments to facilitate planning decisions required to be documented and substantiated in the business case.","PeriodicalId":287132,"journal":{"name":"Online World Conference on Soft Computing in Industrial Applications","volume":"45 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1999-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125698500","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}
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