Engaging Stakeholders To Extend The Lifecycle Of Hybrid Simulation Models

Developing a simulation model of a complex system requires a significant investment of time, expertise and expense. In order to realize the greatest return on such an investment, it is desirable to extend the lifecycle of the simulation model as much as possible. Existing studies typically end after the `first loop' of the lifecycle, with the computer model suitable for addressing the initial requirements of the stakeholders. We explore extending the modeling lifecycle to a `second loop' by introducing an existing hybrid simulation model to a new group of stakeholders and further developing it to capture new requirements. With the aid of an example application, we explain how the hybrid model facilitated stakeholder engagement by closely reflecting the real world and how the model lifecycle has been successfully extended to maximize the benefit to Eurostar International Limited

Hybrid simulation challenges and opportunities: a life-cycle approach

The last 10 years have witnessed a marked upsurge of attention on Hybrid Simulation (HS). The majority of authors define HS as a joint modelling approach which includes two or more simulation approaches (mainly Discrete Event Simulation, System Dynamics and Agent Based Simulation). Whilst some may argue that HS has been in existence for more than 5 decades, the recent rise tended to be more problem driven rather than technical experimentation. Winter Simulation Conference (WSC) 2015, 2016, 2017 have witnessed 3 panels on the purpose, history and definition of HS, respectively. This paper reports on a comprehensive review conducted by the panelists on HS and its applications. The aim of the paper is to move the debate forward by exploring potential platforms for developing concrete avenues for research on HS in conjunction with the modelling life cycle

Layout Evaluation by Simulation Protocol for Identifying Potential Inefficiencies Created by Medical Building Configuration

With the healthcare industry in a state of change, one focus is on efficiency in the healthcare environment. The trend for architects is a focus on an evidence-based design decision making process. In this context, simulation is gaining acceptance as a source of evidence. This research developed the Layout Evaluation by Simulation (LES) protocol to evaluate the design of a healthcare facility layout. The approach contains a Systems-of-Systems analysis for developing a healthcare delivery (HD) model, a computer model and simulation of an existing medical facility validated by existing data. Then simulations are run through the validated model inserting the future facility design to evaluate efficiency in a proposed new spatial layout. Through a real-world case study, the research contains an evaluation of the predictive capacity of the LES protocol. In the research, a completely Agent Based Modeling and Simulation, a completely Discrete Event Simulation, and a hybrid were investigated. As detail was added to all models, simulations were run creating a matrix of results for comparison to existing data. The LES protocol was confirmed to be effective. The results demonstrate that the healthcare delivery (HD) model provides a sufficient basis from which to develop the computer model and simulation. The LES protocol is a valuable tool for evaluating situations for emergent behavior. The research also confirmed the need for some degree of agent based modeling to detect emergent behavior

Purpose and benefits of hybrid simulation: Contributing to the convergence of its definition

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A Methodology for Internet of Things Business Modeling and Analysis using Agent-Based Simulation

Internet of Things (IoT) is a new vision of an integrated network covering physical objects that are able to collect and exchange data. It enables previously unconnected devices and objects to become connected using equipping devices with communication technology such as sensors and radio frequency identification tags (RFID). As technology progresses towards new paradigm such as IoT, there is a need for an approach to identify the significance of these projects. Conventional simulation modeling and data analysis approaches are not able to capture the system complexity or suffer from a lack of data needed that can help to build a prediction. Agent-based Simulation (ABM) proposes an efficient simulation scheme to capture the structure of this dimension and offer a potential solution. Two case studies were proposed in this research. The first one introduces a conceptual case study addressing the use of agent-based simulations to verify the effectiveness of the business model of IoT. The objective of the study is to assess the feasibility of such application, of the market in the city of Orlando (Florida, United States). The second case study seeks to use ABM to simulate the operational behavior of refrigeration units (7,420) in one of largest retail organizations in Saudi Arabia and assess the economic feasibility of IoT implementation by estimating the return on investment (ROI)

Building Resilience in a Major City Evacuation Plan Using Simulation Modeling

This study provides data on the optimal staff, materials, space, and time resources required to operate a regional hub reception center, a “short-term facility with the goal to process and transport displaced survivors (evacuees) to temporary or permanent shelters following a catastrophic incident,” (Regional Catastrophic Planning Team, 2012). The facility will process approximately 20,000 evacuees over its entire seven-day duration following a disaster to assist in community resilience. The study was performed using a model created using the computer simulation software, AnyLogic. The results of the study demonstrated that the goals set forth by the Illinois-Indiana-Wisconsin Regional Catastrophic Planning Team could be improved upon and that the largest contributing factor to optimizing the RHRC is finding the optimal number of total staff members to operate the facility

Including Generative Mechanisms in Project scheduling using Hybrid Simulation

Scheduling is central to the practice of project management and a topic of significant interest for the operations research and management science academic communities. However, a rigour-relevance gap has developed between the research and practice of scheduling that mirrors similar concerns current in management science. Closing this gap requires a more accommodative philosophy that can integrate both hard and soft factors in the construction of project schedules. This paper outlines one interpretation of how this can be achieved through the combination of discrete event simulation for schedule construction and system dynamics for variable resource productivity. An implementation was built in a readily available modelling environment and its scheduling capabilities tested. They compare well with published results for commercial project scheduling packages. The use of system dynamics in schedule construction allows for the inclusion of generative mechanisms, models that describe the process by which some observed phenomenon is produced. They are powerful tools for answering questions about why things happen the way they do, a type of question very relevant to practic

Toward a Theory of Multi-Method Modeling and Simulation Approach

The representation via simulation models can easily lead to simulation models too simple for their intended purpose, or with too much detail, making them hard to understand. This problem is related to limitations of the modeling and simulation methods. A multi-method Modeling and Simulation (M&S) approach has the potential for improved representation by taking advantage of methods\u27 strengths and mitigating their weaknesses. Despite a high appeal for using multiple M&S methods, several related problems should be addressed first. The current level of theoretical, methodological, and pragmatic knowledge related to a multi-method M&S approach is limited. It is problematic that there is no clearly identified purpose and definition of the multi-method M&S approach. Theoretical and methodological advances are vital to enhancing the application of a multi-method M&S approach to address a broader range of scientific inquiries, improve quality of research, and enable finding common ground between scientific domains. This dissertation explored theoretical principles and research guidelines of a multi-method M&S approach. The analyzed literature offered perspectives related to the purpose, terms, and research guidelines of a multi-method M&S approach. A pragmatic philosophical stance was used to provide the basis for the choice of terms and definitions relevant to a multi-method M&S approach were proposed. The degrees of falsifiability are adapted to the M&S domain, which allowed for developing complementarity principles as the theoretical basis of a multi-method M&S approach. Next, a blueprint of a multi-method M&S approach called method formats was derived, because transitions toward formats must seek justifications in order to increase research objectivity and transparency. A sample set of methods was explored in the context of a proposed sample set of criteria. None of the methods were evaluated with the maximum score for every criterion, which implied that if all those characteristics were required within a research context, then, none of the methods could provide the highest possible score without combining methods. Finally, a case study that included a multi-method simulation model was developed, providing a data layer for evaluation of complementarity principles. The case study contributed to the credibility of complementarity principles as a reason to use a multi-method M&S approach and value of pseudo-triangulation as a mean of verification of a selected approach

Digital twin-driven real-time planning, monitoring, and controlling in food supply chains

There needs to be more clarity about when and how the digital twin approach could benefit the food supply chains. In this study, we develop and solve an integrated problem of procurement, production, and distribution strategies (PPDs) in a medium-scale food processing company. Using the digital twin approach, the model considers the industrial symbiosis opportunities between the supplier, manufacturer, and customer using interval and sequence variables operating in a constrained environment using mixed-integer linear programming (MILP) and agent-based simulation (ABS) methodology. The study optimizes the make-span and lead time, simultaneously achieving a higher level of digitalization. The analysis demonstrates how digital twin accelerates supply chain productivity by improving makespan time, data redundancy (DR), optimal scheduling plan (OSP), overall operations effectiveness (OOE), overall equipment effectiveness (OEE), and capacity utilization. Our findings provide compelling evidence that the seamless integration PPDs enormously enhance production flexibility, resulting in an excellent service level of 94 %. Managers leverage real-time simulation to accurately estimate the replenishment point with minimal lead time, ensuring optimized operations. Furthermore, our results demonstrate that implementing PPDs has yielded considerable benefits. Specifically, we observed a remarkable 65 % utilization of the pasteurizer and aging vessel and an impressive 97 % utilization of the freezer. Moreover, by applying the DT model, the present model found a notable 6 % reduction in backlog, further streamlining operations and enhancing efficiency

A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic.

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. It is similar to influenza viruses and raises concerns through alarming levels of spread and severity resulting in an ongoing pandemic worldwide. Within eight months (by August 2020), it infected 24.0 million persons worldwide and over 824 thousand have died. Drones or Unmanned Aerial Vehicles (UAVs) are very helpful in handling the COVID-19 pandemic. This work investigates the drone-based systems, COVID-19 pandemic situations, and proposes an architecture for handling pandemic situations in different scenarios using real-time and simulation-based scenarios. The proposed architecture uses wearable sensors to record the observations in Body Area Networks (BANs) in a push-pull data fetching mechanism. The proposed architecture is found to be useful in remote and highly congested pandemic areas where either the wireless or Internet connectivity is a major issue or chances of COVID-19 spreading are high. It collects and stores the substantial amount of data in a stipulated period and helps to take appropriate action as and when required. In real-time drone-based healthcare system implementation for COVID-19 operations, it is observed that a large area can be covered for sanitization, thermal image collection, and patient identification within a short period (2 KMs within 10 min approx.) through aerial route. In the simulation, the same statistics are observed with an addition of collision-resistant strategies working successfully for indoor and outdoor healthcare operations. Further, open challenges are identified and promising research directions are highlighted