Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9384099
Bastian C. Schumacher, H. Kohl
In this paper, ways are shown how students can be familiarized with executing simulation studies for the design and improvement of new and existing material flow systems using flexible discrete-event simulation (DES) tools. The prototypical app "Production Simulation Application" is described. It combines learning-conducive components that are used to familiarize users with objects, the graphical model buildup, and the use of programming language. Game elements such as levels, badges, and points are shaped to motivate learners to interact frequently. They enable immediate feedback. A test shows that the app has been used repeatedly at short intervals beyond the course. A procedure for experience-based learning for conducting simulation studies is developed, in which a so-called learning factory enables learners to complete a simulation study. It is shown that the developments can contribute to the dissemination of DES and to increasing the planning quality in times of rising complexity of production systems.
{"title":"Learning Environment for Introduction in Discrete-Event Simulation for Design and Improvement of New and Existing Material Flow Systems","authors":"Bastian C. Schumacher, H. Kohl","doi":"10.1109/WSC48552.2020.9384099","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384099","url":null,"abstract":"In this paper, ways are shown how students can be familiarized with executing simulation studies for the design and improvement of new and existing material flow systems using flexible discrete-event simulation (DES) tools. The prototypical app \"Production Simulation Application\" is described. It combines learning-conducive components that are used to familiarize users with objects, the graphical model buildup, and the use of programming language. Game elements such as levels, badges, and points are shaped to motivate learners to interact frequently. They enable immediate feedback. A test shows that the app has been used repeatedly at short intervals beyond the course. A procedure for experience-based learning for conducting simulation studies is developed, in which a so-called learning factory enables learners to complete a simulation study. It is shown that the developments can contribute to the dissemination of DES and to increasing the planning quality in times of rising complexity of production systems.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"263 6-10","pages":"3224-3235"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91509037","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/WSC48552.2020.9383957
Zhanyang Zhang, Michael E. Kress, Tobias Schäfer
The focus of our study is to investigate the feasibility and effectiveness of using Lattice Boltzmann Advection Diffusion Equation (LBM-ADE) to model and simulate ocean oil spill transport at the surface level. We present some preliminary results from a prototype model and simulation in limited scale (a sub area of Gulf of Mexico) with assimilation of real ocean current data from the Unified Wave Interface-Coupled Model (UWIN-CM). We validate our model in a benchmark study against GNOME, a tool developed and used by NOAA for ocean oil spill forecast, under two scenarios: (i) a Gaussian hill concentration using a linear ocean current with the analytical solution as a reference; (ii) a Gaussian hill concentration using real ocean current from (UWIN-CM). Our benchmark results in both cases show the LBM-ADE model solutions are very close to the targeted analytical and GNOME solutions with the same initial oil spill and location.
{"title":"A Lattice Boltzmann Advection Diffusion Model for Ocean Oil Spill Surface Transport Prediction","authors":"Zhanyang Zhang, Michael E. Kress, Tobias Schäfer","doi":"10.1109/WSC48552.2020.9383957","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383957","url":null,"abstract":"The focus of our study is to investigate the feasibility and effectiveness of using Lattice Boltzmann Advection Diffusion Equation (LBM-ADE) to model and simulate ocean oil spill transport at the surface level. We present some preliminary results from a prototype model and simulation in limited scale (a sub area of Gulf of Mexico) with assimilation of real ocean current data from the Unified Wave Interface-Coupled Model (UWIN-CM). We validate our model in a benchmark study against GNOME, a tool developed and used by NOAA for ocean oil spill forecast, under two scenarios: (i) a Gaussian hill concentration using a linear ocean current with the analytical solution as a reference; (ii) a Gaussian hill concentration using real ocean current from (UWIN-CM). Our benchmark results in both cases show the LBM-ADE model solutions are very close to the targeted analytical and GNOME solutions with the same initial oil spill and location.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"30 1","pages":"680-691"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73886689","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/WSC48552.2020.9384053
R. D. Tordecilla, Javier Panadero, A. Juan, C. L. Quintero-Araújo, J. Montoya-Torres
Location routing is a well known problem in which decisions about facility location and vehicle routing must be made. Traditionally, a fixed size or capacity is assigned to an open facility as the input parameter to the problem. However, real-world cases show that decision-makers usually have a set of size options. If this size is selected accurately according to the demand of allocated customers, then location decisions and routing activities would raise smaller cost. Nevertheless, choosing this size implies additional variables that make an already NP-hard problem even more challenging. In addition, considering stochastic demands contributes to making the optimization problem more difficult to solve. Hence, a simheuristic algorithm is proposed in this work. It combines the efficiency of metaheuristics and the capabilities of simulation to deal with uncertainty. A series of computational experiments show that our approach can efficiently deal with medium-large instances.
{"title":"A Simheuristic Algorithm for the Location Routing Problem with Facility Sizing Decisions and Stochastic Demands","authors":"R. D. Tordecilla, Javier Panadero, A. Juan, C. L. Quintero-Araújo, J. Montoya-Torres","doi":"10.1109/WSC48552.2020.9384053","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384053","url":null,"abstract":"Location routing is a well known problem in which decisions about facility location and vehicle routing must be made. Traditionally, a fixed size or capacity is assigned to an open facility as the input parameter to the problem. However, real-world cases show that decision-makers usually have a set of size options. If this size is selected accurately according to the demand of allocated customers, then location decisions and routing activities would raise smaller cost. Nevertheless, choosing this size implies additional variables that make an already NP-hard problem even more challenging. In addition, considering stochastic demands contributes to making the optimization problem more difficult to solve. Hence, a simheuristic algorithm is proposed in this work. It combines the efficiency of metaheuristics and the capabilities of simulation to deal with uncertainty. A series of computational experiments show that our approach can efficiently deal with medium-large instances.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"245 1","pages":"1265-1275"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74120998","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/WSC48552.2020.9383925
Jaume Figueras i Jové, A. Guasch, Josep Casanovas-García
Wildfire simulation tools focus on how fire spreads in the natural environment. Simulation of fire containment operations can provide managers with a tool that combine wildfire evolution with suppression operations. Combined simulation tools are useful to evaluate different strategies and tactics in firefighting wildfires. This paper presents the modelling and simulation of aerial containment operations integrated into a wildfire spread simulator. A continuous space wildfire spread simulator has been used for fire spread simulation. This simulator called GisFIRE integrates aerial operations and uses QGIS geographical information system to integrate both simulation tools with geographical information. The information system combines air facilities locations, usable bodies of water, and other relevant geo-information with the simulation procedures. Open source software is a requirement to allow integration of different software packages and usage of OGC standards to represent geographical information.
{"title":"Simulation of Aerial Supression Tasks in Wildfire Events Integrated with Gisfire Simulator","authors":"Jaume Figueras i Jové, A. Guasch, Josep Casanovas-García","doi":"10.1109/WSC48552.2020.9383925","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383925","url":null,"abstract":"Wildfire simulation tools focus on how fire spreads in the natural environment. Simulation of fire containment operations can provide managers with a tool that combine wildfire evolution with suppression operations. Combined simulation tools are useful to evaluate different strategies and tactics in firefighting wildfires. This paper presents the modelling and simulation of aerial containment operations integrated into a wildfire spread simulator. A continuous space wildfire spread simulator has been used for fire spread simulation. This simulator called GisFIRE integrates aerial operations and uses QGIS geographical information system to integrate both simulation tools with geographical information. The information system combines air facilities locations, usable bodies of water, and other relevant geo-information with the simulation procedures. Open source software is a requirement to allow integration of different software packages and usage of OGC standards to represent geographical information.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"1 1","pages":"736-746"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74905904","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/WSC48552.2020.9384112
David King, David Jacques, Jeremy Gray, Katherine Cheney
The implementation and testing of autonomous and cooperative unmanned systems is challenging due to the inherent design complexity, infinite test spaces, and lack of autonomy specific measures. Simulation provides a low cost alternative to flight tests, allowing researchers to rapidly iterate on the design before fielding. To expedite this process, an Autonomous System Reference Architecture allows researchers to utilize existing software modules to rapidly develop algorithms for autonomous systems and test them in included simulation environments. In this paper, we implement ASRA on a cooperative wide area search scenario as a test bed to study ASRA’s utility for rapid prototyping and evaluation of autonomous and cooperative systems. Through a face centered cubic design of experiments, selected autonomy metrics are studied to provide a response surface model to characterize the system and provide a tool to optimize mission control parameters and maximize mission performance.
{"title":"Design and Simulation of a Wide Area Search Mission: An Implementation of an Autonomous Systems Reference Architecture","authors":"David King, David Jacques, Jeremy Gray, Katherine Cheney","doi":"10.1109/WSC48552.2020.9384112","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384112","url":null,"abstract":"The implementation and testing of autonomous and cooperative unmanned systems is challenging due to the inherent design complexity, infinite test spaces, and lack of autonomy specific measures. Simulation provides a low cost alternative to flight tests, allowing researchers to rapidly iterate on the design before fielding. To expedite this process, an Autonomous System Reference Architecture allows researchers to utilize existing software modules to rapidly develop algorithms for autonomous systems and test them in included simulation environments. In this paper, we implement ASRA on a cooperative wide area search scenario as a test bed to study ASRA’s utility for rapid prototyping and evaluation of autonomous and cooperative systems. Through a face centered cubic design of experiments, selected autonomy metrics are studied to provide a response surface model to characterize the system and provide a tool to optimize mission control parameters and maximize mission performance.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"129 1","pages":"540-551"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76560172","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/WSC48552.2020.9384079
K. P. White, R. G. Ingalls
Simulation is experimentation with a model. The behavior of the model imitates some salient aspect of the behavior of the system under study and the user experiments with the model to infer this behavior. This general framework has proven a powerful adjunct to learning, problem solving, design, and control. In this tutorial, we focus principally on discrete-event simulation – its underlying concepts, structure, and application.
{"title":"The Basics of Simulation","authors":"K. P. White, R. G. Ingalls","doi":"10.1109/WSC48552.2020.9384079","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384079","url":null,"abstract":"Simulation is experimentation with a model. The behavior of the model imitates some salient aspect of the behavior of the system under study and the user experiments with the model to infer this behavior. This general framework has proven a powerful adjunct to learning, problem solving, design, and control. In this tutorial, we focus principally on discrete-event simulation – its underlying concepts, structure, and application.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"27 1","pages":"1087-1101"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80102613","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/WSC48552.2020.9384039
Gongbo Zhang, Haidong Li, Yijie Peng
We study a ranking and selection problem with exponential sampling distributions. Under a Bayesian framework, we derive the posterior distribution of the performance parameter, and provide a normal approximation for the posterior distribution based on a central limit theorem to efficiently learn about the performance parameter. We formulate dynamic sampling decision as a stochastic control problem, and propose a sequential sampling procedure, which maximizes a value function approximation one-step ahead and is proved to be consistent. Numerical results demonstrate the efficiency of the proposed method.
{"title":"Sequential Sampling for a Ranking and Selection Problem with Exponential Sampling Distributions","authors":"Gongbo Zhang, Haidong Li, Yijie Peng","doi":"10.1109/WSC48552.2020.9384039","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384039","url":null,"abstract":"We study a ranking and selection problem with exponential sampling distributions. Under a Bayesian framework, we derive the posterior distribution of the performance parameter, and provide a normal approximation for the posterior distribution based on a central limit theorem to efficiently learn about the performance parameter. We formulate dynamic sampling decision as a stochastic control problem, and propose a sequential sampling procedure, which maximizes a value function approximation one-step ahead and is proved to be consistent. Numerical results demonstrate the efficiency of the proposed method.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"11 1","pages":"2984-2995"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81141614","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/WSC48552.2020.9383865
Xingyu Wang, C. Rhee
In this paper we address the problem of rare-event simulation for heavy-tailed Lévy processes with infinite activities. We propose a strongly efficient importance sampling algorithm that builds upon the sample path large deviations for heavy-tailed Lévy processes, stick-breaking approximation of extrema of Lévy processes, and the randomized debiasing Monte Carlo scheme. The proposed importance sampling algorithm can be applied to a broad class of Lévy processes and exhibits significant improvements in efficiency when compared to crude Monte-Carlo method in our numerical experiments.
{"title":"Rare-Event Simulation for Multiple Jump Events in Heavy-Tailed Lévy Processes with Infinite Activities","authors":"Xingyu Wang, C. Rhee","doi":"10.1109/WSC48552.2020.9383865","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383865","url":null,"abstract":"In this paper we address the problem of rare-event simulation for heavy-tailed Lévy processes with infinite activities. We propose a strongly efficient importance sampling algorithm that builds upon the sample path large deviations for heavy-tailed Lévy processes, stick-breaking approximation of extrema of Lévy processes, and the randomized debiasing Monte Carlo scheme. The proposed importance sampling algorithm can be applied to a broad class of Lévy processes and exhibits significant improvements in efficiency when compared to crude Monte-Carlo method in our numerical experiments.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"78 1","pages":"409-420"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82845539","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/WSC48552.2020.9384062
Anne Schwientek, Ann-Kathrin Lange, C. Jahn
Given the growth in ship size and increasing demands, it is essential for seaport container terminals to make well-founded tactical and operational decisions. Here, horizontal transport is an important element of container terminals, connecting seaside handling with container storage. An efficient design of horizontal transport, especially the assignment of vehicles to orders, strongly influences the terminal performance. Most existing scientific studies vary only individual parameters or dispatching strategies neglecting different terminal targets. This study approaches this research gap. In a discrete-event simulation model, the influence of various terminal parameters on dispatching strategies is examined, taking into account the terminal targets. The two most influencing terminal parameters, terminal size and yard block assignment to containers, are analyzed in detail. The results show that the best choice of yard block assignment and dispatching method for a given terminal size depends on the combination of both parameters and the aspired targets.
{"title":"Effects of Terminal Size, Yard Block Assignment, and Dispatching Methods on Container Terminal Performance","authors":"Anne Schwientek, Ann-Kathrin Lange, C. Jahn","doi":"10.1109/WSC48552.2020.9384062","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384062","url":null,"abstract":"Given the growth in ship size and increasing demands, it is essential for seaport container terminals to make well-founded tactical and operational decisions. Here, horizontal transport is an important element of container terminals, connecting seaside handling with container storage. An efficient design of horizontal transport, especially the assignment of vehicles to orders, strongly influences the terminal performance. Most existing scientific studies vary only individual parameters or dispatching strategies neglecting different terminal targets. This study approaches this research gap. In a discrete-event simulation model, the influence of various terminal parameters on dispatching strategies is examined, taking into account the terminal targets. The two most influencing terminal parameters, terminal size and yard block assignment to containers, are analyzed in detail. The results show that the best choice of yard block assignment and dispatching method for a given terminal size depends on the combination of both parameters and the aspired targets.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"177 1","pages":"1408-1419"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79880053","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/WSC48552.2020.9384089
N. Feldkamp, S. Bergmann, S. Strassburger
Modular production systems aim to supersede the traditional line production in the automobile industry. The idea here is that highly customized products can move dynamically and autonomously through a system of flexible workstations without fixed production cycles. This approach has challenging demands regarding planning and organization of such systems. Since each product can define its way through the system freely and individually, implementing rules and heuristics that leverage the flexibility in the system in order to increase performance can be difficult in this dynamic environment. Transport tasks are usually carried out by automated guided vehicles (AGVs). Therefore, integration of AI-based control logics offer a promising alternative to manually implemented decision rules for operating the AGVs. This paper presents an approach for using reinforcement learning (RL) in combination with simulation in order to control AGVs in modular production systems. We present a case study and compare our approach to heuristic rules.
{"title":"Simulation-Based Deep Reinforcement Learning For Modular Production Systems","authors":"N. Feldkamp, S. Bergmann, S. Strassburger","doi":"10.1109/WSC48552.2020.9384089","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384089","url":null,"abstract":"Modular production systems aim to supersede the traditional line production in the automobile industry. The idea here is that highly customized products can move dynamically and autonomously through a system of flexible workstations without fixed production cycles. This approach has challenging demands regarding planning and organization of such systems. Since each product can define its way through the system freely and individually, implementing rules and heuristics that leverage the flexibility in the system in order to increase performance can be difficult in this dynamic environment. Transport tasks are usually carried out by automated guided vehicles (AGVs). Therefore, integration of AI-based control logics offer a promising alternative to manually implemented decision rules for operating the AGVs. This paper presents an approach for using reinforcement learning (RL) in combination with simulation in order to control AGVs in modular production systems. We present a case study and compare our approach to heuristic rules.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"10 1","pages":"1596-1607"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81867415","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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