Pub Date : 2016-12-11DOI: 10.1109/WSC.2016.7822270
A. Akçay, Tugce G. Martagan
We consider a pharmaceutical company that sources a biological product from a set of unreliable contract manufacturers. The likelihood of a manufacturer to successfully deliver the product is estimated via logistic regression as a function of the product attributes. The assignment of a product to the right contract manufacturers is of critical importance for the pharmaceutical company, and simulation-based optimization is used to identify the optimal sourcing decision. However, the input uncertainty due to the uncertain parameters of the logistic regression model often leads to poor sourcing decisions. We quantify the decrease in the expected profit due to input uncertainty as a function of the size of the historical data set, the level of dispersion in the historical data of a product attribute, and the number of products. We also introduce a sampling-based algorithm that reduces the expected decrease in the expected profit.
{"title":"Stochastic simulation under input uncertainty for contract-manufacturer selection in pharmaceutical industry","authors":"A. Akçay, Tugce G. Martagan","doi":"10.1109/WSC.2016.7822270","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822270","url":null,"abstract":"We consider a pharmaceutical company that sources a biological product from a set of unreliable contract manufacturers. The likelihood of a manufacturer to successfully deliver the product is estimated via logistic regression as a function of the product attributes. The assignment of a product to the right contract manufacturers is of critical importance for the pharmaceutical company, and simulation-based optimization is used to identify the optimal sourcing decision. However, the input uncertainty due to the uncertain parameters of the logistic regression model often leads to poor sourcing decisions. We quantify the decrease in the expected profit due to input uncertainty as a function of the size of the historical data set, the level of dispersion in the historical data of a product attribute, and the number of products. We also introduce a sampling-based algorithm that reduces the expected decrease in the expected profit.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115658014","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 : 2016-12-11DOI: 10.1109/WSC.2016.7822204
B. Mielczarek, J. Zabawa
This paper describes a hybrid simulation model that uses a system dynamics and discrete event simulation to study the influence of long-term population changes on the demand for healthcare services. A dynamic simulation model implements an aging chain approach to forecast the number of individuals who belong to their respective age-sex cohorts. The demographic parameters that were calculated from a Central Statistical Office Local Data Base were applied to the Wroclaw Region population from 2002 to 2014, and the basic scenario for the projected trends was adopted for a time horizon from 2015 to 2035. The historical data on hospital admissions were obtained from the Regional Health Fund. A discrete event model generates batches of patients with cardiac diseases and modifies the demand according to the demographic changes that were forecasted by a population model. The results offer a well-defined starting point for future research in the health policy field.
{"title":"Modeling healthcare demand using a hybrid simulation approach","authors":"B. Mielczarek, J. Zabawa","doi":"10.1109/WSC.2016.7822204","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822204","url":null,"abstract":"This paper describes a hybrid simulation model that uses a system dynamics and discrete event simulation to study the influence of long-term population changes on the demand for healthcare services. A dynamic simulation model implements an aging chain approach to forecast the number of individuals who belong to their respective age-sex cohorts. The demographic parameters that were calculated from a Central Statistical Office Local Data Base were applied to the Wroclaw Region population from 2002 to 2014, and the basic scenario for the projected trends was adopted for a time horizon from 2015 to 2035. The historical data on hospital admissions were obtained from the Regional Health Fund. A discrete event model generates batches of patients with cardiac diseases and modifies the demand according to the demographic changes that were forecasted by a population model. The results offer a well-defined starting point for future research in the health policy field.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124273484","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 : 2016-12-11DOI: 10.1109/WSC.2016.7822085
I. Martinez-Moyano, C. Macal
In dealing with complex systems, there is no single “best” possible modeling approach, as each specific system and modeling purpose has subtleties and specific needs. Consequently, in developing models that capture the complexity of real systems, it is useful to combine modeling approaches yielding what is referred to as a hybrid modeling approach. By combining different modeling paradigms, hybrid modeling and simulation provide a more comprehensive and holistic view of the system under investigation and a very powerful approach to understanding complexity. This paper discusses the uses and applications of hybrid modeling, general lessons related to how and when to use such an approach, and relevant tools.
{"title":"A primer for hybrid modeling and simulation","authors":"I. Martinez-Moyano, C. Macal","doi":"10.1109/WSC.2016.7822085","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822085","url":null,"abstract":"In dealing with complex systems, there is no single “best” possible modeling approach, as each specific system and modeling purpose has subtleties and specific needs. Consequently, in developing models that capture the complexity of real systems, it is useful to combine modeling approaches yielding what is referred to as a hybrid modeling approach. By combining different modeling paradigms, hybrid modeling and simulation provide a more comprehensive and holistic view of the system under investigation and a very powerful approach to understanding complexity. This paper discusses the uses and applications of hybrid modeling, general lessons related to how and when to use such an approach, and relevant tools.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117060595","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 : 2016-12-11DOI: 10.1109/WSC.2016.7822088
H. Lam
Input uncertainty refers to errors caused by a lack of complete knowledge about the probability distributions used to generate input variates in stochastic simulation. The quantification of input uncertainty is one of the central topics of interest and has been studied over the years among the simulation community. This tutorial overviews some methodological developments in two parts. The first part discusses major established statistical methods, while the second part discusses some recent results from a robust-optimization-based viewpoint and their comparisons to the established methods.
{"title":"Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation","authors":"H. Lam","doi":"10.1109/WSC.2016.7822088","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822088","url":null,"abstract":"Input uncertainty refers to errors caused by a lack of complete knowledge about the probability distributions used to generate input variates in stochastic simulation. The quantification of input uncertainty is one of the central topics of interest and has been studied over the years among the simulation community. This tutorial overviews some methodological developments in two parts. The first part discusses major established statistical methods, while the second part discusses some recent results from a robust-optimization-based viewpoint and their comparisons to the established methods.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120936255","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 : 2016-12-11DOI: 10.1109/WSC.2016.7822250
A. Ajdari, A. Ghate
We consider an optimization problem in radiotherapy, where the goal is to maximize the biological effect on the tumor of radiation intensity profiles across multiple treatment sessions, while limiting their toxic effects on nearby healthy tissues. We utilize the standard linear-quadratic dose-response model, which yields a nonconvex quadratically constrained quadratic programming (QCQP) formulation. Since nonconvex QCQPs are in general computationally difficult, recent work on this problem has only considered stationary solutions. This restriction allows a convex reformulation, enabling efficient solution. All other generic convexification methods for nonconvex QCQPs also yield a stationary solution in our case. While stationary solutions could be sub-optimal, currently there is no efficient method for finding nonstationary solutions. We propose a model predictive control approach that can, in principle, efficiently discover nonstationary solutions. We demonstrate via numerical experiments on head-and-neck cancer that these nonstationary solutions could produce a larger biological effect on the tumor than stationary.
{"title":"A model predictive control approach for discovering nonstationary fluence-maps in cancer radiotherapy fractionation","authors":"A. Ajdari, A. Ghate","doi":"10.1109/WSC.2016.7822250","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822250","url":null,"abstract":"We consider an optimization problem in radiotherapy, where the goal is to maximize the biological effect on the tumor of radiation intensity profiles across multiple treatment sessions, while limiting their toxic effects on nearby healthy tissues. We utilize the standard linear-quadratic dose-response model, which yields a nonconvex quadratically constrained quadratic programming (QCQP) formulation. Since nonconvex QCQPs are in general computationally difficult, recent work on this problem has only considered stationary solutions. This restriction allows a convex reformulation, enabling efficient solution. All other generic convexification methods for nonconvex QCQPs also yield a stationary solution in our case. While stationary solutions could be sub-optimal, currently there is no efficient method for finding nonstationary solutions. We propose a model predictive control approach that can, in principle, efficiently discover nonstationary solutions. We demonstrate via numerical experiments on head-and-neck cancer that these nonstationary solutions could produce a larger biological effect on the tumor than stationary.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127152501","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 : 2016-12-11DOI: 10.1109/WSC.2016.7822364
J. Ock, R. Issa, I. Flood
Stormwater discharges from construction activities can have significant impact on water quality by contributing sediments and pollutants to waterbodies. The National Pollutant Discharge Elimination System (NPDES) for most States and the Construction General Permit (CGP) for a few states in the U.S. require the development and implementation of Storm Water Pollution Prevention Plan (SWPPP) and Best Management Practices (BMPs), which should contain storm water collection and discharge points, and drainage patterns across construction projects. Generally, erosion and sedimentation from disturbed construction sites need to be controled before and after construction. This regulatory compliance frequently results in schedule delays or decreased productivity at the beginning of construction process and violations or failure to implement stormwater management on construction sites increases construction costs. Therefore, an appropriate SWPPP needs to be developed at the planning phase. This study explores the feasibility of utilizing BIM tools for SWPPP and BMPs developments.
{"title":"Analysis tools for stormwater controls on construction sites","authors":"J. Ock, R. Issa, I. Flood","doi":"10.1109/WSC.2016.7822364","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822364","url":null,"abstract":"Stormwater discharges from construction activities can have significant impact on water quality by contributing sediments and pollutants to waterbodies. The National Pollutant Discharge Elimination System (NPDES) for most States and the Construction General Permit (CGP) for a few states in the U.S. require the development and implementation of Storm Water Pollution Prevention Plan (SWPPP) and Best Management Practices (BMPs), which should contain storm water collection and discharge points, and drainage patterns across construction projects. Generally, erosion and sedimentation from disturbed construction sites need to be controled before and after construction. This regulatory compliance frequently results in schedule delays or decreased productivity at the beginning of construction process and violations or failure to implement stormwater management on construction sites increases construction costs. Therefore, an appropriate SWPPP needs to be developed at the planning phase. This study explores the feasibility of utilizing BIM tools for SWPPP and BMPs developments.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125179748","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 : 2016-12-11DOI: 10.1109/WSC.2016.7822401
Ozge Yapar, Noah Gans, S. Chick
We focus on the design of multiarm multistage (MAMS) clinical trials, using ideas from simulation optimization, biostatistics, and health economics. From a trial design perspective, we build on the trend of comparing multiple treatments with a single control by allowing for more than two arms in a trial, and we allow for arbitrarily many stages of sampling by using a diffusion approximation that allows for adaptive stopping rules. From a simulation perspective, our techniques extend the correlated knowledge-gradient concept, which has been used in one-stage lookahead (knowledge gradient) procedures, to Bayesian fully sequential selection procedures.
{"title":"Proposal for fully sequential multiarm trials with correlated arms","authors":"Ozge Yapar, Noah Gans, S. Chick","doi":"10.1109/WSC.2016.7822401","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822401","url":null,"abstract":"We focus on the design of multiarm multistage (MAMS) clinical trials, using ideas from simulation optimization, biostatistics, and health economics. From a trial design perspective, we build on the trend of comparing multiple treatments with a single control by allowing for more than two arms in a trial, and we allow for arbitrarily many stages of sampling by using a diffusion approximation that allows for adaptive stopping rules. From a simulation perspective, our techniques extend the correlated knowledge-gradient concept, which has been used in one-stage lookahead (knowledge gradient) procedures, to Bayesian fully sequential selection procedures.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123206793","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 : 2016-12-11DOI: 10.1109/WSC.2016.7822213
Mohammed Mesabbah, A. Mahfouz, Mohamed A. F. Ragab, A. Arisha
Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in the vineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, and increasing in grape losses rates. The objective of this research is to investigate how the variation in workers experience could impact vineyard harvesting productivity and operational cost. The complexity of the problem means that it is difficult to analyze the system parameters and their relationships using individual analytical model. Hence, a hybrid model integrating discrete event simulation (DES) and agent based modeling (ABM) is developed and applied on a vineyard to achieve research objective. DES models harvesting operation and simulates process performance, while ABM addresses the seasonal workers heterogeneous characteristics, particularly experience variations and disparity of working days in the vineyard. The model is used to evaluate two seasonal recruiting policies against vineyard productivity, grape losses quantities, and total operational cost.
根据季节性招聘合同雇用工人会导致葡萄园工人技能的显著差异。这导致工人表现不一致，收获效率降低，葡萄损失率增加。本研究的目的是调查工人经验的变化如何影响葡萄园的收获生产力和运营成本。问题的复杂性意味着很难用单个的分析模型来分析系统参数和它们之间的关系。为此，本文建立了离散事件仿真(DES)与agent based modeling (ABM)相结合的混合模型，并将其应用于某葡萄园的研究中。DES模拟收获操作并模拟过程性能，而ABM则解决了季节性工人的异质性特征，特别是葡萄园工作日的经验变化和差异。该模型用于评估两种季节性招聘政策对葡萄园生产力、葡萄损失数量和总运营成本的影响。
{"title":"Hybrid modeling for vineyard harvesting operations","authors":"Mohammed Mesabbah, A. Mahfouz, Mohamed A. F. Ragab, A. Arisha","doi":"10.1109/WSC.2016.7822213","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822213","url":null,"abstract":"Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in the vineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, and increasing in grape losses rates. The objective of this research is to investigate how the variation in workers experience could impact vineyard harvesting productivity and operational cost. The complexity of the problem means that it is difficult to analyze the system parameters and their relationships using individual analytical model. Hence, a hybrid model integrating discrete event simulation (DES) and agent based modeling (ABM) is developed and applied on a vineyard to achieve research objective. DES models harvesting operation and simulates process performance, while ABM addresses the seasonal workers heterogeneous characteristics, particularly experience variations and disparity of working days in the vineyard. The model is used to evaluate two seasonal recruiting policies against vineyard productivity, grape losses quantities, and total operational cost.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125341520","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 : 2016-12-11DOI: 10.1109/WSC.2016.7822189
Andrew J. Tekippe, Caroline C. Krejci
Veterinarians have experienced disturbing trends related to workplace-induced stress. This is partly attributed to high levels of compassion fatigue, the emotional strain of unalleviated stress from interactions with those suffering from traumatic events. This paper presents a three-stage hybrid model designed to study the dynamics of compassion fatigue in veterinarians. A discrete event simulation that represents the work environment is used to generate client and patient attributes, and the veterinarian's utilization throughout the day. These values become inputs to a system dynamics model that simulates the veterinarian's interpretation of the work environment to produce quantifiable emotional responses in terms of eight emotions. The emotional responses are mapped to the Professional Quality of Life Scale, which enables the calculation of compassion satisfaction, burnout, and secondary traumatic stress measures. A pilot study using the hybrid model was conducted to assess the viability of the proposed approach, which yielded statistically significant results.
{"title":"Using hybrid simulation modeling to assess the dynamics of compassion fatigue in veterinarian general practitioners","authors":"Andrew J. Tekippe, Caroline C. Krejci","doi":"10.1109/WSC.2016.7822189","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822189","url":null,"abstract":"Veterinarians have experienced disturbing trends related to workplace-induced stress. This is partly attributed to high levels of compassion fatigue, the emotional strain of unalleviated stress from interactions with those suffering from traumatic events. This paper presents a three-stage hybrid model designed to study the dynamics of compassion fatigue in veterinarians. A discrete event simulation that represents the work environment is used to generate client and patient attributes, and the veterinarian's utilization throughout the day. These values become inputs to a system dynamics model that simulates the veterinarian's interpretation of the work environment to produce quantifiable emotional responses in terms of eight emotions. The emotional responses are mapped to the Professional Quality of Life Scale, which enables the calculation of compassion satisfaction, burnout, and secondary traumatic stress measures. A pilot study using the hybrid model was conducted to assess the viability of the proposed approach, which yielded statistically significant results.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116000074","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 : 2016-12-11DOI: 10.1109/WSC.2016.7822234
F. Wieland, Rohit Sharma, A. Tyagi, M. Santos, Jyotirmaya Nanda, Yingchuan Zhang
Modeling and simulation in the aviation community is characterized by specialized models built to solve specific problems. Some models are statistically-based, relying on averages and distribution functions using Monte-Carlo techniques to answer policy questions. Others are physics-based, relying on differential equations describing such phenomena as the physics of flight, communication errors and frequency congestion, noise production, atmospheric wake generation, and other phenomena to provide detailed insight into study questions. Several years ago, researchers at Intelligent Automation, Incorporated (IAI) recognized that many of the physics-based aviation models, while conceptually similar, were difficult to interoperate because of varying assumptions regarding particular aspects of flight dynamics. Despite this difficulty, the aviation community routinely use these diverse physics-based models for a single coherent study. IAI researchers have since constructed an automated method for interoperating these models in a manner that produces consistent, coherent, and comparable results even with computations that otherwise use different assumptions.
{"title":"Applying a disparate network of models for complex airspace problems","authors":"F. Wieland, Rohit Sharma, A. Tyagi, M. Santos, Jyotirmaya Nanda, Yingchuan Zhang","doi":"10.1109/WSC.2016.7822234","DOIUrl":"https://doi.org/10.1109/WSC.2016.7822234","url":null,"abstract":"Modeling and simulation in the aviation community is characterized by specialized models built to solve specific problems. Some models are statistically-based, relying on averages and distribution functions using Monte-Carlo techniques to answer policy questions. Others are physics-based, relying on differential equations describing such phenomena as the physics of flight, communication errors and frequency congestion, noise production, atmospheric wake generation, and other phenomena to provide detailed insight into study questions. Several years ago, researchers at Intelligent Automation, Incorporated (IAI) recognized that many of the physics-based aviation models, while conceptually similar, were difficult to interoperate because of varying assumptions regarding particular aspects of flight dynamics. Despite this difficulty, the aviation community routinely use these diverse physics-based models for a single coherent study. IAI researchers have since constructed an automated method for interoperating these models in a manner that produces consistent, coherent, and comparable results even with computations that otherwise use different assumptions.","PeriodicalId":367269,"journal":{"name":"2016 Winter Simulation Conference (WSC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122893282","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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