{"title":"Multi-Agent System Model for Dynamic Scheduling in Flexibile Job Shops","authors":"A. Ebufegha, Simon Li","doi":"10.1109/WSC52266.2021.9715441","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715441","url":null,"abstract":"One of the hallmarks of industry 4.0 is the development of a smart manufacturing system (SMS). These are highly modular systems, with every physical resource being autonomous and capable of exchanging information with each other over an industrial network. The resources can self-organize to schedule job shop operations in real-time. The ability to schedule in real-time allows for better use of the flexibility in part processing operation sequences than with conventional manufacturing systems. This could potentially result in reduced order completion times and increased average machine utilization. However, it is difficult to investigate the benefits of such a system as they are expensive to build as such a simulation is necessary. This paper presents model for a dynamic scheduling in an SMS well as a multi-method model for simulating its operation. The paper also presents a preliminary investigation into the benefits of the proposed scheduling strategy.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132122003","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":"Reflections On Simulation Optimization","authors":"S. Henderson","doi":"10.1109/WSC52266.2021.9715361","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715361","url":null,"abstract":"I provide some perspectives on simulation optimization. First, more attention should be devoted to the finite-time performance of solvers than on ensuring convergence properties that may only arise in asymptotic time scales that may never be reached in practice. Both analytical results and computational experiments can further this goal. Second, so-called sample-path functions can exhibit extremely complex behavior that is well worth understanding in selecting a solver and its parameters. Third, I advocate the use of a layered approach to formulating and solving optimization problems, whereby a sequence of models are built and optimized, rather than first building a simulation model and only later “bolting on” optimization.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128791644","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":"Customer Path Generation Simulation for Selection from Proposed Grocery Store Layouts","authors":"Kimberly Holmgren","doi":"10.1109/WSC52266.2021.9715297","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715297","url":null,"abstract":"Before a grocery store opens, key operational decisions must be made with no historical data. One important decision is how to optimally lay out the store to maximize consumer spending. This work reviews existing literature on simulation to optimize grocery store layout, uses computer vision techniques to transform a store diagram into a digital representation, and applies simulation methods to approximate which of several layouts proposed by a store designer would result in the highest amount of impulse purchasing. Output analysis methods are used to compare these results to determine whether one design outperforms the others.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"430 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115933352","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":"Sufficient Conditions for a Central Limit Theorem to Assess the Error of Randomized Quasi-Monte Carlo Methods","authors":"Marvin K. Nakayama, B. Tuffin","doi":"10.1109/WSC52266.2021.9715427","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715427","url":null,"abstract":"Randomized quasi-Monte Carlo (RQMC) can produce an estimator of a mean (i.e., integral) with root-mean-square error that shrinks at a faster rate than (standard) Monte Carlo's. While RQMC is commonly employed to provide a confidence interval (CI) for the mean, this approach implicitly assumes that the RQMC estimator obeys a central limit theorem (CLT), which has not been established for most RQMC settings. To address this, we provide various conditions that ensure an RQMC CLT, as well as an asymptotically valid CI, and examine the tradeoffs in our restrictions. Our sufficient conditions, depending on the regularity of the integrand, often require that the number of randomizations grows sufficiently fast relative to the number of points used from the low-discrepancy sequence.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115975839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A New Ethical Principle for Analysts who use Models","authors":"P. Davis","doi":"10.1109/WSC52266.2021.9715331","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715331","url":null,"abstract":"This paper reviews some existing ethical principles applying to modelers and analysts. It then proposes a new principle motivated by modern advances that allow modeling and analysis to confront uncertainty-even deep uncertainty-and to do so effectively. Given these advances and the high stakes that are often involved, analysts have an obligation to convey more information than has been expected in the past-information to help decisionmakers choose strategies that will hedge as well as feasible against uncertainties. Using dilemmas familiar to analysts, including some that draw on topical events, the paper then discusses challenges involved in following the principle and suggest tactics that can help in doing so.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116973089","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":"Model Transformation Across Devs and Event Graph Formalisms","authors":"Neal DeBuhr, H. Sarjoughian","doi":"10.1109/WSC52266.2021.9715356","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715356","url":null,"abstract":"This paper develops a model transformation mechanism across the Discrete Event System Specification (DEVS) and Event Graph (EG) modeling formalisms. We detail this cross-formalism model transformation from methodological and software implementation perspectives. By using simple, well-defined, and automated mechanisms of cross-formalism model transformation, modelers establish a plurality of vantage points, from which to understand and communicate model behavior. Model characteristics may be clarified, emphasized, obfuscated, or hidden across these different vantage points. This paper, therefore, serves as a step toward research into better modeling that can improve soft factors such as model reasoning and collaborative model design for developing better simulations.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116717771","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":"Data-Driven Two-Stage Stochastic Programming with Marginal Data","authors":"Ke Ren, H. Bidkhori","doi":"10.1109/WSC52266.2021.9715339","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715339","url":null,"abstract":"We present new methodologies to solve data-driven two-stage stochastic optimization when only the marginal data are available. We propose a novel data-driven distributionally robust framework that only uses the available marginal data. The proposed model is distinguished from the traditional techniques of solving missing data in that it conducts an integrated analysis of missing data and optimization problems, whereas classical methods conduct separate analyses by first recovering the missing data and then finding the optimal solutions. On the theoretical side, we show that our model produces risk-averse solutions and guarantees finite sample performance. Empirical experiments are conducted on two applications based on synthetic data and real-world data. We validate the proposed finite sample guarantee and show that the proposed approach achieves better out-of-sample performance and higher reliability than the classical data imputation-based approach.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114414181","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}
Sojung Kim, B. Feng, Katy Smith, Sara Masoud, Zeyu Zheng
{"title":"Instructions for Authors of Papers Using Latex","authors":"Sojung Kim, B. Feng, Katy Smith, Sara Masoud, Zeyu Zheng","doi":"10.1109/WSC52266.2021.9715327","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715327","url":null,"abstract":"This set of instructions for producing a proceedings paper for the 2021 Winter Simulation Conference (WSC) with LATEX also serves as a sample file that you can edit to produce your submission, and a checklist to ensure that your submission meets the WSC 2021 requirements. Please follow the guidelines herein when preparing your paper. Failure to do so may result in a paper being rejected, returned for appropriate revision, or edited without your knowledge.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127069370","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":"Nonparametric Kullback-Liebler Divergence Estimation Using M-Spacing","authors":"Linyun He, Eunhye Song","doi":"10.1109/WSC52266.2021.9715376","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715376","url":null,"abstract":"Entropy of a random variable with unknown distribution function can be estimated nonparametrically by spacing methods when independent and identically distributed (i.i.d.) observations of the random variable are available. We extend the classical entropy estimator based on sample spacing to define an m-spacing estimator for the Kullback-Liebler (KL) divergence between two i.i.d. observations with unknown distribution functions, which can be applied to measure discrepancy between real-world system output and simulation output as well as between two simulators' outputs. We show that the proposed estimator converges almost surely to the true KL divergence as the numbers of outputs collected from both systems increase under mild conditions and discuss the required choices for $m$ and the simulation output sample size as functions of the real-world sample size. Additionally, we show Central Limit Theorems for the proposed estimator with appropriate scaling.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126376317","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":"Work Smarter, Not Harder: A Tutorial On Designing and ConductingSimulation Experiments","authors":"S. Sanchez, Paul J. Sanchez, Hong Wan","doi":"10.1109/WSC52266.2021.9715422","DOIUrl":"https://doi.org/10.1109/WSC52266.2021.9715422","url":null,"abstract":"Simulation models are integral to modern scientific research, national defense, industry and manufacturing, and in public policy debates. These models tend to be extremely complex, often with thousands of factors and many sources of uncertainty. To understand the impact of these factors and their interactions on model outcomes requires efficient, high-dimensional design of experiments. Unfortunately, all too often, many large-scale simulation models continue to be explored in ad hoc ways. This suggests that more simulation researchers and practitioners need to be aware of the power of designed experiments in order to get the most from their simulation studies. In this tutorial, we demonstrate the basic concepts important for designing and conducting simulation experiments, and provide references to other resources for those wishing to learn more. This tutorial (an update of previous WSC tutorials) will prepare you to make your next simulation study a simulation experiment.","PeriodicalId":369368,"journal":{"name":"2021 Winter Simulation Conference (WSC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124856901","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}