{"title":"A Data Farming Analysis of A Simulation of Armstrong’s Stochastic Salvo Model","authors":"Gökhan Kesler, Thomas W. Lucas, P. Sánchez","doi":"10.1109/WSC40007.2019.9004900","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004900","url":null,"abstract":"In 1995, Retired Navy Captain Wayne Hughes formulated a salvo model for assessing the military worth of warship capabilities in the missile age. Hughes’ model is deterministic, and therefore provides no information about the distribution of outcomes that result from inherently stochastic salvo exchanges. To address this, Michael Armstrong created a stochastic salvo model by transforming some of Hughes’ fixed inputs into random variables. Using approximations, Armstrong provided closed-form solutions that obtain probabilistic outcomes. This paper investigates Armstrong’s stochastic salvo model using data farming. By using a sophisticated design of experiments to run a simulation at thousands of carefully selected input combinations, responses such as ship losses are formulated as readily interpretable regression and partition tree metamodels of the inputs. The speed at which the simulation runs suggests that analysts should directly use the simulation rather than resorting to approximate closed-form solutions.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129789845","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":"Towards Adaptive Enterprises Using Digital Twins","authors":"V. Kulkarni, Souvik Barat, T. Clark","doi":"10.1109/WSC40007.2019.9004956","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004956","url":null,"abstract":"Modern enterprises are large complex systems operating in highly dynamic environments thus requiring quick response to a variety of change drivers. Moreover, they are systems of systems wherein understanding is available in localized contexts only and that too is typically partial and uncertain. With the overall system behaviour hard to know a-priori and conventional techniques for system-wide analysis either lacking in rigour or defeated by the scale of the problem, the current practice often exclusively relies on human expertise for monitoring and adaptation. We present an approach that combines ideas from modeling & simulation, reinforcement learning and control theory to make enterprises adaptive. The approach hinges on the concept of Digital Twin - a set of relevant models that are amenable to analysis and simulation. The paper describes illustration of approach in two real world use cases.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129387283","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}
Antoine Perraudat, S. Dauzére-Pérés, P. Vialletelle
{"title":"Evaluating the Impact of Dynamic Qualification Management in Semiconductor Manufacturing","authors":"Antoine Perraudat, S. Dauzére-Pérés, P. Vialletelle","doi":"10.1109/WSC40007.2019.9004687","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004687","url":null,"abstract":"In semiconductor manufacturing, before executing any operation on a product, a machine must be qualified, i.e., certified, to ensure quality and yield requirements. The qualifications of machines in a work-center are essential to the overall performance of the manufacturing facility. However, performing a qualification can be expensive and usually takes time, although the more qualified the machines, the more flexible the production system. Qualification management aims at determining the right qualifications at the lowest cost. We first discuss the limitations of a single-period optimization model, in particular due to capacity losses and delays inherent to qualification procedures. Then, we motivate and briefly introduce a multi-period optimization model. Finally, we compare both optimization models in a computational study on industrial instances from a High Mix/Low Volume (HMLV) production facility with a high production variability.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129425274","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":"Modeling Home Grocery Delivery Using Electric Vehicles: Preliminary Results of an Agent-Based Simulation Study","authors":"D. Utomo, Adam Gripton, P. Greening","doi":"10.1109/WSC40007.2019.9004713","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004713","url":null,"abstract":"This paper presents preliminary results of an agent-based simulation study aimed at establishing whether a fleet of electric vans with different charging options can match the performance of a diesel fleet. We describe a base model imitating the operations of a real-world retailer using agents. We then introduce electric vehicles and charging hubs into our model. We evaluate how the use of electric vehicles, charging power and charging hubs influence the retailer’s operations. Our simulation experiment suggests that, though they are useful, technological interventions alone are not sufficient to match the performance of a diesel fleet. Hence, reorganization of the urban delivery system is required in order to reduce carbon emissions significantly.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128639112","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":"Simulation Trust and the Internet of Things","authors":"M. Loper","doi":"10.1109/WSC40007.2019.9004912","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004912","url":null,"abstract":"The urban environment is becoming increasingly more connected and complex. In the coming decades, we will be surrounded by billions of sensors, devices, and machines, the Internet of Things (IoT). As the world becomes more connected, we will become dependent on machines and simulation to make decisions on our behalf. When simulation systems use data from sensors, devices and machines (i.e., things) to make decisions, they need to learn how to trust that data, as well as the things they are interacting with. As embedded simulation becomes more commonplace in IoT and smart city applications, it is essential that decision makers are able to trust the simulation systems making decisions on their behalf. This paper looks at trust from an IoT perspective, describing a set of research projects conducted that span multiple dimensions of trust, and discusses whether these concepts of trust apply to simulation.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124978290","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}
Damon Frezza, J. Thompson, David M. Slater, G. Jacyna
{"title":"Simulating Multifractal Signals for Risk Assessment","authors":"Damon Frezza, J. Thompson, David M. Slater, G. Jacyna","doi":"10.1109/WSC40007.2019.9004676","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004676","url":null,"abstract":"Many data sets collected from physical processes or human engineered systems exhibit self-similar properties that are best understood from the perspective of multifractals. These signals fail to satisfy the mathematical definition of stationarity and are therefore incompatible with Gaussian-based analysis. Efficient algorithms for analyzing the multifractal properties exist, but there is a need to simulate signals that exhibit the same multifractal spectrum as an empirical data set. The following work outlines two different algorithms for simulating multifractal signals and addresses the strengths and weaknesses of each approach. We introduce a procedure for fitting the parameters of a multifractal spectrum to one extracted empirically from data and illustrate how the algorithms can be employed to simulate potential future paths of a multifractal process. We illustrate the procedure using a high-frequency sample of IBM’s stock price and demonstrate the utility of simulating multifractals in risk management.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125014225","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":"On the Almost Sure Convergence Rate for A Series Expansion of Fractional Brownian Motion","authors":"Yi Chen, Jing Dong","doi":"10.1109/WSC40007.2019.9004731","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004731","url":null,"abstract":"Fractional Brownian motions (fBM) and related processes are widely used in financial modeling to capture the complicated dependence structure of the volatility. In this paper, we analyze an infinite series representation of fBM proposed in (Dzhaparidze and Van Zanten 2004) and establish an almost sure convergence rate of the series representation. The rate is also shown to be optimal. We then demonstrate how the strong convergence rate result can be applied to construct simulation algorithms with path-by-path error guarantees.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126989629","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":"Estimation and Inference for Non-Stationary Arrival Models with a Linear Trend","authors":"P. Glynn, Zeyu Zheng","doi":"10.1109/WSC40007.2019.9004779","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004779","url":null,"abstract":"This paper is concerned with building statistical models for non-stationary input processes with a linear trend. Under a Poisson assumption, we investigate the use of the maximum likelihood (ML) method to estimate the model and establish limiting behavior for the ML estimator in an asymptotic regime that naturally arises in applications with high-volume inputs. We also develop likelihood ratio tests for the presence of a linear trend and discuss the asymptotic efficiency. Change-point detection procedures are discussed to identify an unknown point when the model switches from a stationary mode to non-stationarity with a linear trend. Numerical experiments on an e-commerce data set are included. Incorporating a linear trend into an input model can improve prediction accuracy and potentially enhance associated performance evaluations and decision making.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"183 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120888689","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":"R-Mgspline: Retrospective Multi-Gradient Search for Multi-Objective Simulation Optimization on Integer Lattices","authors":"Eric A. Applegate, S. R. Hunter","doi":"10.1109/WSC40007.2019.9004719","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004719","url":null,"abstract":"We introduce the R-MGSPLINE (Retrospective Multi-Gradient Search with Piecewise Linear Interpolation and Neighborhood Enumeration) algorithm for finding a local efficient point when solving a multi-objective simulation optimization problem on an integer lattice. In this nonlinear optimization problem, each objective can only be observed with stochastic error and the decision variables are integer-valued. R-MGSPLINE uses a retrospective approximation (RA) framework to repeatedly call the MGSPLINE sample-path solver at a sequence of increasing sample sizes, using the solution from the previous RA iteration as a warm start for the current RA iteration. The MGSPLINE algorithm performs a line search along a common descent direction constructed from pseudo-gradients of each objective, followed by a neighborhood enumeration for certification. Numerical experiments demonstrate R-MGSPLINE’s empirical convergence to a local weakly efficient point.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116456756","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":"Estimating Quantile Sensitivity for Financial Models with Correlations and Jumps","authors":"Yijie Peng, M. Fu, Jianqiang Hu, Lei Lei","doi":"10.1109/WSC40007.2019.9004858","DOIUrl":"https://doi.org/10.1109/WSC40007.2019.9004858","url":null,"abstract":"We apply a generalized likelihood ratio (GLR) derivative estimation method in previous works to estimate quantile sensitivity of financial models with correlations and jumps. Examples illustrate the wide applicability of the GLR method by providing several practical settings where other techniques are difficult to apply, and numerical results demonstrate the effectiveness of the new estimator.","PeriodicalId":127025,"journal":{"name":"2019 Winter Simulation Conference (WSC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127215379","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}