Socioeconomic and demographic factors predictive of same day access utilization in outpatient radiation oncology.

PURPOSE: Access to medical care extends to not only the timely and appropriate receipt of services but also addresses inclusivity and underlying determinants of health. Given that patients from disadvantaged backgrounds have been shown to be more likely to experience delays in care, a same day access scheduling initiative was proposed to address this equity issue. Therefore, this study aims to evaluate our experience, focusing on identifying socioeconomic and demographic patterns of same day access utilization. DESIGN/METHODOLOGY/APPROACH: From March 2021 to January 2023, all patients referred for new consultation to a tertiary care-based radiation oncology department were offered same day appointments as part of a prospective pilot initiative. Descriptive statistics were used to identify factors predictive of utilization. FINDINGS: On multivariate analysis, patient characteristics independently associated with higher odds of same day access utilization included low-income status ([OR] = 3.70, 95% CI (1.47-6.14)) and Black or Latino race ([OR] = 4.05, 95% CI: 1.72-9.11). RESEARCH LIMITATIONS/IMPLICATIONS: While we were unable to acquire data on actual clinical outcomes for patients opting for same day appointments, the enthusiasm for this program was obvious. PRACTICAL IMPLICATIONS: Patients from disadvantaged backgrounds and vulnerable segments of the population were more likely to elect for same day appointments. Implications on health equity are discussed. SOCIAL IMPLICATIONS: Patient-centered approaches to overcome barriers of access can potentially help ensure that care is equitable. ORIGINALITY/VALUE: Our findings, representing the first published data analyzing a longitudinal experience with same day appointments in oncology, strongly suggest that certain disadvantaged populations may benefit more from access initiatives.

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids.

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm.

An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.

An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments.

Improving access to outpatient computed tomography.

Demand for diagnostic imaging services in the United States continues to rise, posing challenges for health systems to maintain efficient scheduling processes. This study documents a quality improvement initiative undertaken at our institution in response to a surge in demand for outpatient imaging during 2022, which led to a notable scheduling backlog. By October 2022, the average scheduling interval, defined as the time from order placement to scheduled examination date, had increased from 2 weeks to 6 weeks. The objective of this initiative was to reduce the scheduling interval from 6 weeks to 10 days by January 2023. Utilizing feedback from schedulers, technologists, and radiologists, several interventions were implemented. The impact of each intervention was monitored with a control chart with weekly appointment delays tracked as a balancing measure. Initially, examination slots were double-booked for a period of 4 weeks to address the backlog, resulting in a reduction of the scheduling interval to 12 days (72 % decrease). Subsequently, examination slot duration was shorted from 20 to 15 min and contrast protocols were standardized across all sites. These adjustments further decreased the interval to 7 days (41 % reduction) over the following 9 weeks. While staffing shift adjustments had no impact on the scheduling interval, the introduction of an extra CT scanner reduced the interval to 3 days (57 % decrease). These interventions resulted in a notable increase in examination volume, from a weekly average of 722 to 860 examinations (19 % increase), approximately an additional $1,612,000 in annual revenue. Importantly, there was no change in the average appointment delay, which remained at 15 min over the study period. These improvements were sustained across the subsequent months and received favorable subjective feedback from staff. While the initiative successfully addressed scheduling inefficiencies across our health system, the rise in examination volumes has led to an increased turnaround time for completed reports. Future directions for enhancing the outpatient scheduling process include expanding online scheduling platforms, implementing systems to assess imaging appropriateness, and developing urgency stratification to prioritize time-sensitive examinations.

Enhanced Harmonic Partitioned Scheduling of Periodic Real-Time Tasks Based on Slack Analysis.

The adoption of multiprocessor platforms is growing commonplace in Internet of Things (IoT) applications to handle large volumes of sensor data while maintaining real-time performance at a reasonable cost and with low power consumption. Partitioned scheduling is a competitive approach to ensure the temporal constraints of real-time sensor data processing tasks on multiprocessor platforms. However, the problem of partitioning real-time sensor data processing tasks to individual processors is strongly NP-hard, making it crucial to develop efficient partitioning heuristics to achieve high real-time performance. This paper presents an enhanced harmonic partitioned multiprocessor scheduling method for periodic real-time sensor data processing tasks to improve system utilization over the state of the art. Specifically, we introduce a general harmonic index to effectively quantify the harmonicity of a periodic real-time task set. This index is derived by analyzing the variance between the worst-case slack time and the best-case slack time for the lowest-priority task in the task set. Leveraging this harmonic index, we propose two efficient partitioned scheduling methods to optimize the system utilization via strategically allocating the workload among processors by leveraging the task harmonic relationship. Experiments with randomly synthesized task sets demonstrate that our methods significantly surpass existing approaches in terms of schedulability.

An improved intelligent optimization algorithm for small-batch order production scheduling.

Although the critical path method (CPM) is effective for the integrated scheduling of small-batch orders, its overemphasis on vertical process relationships and neglect of horizontal parallel relationships have imposed limitations on scheduling, often leading to suboptimal outcomes in terms of the total product completion time. This study introduces an innovative algorithm designed to overcome these limitations and further optimize the total processing time of products. We propose a strategy of "exchanging adjacent processes on the same device", which operates based on the scheduling results of the CPM. By swapping adjacent and interchangeable processes within the constraints of the problem, this algorithm generates multiple new scheduling schemes, effectively expanding the solution space. This expansion enables the discovery of optimized solutions that leverage "horizontal parallel relationships", which is crucial for reducing the "total processing time of products". Finally, the effectiveness of the proposed algorithm is verified through experiments.

A machine learning based EMA-DCPM algorithm for production scheduling.

Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, "5+X" was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications.

Procedural times with robotic-assisted bronchoscopy: a high volume single-center study.

BACKGROUND: Incidental and screen-detected pulmonary nodules are common. The increasing capabilities of advanced diagnostic bronchoscopy will increase bronchoscopists' procedural volume necessitating optimization of procedural scheduling and workflow. OBJECTIVES: The objectives of this study were to determine total time in the procedure room, total bronchoscopy procedure time, and robotic-assisted bronchoscopy procedure time longitudinally and per specific procedure performed. DESIGN: A single-center observational study of all consecutive patients undergoing shape-sensing robotic-assisted bronchoscopy (RAB) biopsy procedures for the evaluation of pulmonary lesions with variable probability for malignancy. METHODS: Chart review to collect patient demographics, lesion characteristics, and procedural specifics. Descriptive and comparative statistics are reported. RESULTS: Actual bronchoscopy procedure time may decrease with increased institutional experience over time, however, there is limited ability to reduce non-bronchoscopy related time within the procedure room. The use of cone beam computed tomography (CBCT), rapid on-site evaluation (ROSE), and performance of staging endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) in a single procedure are each associated with additional time requirements. CONCLUSION: Institutional procedural block times should adapt to the nature of advanced diagnostic bronchoscopy procedures to allow for the accommodation of new modalities such as RAB combined with other technologies including radial endobronchial ultrasound, CBCT, ROSE, and staging linear EBUS. Identifying institutional median procedural times may assist in scheduling and ideal block time utilization.

Times necessary to perform robotic assisted bronchoscopy biopsy procedures at a single hospitalBackground: Lung lesions and nodules are commonly seen on computed tomography (CT) scans. With advances in technology, more of these lesions are being biopsied with robotic assisted bronchoscopy (RAB) procedures, leading to increased demand. Health care providers who perform these procedures have finite available time in which they must accommodate all their procedures. Understanding procedure times is necessary to fully utilize schedules. Methods and aims overview: We describe our experience of 5 pulmonologists performing 700 robotic assisted bronchoscopies at a single hospital. Our aim is to describe the time needed for the robotic bronchoscopies over time and with specific procedures. Results and conclusion: We find that as more robotic assisted bronchoscopies are performed, the overall procedure time may decrease. Using cone beam computed tomography during the procedure, having on- site pathology review of biopsies, and obtaining biopsies of lymph nodes may lengthen the procedure time. The time spent preparing the patient for the procedure excluding the bronchoscopy remained stable. Understanding the time necessary based on what is performed during the procedure will allow it to be scheduled for the appropriate amount of time. As a result, procedure days can be fully optimized, minimizing scheduling impacts on patients and health care workers.

A multi-objective brain storm optimization for integrated distributed flexible job shop and distribution problems.

Production and distribution are critical components of the furniture supply chain, and achieving optimal performance through their integration has become a vital focus for both the academic and business communities. Moreover, as economic globalization progresses, distributed manufacturing has become a pioneering production technique. Via leveraging a distributed flexible manufacturing system, mass flexible production at lower costs can be achieved. To this end, this study presents an integrated distributed flexible job shop and distribution problem to minimize makespan and total tardiness. In our research, a set of custom furniture orders from different customers are processed among flexible job shops and then delivered by vehicles to customers as the due date. To distinctly show the presented problem, a mixed integer mathematical programming model is created, and a multi-objective brain storm optimization method is introduced considering the problem's features. In comparison to the other three advanced methods, the superiority of the algorithm created is showcased. The findings of the experiments demonstrate that the constructed model and the introduced algorithm have remarkable competitiveness in addressing the problem being examined.

Energy and throughput aware adequate routing for wireless sensor networks using integrated game theory method.

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR.

Hybrid quantum-classical computation for automatic guided vehicles scheduling.

Motivated by recent efforts to develop quantum computing for practical, industrial-scale challenges, we demonstrate the effectiveness of state-of-the-art hybrid (not necessarily quantum) solvers in addressing the business-centric optimization problem of scheduling Automatic Guided Vehicles (AGVs). Some solvers can already leverage noisy intermediate-scale quantum (NISQ) devices. In our study, we utilize D-Wave hybrid solvers that implement classical heuristics with potential assistance from a quantum processing unit. This hybrid methodology performs comparably to existing classical solvers. However, due to the proprietary nature of the software, the precise contribution of quantum computation remains unclear. Our analysis focuses on a practical, business-oriented scenario: scheduling AGVs within a factory constrained by limited space, simulating a realistic production setting. Our approach maps a realistic AGVs problem onto one reminiscent of railway scheduling and demonstrates that the AGVs problem is better suited to quantum computing than its railway counterpart, the latter being denser in terms of the average number of constraints per variable. The main idea here is to highlight the potential usefulness of a hybrid approach for handling AGVs scheduling problems of practical sizes. We show that a scenario involving up to 21 AGVs, significant due to possible deadlocks, can be efficiently addressed by a hybrid solver in seconds.

Optimal electric vehicle charge scheduling algorithm using war strategy optimization approach.

In this work, a new kind of charge scheduling algorithm is proposed by utilizing the War Strategy Optimization (WSO) algorithm. The strategies used in the war such as attack, defense, assigning soldiers to take positions are the inspiration to this algorithm. The proposed WSO algorithm is validated in a constructed geographic area which consists of Six starting/destination points, sixteen nodes, and twelve charging stations. In terms of waiting time and charging cost, the experimental results show that the WSO method much improves over current methods. The average waiting time and average charging cost of EVs are validated in MATLAB, with different considerations such as different number of EVs varied from 25 to 100, and different number of charging piles varied from 1 to 4. The WSO algorithm specifically lowered charging costs by up to 13.67% compared to the same and waiting time by up to 83.25% relative to the First Come First Serve algorithm. Comparatively to the Chaotic Harris Hawk Optimization and Harris Hawk Optimization algorithms, the WSO method demonstrated declines in waiting time by 11.17% and 39.09%, respectively, and declines in charging costs by 3.61% and 12.45%, respectively. Especially in situations with limited charging infrastructure, these findings show that the WSO algorithm may improve the efficiency and cost-effectiveness of EV charging management systems. For real-world EV charging management systems, the method's capacity to efficiently allocate EVs among charging stations, lower waiting times, and lower charging costs makes it a potential solution.

Planning cost-effective operational forest inventories.

We address a Bayesian two-stage decision problem in operational forestry where the inner stage considers scheduling the harvesting to fulfill demand targets and the outer stage considers selecting the accuracy of pre-harvest inventories that are used to estimate the timber volumes of the forest tracts. The higher accuracy of the inventory enables better scheduling decisions but also implies higher costs. We focus on the outer stage, which we formulate as a maximization of the posterior value of the inventory decision under a budget constraint. The posterior value depends on the solution to the inner stage problem and its computation is analytically intractable, featuring an NP-hard binary optimization problem within a high-dimensional integral. In particular, the binary optimization problem is a special case of a generalized quadratic assignment problem. We present a practical method that solves the outer stage problem with an approximation which combines Monte Carlo sampling with a greedy, randomized method for the binary optimization problem. We derive inventory decisions for a dataset of 100 Swedish forest tracts across a range of inventory budgets and estimate the value of the information to be obtained.

Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network.

The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.

A Self-Learning Hyper-Heuristic Algorithm Based on a Genetic Algorithm: A Case Study on Prefabricated Modular Cabin Unit Logistics Scheduling in a Cruise Ship Manufacturer.

Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on a genetic algorithm (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. This problem can be regarded as a multi-objective fuzzy logistics collaborative scheduling problem. Hyper-heuristic algorithms effectively avoid the extensive evaluation and repair of infeasible solutions during the iterative process, which is a common issue in meta-heuristic algorithms. The GA-SLHH employs a genetic algorithm combined with a self-learning strategy as its high-level strategy (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic scheduling rules as solution support. Multiple sets of numerical experiments demonstrate that the GA-SLHH exhibits a stronger comprehensive optimization ability and stability when solving this problem. Finally, the validity of the GA-SLHH in addressing real-world decision-making issues in cruise ship manufacturing companies is validated through practical enterprise cases. The results of a practical enterprise case show that the scheme solved using the proposed GA-SLHH can reduce the transportation time by up to 37%.

Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes.

With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node's Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely "the alternating backpack problem", and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes' energy requirements and MC's capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

Combining 5G New Radio, Wi-Fi, and LiFi for Industry 4.0: Performance Evaluation.

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi -complementary WATs in the 5G-CLARITY project-in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms.

Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System.

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to "double near and far effect" in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the "double near and far effect" and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG.

Criteria-based outpatient scheduling at a nephrology clinic: prospective evaluation of patient pre-assessment and its corresponding adaptive scheduling strategy.

BACKGROUND: Outpatient Clinics (OCs) are under pressure because of increasing patient volumes and provider shortages. At the same time, many patients with chronic diseases receive routine follow-up consultations that are not always necessary. These patients block access to care for patients that are in actual need for care. Pre-assessing patient charts has shown to reduce unnecessary outpatient visits. However, the resulting late cancellations due to the pre-assessment, challenge efficient alignment of capacity with actual patient demand, leading to either empty slots or overtime. This study aims to develop a method to analyse the effect of pre-assessing patients before inviting them to the OC. This involves 1) to select who should come and 2) to optimize the impact of pre-assessment on the schedule and efficient use of OC staff. METHODS: This prospective mixed-methods evaluation study consists of 1) an expert meeting to determine a pre-assessment strategy; 2) a retrospective cohort study to review the impact of this strategy (12 months of a Dutch nephrology OC); 3) mathematical optimization to develop an optimal criteria-based scheduling strategy; and 4) a computer simulation to evaluate the developed strategy. Primary outcomes are the staff idle time and staff overtime. Secondary outcomes evaluate the number of weekly offered appointments. RESULTS: The expert group reached consensus about the pre-assessment criteria. 875 (18%) of the realized appointments in 2022 did not meet the OC visit pre-assessment criteria. In the best performing scheduling strategy, 94 slots (87% of the available capacity) should be scheduled on a weekly basis. For this schedule, 26.8% of the OC weeks will experience idle time ( µ =2.51, σ =1.44 appointment slots), and 21% of the OC weeks will experience overtime ( µ =2.26, σ =1.65 appointment slots) due to the variation in patient appointment requests. Using the pre-assessment strategy combined with the best performing scheduling strategy under full capacity (108 slots), up to 20% increase in patient demand can be handled with equal operational performance. CONCLUSIONS: This evaluation study allows OC managers to virtually test operational impact of pre-assessment strategies on the capacity of their OC, and shows the potential of increasing efficient use of scarce healthcare capacity. TRIAL REGISTRATION: Not applicable.