Articles 2020 (A...H)

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Ababii V., Sudacevschi V., Braniste R., Turcan A., Ababii C., Munteanu S. Adaptive computing system for distributed process control // International Journal of Progressive Sciences and Technologies. Vol. 22, No 2, September 2020, pp. 258-264. ISSN: 2509-0119.

Abdellaoui M.E.A.E., Bricard E., Grimaud F., Gianessi P., Delorme X. Scalable, reconfigurable simulation models in Industry4.0-oriented enterprise 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. 2755-2766.
This article shows how to use it to derive scalable, flexible simulation models, so as to improve and monitor the manufacturing systems processes and finally address this challenge. An example to generate a simulation model for a real case study is presented.

Afridi M.T., Nieto-Isaza S., Ehm H., Ponsignon T., Hamed A. A deep reinforcement learning approach for optimal replenishment policy in a vendor managed inventory setting for semiconductors // 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. 1753-1764.

Ageev K.A., Sopin E.S., Samouylov K.E. Resource sharing model with minimum allocation for the performance analysis of network slicing, information technologies and mathematical modelling. queueing theory and applications // ITMM 2020. Communications in Computer and Information Science.

Ahn D., Shin D. Ordinal optimization with generalized linear 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. 3008-3019.
Given a number of stochastic systems, we consider an ordinal optimization problem to find an optimal allocation of a finite sampling budget, which maximizes the likelihood of selecting the “best” system, where the “best” is defined as the one with the highest mean.

Alban A., Chick S.E., Lvova O., Sent D. A simulation model to evaluate the patient flow in an intensive care unit under different levels of specialization // 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. 922-933.
This paper presents a simulation model which is used to assess trade-offs in these operational design issues with respect to three performance measures (rejection rate, rescheduling rate, and bed occupancy rate), using data and design options for the Academic Medical Center, one of two locations forming the Amsterdam University Medical Centers.

Alexopoulos C., Boone J.H., Goldsman D., Lolos A., Dingeç K.D., Wilson J.R. Steady-state quantile estimation using standardized time series// 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. 289-300.

Alsakka F., Khalife S., Darwish M., Al-Hussein M., Mohamed Y. Deploying discrete-event simulation and continuous improvement to increase production rate in a modular construction 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. 1676-1687.
This study employed simulation to model five major production lines in the factory to evaluate their performance concurrently and suggest improvements.

Alsassa S., Lefevre T., Laugier V., Stindel E., Ansart S. (2020). Modeling Early Stages of Bone and Joint Infections Dynamics in Humans: A Multi-Agent, Multi-System Based Model // Frontiers in molecular biosciences, 7, 26.

Alvarado M., Basinger K., Lahijanian B., Alvarado D. Teaching simulation to generation Z engineering students: lessons learned from a flipped classroom pilot 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. 3248-3259.

Al-Zoubi K., Wainer G. Modelling Fog & Cloud collaboration methods on large scale // 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. 2161-2172.

Ambra T., Macharis C. Agent-based digital twins (ABM-DT) in synchromodal transport and logistics: the fusion of virtual and physical 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. 159-169.
The paper demonstrates a first proof-of-concept for long-distance Digital Twin solutions by connecting real-time data feeds from the physical system to a virtual GIS environment that can be utilized in real-time synchromodal deliveries.

Amorim G.A., Lopes L.A.S. and Silva Junior O.S. (2020) Discrete event-based railway simulation model for eco-efficiency evaluation // International Journal of Simulation Modelling, 19(3), 375-386. Available: https://doi.org/10.2507/IJSIMM19-3-517.

Anderson S., Anderson S.D. (2020, June). Coding and Music Creation in a Multi-Agent Environment // In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 527-528).

Ansari M., Smith J.S. Gravity clustering: a correlated storage location assignment problem 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. 1288-1299.
In this paper, we propose a clustering method based on the gravity model. We show that for warehouses in which there is more than one pick per trip, our proposed method improves the performance.

Antokhina Yu.A, Balashov V.M, Semenova E.G, Varzhapetyan A.G. Computer simulation of processes in technical systems // Journal of Physics: Conference Series. 1691 (2020) 012069, doi:10.1088/1742-6596/1691/1/012069.

Arani M., Liu X., Abdolmaleki S. Scenario-based simulation approach for an integrated inventory blood supply chain system // 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. 1348-1359.
This study conducts a comparison between a newly proposed integrated inventory blood supply chain and the current practice of the blood product distribution system.

Araujo-Granda P., Gras A., Ginovart M., & Moulton V. (2020). INDISIM-Denitrification, an individual-based model for study the denitrification process // Journal of industrial microbiology & biotechnology, 47(1), 1-20.

Aresi L., Dauzere-Peres S., Yugma C., Ndiaye M., Rulliere L. Optimizing the allocation of single-lot stockers in an AMHS 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. 1700-1700.
This paper addresses the problem of optimally allocating single-lot stockers, also called bins, to machines in an Automated Material Handling System (AMHS) of a semiconductor wafer manufacturing facility.

Arani M., Liu X., Abdolmaleki S. Scenario-based simulation approach for an integrated inventory blood supply chain system // 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. 1348-1359.
This study conducts a comparison between a newly proposed integrated inventory blood supply chain and the current practice of the blood product distribution system.

Asgari S. A green performance bond framework for managing greenhouse gas emissions during construction: proof of concept using agent-based 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. 2549-2555.
This study introduces a green performance bond framework as a potential solution and evaluates its feasibility and effectiveness in discouraging opportunistic bidding behaviors.

Asgharpourmasouleh A., Fattahzadeh M., Mayerhoffer D., & Lorenz J. (2020). On the Fate of Protests: Dynamics of Social Activation and Topic Selection Online and in the Streets // In Computational Conflict Research (pp. 141-164). Springer, Cham.

Azim M.A., Sathasivam S., Alzaeemi S.A.S., Mahmood M. (2019). Agent Based Modeling for Comparing the Performances of Hyperbolic and Zeng and Martinez Activations Functions // International Journal of Computer Networks and Communications Security, 7(12), 250-257.

Bagamanova M., Mota M.M. Reduction of taxi-related airport emissions with disruption-aware stand assignment: case of Mexico city international airport // 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. 516-527.
This paper illustrates the application of the presented methodology combined with simulation and demonstrates the impact of the application of Bayesian modeling and metaheuristic optimization for reduction of taxi-related emissions.

Bai J., Brunner J.O., Gerstmeyr S. Simulation and evaluation of ICU management policies // 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. 864-875.

Bai S. (2020). Simulations of COVID-19 spread by spatial agent-based model and ordinary differential equations // International Journal of Simulation and Process Modelling, 15(3), 268-277.

Bai Y., Lam H. Calibrating input parameters via eligibility sets // 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. 2114-2125.
In this paper we introduce the concept of eligibility set to bypass non-identifiability, by relaxing the need of consistent estimation to obtaining bounds on the input parameter values. We reason this concept from the worst-case notion in robust optimization, and demonstrate how to compute eligibility set via empirical matching between the simulated and the real outputs.

Bai Y., Lam H. On the error of naive rare-event Monte Carlo estimator // 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. 397-408.
We consider the estimation of rare-event probabilities using sample proportions output by naive Monte Carlo. In this paper we investigate this naive rare-event estimator, particularly its conservativeness level and the guarantees in using it to construct confidence bounds for the target probability. We show that the half-width of a valid confidence interval is typically scaled proportional to the magnitude of the target probability and inverse square-root with the number of positive outcomes in the Monte Carlo.

Baldwa V., Sehgal S., Ramamohan V., Tandon V. A combined simulation and machine learning approach for real-time delay prediction for waitlisted neurosurgery candidates // 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. 956-967.
In this study, we present a method to predict whether a patient seeking admission to the neurosurgery ward of a large public tertiary care hospital in north India receives admission within a prespecified duration.

Bao H., Dong H., Jia J., Peng Y., & Li Q. (2020). Impacts of land expropriation on the entrepreneurial decision-making behavior of land-lost peasants: An agent-based simulation // Habitat International, 95, 102096.

Barazza E., & Strachan N. (2020). The co-evolution of climate policy and investments in electricity markets: Simulating agent dynamics in UK, German and Italian electricity sectors. Energy Research & Social Science, 65, 101458.

Barbet V., Bourles R., & Rouchier J. (2020). Informal risk-sharing cooperatives: the effect of learning and other-regarding preferences // Journal of Evolutionary Economics, 1-28.

Barbosa P., Schumaker N.H., Brandon K.R., Bager A., Grilo C. (2020). Simulating the consequences of roads for wildlife population dynamics // Landscape and Urban Planning, 193, 103672.

Barker A.K., Scaria E., Alagoz O., Sethi A.K., & Safdar N. (2020). Reducing C. difficile in children: An agent-based modeling approach to evaluate intervention effectiveness // Infection Control & Hospital Epidemiology, 1-9.

Barrera J., Lagos G. Approximating the Levy-frailty Marshall-Olkin model for failure times // 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. 2389-2399.
In this paper we approximate the last, close-to-first, and what we call quantile failure times of a system, when the system-components’ failure times are modeled according to a Levy-frailty Marshall-Olkin distribution.

Barrios B.B., Juan A.A., Panadero J. Altendorfer K., Peirleitner A.J., Estrada-Moreno A. On the use of simheuristics to optimize safety-stock levels in material requirements planning with random 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. 1539-1550.
This paper analyzes a MRP version in which the demand of final products in each period is a random variable. The goal is then to find the optimal safety-stock configuration of both the product and the parts, i.e.: the configuration that minimizes the expected total cost.

Barton R.R. Tutorial: metamodeling for 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. 1102-1116.
This introductory tutorial will highlight uses of metamodels, commonly used metamodel types, the linkage between metamodel type and the set of simulation model runs used to fit the metamodel, and basic issues in building and validating metamodels.

Bayliss C., Copado-Mendez P.J., Panadero J., Juan A.A., Martins L.do C. A simheuristic-learnheuristic algorithm for the stochastic team orienteering problem with dynamic rewards // 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. 1254-1264.
In this paper, we consider the stochastic team orienteering problem with dynamic rewards and stochastic travel times.

Bayliss C., Panadero J., Calvet L., Marques J.M. A simulation model for volunteer computing micro-blogging services // 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. 552-562.

Bayliss C., Serra M., Gandouz M., Juan A.A., Nieto A. A simheuristic algorithm for reliable asset and liability management under uncertainty scenarios // 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. 2093-2104.

Beauchemin M., Gaudreault J., Dumetz L., Agnard S. Evaluating workers allocation policies through the simulation of a high precision machining workshop // 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 of the metal parts manufacturing production line is built in order to test different allocation policies. We measure how more advanced policies lead to increased efficiency.

Belsare A. V., Gompper M. E., Keller B., Sumners J., Hansen L., & Millspaugh J.J. (2020). An agent-based framework for improving wildlife disease surveillance: A case study of chronic wasting disease in Missouri white-tailed deer // Ecological Modelling, 417, 108919.

Bemthuis R., Mes M., Iacob M.-E., Havinga P. Using agent-based simulation for emergent behavior detection in 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. 230-241.
In this paper, we propose a conceptual agent-based simulation framework to help not only discover complex business processes but also to analyze and learn from emergent behavior arising in cyber-physical systems.

Benzoni A., Yugma C., Bect P., Planchais A. Allocating reticles in an automated stocker 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. 1711-1717.
This article addresses the problem of reticle allocation in a stocker of an existing photolithography workshop of a 200 mm semiconductor wafer manufacturing facility.

Berger C., Mahdavi A. (2020). Review of current trends in agent-based modeling of building occupants for energy and indoor-environmental performance analysis // Building and Environment, 173, 106726.

Bijak J., Higham P.A., Hilton J., Hinsch M., Nurse S., Prike T., Smith P.W.F., Reinhardt O., Uhrmacher A.M. Modelling migration: decisions, processes and outcomes // 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. 2613-2624.

Bina K., Moghadas N. (2020). BIM-ABM simulation for emergency evacuation from conference hall, considering gender segregation and architectural design // Architectural Engineering and Design Management, 1-15.

Bocciarelli P., D_Ambrogio A., Durak U. ArTIC-M&S: an architecture for TOSCA-based inter-cloud modeling 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. 2018-2029.
This work proposes ArTIC-MS, a Modeling & Simulation-as-a-Service (MSaaS) architecture that aims at investigating innovative approaches to ease the building of inter-cloud MSaaS applications. ArTIC-MS’s main objective is to provide an effective interoperability among Modeling & Simulation services provided by different nations to seamlessly build complex MSaaS applications.

Bondareva I.O., Shendo M.V., Luneva T.V., Khanova A.A. Logical-probabilistic and simulation modeling as a toolkit for complex analysis and risk management of a cargo port // E3S Web of Conferences. TPACEE-2020. 2020. V. 224. P. 02027. DOI.org/10.1051/e3sconf/202022402027.

Brady C., Stroup W.M., Petrosino A. Wilensky U.J. (2020) Amplifying the Restructuration Potential of Agent-Based Modeling Through Group-Based Activity Structures // AERA Annual Meeting San Francisco, CA http://tinyurl.com/szuaxl3 (Conference Canceled).

Brainard J., Hunter P.R., & Hall I. R. (2020). An agent-based model about the effects of fake news on a norovirus outbreak // Revue d'Epidemiologie et de Sante Publique.

Brown P., Kawazoe C., Nguyen A. Modeling and simulation: balancing performance, schedule, and cost // 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. 2042-2048.

Brunetti M., Mes M., van Heuveln J. A general simulation framework for smart yards // 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. 2743-2754.
This paper presents a simulation framework for the logistics operations at Smart Yards. A Smart Yard is a digital and physical system enabling the collaboration of various companies at a logistics hub, e.g., seaport, airport or hinterland distribution center.

Bryce R.M., Henderson J.A. Workforce populations: empirical versus Markovian dynamics // 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. 1983-1993.
We present a study where different populations in the Canadian Armed Forces are considered. We contrast empirical survival time distributions with the matched exponential, and find distributions ranging from being close to exponential (e.g., Reserve Force) to distinctly non-exponential (e.g., Regular Force).

Calabro G., Inturri G., Le Pira M., Pluchino A., Ignaccolo M. (2020). Bridging the gap between weak-demand areas and public transport using an ant-colony simulation-based optimization // Transportation Research Procedia, 45, 234-241.

Calabro G., Torrisi V., Inturri G., Ignaccolo M. (2020). Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization // European Transport Research Review, 12, 1-11.

Campos R.F.D.A., Cunha D.A.D., Bueno N.P. (2020). Information dissemination in socio-ecological systems: Analysis of a hybrid model of System Dynamics and Agent-Based Modeling // Nova Economia, 30(1), 257-286.

Cao S. et al. An Agent-Based Model of Leader Emergence and Leadership Perception within a Collective // Complexity. 2020. Vol. 2020. P. 1-11. doi:10.1155/2020/6857891.

Cardenas R., Henares K., Arroba P., Wainer G., Risco-Martín J.L. A DEVS simulation algorithm based on shared memory for enhancing performance // 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. 2184-2195.
In this research, we present a chained DEVS simulator, a DEVS-compliant, function-oriented simulation algorithm that exploits shared memory patterns to improve the performance of sequential and parallel simulations.

Carletti M., Maggipinto M., Beghi A., Susto G.A., Gentner N., Yang Y., Kyek A. Interpretable anomaly detection for knowledge discovery 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. 1875-1885.
In this work, we show the effectiveness of a method, called DIFFI, to equip Isolation Forest, one of the most popular Anomaly Detection algorithms, with interpretability traits that can help corrective actions and knowledge understanding.

Casale G. Integrated performance evaluation of extended queueing network models with line // 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. 2377-2388.
In this tool paper, we present LINE 2.0, an integrated software package to specify and analyze extended queueing network models. This new version of the tool is underpinned by an object-oriented language to declare a fairly broad class of extended queueing networks.

Cen W., Herbert E.A., Haas P.J. NIM: modeling and generation of simulation inputs via generative neural 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. 584-595.
We present Neural Input Modeling (NIM), a generative-neural-network framework that exploits modern data-rich environments to automatically capture simulation input distributions and then generate samples from them.

Chan C.W., Cai W., Gan B.P. Towards situation aware dispatching in a dynamic and complex manufacturing environment // 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. 528-539.
In this work, we use simulation and machine learning methods to generate dispatching knowledge and define features that are relevant in a dynamic product mix situation.

Chang T.H., Larson J., Watson L.T. Multiobjective optimization of the variability of the high-performance LINPACK solver // 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. 3081-3092.

Chappin E. J., Nikolic I., & Yorke-Smith N. (2020). Agent-based modelling of the social dynamics of energy end use // In Energy and Behaviour (pp. 321-351). Academic Press.

Chen G. Unbiased gradient simulation for Zeroth-order 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. 2947-2959.
We apply the Multi-Level Monte Carlo technique to get an unbiased estimator for the gradient of an optimization function. Under mild assumptions, our algorithm achieves a complexity bound independent of the dimension, compared with the typical one that grows linearly with the dimension.

Chen S., He Q., & Xiao H. (2020). A study on cross-border e-commerce partner selection in B2B mode // Electronic Commerce Research, 1-21.

Chen S., Zhang H., Guan J., Rao Z. (2020, March). Agent-based modeling and simulation of stochastic heat pump usage behavior in residential communities // In Building Simulation (pp. 1-19). Tsinghua University Press.

Chen X., Wang X. Perfect sampling of multivariate Hawkes processes // 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. 469-480.
In this paper, we present a perfect sampling algorithm that can generate i.i.d. stationary sample paths of multivariate Hawkes process without any transient bias. In addition, we provide an explicit expression of algorithm complexity in model and algorithm parameters and provide numerical schemes to find the optimal parameter set that minimizes the complexity of the perfect sampling algorithm.

Cheng C., Luo Y., & Yu C. (2020). Dynamic mechanism of social bots interfering with public opinion in network // Physica A: Statistical Mechanics and its Applications, 124163.

Cherkesly M., Maizi Y. A simulation model for short and long term humanitarian supply chain operations 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. 1360-1371.
In this paper, we develop a sustainable humanitarian supply chain network for the relief-to-development continuum. Hence, this network ensures an effective and smooth transition from response to reconstruction operations.

Optimal switching in a dynamic, stochastic, operating environment // 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. 2083-2092.
This paper considers the dynamic stochastic problem of water allocation for hydroelectric generation and downstream use. Our main contribution is to present a novel numerical solution to this problem.

Chudzinska M., Dupont Y.L., Nabe-Nielsen J., Maia K.P., Henriksen M.V., Rasmussen C., ... & Trøjelsgaard K. (2020). Combining the strengths of agent-based modelling and network statistics to understand animal movement and interactions with resources: example from within-patch foraging decisions of bumblebees // Ecological Modelling, 430, 109119.

Collard P. (2020). Second-order micromotives and macrobehaviour // Journal of Computational Social Science, 1-21.

Collins A.J., Etemadidavan S., Pazos-Lago P. A human experiment using a hybrid agent-based 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. 2221-2232.

Corlu C.G., Biller B., Wolf E., Yucesan E. Inventory management with disruption risk // 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. 2625-2636.

Corlu C.G., Maleyeff J., Wang J., Yip K., Farris J. Real-time nurse dispatching using dynamic priority decision 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. 782-793.
This article details a framework that uses a discrete-event simulation, programmed in Python, to determine how priorities should be assigned in real time based on characteristics of patient needs.

Corlu C.G., Panadero J., Juan A.A. On the scarcity of observations when modelling random inputs and the quality of solutions to stochastic optimization problems // 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. 2105-2113.
This paper considers an inventory-routing problem with stochastic demands, in which the retailers have access to limited amounts of historical demand data.

Cortes E., Rabelo L., Sarmiento A.T., and Gutierrez E. Design of distributed discrete-event simulation systems using deep belief networks // Information 2020, 11, 467; doi:10.3390/info11100467.

Costa L., Araujo M., Silva T., Junior R., Andrade J., & Campos G. (2020, January). Comparative Study of Neural Networks Techniques in the Context of Cooperative Observations // In Anais do XVI Encontro Nacional de Inteligencia Artificial e Computacional (pp. 563-574). SBC.

Cuevas E. (2020). An agent-based model to evaluate the COVID-19 transmission risks in facilities // Computers in Biology and Medicine, 103827.

Cui L., He T., Jiang Y., Li M., Wang O., Jiajue R., ... & Xia W. (2020). Predicting the intervention threshold for initiating osteoporosis treatment among postmenopausal women in China: a cost-effectiveness analysis based on real-world data // Osteoporosis International, 31(2), 307-316.

Daems D. (2020). A Review and Roadmap of Online Learning Platforms and Tutorials in Digital Archaeology // Advances in Archaeological Practice, 8(1), 87-92.

Dacrema E., Benati S. The mechanics of contentious politics: an agent-based modeling approach // The Journal of Mathematical Sociology. 2020. Vol. 44, N 3. P. 163-198. DOI: https://doi.org/10.1080/0022250X.2020.1753187.

de Celis R., Solano-Lopez P., Cadarso L. A neural network for sensor hybridization in rocket guidance // 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. 2006-2017.
This research presents a new non-linear hybridization algorithm to feed navigation and control systems, which is based on neural networks.

Delcea C., Cotfa, L.A., Craciun L., & Molanescu A.G. (2020). An agent-based modeling approach to collaborative classrooms evacuation process // Safety science, 121, 414-429.

de Mingo Lopez L.F., Blas N.G., Castellanos Penuela A.L., & Castellanos Penuela J.B. (2020). Swarm Intelligence Models: Ant Colony Systems Applied to BNF Grammars Rule Derivation // International Journal of Foundations of Computer Science, 31(01), 103-116.

de Oca E.S.M., Suppi R., De Gisuti L.C., Naiouf M. (2020). Green High Performance Simulation for AMB models of Aedes aegypti // Journal of Computer Science and Technology, 20(1), e02-e02.

de Oliveira Zamberlan A., Bordini R.H., Kurtz G.C., Fagan S.B. (2020). Multi-Agent Systems, Simulation and Nanotechnology // In Multi Agent Systems-Strategies and Applications. IntechOpen.

Dhou K. (2020). A new chain coding mechanism for compression stimulated by a virtual environment of a predator–prey ecosystem // Future Generation Computer Systems, 102, 650-669.

Diaz C.A.B., Aslam T., Ng A.HC., Flores-Garcia E., Wiktorsson M. Simulation-based multi-objective optimization for reconfigurable manufacturing system configurations analysis // 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. 1527-1538.
The purpose of this study is to analyze the use of simulation-based multi-objective optimization for reconfigurable manufacturing system configuration analysis.

Dimitriou N. (2020) A deep learning framework for simulation and defect prediction applied in microelectronics // Simulation Modelling Practice and Theory, 100 (6), 102063. DOI: 10.1016/j.simpat.2019.102063.

Doddavaram R., Corlu C.G. Teaching risk analytics using 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. 3272-3281.
We discuss our experience with using R, which is a free software that is particularly suitable for computer simulation, in a risk analytics course offered to students having different experience levels and technical sophistication.

Dominguez R., Cannella S. (2020). Insights on Multi-Agent Systems Applications for Supply Chain Management // Sustainability, 12(5), 1935.

Dos Reis F.B. (2020) Synthetic residential load models for smart city energy management simulations. IET Smart Grid, 3 (3). P.352-364.

dos Santos V.H., Kotiadis K., Scaparra M.P. A review of hybrid simulation in healthcare // 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. 1004-1015.
This review explores applications of Hybrid Simulation (HS) in healthcare, to outline research gaps and foster new research in HS to solve complex real healthcare problems.

Dubovi I., Levy S.T., Levy M., Zuckerman Levin N., & Dagan E. (2020). Glycemic control in adolescents with type 1 diabetes: Are computerized simulations effective learning tools? // Pediatric Diabetes, 21(2), 328-338.

Duran J.M. What is a Simulation Model? // Minds & Machines. 2020. V. 30. P. 301–323. DOI.org/10.1007/s11023-020-09520-z.

Eckman D.J., Henderson S.G. Biased gradient estimators in 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. 2935-2946.
We focus on the infinitesimal perturbation analysis gradient estimator, which is biased when an interchange of differentiation and expectation fails. Although a local-search algorithm guided by biased gradient estimators will likely not converge to a local optimal solution, it might be expected to reach a neighborhood of one. We test such a gradient-based search on an ambulance base location problem, demonstrating its effectiveness in a non-trivial example, and present some supporting theoretical results.

Eckman D.J., Plumlee M., Nelson B.L. Revisiting subset 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. 2972-2983.
In the subset-selection approach to ranking and selection, a decision-maker seeks a subset of simulated systems that contains the best with high probability. We present a new, generalized framework for constructing these subsets and demonstrate that some existing subset-selection procedures are situated within this framework.

Elbert R., Lehner R. Simulation-based analysis of a cross-actor pallet exchange platform // 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. 1396-1407.
In this paper, a fictitious cross-actor pallet exchange platform is analyzed, which manages pallet debts and receivables between the different actors of a supply chain. A claim transfer is performed, and the actors no longer owe pallets to each other, but to the system.

Eom H., Li Y. Developing high-quality microsimulation models using r in health decision sciences // 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. 1167-1177.
This paper describes several modeling approaches and programming languages widely used in health decision sciences.

Fard M.D., Sarjoughian H.S., Mahmood I., Mounir A., Guan X., Mascaro G. Modeling the water-energy nexus for the phoenix active management area // 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. 2317-2328.
In this paper, the integrated Water Evaluation and Planning and Long-range Energy Alternatives Planning tools and a modeling framework that externalizes their hidden linkage to an interaction model are described and compared using the Phoenix Active Management Area.

Farhan M., Gohre B., Junprung E. Reinforcement learning in AnyLogic simulation models: a guiding example using pathmind // 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. 3212-3223.
In this paper, we demonstrate the use of reinforcement learning in AnyLogic software models using Pathmind. A coffee shop simulation is built to train a barista to make correct operational decisions and improve efficiency that directly affects customer service time. The trained policy outperforms rule-based functions in terms of customer service time and throughput.

Faria, L. F. F. D., Asevedo, L. F. D., Vieira J.G.V., Silva J.E.A.R.D. (2020). A combined approach of multiple-criteria decision analysis and discrete-event simulation: lessons learned from a fleet composition study // World Review of Intermodal Transportation Research, 9(2), 97-119.

Farjam M., Bravo G. (2020). Fixing Sample Biases in Experimental Data Using Agent-Based Modelling // In Advances in Social Simulation (pp. 155-159). Springer, Cham.

Farjamirad M., Niknami K.A. (2020). Frequency of Using Stone Ossuaries in Marvdasht Plain (Fourth–Seventh Century AD): Explaining Funerary Patterns Through Agent-Based Modelling // In Archaeology of Iran in the Historical Period (pp. 363-371). Springer, Cham.

Fatma N., Mohd S., Ramamohan V., Mustafee N. Primary healthcare delivery network simulation using stochastic metamodels // 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. 818-829.
We present a discrete-event simulation (DES) of a network of primary health centers (PHCs) using stochastic metamodels developed from more detailed DES models of PHCs (‘parent’ simulations), which were developed separately for comprehensively analyzing individual PHC operations.

Feinberg A., Hooijschuur E., Ghorbani A. (2020). Simulation of Behavioural Dynamics Within Urban Gardening Communities // In Advances in Social Simulation (pp. 161-167). Springer, Cham.

Feldkamp N., Bergmann S., Strassburger S. Simulation-based deep reinforcement learning for modular production 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. 1596-1607.
This paper presents an approach for using reinforcement learning in combination with simulation in order to control automated guided vehicles in modular production systems. We present a case study and compare our approach to heuristic rules.

Feng B.M., Liu K. Path generation methods for valuation of large variable annuities portfolio using Quasi-Monte Carlo 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. 481-491.
In this study, we propose and analyze three Quasi-Monte Carlo path generation methods, Cholesky decomposition, Brownian Bridge, and Principal Component Analysis, for the valuation of large Variable annuities portfolios.

Feng H., Li Z., Alvarado M.M., Colon-Morales C.M. A simulation study of outpatient surgery clinic with stochastic patient re-entrance // 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. 910-921.
We develop a simulation model to study the operational performance of an Mohs Micrographic Surgeryclinic with a given appointment schedule used in practice. Our study reveals how the waiting time and clinic overtime is affected by different stochastic factors.

Fichera A., Pluchino A., Volpe R. (2020). From self-consumption to decentralized distribution among prosumers: A model including technological, operational and spatial issues // Energy Conversion and Management, 217, 112932.

Figueras J., Guasch A., Casanovas-García J. Simulation of aerial supression tasks in wildfire events integrated with gisfire simulator // 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. 736-746.
This paper presents the modelling and simulation of aerial containment operations integrated into a wildfire spread simulator.

Fouladvand J., Mouter N., Ghorbani A., Herder P. (2020). Formation and Continuation of Thermal Energy Community Systems: An Explorative Agent-Based Model for the Netherlands // Energies, 13(11), 2829.

Franck T., Bercelli P., Aloui S., Augusto V. A generic framework to analyze and improve patient pathways within a healthcare network using process mining and 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. 968-979.
We propose a generic simulation model in order to analyze patient pathways from the Emergency Department to hospital discharge. The model is adaptable for all pathologies and can include several hospitals within a healthcare network.

Fu M.C. A tutorial introduction to Monte Carlo tree search // 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. 1178-1193.
This tutorial provides an introduction to Monte Carlo tree search, which is a general approach to solving sequential decision-making problems under uncertainty using stochastic (Monte Carlo) simulation.

Gao M. (2020) Real-time visualization optimization management simulation of big data stream on industrial heritage cloud platform. Complexity, 2020, 8885191. DOI: 10.1155/2020/8885191.

Gao X., Kong N., Griffin P.M. Dynamic optimization of drone dispatch for substance overdose rescue // 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. 830-841.

Garbers A., Nolletti V., Kuhl M.E., Stanislow K. Design and simulation of a new biomedical production 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. 1631-1640.
This paper presents the design and analysis of a lean production system for a new biomedical technology product that has the potential to accelerate the screening process for cancer treatments.

Garcia Filho C. (2020). Simulating social distancing measures in household and close contact transmission of SARS-CoV-2 // Cadernos de Saúde Pública, 36(5).

Garcia-Vicuna D., Mallor F., Esparza L. Planning ward and intensive care unit beds for COVID-19 patients using a discrete event 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. 759-770.
This paper reports the construction of a simulation model used to support the decision-making concerned with the short-term planning of the necessary hospital beds to face the COVID-19 in Navarre, Spain.

Genc Y., Ozkok M. Simulation-based optimization of the sea trial on ships // Journal of ETA Maritime Science (JEMS), 2020; 8(4): 274-285.
The purpose of this study is to plan the tests performed in the sea trial by the means of computer programs and to suggest shorter completion period for the tests.

Gentner N., Kyek A., Yang Y., Susto G.A. Enhancing scalability of virtual metrology: a deep learning-based approach for domain adaptation // 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. 1898-1909.

Gerrits B., Mes M., Schuur P. Mixing it up: simulation of mixed traffic container terminals // 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. 1384-1395.

Ghaitaranpour A., Mohebbi M., Koocheki A., Ngadi M.O. (2020). An agent-based coupled heat and water transfer model for air frying of doughnut as a heterogeneous multiscale porous material // Innovative Food Science & Emerging Technologies, 102335.

Ghorpade T., Corlu C.G. Lective pick-up and delivery problem: a simheuristic 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. 1468-1479.
This paper considers a stochastic selective pick-up and delivery problem and proposes a simheuristic algorithm that integrates a GRASP metaheuristic with Monte Carlo simulation.

Glake D., Ritter N., Clemen T. Utilizing spatio-temporal data in multi-agent 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. 242-253.
In this paper, we present the concepts and semantics of data-driven simulations with vector and raster data and extend them by a time dimension that applies at run-time within the simulation execution or in conjunction with the definition of multi-agent simulations models.

Glynn P.W., Nakayama M.K., Tuffin B. Comparing regenerative-simulation-based estimators of the distribution of the hitting time to a rarely visited set // 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. 421-432.
We consider the estimation of the distribution of the hitting time to a rarely visited set of states for a regenerative process.

Gok Y.S., Tomasella M., Guimarans D., Ozturk C. Simheuristic approach for robust scheduling of airport turnaround teams // 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. 1336-1347.

Grabis J. Product life cycle perspective on ICT product supply chain resilience // 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. 1444-1455.
The paper elaborates a simulation model for analyzing relationships between the cost of treating the vulnerabilities and the supply chain configuration. We show that flexible supply chain configurations have the lowest cost and are the most resilient to vulnerabilities.

Greco A., Pluchino A., Caddemi S., Calio I., & Cannizzaro F. (2020). On profile reconstruction of Euler–Bernoulli beams by means of an energy based genetic algorithm // Engineering with Computers, 36(1), 239-250.

Greenwood A.G. A specification for effective simulation project 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. 2482-2492.
This paper proposes a specification for effectively defining and managing simulation projects.

Gros T.P., Grob J., Wolf V. Real-time decision making for a car manufacturing process using deep reinforcement learning // 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. 3032-3044.
We combine a simulation model of a central production part, the line buffer, with deep reinforcement learning algorithms, in particular with deep Q-Learning and Monte Carlo tree search.

Guerrin F. (2020). Agent-Based Modelling of a Simple Synthetic Rangeland Ecosystem // In Landscape Modelling and Decision Support (pp. 179-215). Springer, Cham.

Guizzi G., Vespoli S., Grassi A., Santillo L.C. Simulation-based performance assessment of a new job-shop dispatching rule for the semi-heterarchical Industry 4.0 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. 1664-1675.
This paper proposes a decentralized scheduling approach able to improve the performances of a Job-Shop production system, compliant to a semi-heterarchical Industry 4.0 architectures.

Gulied M., Al Nouss A., Khraisheh M., AlMomani F. (2020). Modeling and simulation of fertilizer drawn forward osmosis process using Aspen Plus-MATLAB model // Science of The Total Environment, 700, 134461.

Gumzej R., & Rakovska M. (2020). Simulation Modeling and Analysis for Sustainable Supply Chains // In Sustainable Logistics and Production in Industry 4.0 (pp. 145-160). Springer, Cham.

Gyulai D., Bergmann J., Lengyel A., Kadar B., Czirko D. Simulation-based digital twin of a complex shop-floor logistics system // 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. 1849-1860.
In this paper, a novel discrete-event simulation model is proposed for the detailed representation of a complex shop-floor logistics system, employing automated robotic vehicles (AGV). The simulation model is applied to test new AGV management policies, involving both vehicle capacity planning and dispatching decisions.

Haghpanah F., Ghobadi K., Schafer B.W. How to evacuate an emergency department during pandemics: a COVID-19 agent-based 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.
In this study, we developed an agent-based model to simulate the evacuation of the emergency department at the Johns Hopkins Hospital during the COVID-19 pandemic. The results show a larger nursing team can reduce the average and maximum probable evacuation times by 12 and 19 minutes, respectively.

Hajmohammad S., Shevchenko A. (2020). Mitigating sustainability risk in supplier populations: an agent-based simulation study // International Journal of Operations & Production Management.

Ham A., Park M.-J., Shin H.-J., Choi S.-Y., Fowler J.W. Integrated scheduling of jobs, reticles, machines, AMHS and ARHS in a 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. 1966-1973.
This paper studies simultaneous scheduling of production and material transfer in the semiconductor photolithography area.

Hasan M., Lu M., AbouRizk S., Neufeld J. Planning and scheduling drainage infrastructure maintenance operations under hard and soft constraints: 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. 2505-2516.

Hai Le, Xiaolin Hu. Extended model space specification for mobile agent-based systems to support automated discovery 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. 2233-2244.

Healy C., Pekins P.J., Atallah S., Congalton R.G. (2020). Using agent-based models to inform the dynamics of winter tick parasitism of moose // Ecological Complexity, 41, 100813.

Heger J., Voss T. Dynamically changing sequencing rules with reinforcement learning in a job shop system with stochastic influences // 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. 1608-1618.

Henriette A., Christian F., Jacob L. Rainer L., Germar S., Zettler B. Simulation-based evaluation of lot release policies in a power semiconductor facility – a 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. P. 1503-1514.
In this paper, we focus on a real-world pre-assembly facility in a high-volume and high-mix semiconductor wafer fab. We conduct an in-depth, deterministic discrete-event simulation in two stages, using real production data and demands.

Hjorth A., Head B., Brady C. & Wilensky U. (2020). LevelSpace – a NetLogo Extension for Multi-Level Agent-Based Modeling // Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-4.

Hughes M., Kelbaugh M., Campbell V., Reilly E., Agarwala S., Wilt M., etc. System integration with multiscale networks (SIMoN): a modular framework for resource management 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. 656-667.

Hutchins N.M., Biswas G., Maróti M., Ledeczi A., Grover S., Wolf R., ... & McElhaney K. (2020). C2STEM: a System for Synergistic Learning of Physics and Computational Thinking // Journal of Science Education and Technology, 29(1), 83-100.

Hwang I. (2020). An Agent-Based Model of Firm Size Distribution and Collaborative Innovation // Journal of Artificial Societies and Social Simulation, 23(1), 1-9.