Scheduling Simulation Research Articles

Real-Time Fixed Priority Scheduling Synthesis using Affine DataFlow Graphs: from Theory to Practice

The major drawback of using static schedules to execute dataflow applications is their high inflexibility. In real-time systems, periodic schedules make it easier to assert safety guarantees and to decrease the schedule size, but their characteristics remain hard to compute. This article presents an approach to automatically generate fixed priority schedules from a dataflow specification. To do so, precedence dependencies between actors in the dataflow graphs are abstracted, as well as the task periods, by using affine relations . This abstraction allows us to synthesize schedules efficiently considering two main objectives: the maximization of throughput and the minimization of buffer sizes. Given a dataflow graph to execute in a real-time environment, we transform it into an Affine DataFlow Graph (ADFG) and compute the task priorities, their mapping, the number of delays in the buffers, and the buffer sizes. This article is the first to present an overview of both theoretical and practical aspects of ADFG. On the theoretical side, it presents corrections and improvements on the fixed priority case. On the practical side, benchmark evaluations demonstrate the robustness and maturity of the approach that our scheduling synthesizer implements. Synthesized schedules are evaluated by using scheduling simulation and real-time implementation. Last but not least, the synthesized periods reach the optimal throughput if enough processors are available, and most of the time the periods reach the maximal processor utilization factor in the uni-processor case. Moreover, execution time of the synthesis is about only one second for the main proposed algorithms.

Radiation in Combination with Immune Checkpoint Blockade and DNA Damage Response Inhibitors in Mice: Dosage Optimization in MC38 Syngeneic Tumors via Modelling and Simulation.

Clinical trials assessing the impact of radiotherapy (RT) in combination with DNA damage response pathway inhibitors (DDRis) and/or immune checkpoint blockade are currently ongoing. However, current methods for optimizing dosage and schedule are limited. A mathematical model was developed to capture the impacts of RT in combination with DDRi and/or anti-PD-L1 [immune checkpoint inhibitor (ICI)] on tumor immune interactions in the MC38 syngeneic tumor model. The model was fitted to datasets that assessed the impact of RT in combination with the DNA protein kinase inhibitor (DNAPKi) AZD7648. The model was further fitted to datasets from studies that were used to assess both RT/ICI combinations as well as RT/ICI combinations followed by concurrent administration of the poly ADP ribose polymerase inhibitor (PARPi) olaparib. Nonlinear mixed-effects modeling was performed followed by internal validation with visual predictive checks (VPC). Simulations of alternative dosage regimens and scheduling were performed to identify optimal candidate dosage regimens of RT/DNAPKi and RT/PARPi/ICI. Model fits and VPCs confirmed a successful internal validation for both datasets and demonstrated very small differences in the median, lower, and upper percentile values of tumor diameters between RT/ICI and RT/PARPi/ICI, which indicated that the triple combination of RT/PARPi/ICI at the given dosage and schedule does not provide additional benefit compared with ICI in combination with RT. Simulation of alternative dosage regimens indicated that lowering the dosage of ICI to between 2 and 4 mg/kg could induce similar benefits to the full dosage regimen, which could be of translational benefit. SIGNIFICANCE STATEMENT: This work provides a mixed-effects model framework to quantify the effects of combination radiotherapy/DNA damage response pathway inhibitors/immune checkpoint inhibitors in preclinical tumor models and identify optimal dosage regimens, which could be of translational benefit.

Provision of reactive power services by energy communities in MV distribution networks

The paper presents a procedure for the optimal operation of a community of prosumers connected to a medium voltage distribution network equipped with generation and storage units that considers the penalization for low power factor operation, the exploitation of direct exchanges of both active and reactive power between the prosumers and the provision of reactive power services by the community to the local distribution system operator and the transmission system operator. The proposed procedure calculates the maximum and minimum reactive power deviations that each community participant can provide with respect to the reference profile. A deterministic day-ahead scheduling problem is considered assuming the forecast of load and photovoltaic production known without uncertainties. The formulation of the optimization problems and the solution computational requirements are suitable for the inclusion in a stochastic approach. The effectiveness of the approach is supported by numerical simulations of the daily scheduling for different test cases.

V05-11 DEVELOPMENT OF PERFUSED ROBOT-ASSISTED PARTIAL NEPHRECTOMY PROCEDURAL SIMULATION PLATFORM FOR SINGLE PORT, RETROPERITONEAL APPROACHES

You have accessJournal of UrologyCME1 Apr 2023V05-11 DEVELOPMENT OF PERFUSED ROBOT-ASSISTED PARTIAL NEPHRECTOMY PROCEDURAL SIMULATION PLATFORM FOR SINGLE PORT, RETROPERITONEAL APPROACHES Nathan Schuler, Lauren Shepard, Craig Rogers, Riccardo Autorino, Sammy Elsamara, Simone Crivellaro, Jean Joseph, and Ahmed Ghazi Nathan SchulerNathan Schuler More articles by this author , Lauren ShepardLauren Shepard More articles by this author , Craig RogersCraig Rogers More articles by this author , Riccardo AutorinoRiccardo Autorino More articles by this author , Sammy ElsamaraSammy Elsamara More articles by this author , Simone CrivellaroSimone Crivellaro More articles by this author , Jean JosephJean Joseph More articles by this author , and Ahmed GhaziAhmed Ghazi More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003263.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Retroperitoneal robot assisted partial nephrectomy (RAPN) has shown restricted diffusion, limited to experienced surgeons at select institutions]. However, the recent introduction of the da Vinci single port (SP) surgical platform for RAPN with its slender profile is ideal for navigating narrow spaces. We have previously developed and validated a realistic, perfused hydrogel platform for transperitoneal RAPN robotic surgery as an adequate training tool. Our objective was to develop a similar platform using the same methodology. METHODS: A contoured box was designed to replicate the intraabdominal space surrounding the kidney, emphasizing realistic responses to initial incisions, balloon dilatation, and robot docking, then subsequently 3D printed. Patient-specific renal tissue phantoms and supporting structures were created using previously validated methods. Previous work with tensile testing on PVA hydrogels validated their ability to mimic the physical properties of renal tissue. The phantom was wrapped in a simulated Gerota’s Fascia, attached to a PVA “muscle” sheet, and connected to a hydrogel AA and IVC. The completed model was fixed to the inner contour of the 3D printed body cavity section, which was then covered with a hydrogel abdominal wall with a simulated peritoneal pouch, containing simulated bowels. Experts at the University of Rochester, Cleveland clinic and Henry ford hospitals tested the model during scheduled simulation events. 6 experts with >50 case experience in Single port robotic surgery and >1000 multiport robotic cases completed a survey to evaluate the realism and educational effectiveness of this model. RESULTS: Respondents completed surveys on the novelty and effectiveness of the platform. Agreement on simulator novelty was unanimously positive (100% agree or better) (Figure 1a). Performance evaluations reached a minimum of 80% agreement for all categories, with 100% of feedback rated as neutral or better. (Figure 1b). CONCLUSIONS: This realistic simulation procedural platform for robot assisted single port retroperitoneal renal surgery provides a suitable training medium for training and experienced urologists to improve the skills required to adopt this approach. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e427 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nathan Schuler More articles by this author Lauren Shepard More articles by this author Craig Rogers More articles by this author Riccardo Autorino More articles by this author Sammy Elsamara More articles by this author Simone Crivellaro More articles by this author Jean Joseph More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF downloadLoading ...

Interpolation of scheduled simulation results for real-time auralization

The approach of auralization allows rendering plausible audio signals based on purely synthesized data. One key aspect for this is the simulation of sound propagation. Using sophisticated physics-based models, even complex situations—like sound propagation through an inhomogeneous, moving atmosphere—can be considered. However, even with fast approaches based on geometrical acoustics such simulations might not be real-time capable when being integrated in the auralization processing chain. This is because the update rate of the simulations is significantly lower than the audio block rate which can lead to artifacts, especially when considering effects like the Doppler shift. In this contribution, we propose a method aiming to run an artifact-free auralization despite a relatively low and potentially irregular simulation update rate. For this purpose, the simulations are run in a thread separate from the main processing chain and the respective outputs are interpolated. The method is applied to an aircraft flyover scenario considering curved sound propagation in a stratified, moving atmosphere.

Agent-based modelling and simulation for life-cycle airport flight planning and scheduling

ABSTRACT The airport flight planning and scheduling (AFPS) process involves many sub-systems in airport management, including flight arrival prediction, flight landing prioritisation, flight route scheduling, flight departure prioritisation and airport big-data planning. Existing literature has mainly focused on advancing the efficiency of specific sub-system(s) of AFPS. Therefore, there is a call to investigate the AFPS comprehensively and improve its performance systematically, instead of solely enhancing partial sub-system(s) in an airport. This paper proposes a life cycle analysis (LCA) framework to describe the “life cycle” that how an aircraft interacts with the AFPS system of an airport. The agent-based modelling and simulation technique is used to simulate the AFPS system under the LCA framework by treating each aircraft as a passive agent following the orders from the AFPS system. We demonstrate that the naive first-in-first-out principle can be modified to some simple prioritising rules to significantly reduce the overall delay (including importance-weighted delay) and processing time of flights, as well as to increase the number of departed aircraft. Hence, the produced agent-based model allows us to improve AFPS performance by optimising multiple sub-systems simultaneously.

Water Budget of Urban Turf Field and Optimal Irrigation Schedule Simulation in an Ecotone between Semi-Humid and Semi-Arid Regions, Northern China

Water security in the ecotone between semi-humid and semi-arid regions (EHA) is very vulnerable and sensitive to climate change and human interferences. Urban turf irrigation is a primary consumer of urban water resources in the EHA, which places huge pressures on water security by substantial irrigated water use due to the expansion of urban turf planting. Based on a 2-year (2020–2021) turf experiment in Zhangjiakou City, a typical water-deficit city in the EHA of northern China, the water budget for turf was measured and analyzed. Furthermore, the Root Zone Water Quality Model (RZWQM2) was employed to evaluate the optimal irrigation scheme for turf. The results showed that the average volumetric water content in the 0–40 cm soil layer was maintained above 23% in 2020–2021. The evapotranspiration in growth period of turf accounted for more than 70% of the annual evapotranspiration, and the deep seepage in turf soil accounted for 49.67% and 60.28% of the total precipitation and irrigation in 2020 and 2021, respectively, during the vigorous growth period of the turf from May to September. The calibrated RZWQM2 showed a robust ability to simulate the water changes in turf. The d-values (consistency index) between the simulated and observed volumetric water contents and evapotranspiration were both greater than 0.90. In the aspects of irrigation scenarios, the T60%-12 scenario (TA-B, where A is 100%, 80%, 60% or 40% of the total irrigation amount and B is the number of irrigation events corresponding to A) was determined as the best irrigation schedule in our study area because of lower evapotranspiration, seepage and higher turf soil water storage under this irrigation scenario, also resulting from the comparison of different irrigation scenarios using the entropy-weight-TOPSIS method. In such an optimal scenario, T60%-12 irrigation treatment reduced the irrigated water requirement of turf by 40% (142.06 mm) and the seepage amount by 28.07% (39.05 mm), and had the lowest negative impacts on the turf growth.

Multi-Task Multi-Agent Reinforcement Learning for Real-Time Scheduling of a Dual-Resource Flexible Job Shop with Robots

In this paper, a real-time scheduling problem of a dual-resource flexible job shop with robots is studied. Multiple independent robots and their supervised machine sets form their own work cells. First, a mixed integer programming model is established, which considers the scheduling problems of jobs and machines in the work cells, and of jobs between work cells, based on the process plan flexibility. Second, in order to make real-time scheduling decisions, a framework of multi-task multi-agent reinforcement learning based on centralized training and decentralized execution is proposed. Each agent interacts with the environment and completes three decision-making tasks: job sequencing, machine selection, and process planning. In the process of centralized training, the value network is used to evaluate and optimize the policy network to achieve multi-agent cooperation, and the attention mechanism is introduced into the policy network to realize information sharing among multiple tasks. In the process of decentralized execution, each agent performs multiple task decisions through local observations according to the trained policy network. Then, observation, action, and reward are designed. Rewards include global and local rewards, which are decomposed into sub-rewards corresponding to tasks. The reinforcement learning training algorithm is designed based on a double-deep Q-network. Finally, the scheduling simulation environment is derived from benchmarks, and the experimental results show the effectiveness of the proposed method.

Flexibility and Consistency in Long-Term Care Rostering

Problem definition: We consider the rostering decisions—that is, the assignment of workers scheduled for a shift to units—of a long-term care facility. The facility’s objective is to minimize the monthly inconsistency level, a widely promoted quality metric representing the number of different caregivers working in each unit over one month. Methodology/results: We introduce simple rostering heuristics that prioritize either part-time or full-time workers and present a stochastic model of the repeated rostering problem to compare the performance of different strategies analytically. Our analysis shows that in order to minimize the inconsistency level, part-time workers should receive higher priority than full-time workers for assignment to their home units. We also establish an analytical upper bound for a threshold on the time horizon above which a policy giving assignment priority to part-time workers is guaranteed to outperform one giving priority to full-time workers. Using data from more than 15,000 shifts worked by nursing assistants at three nursing homes, we compare the actual rosters to the hindsight optimal consistency-maximizing schedules, demonstrating a significant opportunity for improvement. We then compare the performance of our rostering heuristics via trace-based simulation of the historical schedules. This reinforces the superiority of prioritizing part-time workers, yielding reductions in the inconsistency level between 20% and 30% compared with the historical rosters. Managerial implications: Contrary to popular guidance, our results show that managers should focus on part-time workers and assign them as consistently as possible. Even if some full-time workers are always assigned to their home units (because of preferences or work rules), assignment flexibility among the remaining full-time workers still enables significant improvements in the consistency of care. This flexibility among full-time workers helps because their higher work frequency tends to make a reassignment away from their home unit contribute less to inconsistency, because they are able to work multiple shifts in these nonhome units. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1174 .

An integrated framework for improving the efficiency and safety of hydraulic tunnel construction

Drilling and blasting is the most flexible tunnel construction method considering the excavation of tunnel size and shape. Schedule and safety are two critical issues for project stakeholders during construction. To evaluate construction quality, various tools such as Building Information Modelling (BIM), schedule simulation, and numerical analysis are commonly employed. However, in current practices, these processes are often independent of each other, requiring data conversion and operation between multiple platforms. In this paper, we present a novel integrated framework for improving the safety and efficiency of hydraulic tunnel construction. To this end, a geometric tunnel model with time attribute is created, which connects the schedule information obtained by construction simulation via secondary development. Then a numerical simulation method coupled with construction schedule is introduced to the integrated framework, which can automatically process the numerical model and perform analysis and calculation combined with the schedule information. In addition, this paper proposes a parameterization method of the rock bolt numerical model to realize the evaluation of different support schemes. The proposed method can avoid tedious manual operations to reduce possible errors. Finally, we propose a multistep prediction model of surrounding rock deformation based on Long Short-Term Memory (LSTM). The framework integrates the above functions via secondary development, scripting, and other technologies, and demonstrates the reliability of the framework with a tunnel project. The proposed integrated framework provides an intuitive and efficient tunnel construction management method hence promoting the information flow of multiple participants and multiple professionals in tunnel construction and providing decision support.

Towards QoS-Based Embedded Machine Learning

Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the hardware, software and system levels. To this end, we present a novel quality-of-service based resource allocation scheme that uses feedback control to adjust compute resources dynamically to cope with the varying and unpredictable workloads of ML applications while still maintaining an acceptable level of service to the user. To evaluate the feasibility of our approach we implemented a feedback control scheduling simulator that was used to analyze our framework under various simulated workloads. We also implemented our framework as a Linux kernel module running on a virtual machine as well as a Raspberry Pi 4 single board computer. Results illustrate that our approach was able to maintain a sufficient level of service without overloading the processor as well as providing an energy savings of almost 20% as compared to the native resource management in Linux.

Optimal operation of regional integrated energy system based on multi-agent deep deterministic policy gradient algorithm

The complex energy coupling and uncertainties of renewable energy and load make the dynamic scheduling of the integrated energy system (IES) very difficult. Therefore, an optimal operation method based on the multi-agent deep deterministic policy gradient algorithm (MADDPG) is proposed. Firstly, the IES model is established, and the optimal scheduling problem is transformed into the Markov decision problem; Then, the action-space, state-space and reward function of agents are designed which control energy conversion equipment and energy storage equipment respectively. Furtherly, a multi-agent framework is established based on the MADDPG; Finally, a scheduling simulation example was carried out. The simulation results indicate that compared with the single agent, the multi-agent framework can improve the stability of training for agents and the ability to explore the optimal solution.

A systematic approach to parameter optimization and its application to flight schedule simulation software

Industrial software often has many parameters that critically impact performance. Frequently, these are left in a sub-optimal configuration for a given application because searching over possible configurations is costly and, except for developer instinct, the relationships between parameters and performance are often unclear and complex. While there have been significant advances in automated parameter tuning approaches recently, they are typically black-box. The high-quality solutions produced are returned to the user without explanation. The nature of optimisation means that, often, these solutions are far outside the well-established settings for the software, making it difficult to accept and use them. To address the above issue, a systematic approach to software parameter optimization is presented. Several well-established techniques are followed in sequence, each underpinning the next, with rigorous analysis of the search space. This allows the results to be explainable to both end users and developers, improving confidence in the optimal solutions, particularly where they are counter-intuitive. The process comprises statistical analysis of the parameters; single-objective optimization for each target objective; functional ANOVA to explain trends and inter-parameter interactions; and a multi-objective optimization seeded with the results from the single-objective stage. A case study demonstrates application to business-critical software developed by the international airline Air France-KLM for measuring flight schedule robustness. A configuration is found with a run-time of 80% that of the tried-and-tested configuration, with no loss in predictive accuracy. The configuration is supplemented with detailed analysis explaining the importance of each parameter, how they interact with each other, how they influence run-time and accuracy, and how the final configuration was reached. In particular, this explains why the configuration included some parameter settings that were outwith the usually recommended range, greatly increasing developer confidence and encouraging adoption of the new configuration.

Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems

Due to poor predictability of resources and difficulty in perception of task execution status, traditional Automatic Guide Vehicle (AGV) scheduling systems need a lot of extra time in the charging process. To solve this problem, a digital twin-based dynamic AGV scheduling (DTDAS) method is proposed, including four functions, namely the knowledge support system, the scheduling model, the scheduling optimization, and the scheduling simulation. With the features of virtual reality data interaction, symbiosis, and fusion from the digital twin technology, the proposed DTDAS method can solve the AGV charging problem in the AGV scheduling system, effectively improving the operating efficiency of the workshop. An AGV scheduling process in a discrete manufacturing workshop is taken as a case study to verify the effectiveness of the proposed method. The results show that, compared with the traditional AGV scheduling method, the DTDAS method proposed in this article can reduce makespan 10.7% and reduce energy consumption by 1.32%.

Which Way Would You Slice It? Evaluation of 3 Educational Models for the Loop Drainage Technique.

Emergency department visits for cutaneous abscesses are increasing. It is important for healthcare professionals to be proficient in identifying and treating abscesses. Loop drainage technique (LDT) is a newer technique which has been described in several articles but limited resources for teaching have been studied. The objective of this study was to compare 3 models for learning and teaching the LDT. This was a prospective survey study of a convenience sample of emergency medicine residents at a large urban academic center. Residents volunteered to participate during a scheduled cadaver and simulation session. After a self-directed review of the LDT, each participant performed ultrasound visualization and then the LDT on 3 simulated abscesses: a cadaveric model, a commercial abscess pad, and a homemade phantom. Participants completed pre- and post-simulation surveys. Of 57 residents, 28 participated in the 1-day simulation. The majority (57.1%, p < 0.009) preferred the cadaver model for learning the LDT, and 78.6% reported it to have the most realistic physical examination for an abscess (p = 0.001). Prior to participation, 0% of residents felt proficient performing LDT. After participation, 46.4% of residents felt proficient and 78.6% reported intent to use in clinical practice (p < 0.001). Simulation is an effective educational tool for both learning new skills and improving procedural competency. Residents found cadavers provided the most realistic physical examination, and the majority preferred it for learning the LDT. However, cadavers are not always accessible, an important factor when considering various educational settings.

Real-time scheduling simulation optimisation of job shop in a production-logistics collaborative environment

ABSTRACT In a complex and dynamic job shop containing logistics factor, schedule needs to be generated rapidly, so the real-time scheduling method is more suitable for such scenario. Such method takes advantage of local information within a short time due to the rapid changes of information under uncertain environment. Therefore, how to make use of the future information by prediction while ensuring the robustness of schedule is a valuable problem. To solve it, firstly, a new real-time scheduling model and algorithm is proposed. There is a new kind of release moment of task information which can give AGVs the longest time to prepare for the task than existing research. Secondly, a real-time information update mechanism is designed to increase schedule’s robustness. Finally, a large-scale and dynamic job shop simulation experimental platform is developed. Dynamic factors include the random insertion of orders and failures of equipment. Results show that the method proposed outperforms existing research in terms of customer satisfaction, equipment utilisation and energy consumption. The robustness of schedule can also be acceptable. This paper also finds a rule that in job shop with the large proportion of logistics transportation time, the above method can achieve more competitive results.

Modeling Wireless Mesh Networks for Load Management

Developing a simulation model for multi-hop multi-gateway wireless mesh networks (WMNs) is a challenging task. In this paper, a multi-hop multi-gateway WMN simulation model is developed in a step-by-step approach. This paper presents a MATLAB Simulink-based simulation model of Wireless Mesh Network (WMN) designed for easy optimization of layer 2. The proposed model is of special utility for the simulation of scheduling of GateWay (GW) and packet within a multi-hop multi gateway wireless network. The simulation model provides the flexibility of controlling the flow of packets through the networks. Load management among the GWs of WMN is performed in a distributed manner wherein the nodes based on their local knowledge of neighborhood beacons optimize their path to a GW. This paper presents a centralized Load Management Scheme (LMS). The LMS is based on the formation of Gateway Service Sets (GSS). The GSS is formed on basis of equal load distribution among the GWs. The proposed LMS is then analyzed for throughput improvement by leveraging the MATLAB Simulink model developed in the paper. A throughput improvement of almost 600% and a 40% reduction in packet loss was observed through simulations thus indicating the efficacy of the proposed LMS. The uniqueness of the simulation model presented in this paper are its scalability and flexibility in terms of network topology parameters.

흐름 생산 공정문제를 위한 dueling double Q-러닝 기반 강화학습 기법 연구

Flow shop scheduling is an important optimization problem for operating actual manufacturing facilities. In this paper, we propose a novel flow shop scheduling scheme based on the double Q-learning and the dueling architecture. We designed double Q-learning process using two estimation functions rather than a single estimation function to avoid over-estimation problem. In addition, the adaptation of the dueling architecture, which can provide robust policy estimation performance using both the state function and the advantage function, leading to less-variance and efficient learning process. We conducted extensive simulations of flow shop scheduling using multiple datasets with various scheduling scales. From the simulation result, we observed that the proposed scheme outperforms the existing heuristic and reinforcement learning-based scheduling schemes in terms of the final manufacturing time consumption for various flow shop scales.

Ready Patient One: How to Turn an In-Person Critical Care Simulation Scenario Into an Online Serious Game.

IntroductionSerious gaming has become popular in healthcare education as an engaging way to train learners. When coronavirus disease 2019 (COVID-19) forced the cancellation of in-person simulation sessions, we designed a serious game to deliver content in an interactive format with no out-of-pocket expense. We describe the design process and game reception so that others may replicate it.MethodsWe designed an online game using Choose-Your-Own-Adventure (CYOA) and Escape Room concepts. Using online survey software, we presented an interactive story based on an existing simulation scenario and included interactive puzzles as roadblocks to scenario progression. Each puzzle represented a critical care concept, and many contained hyperlinks to prior basic science lecture material to reinforce learning. A post-game survey assessed students’ experience.ResultsAll (N=88) students enrolled in a scheduled simulation session participated in the game, and 75% (66/88) responded to a post-participation survey. All respondents (100%) were able to complete the game. The majority (57.6%) completed the game in 30 minutes to 1 hour. Most students strongly agreed or agreed that the game enhanced their understanding of critical care concepts (93.9-97.0%), and that they were interested in doing more CYOA games (90.9%).ConclusionThe game was well-received, delivered critical care content, and challenged students to apply basic science principles to medical decision-making from the safety of their own homes. The game was self-guided, requiring minimal active facilitator involvement. We plan to expand the use of the game to other settings and explore its use in formative/summative assessment and remediation.

Reactive Scheduling Based on Adaptive Manipulator Operations in a Job Shop Configuration with Two Machines

Manufacturing systems are affected by uncertainties, such as machine failure or tool breakage, which result in system downtime and productivity deterioration. In machining processes, system downtime must be reduces. This study aims to establish an automated scheduling technique that flexibly responds to unforeseen events, such as machine failure, based on adaptive operations of the handling manipulator instead of an operation schedule for the machine tools. We propose an “adaptive manipulation” procedure for establishing a reactive revision policy. The reactive revision policy modifies a portion of the manipulator operation sequence, followed by the machine operation sequence. We conduct a physical scheduling simulation on a material-handling manipulator system imitating a job-shop manufacturing system. Through simulations involving machine breakdown scenarios, the applicability of the reactive revision policy based on adaptive manipulation is demonstrated.