Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9383888
Linjun Lu, F. Dai
Compaction is one of the most important phases in construction of asphalt concrete (AC) pavements, as it directly affects the density and thereby the performance of pavements. This paper proposed a thermal-based compaction technology for real-time roller path tracking and mapping in pavement compaction operations, based on which roller operators can better control their compaction quality. In the proposed method, the incremental change of a roller position in a short interval was decomposed into two motion components (i.e., the change in heading direction and the linear translation). The global position of the roller was then recovered by chaining the frame-by-frame motion in terms of their changes. Two sets of experimental data from different pavement construction sites were used to test the performance of the proposed technology. The results showed that the developed technology is a promising alternative to the current GPS-based intelligent compaction (IC) in pavement compaction operations.
{"title":"A Thermal-Based Technology for Roller Path Tracking and Mapping in Pavement Compaction Operations","authors":"Linjun Lu, F. Dai","doi":"10.1109/WSC48552.2020.9383888","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383888","url":null,"abstract":"Compaction is one of the most important phases in construction of asphalt concrete (AC) pavements, as it directly affects the density and thereby the performance of pavements. This paper proposed a thermal-based compaction technology for real-time roller path tracking and mapping in pavement compaction operations, based on which roller operators can better control their compaction quality. In the proposed method, the incremental change of a roller position in a short interval was decomposed into two motion components (i.e., the change in heading direction and the linear translation). The global position of the roller was then recovered by chaining the frame-by-frame motion in terms of their changes. Two sets of experimental data from different pavement construction sites were used to test the performance of the proposed technology. The results showed that the developed technology is a promising alternative to the current GPS-based intelligent compaction (IC) in pavement compaction operations.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"23 9 1","pages":"2460-2469"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82680283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9384088
J. Woulfe, M. Andersson
Current changes in DoD budgeting processes and in the constraints on available funding have resulted in inadequate support for our warfighter’s needs. The decision environment evolves into a key question impacting our warfighter capabilities: How should the funding be distributed to achieve the optimal balance between readiness, performance, and cost? This paper outlines the fundamentals of successful Product Life Cycle Management, a method to monitor systems towards fulfilling the operational needs at the lowest possible Total Ownership Cost (TOC). The paper discusses critical decision points in different phases of the system’s life cycle and suggests an approach to use modelling and simulation tools to answer key questions and provide the required decision support.
{"title":"Product Supportability Through Lifecyle Modeling and Simulation","authors":"J. Woulfe, M. Andersson","doi":"10.1109/WSC48552.2020.9384088","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384088","url":null,"abstract":"Current changes in DoD budgeting processes and in the constraints on available funding have resulted in inadequate support for our warfighter’s needs. The decision environment evolves into a key question impacting our warfighter capabilities: How should the funding be distributed to achieve the optimal balance between readiness, performance, and cost? This paper outlines the fundamentals of successful Product Life Cycle Management, a method to monitor systems towards fulfilling the operational needs at the lowest possible Total Ownership Cost (TOC). The paper discusses critical decision points in different phases of the system’s life cycle and suggests an approach to use modelling and simulation tools to answer key questions and provide the required decision support.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"11 1","pages":"2049-2059"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84311137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9384018
M. Arani, Xian Liu, S. Abdolmaleki
This study conducts a comparison between a newly proposed integrated inventory blood supply chain (BSC) and the current practice of the blood product distribution system. The importance of a well-designed blood supply chain is indisputable when it comes to human lives and the valuable blood products of the BSC. Our discrete event simulation approach and scenario discussion encompass a set of operational decisions to manage the complexity of the system. Applying the Arena simulation package, we model a carefully designed blood supply chain to provide a critical comparison of the two primary Key Performance Indicators shortage and outdated units of the BSC. We conclude that the proposed method would bring straightforward improvement with respect to the indicators under study.
{"title":"Scenario-Based Simulation Approach for An Integrated Inventory Blood Supply Chain System","authors":"M. Arani, Xian Liu, S. Abdolmaleki","doi":"10.1109/WSC48552.2020.9384018","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384018","url":null,"abstract":"This study conducts a comparison between a newly proposed integrated inventory blood supply chain (BSC) and the current practice of the blood product distribution system. The importance of a well-designed blood supply chain is indisputable when it comes to human lives and the valuable blood products of the BSC. Our discrete event simulation approach and scenario discussion encompass a set of operational decisions to manage the complexity of the system. Applying the Arena simulation package, we model a carefully designed blood supply chain to provide a critical comparison of the two primary Key Performance Indicators shortage and outdated units of the BSC. We conclude that the proposed method would bring straightforward improvement with respect to the indicators under study.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"46 1","pages":"1348-1359"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82359082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9383997
S. Lang, Fabian Behrendt, Nico Lanzerath, T. Reggelin, Marcel Müller
The following paper presents the application of Deep Q-Networks (DQN) for solving a flexible job shop problem with integrated process planning. DQN is a deep reinforcement learning algorithm, which aims to train an agent to perform a specific task. In particular, we train two DQN agents in connection with a discrete-event simulation model of the problem, where one agent is responsible for the selection of operation sequences, while the other allocates jobs to machines. We compare the performance of DQN with the GRASP metaheuristic. After less than one hour of training, DQN generates schedules providing a lower makespan and total tardiness as the GRASP algorithm. Our first investigations reveal that DQN seems to generalize the training data to other problem cases. Once trained, the prediction and evaluation of new production schedules requires less than 0.2 seconds.
{"title":"Integration of Deep Reinforcement Learning and Discrete-Event Simulation for Real-Time Scheduling of a Flexible Job Shop Production","authors":"S. Lang, Fabian Behrendt, Nico Lanzerath, T. Reggelin, Marcel Müller","doi":"10.1109/WSC48552.2020.9383997","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383997","url":null,"abstract":"The following paper presents the application of Deep Q-Networks (DQN) for solving a flexible job shop problem with integrated process planning. DQN is a deep reinforcement learning algorithm, which aims to train an agent to perform a specific task. In particular, we train two DQN agents in connection with a discrete-event simulation model of the problem, where one agent is responsible for the selection of operation sequences, while the other allocates jobs to machines. We compare the performance of DQN with the GRASP metaheuristic. After less than one hour of training, DQN generates schedules providing a lower makespan and total tardiness as the GRASP algorithm. Our first investigations reveal that DQN seems to generalize the training data to other problem cases. Once trained, the prediction and evaluation of new production schedules requires less than 0.2 seconds.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"142 1","pages":"3057-3068"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84599593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9384004
Xiaoquan Gao, N. Kong, P. Griffin
Opioid overdose rescue is very time-sensitive. Hence, drone-delivered naloxone has the potential to be a transformative innovation due to its easily deployable and flexible nature. We formulate a Markov Decision Process (MDP) model to dispatch the appropriate drone after an overdose request arrives and to relocate the drone to its next waiting location after having completed its current task. Since the underlying optimization problem is subject to the curse of dimensionality, we solve it using ad-hoc state aggregation and evaluate it through a simulation with higher granularity. Our simulation-based comparative study is based on emergency medical service data from the state of Indiana. We compare the optimal policy resulting from the scaled-down MDP model with a myopic policy as the baseline. We consider the impact of drone type and service area type on outcomes, which offers insights into the performance of the MDP suboptimal policy under various settings.
{"title":"Dynamic Optimization of Drone Dispatch for Substance Overdose Rescue","authors":"Xiaoquan Gao, N. Kong, P. Griffin","doi":"10.1109/WSC48552.2020.9384004","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384004","url":null,"abstract":"Opioid overdose rescue is very time-sensitive. Hence, drone-delivered naloxone has the potential to be a transformative innovation due to its easily deployable and flexible nature. We formulate a Markov Decision Process (MDP) model to dispatch the appropriate drone after an overdose request arrives and to relocate the drone to its next waiting location after having completed its current task. Since the underlying optimization problem is subject to the curse of dimensionality, we solve it using ad-hoc state aggregation and evaluate it through a simulation with higher granularity. Our simulation-based comparative study is based on emergency medical service data from the state of Indiana. We compare the optimal policy resulting from the scaled-down MDP model with a myopic policy as the baseline. We consider the impact of drone type and service area type on outcomes, which offers insights into the performance of the MDP suboptimal policy under various settings.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"6 1","pages":"830-841"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79799731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9384103
R. Pal, Ziyuan Huang, Xinlong Yin, Mingyan Liu, S. Lototsky, J. Crowcroft
IoT-driven smart cities are popular service-networked ecosystems, whose proper functioning is hugely based on digitally secure and reliable supply chain relationships. However, the naivety in the current security efforts by concerned parties to protect IoT devices, pose tough challenges to scalable and expanding cyber-risk management markets for IoT societies, post a systemic cyber-catastrophe. As firms increasingly turn to cyber-insurance for reliable risk management, and insurers turn to reinsurance for their own risk management, questions arise as to how modern-day cyber risks aggregate and accumulate, and whether reinsurance is a feasible model for reliable catastrophic risk management and transfer in smart cities. In this introductory effort, we analyze (a) whether traditional cyber-risk spreading is a sustainable risk management practice and (b) under what conditions, for the quite conservative scenario when proportions of i.i.d. catastrophic cyber-risks of a significant heavy-tailed nature are aggregated by a cyber-risk manager.
{"title":"Sustainable Catastrophic Cyber-Risk Management in IoT Societies","authors":"R. Pal, Ziyuan Huang, Xinlong Yin, Mingyan Liu, S. Lototsky, J. Crowcroft","doi":"10.1109/WSC48552.2020.9384103","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384103","url":null,"abstract":"IoT-driven smart cities are popular service-networked ecosystems, whose proper functioning is hugely based on digitally secure and reliable supply chain relationships. However, the naivety in the current security efforts by concerned parties to protect IoT devices, pose tough challenges to scalable and expanding cyber-risk management markets for IoT societies, post a systemic cyber-catastrophe. As firms increasingly turn to cyber-insurance for reliable risk management, and insurers turn to reinsurance for their own risk management, questions arise as to how modern-day cyber risks aggregate and accumulate, and whether reinsurance is a feasible model for reliable catastrophic risk management and transfer in smart cities. In this introductory effort, we analyze (a) whether traditional cyber-risk spreading is a sustainable risk management practice and (b) under what conditions, for the quite conservative scenario when proportions of i.i.d. catastrophic cyber-risks of a significant heavy-tailed nature are aggregated by a cyber-risk manager.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"78 1","pages":"3105-3116"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87077864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9384076
C. G. Corlu, J. Maleyeff, Jiaxun Wang, Kaming Yip, J. Farris
The increase in medical treatment complexity can cause experienced nurses to have difficulty determining priorities among patient needs. Electronic health record systems will enable automated decision support to assist medical professionals in making these determinations. 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. The severity of patient needs is dynamic because severity increases over time until the need is addressed. The simulation framework is applied to a cardiac care unit with 14 patients, who collectively have 125 needs. Four different priority schemes are evaluated and their effectiveness compared under the assumption of an 8 or 9 nurse capacity. The results illustrate the importance of modeling the dispatching of nurses according to severity because, although fewer nurses result in longer average queue times, they can handle higher-severity needs effectively.
{"title":"Real-Time Nurse Dispatching Using Dynamic Priority Decision Framework","authors":"C. G. Corlu, J. Maleyeff, Jiaxun Wang, Kaming Yip, J. Farris","doi":"10.1109/WSC48552.2020.9384076","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384076","url":null,"abstract":"The increase in medical treatment complexity can cause experienced nurses to have difficulty determining priorities among patient needs. Electronic health record systems will enable automated decision support to assist medical professionals in making these determinations. 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. The severity of patient needs is dynamic because severity increases over time until the need is addressed. The simulation framework is applied to a cardiac care unit with 14 patients, who collectively have 125 needs. Four different priority schemes are evaluated and their effectiveness compared under the assumption of an 8 or 9 nurse capacity. The results illustrate the importance of modeling the dispatching of nurses according to severity because, although fewer nurses result in longer average queue times, they can handle higher-severity needs effectively.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"129 1","pages":"782-793"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86380193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9384067
Benjamin Rolf, T. Reggelin, Abdulrahman Nahhas, M. Müller, S. Lang
The paper proposes a simulation-based hyperheuristics approach to generate schedules for a two-stage hybrid flow shop scheduling problem with sequence-dependent setup times. The scheduling problem is derived from a company that is assembling printed circuit boards. A genetic algorithm determines sequences of standard dispatching rules that are evaluated by a discrete-event simulation model minimizing a multi-criteria objective composed of makespan and total tardiness. To reduce the computation time of the algorithm a dispatching rule-based chromosome representation is used containing a sequence of dispatching rules and time intervals in which the rules are applied. Different experiment configurations and their impact on solution quality and computation time are analyzed. The optimization model generates efficient schedules for multiple real-world data sets.
{"title":"Scheduling Jobs in a Two-Stage Hybrid Flow Shop with a Simulation-Based Genetic Algorithm and Standard Dispatching Rules","authors":"Benjamin Rolf, T. Reggelin, Abdulrahman Nahhas, M. Müller, S. Lang","doi":"10.1109/WSC48552.2020.9384067","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384067","url":null,"abstract":"The paper proposes a simulation-based hyperheuristics approach to generate schedules for a two-stage hybrid flow shop scheduling problem with sequence-dependent setup times. The scheduling problem is derived from a company that is assembling printed circuit boards. A genetic algorithm determines sequences of standard dispatching rules that are evaluated by a discrete-event simulation model minimizing a multi-criteria objective composed of makespan and total tardiness. To reduce the computation time of the algorithm a dispatching rule-based chromosome representation is used containing a sequence of dispatching rules and time intervals in which the rules are applied. Different experiment configurations and their impact on solution quality and computation time are analyzed. The optimization model generates efficient schedules for multiple real-world data sets.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"27 1","pages":"1584-1595"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89931917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9383988
Barry B. Barrios, A. Juan, Javier Panadero, K. Altendorfer, Andreas J. Peirleitner, A. Estrada-Moreno
Material requirements planning (MRP) integrates the planning of production, scheduling, and inventory activities in a manufacturing process. Many approaches to MRP management focus either on the simulation of the system (without considering optimization aspects) or in its optimization (without considering stochastic aspects). 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. This total cost is given by: (i) the inventory cost; and (ii) a penalty cost generated by the occurrence of stock outs. To solve this stochastic optimization problem, a spreadsheet simulation model is proposed and a heuristic procedure is employed over it. A numerical example illustrates the main concepts of the proposed approach as well as its potential.
{"title":"On the use of Simheuristics to Optimize Safety-Stock Levels in Material Requirements Planning with Random Demands","authors":"Barry B. Barrios, A. Juan, Javier Panadero, K. Altendorfer, Andreas J. Peirleitner, A. Estrada-Moreno","doi":"10.1109/WSC48552.2020.9383988","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383988","url":null,"abstract":"Material requirements planning (MRP) integrates the planning of production, scheduling, and inventory activities in a manufacturing process. Many approaches to MRP management focus either on the simulation of the system (without considering optimization aspects) or in its optimization (without considering stochastic aspects). 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. This total cost is given by: (i) the inventory cost; and (ii) a penalty cost generated by the occurrence of stock outs. To solve this stochastic optimization problem, a spreadsheet simulation model is proposed and a heuristic procedure is employed over it. A numerical example illustrates the main concepts of the proposed approach as well as its potential.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"10 1","pages":"1539-1550"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86560862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9383939
D. Garcia-Vicuña, F. Mallor, L. Esparza
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. The simulation model focusses on estimating the health system’s transitory state. It reproduces the outbreak dynamics by using the Gompertz growth model and the patient flow through the hospital, including the possible admission in the Intensive Care Unit (ICU). The output estimates the number of the necessary ward and ICU beds to provide healthcare to all patients for the next days. The simulation model uses expert opinions at the first stages of the outbreak, but as more data are collected the necessary parameters are fitted by statistical analysis or combining both. Every day, the research team informed the regional logistic team in charge of planning the health resources. Based on these predictions the authorities plan the necessary resources.
{"title":"Planning Ward and Intensive Care Unit Beds for COVID-19 Patients Using a Discrete Event Simulation Model","authors":"D. Garcia-Vicuña, F. Mallor, L. Esparza","doi":"10.1109/WSC48552.2020.9383939","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383939","url":null,"abstract":"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. The simulation model focusses on estimating the health system’s transitory state. It reproduces the outbreak dynamics by using the Gompertz growth model and the patient flow through the hospital, including the possible admission in the Intensive Care Unit (ICU). The output estimates the number of the necessary ward and ICU beds to provide healthcare to all patients for the next days. The simulation model uses expert opinions at the first stages of the outbreak, but as more data are collected the necessary parameters are fitted by statistical analysis or combining both. Every day, the research team informed the regional logistic team in charge of planning the health resources. Based on these predictions the authorities plan the necessary resources.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"41 1","pages":"759-770"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80382226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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