Статьи 2020 года (I...O)



Irgens G.A., Dabholkar S., Bain C., Woods P., Hall K., Swanson H., ... & Wilensky U. (2020). Modeling and Measuring High School Students’ Computational Thinking Practices in Science // Journal of Science Education and Technology, 29(1), 137-161.

Jablonski K.E., Boone R.B., & Meiman P.J. (2020). Predatory plants and patchy cows: modeling cattle interactions with toxic larkspur amid variable heterogeneity // Rangeland Ecology & Management, 73(1), 73-83.

Jager W., Abramczuk K., Komendant-Brodowska A., Baczko-Dombi A., Fecher B., Sokolovska N., Spits T. (2020). Looking into the Educational Mirror: Why Computation Is Hardly Being Taught in the Social Sciences, and What to Do About It // In Advances in Social Simulation (pp. 239-245). Springer, Cham.

Jain S. A tale of two simulations for project managers // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2470-2481.

Statistical inference for approximate Bayesian optimal design // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2138-2148.
This paper studies a generic Bayesian optimal design formulation with chance constraints, where the decision variable lies in a separable, reflexive Banach space.

Jiang F., Zhang J., & Zhao X. (2020). Research on the influence mechanism of resettlers participation in migrant work in the context of relationship network // Peer-to-Peer Networking and Applications, 1-10.

Jiang G., Feng X., Liu W., & Liu X. (2020). Clicking position and user posting behavior in online review systems: A data-driven agent-based modeling approach // Information Sciences, 512, 161-174.

Jin X., Shen Y., Lee L.H., Chew E.P., Shoemaker C.A. A hybrid of shrinking ball method and optimal large deviation rate estimation in continuous contextual simulation optimization with single observation // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2996-3007.
We propose a new method for solving continuous contextual simulation optimization with a single observation. By adopting the estimation on the large deviation rate in the contextual ranking and selection problem, we transfer the old theorem to the continuous setting using a shrinking ball inspired construct.

Jones K., Hadley E., Rhea S., Lofgren E. Assessing strain on hospital capacity during a localized epidemic using a calibrated hospitalization microsimulation // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 102-110.
The ability of healthcare systems to provide patient care can become disrupted and overwhelmed during a major epidemic or pandemic. We adapted an existing hospitalization microsimulation of North Carolina to assess the impact of a localized epidemic of a fictitious pathogen on inpatient hospital bed availability in the same locale.

Jose J., Singh D., Patel A., Hayatnagarkar H.G. Simulating re-configurable multi-rovers for planetary exploration using behavior-based ontology // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 254-265.
In this paper, we present an ontology-based approach to simulate a multi-rover planetary exploration mission, with a focus on resilience, adaptation, heterogeneity, and reconfigurability.

Joshi M.Y., Flacke J., Schwarz N. (2020). Do microfinance institutes help slum-dwellers in coping with frequent disasters? An agent-based modelling study // International Journal of Disaster Risk Reduction, 101627.

Juan A.A., Pedro C., Javier P., Laroque C., de_la_Torre R. A discrete-event heuristic for makespan optimization in multi-server flow-shop problems with machine re-entering // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1492-1502.

Kaaronen R.O., & Strelkovskii N. (2020). Cultural Evolution of Sustainable Behaviors: Pro-environmental Tipping Points in an Agent-Based Model. One Earth, 2(1), 85-97.

Kampik T., Najjar A. (2020). Simulating, Off-Chain and On-Chain: Agent-Based Simulations in Cross-Organizational Business Processes // Information, 11(1), 34.

Kaur H., Kaur H., Singh A. (2020). Multi-agent Based Recommender System for Netflix // In Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India (pp. 211-221). Springer, Singapore.

Kennedy J.J., Khayyer A., Vinel A., Smith A.E. Efficient risk estimation using extreme value theory and simulation metamodeling // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 385-396.
This paper considers a new approach for constructing metamodels for capturing tail behavior in stochastic systems, e.g., simulation outputs.

Khalil H., Wainer G., Dunnigan Z. Cell-DEVS models for CO2 sensors locations in closed spaces // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 692-703.
We present a work in progress method to determine the best placement of carbon dioxide sensors for the accurate occupants’ detection and calculation of latency using the cellular discrete-event specifications formalism. We present several case studies showing resemblance between physical closed spaces and the models and how the simulation replicates real-life scenarios.

Khansari N., Hewitt E. (2020). Incorporating an agent-based decision tool to better understand occupant pathways to GHG reductions in NYC buildings. Cities, 97, 102503.

Forecasting supply chain impact by predicting governmental decisions in the COVID-19 pandemic // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA.

Kilinc D., Shahraki N., Degnim A.C., Hoskin T.L., Horton T.M., Sir M.S., Pasupathy K.S., Gel E.S. Simulation modeling as a decision tool for capacity allocation in breast surgery // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 806-817.

King D., Jacques D., Gray J., Cheney K. Design and simulation of a wide area search mission: an implementation of an autonomous systems reference architecture // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 540-551.
In this paper, we implement Autonomous System Reference Architecture (ASRA) on a cooperative wide area search scenario as a test bed to study ASRA’s utility for rapid prototyping and evaluation of autonomous and cooperative systems.

Kiribuchi D., Tomita M., Nishikawa T., Yokota S., Narasaki R., Koike S. Modification of Bayesian optimization for efficient calibration of simulation models // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2821-2831.
Simulation models contain many parameters that must be adjusted (calibrated) in advance to reduce the error between simulations and experimental results. Bayesian optimization is often applied to minimize error after only a few simulations. However, Bayesian optimization uses only error information, ignoring information on other simulation results. In this paper, we improve Bayesian optimization by utilizing both and show that other simulation results effectively reduce the dimensionality of the parameter space.

Knapčíková L., Behúnová A., Behún M. Using a discrete event simulation as an effective method applied in the production of recycled material // Journal «Advances in Production Engineering & Management», Vol. 15, Num. 4. December 2020. pp 431–440. https://doi.org/10.14743/apem2020.4.376.

Knoos Franzén L., Schon S., Papageorgiou A., Staack I., Olvander J., Krus P., ... & Jouannet C. (2020). A System of Systems Approach for Search and Rescue Missions // In AIAA Scitech 2020 Forum (p. 0455).

Kopanos G.M., Xenos D., Andreev S., O’Donnell T., Feely S. Advanced production scheduling in a seagate technology wafer fab // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1954-1965.
This work focuses on the highly complicated scheduling problem in wafer fabs. We first provide insights on the broader impact of high quality scheduling decisions in semiconductor industries, and then we discuss traditional heuristic-based scheduling practices versus our mathematical optimization approach.

Kopp D., Hassoun M., Kalir A., Monch L. Integrating critical queue time constraints into smt2020 simulation models // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1813-1824.
In this paper, we study the impact of critical queue time constraints in semiconductor wafer fabrication facilities (wafer fabs).

Koralewski T.E., Wang H.H., Grant W.E., Brewer M.J., Elliott N.C., Westbrook J.K., ... & Michaud J.P. (2020). Integrating Models of Atmospheric Dispersion and Crop-Pest Dynamics: Linking Detection of Local Aphid Infestations to Forecasts of Region-Wide Invasion of Cereal Crops // Annals of the Entomological Society of America.

Koretsky M.D. (2020). An interactive virtual laboratory addressing student difficulty in differentiating between chemical reaction kinetics and equilibrium // Computer Applications in Engineering Education, 28(1), 105-116.

Koster T., Giabbanelli P.J., Uhrmacher A. Performance and soundness of simulation: a case study based on a cellular automaton for in-body spread of HIV // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2281-2292.
In this paper, we focus on improving the efficiency of a cellular automaton model known as the ‘dos Santos’ model for the Human Immunodeficiency Virus (HIV).

Kudlay V., Lawson B., Leemis L.M. Animation for simulation education in R // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 3260-3271.

Kuhlman C.J., Ravi S.S., Korkmaz G., Vega-Redondo F. An agent-based model of common knowledge and collective action dynamics on social networks // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 218-229.
We develop an agent-based model of collective action that was the first to combine social structure and individual incentives.

Ky G., Alam S., Duong V. Carousel inspired virtual circulation: a simulation model for UAV arrival and landing procedure under random events // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 504-515.
The purpose of this paper is to model and simulate this method in a landing configuration for large Unmanned Aerial Vehicles (UAV) and evaluate its efficacy.

Laidler G., Morgan L.E., Nelson B.L., Pavlidis N.G. Metric learning for simulation analytics // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 349-360.
In this paper, we take a simulation analytics view of output analysis, turning to machine learning methods to uncover key insights from the dynamic sample path.

Laili Y., Zhang L., Luo Y. (2020). A pattern-based validation method for the credibility evaluation of simulation models // Simulation, 96(2), 151-167.

Lam H., Zhang J. Distributionally constrained stochastic gradient estimation using noisy function evaluations // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 445-456.
We consider gradient estimation with only noisy function evaluation, where the function can only be evaluated at values lying within a probability simplex. We are interested in obtaining gradient estimators where each (pair of) data collection or simulation run applies simultaneously to all directions at once.

Lang S., Behrendt F., Müller M., Lanzerath N., Reggelin T. Integration of deep reinforcement learning and discrete-event simulation for real-time scheduling of a flexible job shop production // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 3057-3068.
The following paper presents the application of Deep Q-Networks for solving a flexible job shop problem with integrated process planning.

Lather J.I., Eldabi T. The benefits of a hybrid simulation hub to deal with pandemics // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 992-1003.

Law A.M. Statistical analysis of simulation output data: the practical state of the art // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1117-1127.
One of the most important but neglected aspects of a simulation study is the proper design and analysis of simulation experiments. In this tutorial we give a state-of-the-art presentation of what the practitioner really needs to know to be successful. We will discuss how to choose the simulation run length, the warmup-period duration (if any), and the required number of model replications (each using different random num-bers).

Impact of COVID-19 epidemics on bed requirements in a healthcare center using data-driven discrete-event simulation // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 771-781.
We propose a case study to illustrate the tool’s ability to monitor bed occupancy in the recovery unit given the admission rate of ED patients during the pandemic of Sars-Cov-2. These results give an interesting insight on the situation, providing decision makers with a powerful tool to establish an enlightened response to this situation.

Lazarova-Molnar S., Niloofar P., Barta G.K. Data-driven fault tree modeling for reliability assessment of cyber-physical systems // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2719-2730.
We present an approach and a tool for Data-Driven Fault Tree Analysis that extract fault trees from time series data of a system, and uses simulation to analyze the extracted fault trees to estimate reliability measures of systems.

Le H.P., Branke J. Bayesian optimization searching for robust solutions // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2844-2855.
This paper considers the use of Bayesian optimization to identify robust solutions, where robust means having a high expected performance given disturbances over the decision variables and independent noise in the output. We propose a variant of the well-known Knowledge Gradient acquisition function that has been proposed for the case of optimizing integrals.

Leathrum J.F., Collins A.J., Cotter T.S., Lynch C.J., Gore R. Education in analytics needed for the modeling & simulation process // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 3236-3247.
This paper discusses integrating analytics with modeling and simulation in a sequence of courses intended to provide organizations the ability to utilize their data to make better-informed decisions.

Lee J.-H., Kim Y., Kim Y.B., Kim B.-H., Chung G.-H., Kim H.-J. A simulation-based sequential search method for multi-objective scheduling problems of manufacturing systems // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1943-1953.
In this study, we propose a systematic sequential search method for dispatching rule weights to provide Pareto-front solutions. The proposed method divides a search space into sub-spaces with decision tree methods generated for each objective and also uses surrogate models to estimate objective values.

Lee J.Y., Sadler N.C., Egbert R.G., Anderton C.R., Hofmockel K.S., Jansson J.K., Song H.S. (2020). Deep Learning Predicts Microbial Interactions from Self-organized Spatiotemporal Patterns // Computational and Structural Biotechnology Journal.

Lee J.S., & Wolf-Branigin M. (2020). Innovations in modeling social good: A demonstration with juvenile justice intervention // Research on Social Work Practice, 30(2), 174-185.

Utilizing simulation to evaluate shuttle bus performance under passenger counts impacted by COVID-19 // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA.
National Institutes of Health (NIH) has utilized simulation modeling to understand the impact of shifting bus schedules and reduced vehicle capacity under varying passenger demand. This simulation tool can be used to understand how bus schedules may need to be altered to accommodate staggered work patterns and how bus frequency should increase as workers begin returning to the NIH campus.

Lei L., Alexopoulos C., Peng Y., Wilson J.R. Confidence intervals and regions for quantiles using conditional monte carlo and generalized likelihood ratios // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2071-2082.

Leitzke B., Pereira L., Adamatt D. (2020, January). Simulacao Multiagente para Controle de Poluicao na Agua // In Anais do XVI Encontro Nacional de Inteligencia Artificial e Computacional (pp. 142-153). SBC.

Lendermann P., Dauzere-Peres S., McGinnis L., Monch L., O’Donnell T., Seidel G., Vialletelle P. Scheduling and simulation in wafer fabs: competitors, independent players or amplifiers? // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1861-1874.
This panel will discuss the inherent conflict between the application of (discrete-event) simulation and scheduling techniques to manage and optimise capacity and material flow in semiconductor frontend manufacturing (wafer fabrication).

Le_Quere E., Dauzere-Peres S., Tamssaouet K., Maufront C., Astie S. Dynamic sampling for risk minimization in semiconductor manufacturing // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1886-1897.
In this paper, the objective is to minimize the number of wafers at risk, i.e. the number of wafers processed on a machine between two lots that are controlled. The problem can be modeled as the maximization of a submodular set function subject to various capacity constraints.

Li H., Cao X., Sharma P., Lee L.H., Chew E.P. Framework of o2des.net digital twins for next generation ports and warehouse solutions // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 3188-3199.

Li H., Lam H. Optimally tuning finite-difference estimators // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 457-468.
We consider stochastic gradient estimation when only noisy function evaluations are available. Central finite-difference scheme is a common method in this setting, which involves generating samples under perturbed inputs.

Li H., Lam H., Liang Z., Peng Y. Context-dependent ranking and selection under a Bayesian framework // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2060-2070.
We consider a context-dependent ranking and selection problem. The best design is not universal but depends on the contexts. Under a Bayesian framework, we develop a dynamic sampling scheme for context-dependent optimization to efficiently learn and select the best designs in all contexts.

Li K., Liu Y., Wan H., Zhang L. Capturing miner and mining pool decisions in a bitcoin blockchain network: a two-layer simulation model // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 3152-3163.
Motivated by the growing interests in Bitcoin blockchain technology, we build a Monte-Carlo simulation model to study the miners’ and mining pool managers’ decisions in the Bitcoin blockchain network. Our simulation model aims to capture the dynamics of participants of these two different parties and how their decisions collectively affect the system dynamics.

Li M.P., Kuhl M.E., Ballamajalu R., Hochgraf C., Ptucha R., Ganguly A., Kwasinski A. Risk-based A* : simulation analysis of a novel task assignment and path planning method // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 563-571.
This paper addresses the task assignment and path planning (TAPP) problem for autonomous mobile robots (AMR) in material handling applications. We introduce risk-based A*, a novel TAPP method, that aims to reduce conflict and travel distance for AMRs considering system uncertainties such as travel speed, turning speed, and loading/unloading time.

Li Y., Ji W. Understanding the dynamics of information flow during disaster response using absorbing Markov chains // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2526-2535.
This paper aims to derive a quantitative model to evaluate the impact of information flow on the effectiveness of disaster response.

Li Y., Ji W., AbouRizk S.M. Automated abstraction of operation processes from unstructured text for simulation modeling // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2517-2525.

Li Y., Zhang Y., Cao L. Evaluation and selection of hospital layout based on an integrated simulation method // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2560-2568.
To facilitate better space planning and management in hospitals, this study integrates discrete-event simulation and agent-based simulation to examine and evaluate different layout designs.

Lidberg S., Aslam T., Ng A.H.C. Multi-level optimization with aggregated discrete-event models // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1515-1526.

Lin Y., Zhou E., Megahed A. A nested simulation optimization approach for portfolio selection // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 3093-3104.
We consider the problem of portfolio selection with risk factors, where the goal is to select the portfolio position that minimizes the value at risk (VaR) of the expected portfolio loss. The problem is computationally challenging due to the nested structure caused by the risk measure VaR of the conditional expectation, along with the optimization over a discrete and finite solution space. We develop a nested simulation optimization approach to solve this problem.

Lindberg T., Johansson F., Peterson A., Tapani A. Microsimulation of bus terminals: a case study from Stockholm // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1206-1217.
In this study, a discrete event simulation model is used in a case study of the Slussen bus terminal in Stockholm, Sweden. The model is calibrated and validated with empirical data that are automatically collected at the terminal.
Liu C.J., Liu Z., Chai Y.J., Liu T.T. (2020). Review of Virtual Traffic Simulation and Its Applications // Journal of Advanced Transportation, 2020.

Simulus: easy breezy simulation in Python // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2329-2340.
This paper introduces Simulus, a full-fledged open-source discrete-event simulator, supporting both event-driven and process-oriented simulation world-views. Simulus is implemented in Python and aspires to be a part of the Python’s ecosystem supporting scientific computing.

Liu T., Zhou E. Simulation optimization by reusing past replications: don’t be afraid of dependence // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2923-2934.
In this paper, we fill this gap by theoretically studying the stochastic gradient descent method with reusing past simulation replications. We show that reusing past replications does not change the convergence of the algorithm, which implies the bias of the gradient estimator is asymptotically negligible. Moreover, we show that reusing past replications reduces the variance of gradient estimators conditioned on the history, which implies that the algorithm can use larger step size sequences to achieve faster convergence.

Liu X., Jin D., Zhang T. Simulation-based evaluation of handover mechanisms in high-speed railway control and communication systems // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 3176-3187.
In this work, we construct a high-fidelity simulation model based on a real-world measurement dataset. We also implement multiple proposed handover mechanisms and conduct a simulation-based comparative study of them in terms of handover quality and network performance.

Liu Y., Yan L., Liu S., Jiang T., Zhang F., Wang Y., Wu S. Enhancing input parameter estimation by machine learning for the simulation of large-scale logistics networks // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 608-619.
This paper proposes a framework to estimate these parameters with high precision through machine learning, in which the impacting factors are divided into static and dynamic groups and used as features to train a learning model for estimation.

Linz D., Ahmadi B., Good R., Musabandesu E., Loge F. Multi-threaded simulation optimization platform for reducing energy use in large-scale water distribution networks with high dimensions // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 704-714.
This study presents the design of a new multi-threaded simulation optimization software platform to determine pump operations for water distribution networks.

Lugaresi G., Matta A. Generation and tuning of discrete event simulation models for manufacturing applications // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2707-2718.
This paper proposes a method that generates a simulation model and adjusts its level of detail exploiting the manufacturing system properties. The method has been applied in two test cases and can be used effectively to generate both Petri Net and simulation graph models.

Luo H., Wang Z., Yang S., Yang H., Gong Y. (2020, June). Influence Among Preferences and Its Transformation to Behaviors in Groups // In International Conference on Group Decision and Negotiation (pp. 104-119). Springer, Cham.

Lynch C.J., Diallo, S.Y., Kavak H., Padilla J.J. (2020). A content analysis-based approach to explore simulation verification and identify its current challenges // Plos one, 15(5), e0232929.

Mahdian S., Blanchet J.H., Glynn P.W. A class of optimal transport regularized formulations with applications to wasserstein gans // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 433-2444.
We propose a regularization of optimal transport costs and study its computational and duality properties. We obtain running time improvements for fitting Wasserstein Generative Adversarial Networks with no deterioration in testing performance, relative to current benchmarks. We also derive finite sample bounds for the empirical Wasserstein distance from our regularization.

Mahfouz A., Choudhary R., Crowe J., Rashwan W., Owida A. Investigating brexit implications on the irish agri-food exports: a simulation-based scenario mapping model // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1480-1491.

Manda A.B., Gopalswamy K., Shashaani S., Uzsoy R. A simulation optimization approach for managing product transitions in multistage production lines // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1730-1741.
We explore the problem of managing releases into a multistage production system transitioning from producing a mature product in high volume to a new one whose production process is initially unreliable but improves as experience is accumulated.

Mao C., Yu X., Zhou Q., Harms R., Fang G. (2020). Knowledge growth in university-industry innovation networks–Results from a simulation study // Technological forecasting and social change, 151, 119746.

Maqbool A., Afzal F., Razia A. (2020). Disaster Mitigation in Urban Pakistan Using Agent Based Modeling with GIS // ISPRS International Journal of Geo-Information, 9(4), 203.

Integrated simulation tool to analyze patient access to and flow during colonoscopy appointments // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 934-943.
This paper presents a simulation tool that analyzes different scheduling policies to see how they impact overall clinic operations. By simultaneously simulating both scheduling and operations, the tool can account for more variability and better predict actual outcomes. This tool can be used to inform clinics on what scheduling policies work best for their clinic and help analyze what the trade-offs will be between different policies.

Martin C.R., Trabes G.G., Wainer G.A. A new simulation algorithm for PDEVS models with time advance zero // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2208-2220.
In this work, we propose a new algorithm that assures that the output bag of a model is transmitted only when all the outputs corresponding to a given simulation time have been collected.

Maziarz Mariusz; Zach Martin (2020). Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal // Journal of Evaluation in Clinical Practice. 26 (5): 1352–1360. doi:10.1111/jep.13459.

Mazur P.G., Lee N., Schoder D. Integration of physical simulations in static stability assessments for pallet loading in air cargo // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1312-1323.
In this research, we propose and compare two approaches for integrating a physical simulation as a fixed component of the problem-solving heuristic and include irregular shapes.

McGill M.M., Decker A. (2020, June). Tools, Languages, and Environments Used in Primary and Secondary Computing Education // In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 103-109).

McGinnis L.F. An analysis-agnostic system model of the Intel Minifab // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1837-1848.
This paper demonstrates an approach for creating an analysis-agnostic system model for a wafer fab, using as the demonstration vehicle a famously simple yet intriguingly complex case study, the Intel Minifab case developed by Karl Kempf 25 years ago.

McLaughlin M.B., Sarjoughian H.S. DEVS-scripting: a black-box test frame for DEVS models // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2196-2207.

McMullen P.R. (2020). An Agent-Based Approach to the Newsvendor Problem with Price-Dependent Demand // American Journal of Operations Research, 10(4), 101-110.

Mendez-Vázquez Y.M., Nembhard D.A., Cabrera-Rios M. Simulation-aided assessment of team performance: the effects of transient underachievement and knowledge transfer // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1652-1663.

Simulation model to select an optimal solution for a milk run internal logistic loop: case study // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA.
A discrete event simulation model was built to develop an optimal milk run strategy for collecting finished products from the production area and transporting them to the dispatch warehouse.

Mierlo S.V., Vangheluwe H., Breslav S., Goldstein R., & Khan A. (2020). Extending Explicitly Modelled Simulation Debugging Environments with Dynamic Structure // ACM Transactions on Modeling and Computer Simulation (TOMACS), 30(1), 1-25.

Mintram K.S., Maynard S.K., Brown A.R., Boyd R., Johnston A.S.A., Sibly R.M., ... & Tyler C.R. (2020). Applying a Mechanistic Model to Predict Interacting Effects of Chemical Exposure and Food Availability on Fish Populations // Aquatic Toxicology, 105483.

Mittal S., Kasdaglis N., Harrell L., Wittman R.L., Gibson J. Rocca D. Autonomous and composable M&S system of systems with the simulation, experimentation, analytics and testing (seat) framework // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2305-2316.
This paper highlights some of the challenges with building a cloud-based simulation System of Systems, proposes an architecture framework using the concept of structural autonomy and leverages Modeling & Simulation (M&S) as a fundamental key enabler for autonomy research.

Mohammadi N., Taylor J.E. Human-infrastructure interactional dynamics: simulating COVID-19 pandemic regime shifts // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 727-735.
In this study, we explore the dynamics of human-infrastructure interactions during the global COVID-19 pandemic for the entire United States and determine the likelihood of regime shifts in human interactions with six different categories of infrastructure.

Monch L., Shen L., Fowler J.W. Heuristics for order-lot pegging in multi-fab settings // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1742-1752.
In this paper, we study order-lot pegging problems in semiconductor supply chains. The problem deals with assigning already released lots to orders and with planning wafer releases to fulfill orders if there are not enough lots.

Montes de Oca E.S., Suppi R., De Giusti L.C., Naiouf M. (2020). Green High Performance Simulation for AMB models of Aedes aegypti // Journal of Computer Science & Technology, 20.

Montevechi J.A.B., dos Santos C.H., Gabriel G.T., de Oliveira M.L.M., de Queiroz J.A., Leal F. A method proposal for conducting simulation projects in Industry 4.0: a cyber-physical system in an aeronautical industry // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2731-2742.
We present work proposed a method adapted from Montevechi et al. (2010) to carry out simulation projects according to Industry 4.0 principles.

Maintenance with production planning constraints in semiconductor manufacturing // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1921-1930.
In this paper, we are interested in optimally planning maintenance operations given a production plan that must be satisfied.

Muller M., Ulrich J.H., Reggelin T., Lang S., Reyes-Rubiano L.S. Comparison of deadlock handling strategies for different warehouse layouts with an AGVs // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1300-1311.
This paper presents a flexible simulation model for a warehouse with various AGVs. We implemented all three typical strategies to handle deadlocks: prevention, avoidance and detection and resolution. The results show that there is no dominant strategy and that the results strongly depend on the individual case and the input parameters.

Muschett G., Morales N.S. (2020). Using Ecological Modelling to Assess the Long-Term Survival of the West-Indian Manatee (Trichechus manatus) in the Panama Canal. Water, 12(5), 1275.

Mustafee N., Harper A., Onggo B.S. Hybrid modelling and simulation (M&S): driving innovation in the theory and practice of M&S // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 3140-3151.
In this paper, we expand on the unified conceptual representation and classification of hybrid M&S, which includes both HS (Model Types A-C), hybrid OR/MS models (D, D.1) and cross disciplinary hybrid models (Type E), and assess their innovation potential.

Nafors D., Johansson B., Gullander P., Erixon S. Simulation in hybrid digital twins for factory layout planning // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1619-1630.
This paper has shown how the process of brownfield facility layout planning could be innovated via intelligent use of digital technologies, in this case 3D laser scanning, VR, and simulation models in order to find new value-producing opportunities and reduce several forms of waste.

Nair D., Yerragunta S., Kandaswamy S., Venkataraman H. Assessing the impact of heterogeneous traffic on highways via agent-based simulations // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 111-122.

Nakayama M.K., Kaplan Z.T., Li Y., Tuffin B., Le_Ecuyer P. Quantile estimation via a combination of conditional Monte Carlo and randomized Quasi-Monte Carlo // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 301-312.
We consider the problem of estimating the p-quantile of a distribution when observations from that distribution are generated from a simulation model. The standard estimator takes the p-quantile of the empirical distribution of independent observations obtained by Monte Carlo.

Nelson B.L., Leemis L.M. The ease of fitting but futility of testing a nonstationary poisson processes from one sample path // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 266-276.

Neuner P., Haeussler S., Ilmer Q. Periodic workload control: a viable alternative for semiconductor manufacturing // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1765-1776.
This paper analyzes a rule based workload control model applied to a scaled-down semiconductor simulation model.

Ng A.H.C., Bernedixen J., Andersson M., Bandaru S., Lezama T. Aircraft assembly ramp-up planning using a hybrid simulation-optimization approach // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 3045-3056.
This paper introduces a hybrid simulation-optimization approach for addressing an assembly production chain ramp-up problem that takes into account: (1) the interdependencies of the ramp-up profiles between final assembly lines and its upstream lines; (2) workforce planning with various learning curves; (3) inter-plant buffer and lead-time optimization, in the problem formulation.

Niloofar P., Lazarova-Molnar S., Francis D.P., Vulpe A., Suciu G., Balanescu M. Modeling and simulation for decision support in precision livestock farming // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 2601-2612.
In this paper, we highlight the role of modeling and simulation in enhancing decision-making processes in precision livestock farming, and provide a comprehensive overview and categorization with respect to the relevant goals and simulation paradigms.

Oh H., Trinh M.P., Vang C., Becerra D. (2020). Addressing Barriers to Primary Care Access for Latinos in the US: An Agent-Based Model // Journal of the Society for Social Work and Research, 11(2), 000-000.

Okazawa S. Methods for estimating incidence rates and predicting incident numbers in military
populations // Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds. December 13-16, 2020. Orlando, Florida, USA. P. 1994-2005.

This paper provides a detailed mathematical development of equations that define incidence rates, Bayesian techniques for estimating rates based on the available evidence and quantifying how certain the estimate is, and a beta-binomial model for predicting the variation in future event numbers.





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