Using Discrete Event Simulation (DES) To Support Performance-Driven Healthcare Design.

The Veteran’s Health Administration (VHA) is the largest integrated health care system in the United States, forming the arm of the Department of Veterans Affairs (VA) that delivers medical services. From a troubled past, the VHA today is regarded as a model for healthcare transformation. The VA has evaluated and adopted a variety of cutting-edge approaches to foster greater efficiency and effectiveness in healthcare delivery as part of their systems redesign initiative. This paper discusses the integration of two health care analysis platforms: Discrete Event Simulation (DES), and Real Time Locating systems (RTLS) presenting examples of work done at the St. Louis VA Medical Center. Use of RTLS data for generation and validation of DES models is detailed, with prescriptive discussion of methodologies. The authors recommend the careful consideration of these relatively new approaches which show promise in assisting systems redesign initiatives across the health care spectrum.

Prospective Health Technology Assessment allows early decision making for innovative health care technologies. The main idea is to combine available domain knowledge with advanced simulation techniques in order to predict the effects of medical products and to find bottlenecks and weaknesses within the health system. In our recent publications a hybrid simulation approach with System Dynamics and Agent-Based Modeling has been presented. Hospital workflows have been modeled by state charts within agent behavioral models and have to be instantiated each time an agent is entering a hospital. This paper presents a mechanism to generate agents dynamically from SD models and extends the previously presented hybrid approach by process-oriented Discrete Event Simulation for hospital modeling. It connects processes to health care institutions and not to persons traversing them. Two extended example case studies show potentials for medical decision making using the three simulation paradigms in a common environment.

Supply chain challenges require not only effective management, but also a new innovative strategy to reduce costs and maximize its efficiency. Traditional problem-solving methodologies specific to the areas of supply chain management (SCM) find their limits when confronted with an inventive problem or a problem containing a contradiction. TRIZ (theory of inventive problem solving) is an effective theory for systematizing innovation and solving complex problems containing contradictions. Thus, the use of the theory TRIZ can be considered as a way to meet future challenges in SCM fields and get innovative solutions. This paper presents a method for solving supply chain problems and achieving a low-cost, based on complementarities between TRIZ and discrete event simulation and specific methods for solving supply chain problems. In the proposed model, a witness simulation model of the initial problem is developed to optimize the problem and find the system limits. Then, specific problem solving methods are applied to change the original description of problem and move towards a space in which a solution can be found. The discrete event simulation allows for experiments on the system to be created and analyzed. Thus, an experimental design was developed to establish the cause-and-effect relationships between the parameters of the system in order to formulate a generalized system of contradictions. And finally, ARIZ 85C, the most mature meta-methods of TRIZ, is used to address related contradictions for searching for an innovative solution, which must be subsequently implemented and evaluated in the discrete event simulation. The suitability of this new approach is finally proven through an industrial case study conducted in a company specialized in the manufacture of electronic devices for automobiles.

Successful merging or consolidation of interdependent healthcare clinics have been shown to have benefits with regards to decreasing operation costs while maintaining patients' quality of care. In order to achieve a successful merger or integration of clinics, an analysis of the effects of integrating patient flows should occur. This is especially important when the merger of clinics involves a transition into a new facility. We utilize a discrete event simulation model to study the effects of integrating three interdependent musculoskeletal clinics, Orthopedics, Rheumatology, and Radiology, into a new facility in advance of implementation. Through use of the simulation, unexpected bottlenecks in the check-in process are identified and the effects of implementing new patient flows, supported by Real-Time Location System (RTLS) technology, are analyzed.

Smart manufacturing needs to handle increased uncertainty by becoming more responsive and more flexible to reconfigure. Advances in technology within industry 4.0 can provide acquisition of large amounts of data, to support decision making in manufacturing. Those possibilities have brought anew attention to the applicability of discrete event simulation for production flow modelling when moving towards design of logistics systems 4.0. This paper reports a study investigating challenges and opportunities for initiation of discrete event simulation, as a tool for decision making in the era of industry 4.0 manufacturing. The research has been approached through action research in combination with a real case study at a manufacturing company in the energy sector. The Covid-19 pandemic fated that adjusted and new ways of communication, collaboration, and data collection, in relation to the methods, had to be explored and tried. Throughout the study, production data, such as processing times, have been collected and analyzed for discrete event simulation modelling. The complexity of introducing discrete event simulation as a new tool for decision making is highlighted, where we emphasize the human knowledge and involvement yet necessary to understand and to draw conclusions from the data. The results also demonstrate that the data analysis has given valuable insights into production characteristics, that need addressing. Thus, revealing opportunities for how the initiative of introducing discrete event simulation as an anew tool in the wake of industry 4.0, can act as a catalyst for improved decision making in future manufacturing.

ABSTRACTIn the UK, healthcare built environment design is guided by a series of long-established design standards and guidance issued by the Department of Health. More recently, healthcare design focus has broadened to encompass new approaches, supported by large bodies of credible research evidence. It is therefore timely to rethink how healthcare design standards and guidance should be best expressed to suit ‘designerly ways’ of using evidence, to improve their use and effectiveness in practice. This research explored how designers use performance and prescriptive approaches during the healthcare design process. Three in-depth healthcare built environment case studies were used to explore how designers employed such approaches during the design of selected exemplar design elements. Results show that design elements in the pre and conceptual design phases significantly employed performance-based approaches, and due to project-unique circumstances, prescriptive solutions were often significantly modified based on performance criteria. For design elements in the detailed and technical design phases, there was a significant use of solutions based on prescriptive approaches, whilst performance-based criteria were used to evaluate design solutions. This research proposes a performance-based, specification-driven healthcare design with supplementary prescriptive specifications provided for optimum healthcare environment design.

Discrete-event simulation (DES) is a powerful tool for modeling and analyzing complex systems where state changes occur at discrete points in time. This paper presents a practical and sustainable approach to implementing DES using free mathematical and programming software, making it accessible to a wider audience including educators, students, and practitioners. This study explores the use of open-source tools, such as Python and Octave, highlighting their capabilities in building and optimizing DES models without the need for expensive and unaffordable software. In the context of Industry 4.0 and smart manufacturing, the ability to simulate and optimize discrete processes with open tools contributes to the development of digital twins, the integration of cyberphysical systems, and data-driven decision-making. Through detailed case studies in industrial fields, including manufacturing, maintenance, and logistics, this study demonstrates the effectiveness of these tools in simulating real processes and promoting their sustainability. Case studies are also re-examined to emphasize their relevance to smart manufacturing, particularly in terms of predictive maintenance, process optimization, and operational flexibility. Several challenges were encountered during the research process, such as adapting DES methodologies to the limitations of general-purpose mathematical software, ensuring accurate time management and event scheduling in environments not specifically designed for simulation, and balancing model complexity with accessibility for nonexpert users. The integration of free software not only reduces costs but also promotes collaborative learning and innovation. Additionally, the paper discusses the best practices for model validation and experimentation, providing a comprehensive guide for those new to DES. By linking open-source DES tools to the objectives of Industry 4.0, we aim to reinforce the applicability of our approach to modern, connected industrial environments. By leveraging free mathematical and programming software, this approach aims to democratize the use of DES, fostering a deeper understanding and broader application of simulation techniques across diverse fields and various regions of the world.

One technique to measure system performance is using discrete event simulation (DES), which models organizational structures and behavior. DES also allows testing of resource configurations to assess their impact on performance. To evaluate their efficiency, data envelopment analysis (DEA) can be used. However, current DES software does not automate DEA for efficiency evaluation, requiring separate analysis of performance measures and resource efficiency. This complicates finding the most efficient resource configuration, especially in healthcare systems. To address this, this paper proposes a framework combining DES and DEA for simpler analysis of their inputs and outputs. The framework automates data transfer mechanisms between DES outputs and DEA inputs and implements an integrated computational approach to DEA. To validate the framework, a case study in an emergency department was conducted, where complex interconnected processes are common, and optimizing resource allocation is critical for patient care and system performance. The case study analyzed 35 resource configurations to identify the most efficient one. The results demonstrated the framework’s potential to simplify resource analysis, identify optimal configurations, and enhance decision-making, thereby improving the system’s operational efficiency. The framework provides a robust, portable, and scalable solution applicable across diverse industries for effectively optimizing system performance and resource allocation.

Comparison of Timed Automata with Discrete Event Simulation for Modeling Personalized Treatment Decisions: the Case of Metastatic Castration Resistant Prostate Cancer

This review systematically analyses and classifies research and review papers focusing on discrete event simulation applied to wood transport, and therefore illustrates the development of the research area from 1997 until 2017. Discrete event simulation allows complex supply chain models to be mapped in a straightforward manner to study supply chain dynamics, test alternative strategies, communicate findings and facilitate understanding of various stakeholders. The presented analyses confirm that discrete event simulation is well-suited for analyzing interconnected wood supply chain transportation issues on an operational and tactical level. Transport is the connective link between interrelated system components of the forest products industry. Therefore, a survey on transport logistics allows to analyze the significance of entire supply chain management considerations to improve the overall performance and not only one part in isolation. Thus far, research focuses mainly on biomass, unimodal truck transport and terminal operations. Common shortcomings identified include rough explanations of simulation models and sparse details provided about the verification and validation processes. Research gaps exist concerning simulations of entire, resilient and multimodal wood supply chains as well as supply and demand risks. Further studies should expand upon the few initial attempts to combine various simulation methods with optimization.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

157 Background: Growing clinics need innovative ways to maximize capacity to ensure efficiency, a core element of quality care. Providers at the Dana-Farber Cancer Institute (DFCI) are assigned specific exam rooms for each clinic day. Despite room utilization below 50%, providers reported feeling constrained, and patients waited an average of 15 minutes to room. Furthermore, in February 2014, DFCI consolidated two facilities requiring that clinics absorb an immediate 10% increase in patient volume using available capacity. We sought to redesign the exam room allocation process by leveraging RTLS. Methods: Using computer simulation, we compared three options for allocating exam rooms. The simulation indicated a “pooled” room approach that did not designate rooms to individual providers would minimize patient wait time. A team was assembled to pilot the intervention in one clinic. An algorithm was developed to prioritize patients based on appointment time, arrival time, and provider availability. We utilized RTLS to automate the algorithm and indicate available exam rooms to coordinate transport of patients from the waiting area to exam rooms. Patient wait time was extracted from RTLS and compared between the baseline (provider-designated rooms) period and the post-intervention (pooled, undesignated rooms) period. Providers were asked post-intervention to provide feedback using a survey. Results: There was a significant decrease in the average wait to exam room despite a simultaneous increase in average appointment volume during the post-intervention period. Cumulative wait time gains averaged 3.3 hours daily. In addition, providers preferred the pooled rooming method over the designated rooming method. Conclusions: Allowing providers to utilize any available room has allowed this clinic to efficiently accommodate a larger number of appointments per day with an overall decrease in patient wait time. RTLS provided the real-time information necessary to succeed and drive improvement initiatives. [Table: see text]

Quality Improvement With Discrete Event Simulation: A Primer for Radiologists

A general framework for dependability modelling coupling discrete-event and time-driven simulation

Construction companies are increasingly utilizing sensing technologies to automatically record different steps of the construction process in detail for effective monitoring and control. This generates a significant amount of event data that can be used to learn the underlying behavior of agents in a construction site using process mining. While process mining can be used to discover the real process and identify and analyze deviations and bottlenecks in operations, it is a backward-looking approach. On the other hand, discrete event simulation (DES) provides a means to forecast future performance from historical data to enable proactive decision-making by operation managers relating to their projects. However, this method is largely unused by the industry due to the specialized knowledge required to create the DES models. This paper thus proposes a framework that extends the utility of collecting event data and their process models, by transforming them into DES models for forecasting future performance. This framework also addresses another challenge of using DES relating to its inability to update itself as the project progresses. This challenge is addressed by using the Bayesian updating technique to continuously update the input parameters of the simulation model for the most up-to-date estimation based on data collected from the field. The proposed framework was validated on a real-world case study of an earthmoving operation. The results show that the process mining techniques could accurately discover the process model from the event data collected from the field. Furthermore, it was noted that continuous updating of DES model input parameters can provide accurate and reliable productivity estimates based on the actual data generated from the field. The proposed framework can help stakeholders to discover the underlying sequence of their operations, and enable timely, data-driven decisions regarding operations control.