Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5679056
Nick Brown
The idea of simulation model “re-use” is a novel term that in theory will allow for quick turn-around times where budgetary constraints can hold back the development of a new model. The intention of this paper is not to examine a specific example of how a simulation was developed and utilized for “re-use”, but rather explain the process of developing a computer simulation flexible enough that will allow for “reuse”. The overall outcome of this type of development is a data-driven simulation model that is flexible enough to expand to many similar systems without significantly altering the code of the simulation. As a result of this data-driven simulation, companies/organizations are able to reap the benefits of reducing future development time, utilizing the model for other similar systems, achieve quick turn-around, and the ability to perform large scale sensitivity analysis.
{"title":"Model flexibility: Development of a generic data-driven simulation","authors":"Nick Brown","doi":"10.1109/WSC.2010.5679056","DOIUrl":"https://doi.org/10.1109/WSC.2010.5679056","url":null,"abstract":"The idea of simulation model “re-use” is a novel term that in theory will allow for quick turn-around times where budgetary constraints can hold back the development of a new model. The intention of this paper is not to examine a specific example of how a simulation was developed and utilized for “re-use”, but rather explain the process of developing a computer simulation flexible enough that will allow for “reuse”. The overall outcome of this type of development is a data-driven simulation model that is flexible enough to expand to many similar systems without significantly altering the code of the simulation. As a result of this data-driven simulation, companies/organizations are able to reap the benefits of reducing future development time, utilizing the model for other similar systems, achieve quick turn-around, and the ability to perform large scale sensitivity analysis.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123409302","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}
Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5678996
A. Kandil, A. Ezeldin, S. Farghal, Tarek Mahfouz
Construction planning methods have been in continuous evolution due to the increasing complexity of construction projects. Construction simulation modeling is one of the later stages of this evolution that has received much attention in research. Many simulation based construction planning methods developed modeling methods that attempt to cluster project activities into smaller sub-models that enhance model reusability. Many of these modeling methods, however, create new modeling elements that are not familiar to traditional construction simulation modelers. Therefore, the objective of this paper is to develop a method for clustering activities of large and repetitive construction projects for enhancing the reusability of those simulation models. The developed method does not create any new modeling elements and is called Clustered Simulation Modeling (CSM). CSM was evaluated in modeling an actual large-scale repetitive construction projects, and the results have illustrated the effectiveness of the method and the proposed clustering scheme.
{"title":"Clustered simulation for the simulation of large repetitive construction projects","authors":"A. Kandil, A. Ezeldin, S. Farghal, Tarek Mahfouz","doi":"10.1109/WSC.2010.5678996","DOIUrl":"https://doi.org/10.1109/WSC.2010.5678996","url":null,"abstract":"Construction planning methods have been in continuous evolution due to the increasing complexity of construction projects. Construction simulation modeling is one of the later stages of this evolution that has received much attention in research. Many simulation based construction planning methods developed modeling methods that attempt to cluster project activities into smaller sub-models that enhance model reusability. Many of these modeling methods, however, create new modeling elements that are not familiar to traditional construction simulation modelers. Therefore, the objective of this paper is to develop a method for clustering activities of large and repetitive construction projects for enhancing the reusability of those simulation models. The developed method does not create any new modeling elements and is called Clustered Simulation Modeling (CSM). CSM was evaluated in modeling an actual large-scale repetitive construction projects, and the results have illustrated the effectiveness of the method and the proposed clustering scheme.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123538326","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}
Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5678863
Yan Liu, S. Takakuwa
To ensure just-in-time shipments from a general non-automated retail-cross-docking center, different items must be handled efficiently by different processes despite the many inbound shipments and frequent demand orders from retail stores. In this paper, a systematic and flexible procedure is proposed that efficiently provides critical decision-making support to logistics managers to help them understand and validate the material handling operation at a real retail-cross-docking center. The proposed procedure considers dynamic logistics operation information, such as inbound schedules of suppliers, demand data from retail-chain stores, and individual operator schedules. This detailed data is required for the performance of simulation. In addition, the procedure is applied to an actual non-automated retail-cross-docking center to confirm its effectiveness. Furthermore, the proposed method was found to be both practical and powerful in assisting logistics managers with their continuous decision-making efforts.
{"title":"Enhancing simulation as a decision-making support tool for a crossdocking center in a dynamic retail-distribution environment","authors":"Yan Liu, S. Takakuwa","doi":"10.1109/WSC.2010.5678863","DOIUrl":"https://doi.org/10.1109/WSC.2010.5678863","url":null,"abstract":"To ensure just-in-time shipments from a general non-automated retail-cross-docking center, different items must be handled efficiently by different processes despite the many inbound shipments and frequent demand orders from retail stores. In this paper, a systematic and flexible procedure is proposed that efficiently provides critical decision-making support to logistics managers to help them understand and validate the material handling operation at a real retail-cross-docking center. The proposed procedure considers dynamic logistics operation information, such as inbound schedules of suppliers, demand data from retail-chain stores, and individual operator schedules. This detailed data is required for the performance of simulation. In addition, the procedure is applied to an actual non-automated retail-cross-docking center to confirm its effectiveness. Furthermore, the proposed method was found to be both practical and powerful in assisting logistics managers with their continuous decision-making efforts.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"98-100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128488100","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}
Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5679126
Allan Clark, J. Hillston, S. Gilmore, P. Kemper
Simulation modeling in systems biology embarks on discrete event simulation only for cases of small cardinalities of entities and uses continuous simulation otherwise. Modern modeling environments like Bio-PEPA support both types of simulation within a single modeling formalism. Developing models for complex dynamic phenomena is not trivial in practice and requires careful verification and testing. In this paper, we describe relevant steps in the verification and testing of a TNFα-mediated NF-κB signal transduction pathway model and discuss to what extent automated techniques help a practitioner to derive a suitable model.
{"title":"VERIFICATION AND TESTING OF BIOLOGICAL MODELS","authors":"Allan Clark, J. Hillston, S. Gilmore, P. Kemper","doi":"10.1109/WSC.2010.5679126","DOIUrl":"https://doi.org/10.1109/WSC.2010.5679126","url":null,"abstract":"Simulation modeling in systems biology embarks on discrete event simulation only for cases of small cardinalities of entities and uses continuous simulation otherwise. Modern modeling environments like Bio-PEPA support both types of simulation within a single modeling formalism. Developing models for complex dynamic phenomena is not trivial in practice and requires careful verification and testing. In this paper, we describe relevant steps in the verification and testing of a TNFα-mediated NF-κB signal transduction pathway model and discuss to what extent automated techniques help a practitioner to derive a suitable model.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128891760","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}
Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5679071
R. Barton, B. Nelson, Wei Xie
We consider the problem of producing confidence intervals for the mean response of a system represented by a stochastic simulation that is driven by input models that have been estimated from “real-world” data. Therefore, we want the confidence interval to account for both uncertainty about the input models and stochastic noise in the simulation output; standard practice only accounts for the stochastic noise. To achieve this goal we introduce metamodel-assisted bootstrapping, and illustrate its performance relative to other proposals for dealing with input uncertainty on two queueing examples.
{"title":"A framework for input uncertainty analysis","authors":"R. Barton, B. Nelson, Wei Xie","doi":"10.1109/WSC.2010.5679071","DOIUrl":"https://doi.org/10.1109/WSC.2010.5679071","url":null,"abstract":"We consider the problem of producing confidence intervals for the mean response of a system represented by a stochastic simulation that is driven by input models that have been estimated from “real-world” data. Therefore, we want the confidence interval to account for both uncertainty about the input models and stochastic noise in the simulation output; standard practice only accounts for the stochastic noise. To achieve this goal we introduce metamodel-assisted bootstrapping, and illustrate its performance relative to other proposals for dealing with input uncertainty on two queueing examples.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128903072","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}
Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5679079
Feng Yang, Jingang Liu
This paper is concerned with characterizing the transient behavior of general queueing systems, which is widely known to be notoriously difficult. The objective is to develop a statistical methodology, integrated with extensive offline simulation and preliminary queueing analysis, for the estimation of a small number of transfer function models (TFMs) that quantify the input-output dynamics of a general queueing system. The input here is the time-varying release rate of entities to the system; the time-dependent output performances include the output rate of entities and the mean of the work in process (i.e., number of entities in the system). The resulting TFMs are difference equations, like the discrete approximations of the ordinary differential equations provided by an analytical approach, while possessing the high fidelity of simulation. The proposed method is expected to overcome the shortcomings of the existing transient analysis approaches, i.e., the computational burden of simulation and the lack of fidelity of analytical queueing models.
{"title":"Transient analysis of general queueing systems via simulation-based transfer function modeling","authors":"Feng Yang, Jingang Liu","doi":"10.1109/WSC.2010.5679079","DOIUrl":"https://doi.org/10.1109/WSC.2010.5679079","url":null,"abstract":"This paper is concerned with characterizing the transient behavior of general queueing systems, which is widely known to be notoriously difficult. The objective is to develop a statistical methodology, integrated with extensive offline simulation and preliminary queueing analysis, for the estimation of a small number of transfer function models (TFMs) that quantify the input-output dynamics of a general queueing system. The input here is the time-varying release rate of entities to the system; the time-dependent output performances include the output rate of entities and the mean of the work in process (i.e., number of entities in the system). The resulting TFMs are difference equations, like the discrete approximations of the ordinary differential equations provided by an analytical approach, while possessing the high fidelity of simulation. The proposed method is expected to overcome the shortcomings of the existing transient analysis approaches, i.e., the computational burden of simulation and the lack of fidelity of analytical queueing models.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116030815","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}
Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5678876
U. Prasad, S. Gavirneni
With the rapid increase in global trade and introduction of new security measures, maritime supply chain costs have increased and so has the need for business intelligence in improving maritime shipping operations. We develop a seaport operations model that simulates the decision making process associated with scheduling and processing of ships with the objective of evaluating the value of Geographical Information System (GIS) information. We consider two scenarios: (1) A traditional model where there is no GIS information on future ship arrivals; and (2) An information-rich model in which the arrival time of the next ship is known. We propose look-ahead based heuristics for the resulting optimization problems, determine the value of information (VOI), and tabulate how VOI varies as a function of the various operational parameters. Adding such operational intelligence to shipping operations improves the performance by as much as 60% (and by 15% on average) and reduces the costs without expanding the physical footprint of the seaport.
{"title":"A simulation approach to estimate the value of information in maritime supply chains","authors":"U. Prasad, S. Gavirneni","doi":"10.1109/WSC.2010.5678876","DOIUrl":"https://doi.org/10.1109/WSC.2010.5678876","url":null,"abstract":"With the rapid increase in global trade and introduction of new security measures, maritime supply chain costs have increased and so has the need for business intelligence in improving maritime shipping operations. We develop a seaport operations model that simulates the decision making process associated with scheduling and processing of ships with the objective of evaluating the value of Geographical Information System (GIS) information. We consider two scenarios: (1) A traditional model where there is no GIS information on future ship arrivals; and (2) An information-rich model in which the arrival time of the next ship is known. We propose look-ahead based heuristics for the resulting optimization problems, determine the value of information (VOI), and tabulate how VOI varies as a function of the various operational parameters. Adding such operational intelligence to shipping operations improves the performance by as much as 60% (and by 15% on average) and reduces the costs without expanding the physical footprint of the seaport.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121056620","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}
Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5679159
T. Allen, David N. Vuckovich
This paper proposes an open-source algorithm for simulation optimization. The intent is to permit many who use a variety of simulation software codes to be able to apply the proposed methods using an MS Excel-Visual Basic interface. First, we review selected literature on simulation optimization and its usefulness. Then, we briefly discuss methods that are commonly used for simulation optimization. Next, we present the proposed Population Indifference Zone (PIZ) algorithm and related software code. Also, we discuss the properties of the proposed method and present the code that runs the Visual Basic program. Finally, we discuss the functionality of the Population Indifference Zone method with examples of problems to which it might be applied and conclude with topics for future research.
{"title":"An open-source Population Indifference Zone-based algorithm for simulation optimization","authors":"T. Allen, David N. Vuckovich","doi":"10.1109/WSC.2010.5679159","DOIUrl":"https://doi.org/10.1109/WSC.2010.5679159","url":null,"abstract":"This paper proposes an open-source algorithm for simulation optimization. The intent is to permit many who use a variety of simulation software codes to be able to apply the proposed methods using an MS Excel-Visual Basic interface. First, we review selected literature on simulation optimization and its usefulness. Then, we briefly discuss methods that are commonly used for simulation optimization. Next, we present the proposed Population Indifference Zone (PIZ) algorithm and related software code. Also, we discuss the properties of the proposed method and present the code that runs the Visual Basic program. Finally, we discuss the functionality of the Population Indifference Zone method with examples of problems to which it might be applied and conclude with topics for future research.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121284719","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}
Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5679089
Sujin Kim, Dali Zhang
Simulation is widely used to evaluate the performance and optimize the design of a complex system. In the past few decades, a great deal of research has been devoted to solving simulation optimization problems, perhaps owing to their generality. However, although there are many problems of practical interests that can be cast in the framework of simulation optimization, it is often difficult to obtain an understanding of their structure, making them very challenging. Direct search methods are a class of deterministic optimization methods particularly designed for black-box optimization problems. In this paper, we present a class of direct search methods for simulation optimization problems with stochastic noise. The optimization problem is approximated using a sample average approximation scheme. We propose an adaptive sampling scheme to improve the efficiency of direct search methods and prove the consistency of the solutions.
{"title":"Convergence properties of direct search methods for stochastic optimization","authors":"Sujin Kim, Dali Zhang","doi":"10.1109/WSC.2010.5679089","DOIUrl":"https://doi.org/10.1109/WSC.2010.5679089","url":null,"abstract":"Simulation is widely used to evaluate the performance and optimize the design of a complex system. In the past few decades, a great deal of research has been devoted to solving simulation optimization problems, perhaps owing to their generality. However, although there are many problems of practical interests that can be cast in the framework of simulation optimization, it is often difficult to obtain an understanding of their structure, making them very challenging. Direct search methods are a class of deterministic optimization methods particularly designed for black-box optimization problems. In this paper, we present a class of direct search methods for simulation optimization problems with stochastic noise. The optimization problem is approximated using a sample average approximation scheme. We propose an adaptive sampling scheme to improve the efficiency of direct search methods and prove the consistency of the solutions.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126575120","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}
Pub Date : 2010-12-05DOI: 10.1109/WSC.2010.5679059
T. Highley, Ross Gore, Cameron Snapp
AggPro predicts baseball statistics by utilizing a weighted average of predictions provided by several other statistics projection systems. The aggregate projection that is generated is more accurate than any of the constituent systems individually. We explored the granularity at which weights should be assigned by considering four possibilities: a single weight for each projection system, one weight per category per system, one weight per player per system, and one weight per player per category per system. We found that assigning one weight per category per system provides better results than the other options. Additionally, we projected raw statistics directly and compared the results to projecting rate statistics scaled by predicted player usage. We found that predicting rate statistics and scaling by predicted player usage produces better results. We also discuss implementation challenges that we faced in producing the AggPro projections.
{"title":"Granularity of weighted averages and use of rate statistics in AggPro","authors":"T. Highley, Ross Gore, Cameron Snapp","doi":"10.1109/WSC.2010.5679059","DOIUrl":"https://doi.org/10.1109/WSC.2010.5679059","url":null,"abstract":"AggPro predicts baseball statistics by utilizing a weighted average of predictions provided by several other statistics projection systems. The aggregate projection that is generated is more accurate than any of the constituent systems individually. We explored the granularity at which weights should be assigned by considering four possibilities: a single weight for each projection system, one weight per category per system, one weight per player per system, and one weight per player per category per system. We found that assigning one weight per category per system provides better results than the other options. Additionally, we projected raw statistics directly and compared the results to projecting rate statistics scaled by predicted player usage. We found that predicting rate statistics and scaling by predicted player usage produces better results. We also discuss implementation challenges that we faced in producing the AggPro projections.","PeriodicalId":272260,"journal":{"name":"Proceedings of the 2010 Winter Simulation Conference","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126931692","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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