{"title":"Modelling the Impact of Ambidextrous Learning on Team Performance Using Agent-Based Simulation","authors":"Yongxing Yan, B. Onggo","doi":"10.36819/sw23.009","DOIUrl":"https://doi.org/10.36819/sw23.009","url":null,"abstract":"","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114910822","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 : 2022-12-22DOI: 10.1101/2022.12.19.22283694
R. Cheng, B. Williams
The Skew-Logistic (SL) function has been proposed to model a real-life dynamic process which rises monotonically to peak followed by a monotonic falling back. It was originally introduced to model the first stage of the covid-19 pandemic when it first appeared with the purpose of forecasting the likely behaviour of covid. In its subsequent behaviour, as covid-19 rose and fell under the influence of different controls and with the onset variants, the prevalence and incidence of covid rose and fell repeatedly producing what we might call a Multi-Wave (MW) behaviour, the waves not necessarily the same size. The purpose of the paper is to show how the method of using the SL function for one wave can be easily modified to model the MW situation. To illustrate our extended method, we apply it to two examples. One is to covid -19, to model its most recent behaviour and examine how things might change. We also apply it to climate change, undoubtedly the most serious issue, as without ensuring the world becomes rapidly carbon will bring to and end the known world
{"title":"USES OF THE SKEW-LOGISTIC FUNCTION FOR MULTI-WAVE FUNCTIONS","authors":"R. Cheng, B. Williams","doi":"10.1101/2022.12.19.22283694","DOIUrl":"https://doi.org/10.1101/2022.12.19.22283694","url":null,"abstract":"The Skew-Logistic (SL) function has been proposed to model a real-life dynamic process which rises monotonically to peak followed by a monotonic falling back. It was originally introduced to model the first stage of the covid-19 pandemic when it first appeared with the purpose of forecasting the likely behaviour of covid. In its subsequent behaviour, as covid-19 rose and fell under the influence of different controls and with the onset variants, the prevalence and incidence of covid rose and fell repeatedly producing what we might call a Multi-Wave (MW) behaviour, the waves not necessarily the same size. The purpose of the paper is to show how the method of using the SL function for one wave can be easily modified to model the MW situation. To illustrate our extended method, we apply it to two examples. One is to covid -19, to model its most recent behaviour and examine how things might change. We also apply it to climate change, undoubtedly the most serious issue, as without ensuring the world becomes rapidly carbon will bring to and end the known world","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123899654","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}
Food systems are socio-ecological systems in which a variety of stakeholders interact through a wide range of activities such as production, packaging, selling and consumption of food (Ericksen, 2008). The objectives for food systems include long-term sustainability of food security and social and environmental outcomes (Ericksen, 2008). A prerequisite for long-term sustainability is the capacity of a system to maintain its functionality without compromising its ability to do so in the future. There is an increased awareness of the vulnerabilities of food systems to changes in the environment like those introduced by climate change (e.g. water scarcity, weather variability) Socio-ecological resilience is essentially understood as a system ability to maintain its functionality even when it is being affected by a disturbance (Folke Holling, While sustainability provides a framework for long-term planning, resilience focuses on adaptive mechanisms that will support a system ’ s functionality in the medium and long-term future. The emphasis on adaptive mechanisms to unpredictable changes has made resilience a compelling forward-looking approach to adaptation (Berkes and Jolly, 2001; Pizzo, 2015). While resilience is a characteristic of the system, resilience management is the active modification of a system with the explicit aim improve its capacity to absorb and adapt to change (Nettier et 2017; Fath Walker These capacities depend on the way the system has been organised and, therefore, resilience management is interested in understanding such organisation and identifying more effective ways for structuring the system. resilience
粮食系统是社会生态系统，其中各种利益相关者通过食品的生产、包装、销售和消费等广泛活动相互作用(Ericksen, 2008)。粮食系统的目标包括粮食安全和社会与环境成果的长期可持续性(Ericksen, 2008)。长期可持续性的一个先决条件是系统有能力维持其功能，而不损害其将来这样做的能力。人们越来越认识到粮食系统对环境变化的脆弱性，如气候变化(如缺水、天气变率)。社会生态恢复力基本上被理解为即使受到干扰也能维持其功能的系统能力(Folke Holling，可持续性为长期规划提供了框架;弹性侧重于在中期和长期未来支持系统功能的自适应机制。对不可预测变化的适应机制的强调，使复原力成为一种引人注目的前瞻性适应方法(Berkes和Jolly, 2001;华人,2015)。虽然弹性是系统的一个特征，但弹性管理是对系统的积极修改，其明确目标是提高其吸收和适应变化的能力(Nettier et 2017;这些能力取决于系统的组织方式，因此，弹性管理感兴趣的是理解这种组织方式，并确定构建系统的更有效方法。弹性
{"title":"Using Microworlds for Resilience Management of Food Systems","authors":"H. Leon, B. Kopainsky","doi":"10.36819/SW21.033","DOIUrl":"https://doi.org/10.36819/SW21.033","url":null,"abstract":"Food systems are socio-ecological systems in which a variety of stakeholders interact through a wide range of activities such as production, packaging, selling and consumption of food (Ericksen, 2008). The objectives for food systems include long-term sustainability of food security and social and environmental outcomes (Ericksen, 2008). A prerequisite for long-term sustainability is the capacity of a system to maintain its functionality without compromising its ability to do so in the future. There is an increased awareness of the vulnerabilities of food systems to changes in the environment like those introduced by climate change (e.g. water scarcity, weather variability) Socio-ecological resilience is essentially understood as a system ability to maintain its functionality even when it is being affected by a disturbance (Folke Holling, While sustainability provides a framework for long-term planning, resilience focuses on adaptive mechanisms that will support a system ’ s functionality in the medium and long-term future. The emphasis on adaptive mechanisms to unpredictable changes has made resilience a compelling forward-looking approach to adaptation (Berkes and Jolly, 2001; Pizzo, 2015). While resilience is a characteristic of the system, resilience management is the active modification of a system with the explicit aim improve its capacity to absorb and adapt to change (Nettier et 2017; Fath Walker These capacities depend on the way the system has been organised and, therefore, resilience management is interested in understanding such organisation and identifying more effective ways for structuring the system. resilience","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803547","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}
Drupad Parmar, Lucy E. Morgan, A. Titman, Richard Williams, S. Sanchez
In stochastic simulation the input models used to drive the simulation are often estimated by collecting data from the real-world system. This can be an expensive and time consuming process so it would therefore be useful to have some guidance on how much data to collect for each input model. Estimating the input models via data introduces a source of variance in the simulation response known as input uncertainty. In this paper we propose a two stage algorithm that guides the initial data collection procedure for a simulation experiment that has a fixed data collection budget, with the objective of minimising input uncertainty in the simulation response. Results show that the algorithm is able to allocate data in a close to optimal manner and compared to two alternative data collection approaches returns a reduced level of input uncertainty.
{"title":"A Two Stage Algorithm for Guiding Data Collection Towards Minimising Input Uncertainty","authors":"Drupad Parmar, Lucy E. Morgan, A. Titman, Richard Williams, S. Sanchez","doi":"10.36819/SW21.013","DOIUrl":"https://doi.org/10.36819/SW21.013","url":null,"abstract":"In stochastic simulation the input models used to drive the simulation are often estimated by collecting data from the real-world system. This can be an expensive and time consuming process so it would therefore be useful to have some guidance on how much data to collect for each input model. Estimating the input models via data introduces a source of variance in the simulation response known as input uncertainty. In this paper we propose a two stage algorithm that guides the initial data collection procedure for a simulation experiment that has a fixed data collection budget, with the objective of minimising input uncertainty in the simulation response. Results show that the algorithm is able to allocate data in a close to optimal manner and compared to two alternative data collection approaches returns a reduced level of input uncertainty.","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131218303","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}
{"title":"Celebrating 20 Years: The Story of the Simulation Workshop","authors":"S. Robinson, Simon J. E. Taylor","doi":"10.36819/sw20.001","DOIUrl":"https://doi.org/10.36819/sw20.001","url":null,"abstract":"","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121328764","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}
{"title":"Hybrid Simulation with SIMUL8 - Tutorial","authors":"F. Lindsay, Christopher Werner","doi":"10.36819/sw20.006","DOIUrl":"https://doi.org/10.36819/sw20.006","url":null,"abstract":"","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134309723","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}
This tutorial paper presents the basics of System Dynamics (SD) modelling, together with an introductory section on Systems Thinking, specifically influence (or causal loop) diagrams. The coverage of SD starts with how a stock-flow diagram is used to commence the conceptualisation process by the creation of a spinal flow(s). Auxiliary variables (contained in the information system) and model parameters are then deployed in equations which explain the various flow rates depicted in the spinal flows. By this means a full working model emerges with a web of information overlaid onto the spinal flows. The paper concludes with a fully worked (but simple) example in the domain of workforce modelling
{"title":"The Basic Principles of Systems Thinking and System Dynamics","authors":"B. Dangerfield","doi":"10.36819/sw20.008","DOIUrl":"https://doi.org/10.36819/sw20.008","url":null,"abstract":"This tutorial paper presents the basics of System Dynamics (SD) modelling, together with an introductory section on Systems Thinking, specifically influence (or causal loop) diagrams. The coverage of SD starts with how a stock-flow diagram is used to commence the conceptualisation process by the creation of a spinal flow(s). Auxiliary variables (contained in the information system) and model parameters are then deployed in equations which explain the various flow rates depicted in the spinal flows. By this means a full working model emerges with a web of information overlaid onto the spinal flows. The paper concludes with a fully worked (but simple) example in the domain of workforce modelling","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133431983","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}
{"title":"Dairy Supply Chain in West Java: Modelling Using Agent-Based Simulation and Reporting using the Stress Guidelines","authors":"S. Onggo, D. Utomo","doi":"10.36819/SW21.032","DOIUrl":"https://doi.org/10.36819/SW21.032","url":null,"abstract":"","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121545713","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}
{"title":"Agent-Based Models: A Tutorial","authors":"D. Robertson","doi":"10.36819/sw20.009","DOIUrl":"https://doi.org/10.36819/sw20.009","url":null,"abstract":"","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130639208","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}
{"title":"Some Formulation Issues in Constructing Metamodels","authors":"R. Barton","doi":"10.36819/sw20.018","DOIUrl":"https://doi.org/10.36819/sw20.018","url":null,"abstract":"","PeriodicalId":149666,"journal":{"name":"Proceedings of SW21 The OR Society Simulation Workshop","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116245197","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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