2020 (A...O)



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.

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.

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.

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).

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.

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.

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.

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 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.

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., 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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., 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.

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 predatorprey ecosystem // Future Generation Computer Systems, 102, 650-669.

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.

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.

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.

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 (FourthSeventh 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.

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.

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 431440. 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).

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.

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.

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.

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 tools 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.

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.

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 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.

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.

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 Pythons ecosystem supporting scientific computing.

Liu T., Zhou E. Simulation optimization by reusing past replications: dont 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.

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.

Mao C., Yu X., Zhou Q., Harms R., Fang G. (2020). Knowledge growth in university-industry innovation networksResults 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.

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): 13521360. 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).

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.

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.

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.

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.

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.

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.

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.

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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