Service Scheduling Research Articles

Emergency department visits as a potential opportunity to promote primary care attachment and modify utilization patterns – results of a pilot study in Berlin, Germany

BackgroundUtilization by low acuity patients contributes to emergency department (ED) crowding. Both knowledge deficits about adequate care levels and access barriers in primary care are important promoters of such presentations. Concurrently, not having a general practitioner (GP) increases the likelihood of low-acuity ED utilization. This pilot study thus investigated feasibility, acceptance, and potential effects of an ED-delivered intervention for low-acuity patients with no regular primary care provider, consisting of an educational leaflet on acute care options and an optional GP appointment scheduling service.MethodsLow-acuity ED consulters not attached to a GP were given an information leaflet about alternative care offers for acute health problems and offered optional personal appointment scheduling at a local GP practice. Patients were surveyed on demographics, medical characteristics, health care utilization, valuation of the intervention, and reasons for not being attached to a GP and visiting the ED. A follow-up survey was conducted after twelve months. Trends in health and health care utilization were evaluated.ResultsBetween December 2020 and April 2022, n = 160 patients were enrolled, n = 114 were followed up. The study population was characterized by young age (mean 30.6 years) and predominantly good general health. Besides good health, personal mobility was a central reason for not being attached to a GP, but general preference for specialists and bad experiences with primary care were also mentioned. Most frequently stated motives for the ED consultation were subjective distress and anxiety, a belief in the superiority of the hospital, and access problems in primary care. The interventional offers were favorably valued, 52.5% (n = 84) accepted the GP appointment scheduling service offer. At follow-up, GP utilization had significantly increased, while there were no significant changes regarding utilization of other providers, including ED. An additional practice survey showed a 63.0% take-up rate for the appointment service.ConclusionsWith this pilot study, we were able to show that a personalized appointment scheduling service seems to be a promising approach to promote GP attachment and increase primary care utilization in patients without a regular GP in a highly urbanized setting. Further larger-scale studies are needed to investigate potential quantitative effects on ED visits.Trial RegistrationGerman Clinical Trials Register (DRKS00023480); date 2020/11/27.

Designing Improvement of Service effectiveness Using M/M/1 and M/M/S Queue Models (Case Study: Customer Arrivals at Jatim Park Tourist Destination During Holidays and Peak Hours)

Queueing is a collection of people lined up with the purpose of waiting to be served, after which they leave the queue. Queuing activities often arise due to ineffective or suboptimal service times, resulting in longer wait times. To maximize and schedule service time optimally, queueing models such as M/M/1 and M/M/S systems are utilized. This study applies these models to analyze the queuing system at Jatim Park tourist destination. The aim of this research is to determine the effectiveness of service at the ticketing counters of Jatim Park during holidays. To initiate the study, data was collected on visitor arrival times and average service times. The data analysis aims to optimize service effectiveness at the Kampung Coklat tourist area by implementing the FCFS (First Come, First Served) queueing method and the M/M/S system. The findings suggest that the optimal number of teller servers needed to serve visitors effectively is 2. The analysis also identifies that the longest queue occurs between 10:00 AM and 11:00 AM, with 3 visitors waiting at its peak. The average number of visitors in the queue during this period is approximately 1.3 or 1 visitor. Based on these results, the study concludes that the service effectiveness at the Kampung Coklat tourist area is sufficiently optimized.

Medical bill shock and imperfect moral hazard

Consumers are sensitive to medical prices when consuming care, but delays in price information may distort moral hazard. We study how medical bills affect household spillover spending following utilization of shoppable services, leveraging variation in insurer claim processing times. Households increase spending by 22% after a scheduled service, but then reduce spending by 11% after the bill arrives. Observed bill effects are consistent with resolving price uncertainty; bill effects are strongest when pricing information is particularly salient. A model of demand for healthcare with delayed pricing information suggests households misperceive pricing signals prior to bills, and that correcting these perceptions reduce average (median) spending by 16% (7%) annually.

Air cargo transportation, loading, and phase-based maintenance service scheduling in demand channel routes

The article proposes an approach for modeling and solving the air cargo transportation, loading, and phase-based maintenance scheduling problems within demand channels. Demand channel transportation presents unique challenges to meeting the earliest pickup and delivery requirements during emergencies. The complexities in transportation involve issues with airport-aircraft compatibility, fleet selection, task assignments, route construction, and effective loading–unloading procedures. The challenges include coping with flight connections due to airport-aircraft compatibility, dealing with limited refueling and maintenance facilities, and swiftly adapting to unforeseen maintenance events by identifying and utilizing available fleets. In this context, the article delineates a demand channel route planning strategy that considers airport-aircraft compatibility, task integrity, priority, partial refueling strategy, and maintenance schedules spanning short to long-haul phases. Formulated as a Mixed Integer Linear Programming (MILP) problem, the solution employs a novel acyclic route construction procedure within a fuel-conscious network. We propose an insertion heuristic with multi-faceted algorithmic interconnections for producing a near-optimal solution suitable for real-world applications. Our insertion procedure has been shown to have very high computational performance, with an average 1.32 percent optimality gap. It can be scaled up to the most critical cargo mission, with up to 190 ports, 95 aircraft, and 380 tasks.

Cloud-edge collaboration composition and scheduling for flexible manufacturing service with a multi-population co-evolutionary algorithm

The Cloud Manufacturing Service Composition and Scheduling (CMfg-SCS) are essential processes in cloud manufacturing. Flexible Manufacturing Services (FMS), such as those provided by industrial robots, are widely used in cloud manufacturing to improve service quality and efficiency. Traditional CMfg-SCS methodologies, however, fall short in effectively managing the inherent temporal-dynamic QoS and flexible capability of FMS. To overcome these challenges, we propose a novel Cloud Manufacturing Service Cloud-edge Collaboration Composition and Scheduling (CMfg-SCCCS) method for FMS. Firstly, the service-task matching hypernetwork is constructed, and the temporal-dynamic QoS and flexible capacity of FMS are modeled. Subsequently, we develop a CMfg-SCCCS optimization model aimed at three objectives, along with a cloud-edge collaboration scheduling mechanism to harmonize cloud and edge-local tasks. Finally, a multi-population co-evolution algorithm with adaptive meta-knowledge transfer mechanism is proposed to solve the complex optimization model. The computational experiments serve to validate the effectiveness of the CMfg-SCCCS method and further reveal the superiority of the co-evolution algorithm in enhancing both the convergence and diversity of the population.

User-Centric Internet of Things and Controlled Service Scheduling Scheme for a Software-Defined Network

User-Centric Internet of Things and Controlled Service Scheduling Scheme for a Software-Defined Network

Predictable Wireless Networked Scheduling for Bridging Hybrid Time-Sensitive and Real-Time Services

Predictable Wireless Networked Scheduling for Bridging Hybrid Time-Sensitive and Real-Time Services

A fully polynomial time approximation scheme for a stop schedule of a transportation line without branches

For a transportation line without branches, passenger service scheduling is mainly concerned with the determination of stop schedules for planned service trips. A mixed-integer programming method is proposed to maximize customer satisfaction by minimizing the total number of in-vehicle passengers during trips. A heuristic algorithm is proposed for a given number of service trips, in which the error bound of the heuristic is restricted by the number of stations. When the given number of service trips is sufficient to provide express services for all origin–destination pairs, the heuristic provides an optimal solution. However, the error bound of the heuristic approaches infinity as the number of stations increases. A fully polynomial time-approximation algorithm is proposed, where the number of service trips is a decision variable. Finally, the proposed algorithm was applied to the stop-schedule problem of a river bus operating along the Han River in Seoul, Korea.

Calibration of simulation model to analyze hospital building energy performance

This research paper addresses the calibration of an energy simulation model for a naturally ventilated hospital building located in Mangalore, Karnataka, India, in a warm, humid climatic zone. The study aims to enhance the accuracy of energy consumption calculations through a calibration process by developing the energy model of a reference hospital building using EnergyPlus software. The required architectural design data, construction details, occupancy and schedule data and services are collected through documents, energy audits and documentation, measured drawings, and semi-structured interviews. Calibration parameters are systematically identified and adjusted through an iterative process. The monthly electricity bill is used to validate the simulation model. The simulation model reached during the validation process has an excellent Coefficient of Variation-Root Mean Squared Error of 4.24% and a Normalized Mean Bias Error of −4.42%, both of which meet the ASHRAE-approved accuracy standards. The paper also discusses the challenges and emphasizes the need for a nuanced approach to modeling healthcare facilities. Notwithstanding the challenges faced, the study offers insightful information about how to calibrate simulation models for hospital energy usage with the significant influence of scheduling for artificial lighting and equipment usage. By pioneering a manual calibration approach tailored to hospital simulation models in the warm humid climate of India, this paper offers a novel and practical solution to address the challenges of energy performance analysis in resource-constrained environments. The calibrated simulation model presented in this study is a valuable tool for assessing and improving the energy performance of naturally ventilated healthcare facilities. By creating alternative space layouts, the research findings aim to foster the development of hospital infrastructure that is both ecologically friendly and energy-efficient in a specific climatic context. This research is assured to make a substantial contribution to the advancement of sustainable hospital design in warm-humid climates, with implications for both academia and industry.

Appointment scheduling for logistics parks under truck heterogeneity and order fulfillment delay

ABSTRACT Appointment scheduling is critical for logistic parks of manufacturing industry firms to manage inventory turnover and schedule service objects. This work studies the appointment scheduling problem of a logistic park considering truck heterogeneity and order fulfillment delay, where the number of time windows in a day, the number of each type of appointment slot, and the service schedule in each time window are decided. The objective is to minimise the expected total cost of the truck waiting time, inventory, and overtime. We introduce a weighted shortest processing time first scheduling rule to determine the service sequence in each time window and prove that it generates optimal scheduling solutions. Based on our proposed scheduling rule, we develop a recursive calculation to formulate the cost function, which provides a tractable approach to solving combinatorially complex problems. The proposed model is solved by a dual-selection based genetic algorithm (DSGA). The performance of the DSGA is validated by comparing it to a genetic algorithm using a single tournament selection operation. Our experimental results show that the DSGA enlarges the size of subgroups with better fitness and therefore performs satisfactorily.

Analysis on the Application of Artificial Intelligence in the Field of Logistics

This paper discusses the wide application of artificial intelligence in the logistics industry, from intelligent distribution to intelligent transportation, through route optimization, automated warehouse management, demand forecasting, inventory management and other means, greatly improve logistics efficiency and reduce costs. Intelligent transport enhances traffic management, ensures road safety and optimizes resource utilization with road condition monitoring, autonomous driving and freight optimization technologies. In the face of challenges such as data security and privacy protection, artificial intelligence continues to promote the transformation of the logistics industry to a smarter and more efficient form. In the future, artificial intelligence will be further integrated into the logistics field, and through data-driven intelligent logistics management and applications in international logistics, more refined supply chain management, better resource scheduling and higher customer service standards will be achieved.

A multiobjective approach for weekly Green Home Health Care routing and scheduling problem with care continuity and synchronized services

Home Health Care (HHC) services are essential for delivering healthcare programs to patients in their homes, with the goal of reducing hospitalization rates and improving patients’ quality of life. However, HHC organizations face significant challenges in scheduling and routing caregivers for home care visits due to complex criteria and constraints. This paper addresses these challenges by considering both caregiver assignments and transportation logistics. The objective is to minimize the total travel distance and CO2 emissions while ensuring a balanced workload for caregivers, meeting patients’ preferences, synchronization, precedence, and availability constraints. To tackle this problem, we propose a multiperiodic Green Home Health Care (GHHC) framework. In the first stage, we utilize multiobjective programming and the NSGA-II algorithm to generate Pareto front solutions that consider travel distance and CO2 emissions. In the second stage, a Mixed-Integer Linear Programming (MILP) model is proposed to balance caregivers’ workload by assigning them to the patient routes generated in the first stage. The results highlight the trade-off between shorter routes and lower emissions. Furthermore, we examine the impact of prioritizing continuity of care and patient satisfaction. This research provides valuable insights into addressing the scheduling and routing challenges in HHC services, contributing to a more efficient and environmentally friendly healthcare delivery.

Market Risk in Project Finance: Approaches to Review and Minimisation

Relevance. Development of an investment project is commonly associated with many risks, and one of the most significant risks is a market risk i.e. is a possible failure to achieve prices, volumes and sales projected in total compared to those planned in the business plan and financial model of a project. Modern literature pays not enough attention to many relevant and practical issues of the market risk (i.e. is a possible failure to achieve prices, volumes and sales projected) review and reduction in the project finance and investment review, as well as to issues of work with independent marketing consultants, possible areas and ways for risk reduction and quality improvement.Purpose. The previous article considered approaches to understanding significance of the market risk and measures to deal with the risk in the review of price forecasts for an investment project. This article is supposed to consider the issues of risk reduction by organising optimal work with an independent consultant on these issues.Objectives. It is supposed to consider the possibility of engaging an independent marketing consultant, the requirements to the consultant by the financing organisation or investor, to provide an example of detailed scope of work for the independent analysis, give examples of comments to the marketing report and proposed measures to adjust the analysis in the report. It is also necessary to consider approaches to the use of the financial model in order to reduce market risk effects in project financing.Methodology. Methods of analysis and synthesis, induction and deduction as well as modelling are used in the article.Results. The article considers two major issues related to the accounting for or reduction of the market risks in the development of investment projects and providing project finance: the range of questions regarding working with a marketing consultant in order to decrease market risk effects is discussed, examples of requirements to the consultant and potential scope of work as well as some possible errors examples are given; author discusses instruments for considering these risks in financial model, including increased required levels for liquidity and debt service ratios in case of high risks and uncertainties.Conclusion. The market risk is one of the key risks in the development of an investment project and project review in project finance. A set of measures is needed to reduce and account for the risk. Proper application of these and other reasonable measures should enable project initiators and sponsors to ensure their successful development and compliance with the debt service schedule to third-party lenders.

A Hybrid Optimized Intelligent Resource-Constrained Service Scheduling for Unified IoT Applications in Smart Cities

A Hybrid Optimized Intelligent Resource-Constrained Service Scheduling for Unified IoT Applications in Smart Cities

Energy-aware cloud manufacturing service selection and scheduling optimization

ABSTRACT Cloud Manufacturing Service Selection and Scheduling (CMSSS) is vital for optimizing resource allocation and meeting task requirements. However, inattention to the preheating process of manufacturing equipment has resulted in wasted energy. To reduce manufacturing energy consumption and ensure the Quality of Service (QoS), this paper establishes a bi-level programming model for CMSSS, quantifies the preheating energy consumption of manufacturing equipment by task cohesion, and proposes an energy-aware cloud manufacturing service selection and scheduling approach. The approach selects the service composition for a task from candidate service sets, schedules subtasks to avoid service occupancy conflicts and maximises task cohesion to reduce the preheating energy consumption of manufacturing equipment by Energy-aware Scheduling Generation Scheme (ESGS). Finally, it integrates ESGS into Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal task execution solution. Experimental results show that ESGS has a better Pareto front than the previous Feasible Scheduling Generation Scheme (FSGS) under seven types of QoS weights. With almost the same QoS satisfaction level, ESGS consumes, on average, 2% to 8% less energy than FSGS for preheating manufacturing equipment. In cloud manufacturing scenarios with preheating processes, ESGS can meet the QoS requirements of demanders as FSGS but with a better energy economy.

Cost-aware Service Placement and Scheduling in the Edge-Cloud Continuum

The edge to data center computing continuum is the aggregation of computing resources located anywhere between the network edge (e.g., close to 5G antennas), and servers in traditional data centers. Kubernetes is the de facto standard for the orchestration of services in data center environments, where it is very efficient. It, however, fails to give the same performance when including edge resources. At the edge, resources are more limited, and networking conditions are changing over time. In this article, we present a methodology that lowers the costs of running applications in the edge-to-cloud computing continuum. This methodology can adapt to changing environments, e.g., moving end-users. We are also monitoring some Key Performance Indicators of the applications to ensure that cost optimizations do not negatively impact their Quality of Service. In addition, to ensure that performances are optimal even when users are moving, we introduce a background process that periodically checks if a better location is available for the service and, if so, moves the service. To demonstrate the performance of our scheduling approach, we evaluate it using a vehicle cooperative perception use case, a representative 5G application. With this use case, we can demonstrate that our scheduling approach can robustly lower the cost in different scenarios, while other approaches that are already available fail in either being adaptive to changing environments or will have poor cost-effectiveness in some scenarios.

An Analysis of the Correct Frequency of the Service Inspections of German Passenger Cars—A Case Study on Kazakhstan and Poland

This article presents a case study on estimating the real service inspection intervals for German-brand passenger cars in Kazakhstan and Poland. This study aimed to identify disparities between the official recommendations of manufacturers for car maintenance and the real data collected in these two countries. The following passenger cars were examined: Audi A6, Q5, and Q8; Porsche Cayenne and Cayenne coupe; and Volkswagen Passat, Polo, Teramont, Tiguan, Touareg, Arteon, Golf, T-Cross, Tiguan all space, Touran, T-Roc, and Up. To assess the difference between real and recommended values, the manufacturer criteria of a recommended mileage of 15,000 and 30,000 km or a time frame of 365 and 730 days to the first service inspection were applied. The data analysis showed that in Kazakhstan, 31.4% of cars did not meet the warranty conditions, while in Poland, it was 21.0%. The dominant criterion that was not met was the time criterion. The assessment of these factors emphasizes the importance of customizing vehicle maintenance schedules to the specific conditions and driving behaviors prevalent in each country. The practical contribution of the article lies in uncovering the discrepancies between official manufacturer recommendations for car maintenance and the actual data collected in Kazakhstan and Poland. By identifying specific models, Volkswagen Touareg and Tiguan in Kazakhstan and Volkswagen Up in Poland, for which the maintenance intervals deviated significantly from those recommended, this study offers valuable insights for optimizing service schedules and improving the efficiency of maintenance practices in these countries. From a scientific perspective, this article contributes by providing empirical evidence of real-world maintenance behaviors for German-brand passenger cars.

Decentralized Scheduling for Data-Parallel Tasks in the Cloud

For latency-sensitive data processing applications in the cloud, concurrent data-parallel tasks need to be scheduled and processed quickly. A data-parallel task usually consists of a set of sub-tasks, generating a set of flows that are collectively referred to as coflows . The state-of-the-art schedulers collect coflow information in the cloud to optimize coflow-level performance. However, most of the coflows, classified as small coflows because they consist of only short flows, have been largely overlooked. This paper presents OptaX , a decentralized network scheduling service that collaboratively schedules data-parallel tasks’ small coflows. OptaX adopts a cross-layer, COTS (commercial off-the-shelf) switch-compatible design that leverages the sendbuffer information in the kernel to adaptively optimize flow scheduling in the network. Specifically, OptaX (i) monitors the system calls (syscalls) in the hosts to obtain their sendbuffer footprints, and (ii) recognizes small coflows and assigns high priorities to their flows. OptaX transfers these flows in a FIFO manner by adjusting TCP’s two attributes: window size and round-trip time. We have implemented OptaX as a Linux kernel module. The evaluation shows that OptaX is at least 2.2 × faster than fair sharing and 1.2 × faster than only assigning small coflows with the highest priority. We further apply OptaX to improve the small I/O performance of Ursa , a distributed block storage system that provides virtual disks where small I/O is dominant. URSA with OptaX achieves significant improvement compared to the original URSA for small I/O latency.

A Resource Allocation Scheme for Edge Computing Network in Smart City Based on Attention Mechanism

In recent years, the number of devices and terminals connected to the smart city has increased significantly. Edge networks face a greater variety of connected objects and massive services. Considering that a large number of services have different QoS requirements, it has always been a huge challenge for smart city to optimally allocate limited computing resources to all services to obtain satisfactory performance. In particular, delay is intolerable for services in certain applications, such as medical, industrial applications, etc, that such applications require the high priority. Therefore, through flexibly dynamic scheduling, it is crucial to schedule services to the optimal node to ensure user experience. In this paper, we propose a resource allocation scheme for hierarchical edge computing network in smart city based on attention mechanism, for extracting a small number of features that can represent services from a large amount of information collected from edge nodes. The attention mechanism is used to quickly determine the priority of the services. Based on this, task deployment and resource allocation for different task priorities are developed to ensure the quality of service in smart cities by introducing Q-learning. Simulation results show that the proposed scheme can effectively improve the edge network resource utilization, reduce the average delay of task processing, and effectively guarantee the quality of service.

Health service and medication costs associated with common mental disorders and subthreshold symptoms in women: Findings from the Geelong Osteoporosis Study in Australia.

This analysis estimated 2013 annual healthcare costs associated with the common mental disorders of mood and anxiety disorders and psychological symptoms within a representative sample of Australian women. Data from the 15-year follow-up of women in the Geelong Osteoporosis Study were linked to 12-month Medicare Benefits Schedule and Pharmaceutical Benefits Scheme data. A Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Non-patient edition identified common mental disorders and the General Health Questionnaire 12 assessed psychological symptoms. Participants were categorised into mutually exclusive groups: (1) common mental disorder (past 12 months), (2) subthreshold (no common mental disorder and General Health Questionnaire 12 score ⩾4) or (3) no common mental disorder and General Health Questionnaire 12 score <4. Two-part and hurdle models estimated differences in service use, and adjusted generalised linear models estimated mean differences in costs between groups. Compared to no common mental disorder, women with common mental disorders utilised more Medicare Benefits Schedule services (mean 26.9 vs 20.0, p < 0.001), had higher total Medicare Benefits Schedule cost ($1889 vs $1305, p < 0.01), received more Pharmaceutical Benefits Scheme prescriptions (35.8 vs 20.6, p < 0.001), had higher total Pharmaceutical Benefits Scheme cost ($1226 vs $740, p < 0.05) and had significantly higher annual out-of-pocket costs for Pharmaceutical Benefits Scheme prescriptions ($249 vs $162, p < 0.001). Compared to no common mental disorder, subthreshold women were less likely to use any Medicare Benefits Schedule service (89.6% vs 97.0%, p < 0.01), but more likely to use mental health services (11.4% vs 2.9%, p < 0.01). The subthreshold group received more Pharmaceutical Benefits Scheme prescriptions (mean 43.3 vs 20.6, p < 0.001) and incurred higher total Pharmaceutical Benefits Scheme cost ($1268 vs $740, p < .05) compared to no common mental disorder. Common mental disorders and subthreshold psychological symptoms place a substantial economic burden on Australian healthcare services and consumers.