Статьи 2020 года (P...Z)



Palau A.S., Liang Z., Lutgehetmann D., & Parlikad A.K. (2020). Collaborative Prognostics in Social Asset Networks // In Value Based and Intelligent Asset Management (pp. 329-349). Springer, Cham.

Learning lindley’s recursion // 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. 644-655.
In this paper, we leverage stochastic simulation and current machine learning methods to learn the Lindley recursion directly from waiting time data of the G/G/1 queue.

Panadero J., Calvet L, Bayliss C., Marques J.M., Selimi M., Freitag F. A simheuristic algorithm for service placement in community 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. 632-643.
Our approach combines Monte Carlo simulation and the multi-criteria optimal placement heuristic, The method is tested using real traces of Guifi.net community network (CN), which is considered to be largest CN worldwide.

Paredis R., Van_Mierlo S., Vangheluwe H. Translating process interaction world view models to DEVS: GPSS to (Python (P)) DEVS // 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. 1016-1026.
We describe a translation of the classic process interaction language GPSS developed by Gordon in the early 1960s onto DEVS, a modular discrete-event modelling and simulation language with precise semantics developed by Zeigler in the late 1970s. We specify and implement a translation that produces, for each GPSS model, a behaviourally equivalent DEVS model.

Parsa A.B., Movahedi A., Taghipour H., Derrible S., Mohammadian A.K. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis // Accident Analysis & Prevention, 136, 105405.

Pasupathy R., Yeh Y. Risk-efficient sequential simulation 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. 2879-2886.

Paul R., Eaton S.E., Laird G., Nelson N., Brennan R. (2020). Using agent-based modelling for EER experimental design: preliminary validation based on student cheating behaviours // Proceedings of the Canadian Engineering Education Association (CEEA).

Payette N. (2020). Collaborating Like Professionals: Integrating NetLogo and GitHub // In Advances in Social Simulation (pp. 343-348). Springer, Cham.

Pei L., Nelson B.L., Hunter S.R. Evaluation of bi-PASS for parallel simulation optimization // 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. 2960-2971.
Cheap parallel computing has greatly extended the reach of ranking & selection (R&S) for simulation optimization. In this paper we present an evaluation of bi-PASS, a R&S procedure created specifically for parallel implementation and very large numbers of system designs. We compare bi-PASS to the state-oftheart Good Selection Procedure and an easy-to-implement subset selection procedure.

Philips I. (2020). An Agent Based Model to Estimate Lynx Dispersal if Re-Introduced to Scotland // Applied Spatial Analysis and Policy, 13(1), 161-185.

Platas-Lopez A., Guerra-Hernández A., Cruz-Ramírez N., Quiroz-Castellanos M., Grimaldo F., Paolucci M., Cecconi F. (2020). Towards an Agent-Based Model for the Analysis of Macroeconomic Signals // In Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications (pp. 551-565). Springer, Cham.

Raczynski S. (2020). Prey-Predator Models Revisited: Uncertainty, Herd Instinct, Fear, Limited Food, Epidemics, Evolution, and Competition // In Interacting Complexities of Herds and Social Organizations (pp. 107-132). Springer, Singapore.

Rahman A., Naufal F., Partiwi S. G. (2020, June). Measuring the entropy of organizational culture using agent-based simulation // In Managing Learning Organization in Industry 4.0: Proceedings of the International Seminar and Conference on Learning Organization (ISCLO 2019), Bandung, Indonesia, October 9-10, 2019 (p. 109). Routledge.

Ralph M. (2020). Emergent patterns in deterministic modeling // International Journal of Mathematical Education in Science and Technology, 1-11.

Reilly E.P., Agarwala S., Kelbaugh M.T., Ciesielski A., Ebeid H.-J.M., Hughes M. Modeling the relationship between food and civil conflict // 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. 715-726.
We built a system of systems model to better understand the relationship between the agricultural sector, other economic factors, and changes in the expected value of conflict. Our model integrates multiple factors, including food production, food trade, population, and civil conflict, and determines their interdependencies based on shared inputs or outputs.

Rippel D., Jathe N., Lutjen M., Freitag M. A mixed-integer formulation to optimize the resupply of components for the installation of offshore wind farms // 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. 1420-1431.
This article proposes a Mixed-Integer formulation for the optimization of supply deliveries to the base port.

Rizana A.F., Ramadhan F. (2020). Penerapan Agent-Based Simulation dalam Memprediksi Penggunaan Berkelanjutan Sistem ERP // Jurnal Teknologi Informasi dan Ilmu Komputer, 7(2).

Simulation and optimization of traction unit circulations // 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. 90-101.
The paper first presents the used data and the delay prediction. Afterwards, the modeling and simulation part and the optimization are presented. At last, the interaction of simulation and optimization are described and promising results of a test case are shown.

Ryder E., Ruiz C., Weaver S., & Gegear R. (2020). Choosing Your Own Adventure: Engaging the New Learning Society through Integrative Curriculum Design // EPiC Series in Education Science, 3, 188-199.

Sabzian H., Shafia M.A., Ghazanfari M., Bonyadi Naeini, A. (2020). Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory. Sustainability, 12(7), 2904.

Sadler T.D., Friedrichsen P., Zangori L., Ke L. (2020). Technology-Supported Professional Development for Collaborative Design of COVID-19 Instructional Materials // Journal of Technology and Teacher Education, 28(2), 171-177.

Sakahira F., Yamaguchi Y., Osawa R., Kishimoto T., Okubo T., Terano T., Tsumura H. Generating hypotheses on prehistoric cultural transformation with agent-based evolutionary 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. 373-384.
We propose an agent-based evolutionary simulation analogous to a genetic algorithm for generating hypotheses on prehistoric cultural transformation. As an application case study, we examine the mechanism of change in the composition of structural remains at the Jomon to Yayoi period sites in Western Japan.

Santoro M., Mazzetti P., Nativi, S. (2020). The VLab Framework: An Orchestrator Component to Support Data to Knowledge Transition // Remote Sensing, 12(11), 1795.

Santos F., Nunes I., Bazzan A.L. (2020). Quantitatively Assessing the Benefits of Model-driven Development in Agent-based Modeling and Simulation // Simulation Modelling Practice and Theory, 102126.

Saoud M.S., Boubetra A., Attia S. (2020). A Simulation Knowledge Extraction-Based Decision Support System for the Healthcare Emergency // Hospital Management and Emergency Medicine: Breakthroughs in Research and Practice: Breakthroughs in Research and Practice, 192.

Savaglio C., Ganzha M., Paprzycki M., Bădica C., Ivanovic M., & Fortino G. (2020). Agent-based Internet of Things: State-of-the-art and research challenges // Future Generation Computer Systems, 102, 1038-1053.

Schaff F. (2020). Conceptualising Artificial Anasazi with an Explicit Knowledge Representation and Population Model // In Advances in Social Simulation (pp. 399-403). Springer, Cham.

Schumacher B.C., Kohl H. Learning environment for introduction in discrete-event simulation for design and improvement of new and existing material flow 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. 3224-3235.
In this paper, ways are shown how students can be familiarized with executing simulation studies for the design and improvement of new and existing material flow systems using flexible discrete-event simulation tools.

Schmaranzer D., Kiefer A., Braune R., Doerner K.F. Simulation-based replacement line and headway optimization // 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. 3069-3080.
We present a study on simulation-based replacement line and headway optimization for the Viennese public transportation system. The discussed problem focuses on scheduled closures of subway lines. A genetic algorithm is proposed to design replacement lines and potentially adjust the headways of all lines in the network.

Sharma A., Tale E., Hernandez M., Phuong V. (2020). Engaging students with computing and climate change through a course in Scientific Computing // Journal of STEM Education: Innovations and Research, 20(2).

Shen Y., Shoemaker C.A. Global optimization for noisy expensive black-box multi-modal functions via radial basis function surrogate // 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. 3020-3031.
This study proposes a new surrogate global optimization algorithm that solves problems with expensive black-box multi-modal objective functions subject to homogeneous evaluation noise. Specifically, we propose a new radial basis function surrogate to approximate noisy functions and extend the Stochastic Response Surface method, which was developed for deterministic problems, to optimize noisy functions.

Shiflet A.B., Shiflet G.W., Cannataro M., Guzzi P H., Zucco C., & Kaplun D.A. (2020). What Are the Chances?—Hidden Markov Models // In An Introduction to Undergraduate Research in Computational and Mathematical Biology (pp. 353-400). Birkhäuser, Cham.

Shinde S.B., Kurhekar M.P. (2020) Agent-Based Modeling of the Adaptive Immune System Using Netlogo Simulation Tool // In: Das K., Bansal J., Deep K., Nagar A., Pathipooranam P., Naidu R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore.

Shrinidhi K.R., Sneha V., Jain V., Nair M.K. (2020). Multi-agent-Based Systems in Machine Learning and Its Practical Case Studies // In Machine Learning for Intelligent Decision Science (pp. 153-189). Springer, Singapore.

Siess J., Gold H., Ponsignon T. Modelling and mathematical optimization for capacity planning of a semiconductor wafer test module // 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. 1910-1920.
This paper focuses on scheduling and capacity planning problems in semiconductor wafer test. The planning of wafer test as the final stage of the semiconductor frontend manufacturing process is very complex due to many uncertain factors.

Silva A., & Oliveira M. (2020, January). Simulando o Jogo de Negociação Pit Game em um Sistema Multi-Agentes Implementado com o Framework JaCaMo // In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional (pp. 938-948). SBC.

Silverman E., Gostoli U. Phase: facilitating agent-based modelling in population health // 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. 135-146.

Singh R.K., Sardar M., & Das D. (2020). Assembling Multi-Robots Along a Boundary of a Region with Obstacles—A Performance Upgradation // In Advances in Computational Intelligence (pp. 201-212). Springer, Singapore.

Sismondo J., Sarhangian V., Borg E., Roberge E., Berger F.H. Emergency imaging after a mass casualty incident: an operational perspective via a simulation 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. P. 853-863.

Sopamena P., Andriansyah R., & Sopamena K. (2020). Analysis of Understanding of Student Concepts in Solving Absolute Value Problems // Matematika dan Pembelajaran, 7(2), 42-50.

Squazzoni Flaminio; Polhill J. Gareth; Edmonds Bruce; Ahrweiler Petra; Antosz Patrycja; Scholz Geeske; Chappin Émile; Borit Melania; Verhagen Harko; Giardini Francesca; Gilbert Nigel (2020). Computational models that matter during a global pandemic outbreak: A call to action // Journal of Artificial Societies and Social Simulation. 23 (2): 10. doi:10.18564/jasss.4298.

Stetsenko I.V., Dyfuchyn A. Petri-object Simulation: Technique and Software. Information, Computing and Intelligent Systems 1, 51-59 (2020).

Stevanovic A., & Mitrovic N. (2020). Impact of conflict resolution parameters on combined alternate-directions lane assignment and reservation-based intersection control // European Transport Research Review, 12(1), 1-10.

Sturrock D.T. Tested success tips for simulation project excellence // 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. 1143-1151.

Sudacevschi V., Ababii V., Munteanu S. Distributed Decision-Making Multi-Agent System in Multi-Dimensional Environment // ARA Journal of Sciences, 3/2020, pp. 74-80, ISSN: 0896-1018.

Suharko A.B. A capacity planning through discrete event simulation // Jurnal Penelitian dan Aplikasi Sistem & Teknik Industri (PASTI). Vol. XIV, No. 2, Agustus 2020, pp.146-156. p-ISSN 2085-5869/ e-ISSN 2598-4853.

Sundar S., Battistoni C., McNulty R., Morales F., Gorky J., Foley H., Dhurjati P. (2020). An agent-based model to investigate microbial initiation of Alzheimer’s via the olfactory system // Theoretical Biology and Medical Modelling, 17(1), pp.1-15.

Szczepanska T., Priebe M., Schroder T. (2020). Teaching the Complexity of Urban Systems with Participatory Social Simulation // In Advances in Social Simulation (pp. 427-439). Springer, Cham.

Tan W.J., Andelfinger P., Cai W., Knoll A., Xu Y., Eckhoff D. Multi-thread state update schemes for microscopic traffic 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. 182-193.
In this paper, we first analyze the common properties of models used in microscopic traffic simulation to understand the impact of their data dependencies. We discuss synchronous and asynchronous agent update schemes and compare them in terms of performance and requirements.

Taylor S.J.E., Anagnostou A., Abubakar N.T., Kiss T., DesLauriers J., Terstyanszky G., Kacsuk P., Kovacs J., Petry J., Kite S., Pattison G. Innovations in simulation: experiences with cloud-based simulation experimentation // 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. 3164-3175.

Pedestrian behavior at intersections: a literature review of models and simulation recommendations // 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. 1194-1205.
This study specifically investigates various modeling studies on pedestrian behavior at intersections. Insights are provided regarding the inputs, algorithms, and application scenarios. Also, this study identifies limitations in the existing traffic simulation tools involving pedestrians and provides recommendations for addressing these issues in future research.

Tomasiello D B., Giannotti M., Feitosa F.F. (2020). ACCESS: An agent-based model to explore job accessibility inequalities //Computers, Environment and Urban Systems, 81, 101462.

Tordecilla R.D., Panadero J., Juan A.A., Quintero-Araujo C.L., Montoya-Torres J.R. A simheuristic algorithm for the location routing problem with facility sizing decisions and stochastic demands // 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. 1265-1275.

Tosselli L., Bogado V., Martínez E. (2020). A repeated-negotiation game approach to distributed (re) scheduling of multiple projects using decoupled learning // Simulation Modelling Practice and Theory, 98, 101980.

Turgut Y., Bozdag C.E. Deep Q-network model for dynamic job shop scheduling problem based on 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. 1551-1559.
In this work deep Q-network model is applied to train an agent to learn how to schedule the jobs dynamically by minimizing the delay time of jobs.

Vahdat K., Shashaani S. Simulation optimization based feature selection, a study on data-driven optimization with input uncertainty // 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. 2149-2160.

Van_der_Valk H., Hunker J., Rabe M., Otto B. Digital twins in simulative applications: a taxonomy // 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. 2695-2706.

VanDeusen A., Liao C.-Y., Venkat A., Cohn A., Kurlander J., Saini S. Evaluating patient triage strategies for non-emergency outpatient procedures under reduced capacity due to 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.
In this case study, we use colonoscopy as a demonstrative example of a non-emergency appointment. A colonoscopy is an outpatient gastroenterology procedure used to screen patients for colon cancer.

van Doren D. (2020). Enabling Innovation Within Public Research Institutes: A Modelling Approach // In Advances in Social Simulation (pp. 441-449). Springer, Cham.

van Weerden J.F., Verbrugge R., Hemelrijk C.K. (2020). Modelling non-attentional visual information transmission in groups under predation // Ecological Modelling, 431, 109073.

Wang A., Chan E.H. (2020). The impact of power-geometry in participatory planning on urban greening // Urban Forestry & Urban Greening, 48, 126571.

Wang A., Wang H., Chan E. (2020). The incompatibility in urban green space provision: An agent-based comparative study // Journal of Cleaner Production, 120007.

Wang K., Xie W., Wang B., Pei J., Wu W., Baker M., Zhou Q. Simulation-based digital twin development for blockchain enabled end-to-end industrial hemp supply chain risk management // 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. 3200-3211.
In this paper, we propose blockchain-enabled Industrial Hemp Supply Chain and develop a preliminary simulation-based digital twin for this distributed cyber-physical system to support the process learning and risk management.

Wang R., Jaiswal P., Honnappa H. Estimating stochastic poisson intensities using deep latent 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. 596-607.
We develop a novel simulation method, using deep neural networks to approximate the path measures induced by the stochastic intensity process, for solving this nonlinear filtering problem.

Wang S., Ng S.H. Partition-based Bayesian optimization for stochastic 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. 2832-2843.
This work proposes a new partition-based Bayesian optimization algorithm where the acquisition function is optimized over a representative set of finite points in each partition instead of the whole design space to reduce the computational complexity. Additionally, to overcome over-exploitation, the algorithm considers regions of different sizes simultaneously in each iteration, providing focus on exploration in larger regions especially at the start of the algorithm. Numerical experiments show that these features help in faster convergence to the optimal point.

Wang X., Rhee C.-H. Rare-event simulation for multiple jump events in heavy-tailed Levy processes with infinite activities // 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. 409-420.
We propose a strongly efficient importance sampling algorithm that builds upon the sample path large deviations for heavy-tailed Levy processes, stick-breaking approximation of extrema of Levy processes, and the randomized debiasing Monte Carlo scheme.

Wang Y., Li X., Zhang F., Wang W., Xiao R. (2020). Effects of rapid urbanization on ecological functional vulnerability of the land system in Wuhan, China: A flow and stock perspective // Journal of Cleaner Production, 248, 119284.

Wang Z., Agrawal A., Carson I., Liu L., Pennathur H., Saab H., Cohn A., Moreno-Hernandez A., Gurm H. Incorporating patient deterioration when simulating utilization of a cardiovascular intensive care unit // 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. 876-887.
We present a discrete-event simulation model to assess how bounce back will impact assessment of bed capacity in the cardiac intensive care unit and step down and other major metrics of the system. We present analyses utilizing data from our collaborators at the Samuel and Jean Frankel Cardiovascular Center at Michigan Medicine.

Werling J., Yugma C., Soukhal A., Mohr T. An agent-based simulation model with human resource integration for semiconductor manufacturing facility // 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. 1801-1812.
This paper presents an agent-based simulation modeling of a real workshop of a semiconductor factory. One of the main characteristics of the factory is the strong involvement of human resources in production operations. The purpose is to build a simulation tool to help decision-makers anticipate production issues.

White K.P., Ingalls R.G. The basics of 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. 1087-1101.
Simulation is experimentation with a model. The behavior of the model imitates some salient aspect of the behavior of the system under study and the user experiments with the model to infer this behavior. This general framework has proven a powerful adjunct to learning, problem solving, design, and control. In this tutorial, we focus principally on discrete-event simulation – its underlying concepts, structure, and application.

Wilensky U.J. (2020) New Developments in Restructuration Theory and Understanding Complex Systems Through Agent-Based Restructurations // AERA Annual Meeting San Francisco, CA http://tinyurl.com/rvb86xr (Conference Canceled).

Winterrose M.L., Carter K.M., Wagner N., & Streilein W.W. (2020). Adaptive attacker strategy development against moving target cyber defenses // In Advances in Cyber Security Analytics and Decision Systems (pp. 1-14). Springer, Cham.

Wozniak M. (2020). Virtualising Space–New Directions for Applications of Agent-Based Modelling in Spatial Economics // Acta Universitatis Lodziensis. Folia Oeconomica, 1(346), p. 7-26.

Xia H., Li L., Cheng X., Liu C., Qiu T. (2020). A dynamic virus propagation model based on social attributes in city IoTs // IEEE Internet of Things Journal.

Joint resource allocation for input data collection and 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. 2126-2137.
We propose a general framework to analyze the joint resource allocation problem for collecting input data and generating simulation replications.

Xuereb M., Ng S.H., Pedrielli G. Stochastic Gaussian Process model averaging for high-dimensional inputs // 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. 373-384.
In this paper, we focus on Gaussian Processes, a family of non-parametric approaches used in machine learning and Bayesian Optimization.

Yang Q., Sun Y., Liu X., Wang J. (2020). MAS-Based Evacuation Simulation of an Urban Community during an Urban Rainstorm Disaster in China // Sustainability, 12(2), 546.

Yao X., Sun H., Fan B. (2020). A novel simulation framework for crowd co-decisions // International Journal of Crowd Science.

Yıldız B., Çagdaş G. (2020). Fuzzy logic in agent-based modeling of user movement in urban space: Definition and application to a case study of a square // Building and Environment, 169, 106597.

Yue T., Long R., Chen H., Liu J., Liu H., Gu Y. (2020). Energy-saving behavior of urban residents in China: A multi-agent simulation // Journal of Cleaner Production, 252, 119623.

Yust A.E., & Smyth D.S. (2020). Simulating Bacterial Growth, Competition, and Resistance with Agent-Based Models and Laboratory Experiments // In An Introduction to Undergraduate Research in Computational and Mathematical Biology (pp. 217-271). Birkhauser, Cham.

Zarrabi A.H., Azarbayjani M., Tavakoli M. (2020) Generative Design Tool: Integrated Approach toward Development of Piezoelectric Façade System.

Zhang G., Li H., Peng Y. Sequential sampling for a ranking and selection problem with exponential sampling distributions // 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. 2984-2995.
We study a ranking and selection problem with exponential sampling distributions. Under a Bayesian framework, we derive the posterior distribution of the performance parameter, and provide a normal approximation for the posterior distribution based on a central limit theorem to efficiently learn about the performance parameter.

Zhang R., Tielbörger K. (2020). Density-dependence tips the change of plant–plant interactions under environmental stress // Nature Communications, 11(1), 1-9.

Zhao J., Bai A., Xi X., Huang Y., Wang S. (2020). Impacts of malicious attacks on robustness of knowledge networks: a multi-agent-based simulation // Journal of Knowledge Management.

Zheng H., Xie W., Feng M.B. Green simulation assisted reinforcement learning with model risk for biomanufacturing learning and control // 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. 337-348.

Zoricak M., Horvath D., Gazda V., Hudec O. (2020). Spatial evolution of industries modelled by cellular automata // Journal of Business Research.

Zou J., Wang K., Sun H. (2020). An implementation architecture for crowd network simulations // International Journal of Crowd Science.

Zukri N.H.A., Rashid N.A.M., Awang N., Zulkifli Z.A. (2020). Agent-Based Encryption for Password Management Application // In Charting the Sustainable Future of ASEAN in Science and Technology (pp. 529-541). Springer, Singapore.

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