Search

The big data era has ushered in huge transformation in the form of major media, with WeMedia nowadays becoming the mainstream. As is witnessed, WeMedia has achieved a rapid and widespread growth in both dissemination and influence, which has posed a series of challenges to the Emergency Intelligence Service System for Think Tanks, including changes to the subject, content, form, and connotation of platforms. Based on big data, this study makes an investigation into the Emergency Intelligence Service for the modern think tanks and applies the method of factor analysis to draw the conclusion on what kind of factors will influence the performance of the Emergency Intelligence Service for Think Tanks. Then, from the perspective of the life-cycle theory, this research not only optimizes the work process of the Emergency Intelligence Service for Think Tanks, but also constructs a multi-dimensional Emergency Intelligence Service System based on the concept of “Data Driven + Technology Application + Expert Decision” from the aspect of theoretical and technical perspectives. Meanwhile, a public health incident emergency information service system is built on this basis, describing the application process with food poisoning as an example. Last but not least, hopefully, this study could help improve the quality of decision and effectiveness of the Emergency Intelligence Service for Think Tanks and enhance the power of influence and communication for agencies of Emergency Intelligence Service and this paper provides a reference for the establishment and improvement of government decision-making and governance mechanisms.

In this paper, we examine older adult care services after a global pandemic and identify new opportunities for care in a post-pandemic world, while recognizing the existing models that will continue successfully. Primary survey data was collected from both the supply side (n=29) and demand side (n=134) of the U.S. older adult care system. The results indicate a trend in the demand side that customers’ preferences for senior care is moving towards de-institutional care options after the outbreak of the Covid-19 pandemic; a development confirmed by the supply side, indicated by occupancy becoming a more challenging issue after pandemic outbreak. Findings also suggest a strong agreement between the demand and supply on the most important aspects of institutional care. Both customers and industry practitioners consider safety and quality of clinical services the top care priority. Lastly, staffing continues to be a major challenge for most institutional practitioners. As the first paper (to the best of our knowledge) that systematically evaluable the situation of the U.S. senior care industry after the pandemic outbreak, our timely results have pinpointed the emerging trends and provide many actionable implications for stakeholders such as care receivers, industry practitioners, and policy makers.

Recently, the Internet-of-Things technique is believed to play an important role as the foundation of the coming Artificial Intelligence age for its capability to sense and collect real-time context information of the world, and the concept Artificial Intelligence of Things (AIoT) is developed to summarize this vision. However, in typical centralized architecture, the increasing of device links and massive data will bring huge congestion to the network, so that the latency brought by unstable and time-consuming long-distance network transmission limits its development. The multi-access edge computing (MEC) technique is now regarded as the key tool to solve this problem. By establishing a MEC-based AIoT service system at the edge of the network, the latency can be reduced with the help of corresponding AIoT services deployed on nearby edge servers. However, as the edge servers are resource-constrained and energy-intensive, we should be more careful in deploying the related AIoT services, especially when they can be composed to make complex applications. In this paper, we modeled complex AIoT applications using directed acyclic graphs (DAGs), and investigated the relationship between the AIoT application performance and the energy cost in the MEC-based service system by translating it into a multi-objective optimization problem, namely the CA$$^3$$ 3 D problem — the optimization problem was efficiently solved with the help of heuristic algorithm. Besides, with the actual simple or complex workflow data set like the Alibaba Cloud and the Montage project, we conducted comprehensive experiments to evaluate the results of our approach. The results showed that the proposed approach can effectively obtain balanced solutions, and the factors that may impact the results were also adequately explored.

Seiring dengan perkembangan teknologi informasi dan komunikasi yang mampu mendukung proses input dan output data secara akurat dan efisien, maka penerapannya dapat dilakukan pada sistem layanan akademik khususnya dalam proses pendaftaran siswa baru. Di masa pandemi Covid-19 sistem pendaftaran dan penerimaan siswa baru sangat perlu dilakukan secara online di sekolah. Sistem pendaftaran dan penerimaan siswa baru yang valid akan menjadi modal awal dalam pelayanan pendidikan di sekolah. SMA BOPKRI 1 Yogyakarta sudah memiliki beberapa aplikasi seperti survey kegiatan sekolah, pengajuan proposal dan kinerja kegiatan, manajemen kegiatan guru, serta peningkatan mutu akreditasi sekolah yang merupakan hasil penelitian kerja sama dengan Universitas Kristen Duta Wacana (UKDW). SMA BOPKRI 1 Yogyakarta membutuhkan sistem aplikasi manajemen pendaftaran dan penerimaan siswa baru. Tim pengabdian FTI UKDW membantu dalam peningkatan layanan administrasi sekolah melalui hilirisasi Sistem Informasi Pendaftaran Siswa Baru (SiBaru). Aplikasi SiBaru dapat membantu calon siswa agar secara mandiri dapat menggunakan fasilitas pendaftaran tersebut. Para staff admin dan tata usaha juga dapat dengan mudah dan cepat mengolah data calon siswa baru. Program pengabdian ini meliputi beberapa tahapan utama yaitu: koordinasi antara tim dan mitra, kegiatan penyempurnaan aplikasi dan evaluasi terhadap program yang telah dilaksanakan. Hasil program pengabdian berupa penyempurnaan aplikasi SiBaru, pelatihan sistem kepada mitra, dan peningkatan kapabilitas penerimaan calon siswa baru oleh sekolah Dari hasil pengujian didapatkan tingkat keberhasilan sebesar 74% untuk proses registrasi dan telah dilakukan perbaikan-perbaikan sesuai dengan hasil pengujian dan evaluasi tersebut. Program pengabdian kepada masyarakat ini masih akan berlanjut pada tahapan berikutnya yaitu pelatihan dan pendampingan mitra dalam implementasi sistem SiBaru.

This paper studies a revenue and social welfare optimization problem for an online service system which provides service-free trials to first-time users. There are two customer types: experienced customers and first-time customers, where type-1 (experienced) customers are fully rational, and they directly purchase service upon arrivals, while type-2 (first-time) customers are boundedly rational, so they may consider to purchase service or not to purchase service after experiencing the free trials. We investigate the optimal pricing strategy of service providers and social planners in two cases: service capacity is exogenous and endogenous. In the latter case, we jointly determine the optimal service price and capacity. We adopt a game theoretical approach: First, for a given price and service rate, we characterize the equilibrium behavior of post-trial customers; next, we solve optimization problems for the service provider and social planners to determine the optimal price and service rate. Our analysis reveals interesting insights and practical implications. For example, in contrast to the conventional wisdom, the service provider’s optimal revenue decreases as customers become less rational.

Artificial intelligence technology is an important transformative force for teaching innovation in the intelligent era. It is being widely used in American school teaching, including the design of intelligent tutoring systems to achieve precise problem solving, the machine learning technology to ensure personalized activity design, the creation of intelligent virtual reality to promote classroom teaching contextualization, and the development of intelligent evaluation systems to ensure the scientific evaluation of capabilities. In the process of advancing the teaching and application of artificial intelligence technology, the United States has built a linkage mechanism of federal leadership, university follow-up, and social collaboration and implemented the smart technology in school teaching and professors’ academic governance. This paper is aimed at studying the professors’ academic governance of American research universities by Internet data mining, historical analysis method, documentary method, survey method, and other methods. Professors’ academic governance is a vital part of the modern university system that causes the institutional reform of the internal governance structure of modern universities. The United States is a powerful country in higher education, and professors in American research universities have always participated in university academic governance for centuries. By studying the definition, history, and development and mode of operation of professors’ academic governance in American research universities, the results indicate a clear division of power and responsibility between the professors and administrators based on an artificial intelligence decision system in American research universities. Also, there is a good communication platform based on artificial intelligence environment for professors to discuss their opinions on academic affairs. Third, professors exercise academic power under the guarantee of diversified guaranteed systems based on the artificial intelligence evaluation system and the ideology of mutual respect based on the artificial intelligence management and service system. Studying the application of artificial intelligence techniques in operating mode and enlightenment of professors’ academic governance in an American research university is of great significance to promote the construction of other modern universities’ professors’ academic governance system.

Permanent supportive housing (PSH) programs are now using Moving On Initiatives (MOIs) to increase homeless service system capacity. These initiatives support tenants with the transition from PSH t...

It is expected that commercial use of drones in the near future will involve delivery service operations by e-commerce companies. We consider relevant strategic and tactical decisions that these retailers will face in drone-based delivery operations, and derive policies on when to offer drone delivery, what delivery capacity to maintain, and what amount to charge for such deliveries. To this end, we develop a Markov decision process (MDP) framework, and introduce two heuristic procedures, through which near-optimal closed-form solutions can be obtained. The results are aimed at helping online retailers to determine in real time whether and to what extent to offer drone-based delivery for given product categories in different service zones. In addition, we study delivery fee structures and identify drone-based delivery pricing strategies under two widely used delivery pricing schemes. For capacity planning decisions, we describe an algorithm to identify the fleet size to utilize to fulfill uncertain demand in a given service region. We also identify structural characteristics on how these decisions and the expected profit are affected by changes in various problem parameters, which can generate generic insights on drone-based delivery operations for e-commerce companies. We find that retailers should prioritize more profitable items when allocating drone delivery capacity, and invest in adding more drones when per order opportunity costs are higher and promised delivery time thresholds are shorter. Retailers can potentially boost their net profits by increasing the effective promised delivery time threshold and/or decreasing the effective delivery delay costs and per order opportunity costs.

Recent years have witnessed the rise of queue scalping in congestion-prone service systems. A queue scalper has no material interest in the primary service but proactively enters the queue in hopes of selling his spot later. This paper develops a queueing-game-theoretic model of queue scalping and generates the following insights. First, we find that queues with either a very small or very large demand volume may be immune to scalping, whereas queues with a nonextreme demand volume may attract the most scalpers. Second, in the short run, when capacity is fixed, the presence of queue scalping often increases social welfare and can increase or reduce system throughput, but it tends to reduce consumer surplus. Third, in the long run, the presence of queue scalping motivates a welfare-maximizing service provider to adjust capacity using a “pull-to-center” rule, increasing (respectively, reducing) capacity if the original capacity level is low (respectively, high). When the service provider responds by expanding capacity, the presence of queue scalping can increase social welfare, system throughput, and even consumer surplus in the long run, reversing its short-run detrimental effect on customers. Despite these potential benefits, such capacity expansion does little to mitigate scalping and may only generate more scalpers in the queue. Finally, we compare and contrast queue scalping with other common mechanisms in practice—namely, (centralized) pay-for-priority, line sitting, and callbacks. This paper was accepted by Victor Martínez de Albéniz, operations management.

Problem definition: Many healthcare providers and payers offer teletriage, a telemedicine service where concerned patients can get advice about their health condition. In theory, teletriage should help patients with an acute illness choose an appropriate provider, reducing unnecessary and duplicate provider visits, yet no study has assessed the overall costs and benefits of teletriage at a system level. Academic/practical relevance: We model the use of teletriage in managing healthcare demand and determine in which cases teletriage is efficient and effective. Our model extends the academic literature on service operations and provides practical suggestions for healthcare payers. Methodology: We use a Markov decision process to model a patient’s choices during an acute illness episode, where the illness severity is partially observable to the patient and triage is subject to errors. We parameterize the model using data from the literature and provide both structural and numerical results. Results: Patients with high uncertainty about their state would use the teletriage service, which may improve their cost outcomes. However, when teletriage is added, the rate of arrivals to the emergency department (ED) may increase when the overtriage rate is above 5%, the lowest value observed in the literature. Patient’s choices and the copayment of other services affect the cost-effectiveness of teletriage. Managerial implications: There are several reasons why adding teletriage to the healthcare system could produce a negative cost outcome. Teletriage should not necessarily be free, which would encourage use by patients with low levels of uncertainty and actually increase the payer’s total cost. The overtriage rate is a key driver of performance and must be managed carefully.

The increasing adoption of omnichannel strategies in recent years has led retail companies worldwide to fundamentally rethink the future role of their network of brick-and-mortar stores. One strategic option being pursued by many retailers is the transformation of the stationary store into a “smart store,” augmented by various digital services. However, an essential prerequisite for the success of smart store services is high quality of the underlying data generated through the use of technologies for tracking products and customer behavior. As a means of investigating the use of machine learning to improve data quality, the present study considers the example of Radio Frequency Identification (RFID) as a technological infrastructure for tracking products in fashion retail. We examine electronic article surveillance and automated checkouts as practical use cases enabled by a classification model for the detection of product movements on the store floor. In order to identify an economically optimal configuration of the classifier, we develop a complementary service operations model that allows for determining the respective cost impact. In addition to the specific results for the considered use cases, the study thus points to a general and novel prescriptive analytics approach.

There is limited understanding of mental health service utilization among youth in the juvenile justice system (JJS). Using administrative data, the current study examined service system trajectories of two cohorts of youth who initially entered the JJS in 2003 (N = 10,170) and 2012 (N = 5,787). We tracked mental health (MH) service utilization and dosage and child welfare system (CWS) involvement for 3 years and found that utilization and dosage of MH services increased between both cohorts. Notably, MH service use positively associated with CWS involvement and concurrent (dual) involvement in the CWS and JJS associated with decreased MH dosage if youth remained at home versus being placed in out-of-home care. Lastly, African American children received less services than their Caucasian peers. Future efforts should focus on preparing MH providers to detect youth who are dually involved and tailor services to enhance collaboration and information sharing across systems.

Background In Australia, sustained nurse home-visiting (SNHV) programs are designed and implemented to promote positive outcomes for mothers with young children experiencing complex life challenges. Despite the crucial role of trained public health nurses, there is a dearth of research about their experiences delivering these programs to culturally and linguistically diverse (CALD) families with limited English proficiency (LEP). Aim This study aimed to explore the experiences of public health nurses in providing services to CALD families with LEP. Method Two focus groups were conducted with 13 public health nurses in two major local health districts in Sydney. The focus groups were audio recorded for transcription purposes, and thematically analyzed. A socioecological framework was applied during the analysis process. Result Five major themes were identified: (1) program issues; (2) service system issues in understanding and responding to diverse women; (3) issues working with interpreters; (4) trusting relationships; and (5) mother-related facilitators. Discussion To achieve better access and engagement for CALD families, it is crucial that public health nurses increase their knowledge and awareness about potential challenges, such as female interpreter availability, and facilitators, such as engaging the broader family, in order to effectively deliver healthcare services to diverse populations.

The COVID-19 pandemic has had a tremendous impact on the world of health, including delivery services at the independent midwife practice. Midwives must change their service system and follow maternity service procedures that the central government has set. The purpose of the study was to explore in-depth the preparation of midwives in providing delivery services at independent midwife practice during the COVID-19 pandemic. This study uses descriptive qualitative research through a phenomenological approach involving seven informants. Data were collected utilizing one-on-one in-depth online interviews using a semi-structured interview guide. Data analysis was done manually using thematic analysis. Data analysis shows that midwives are prepared to provide delivery services at independent midwife practice during the COVID-19 pandemic by modifying infrastructure, providing Personal Protective Equipment (PPE), providing information about COVID-19, disseminating delivery service guidelines, and providing support, vaccinations, rapid screening tests for midwives. © 2021 Universidad Tecnica de Manabi. All Rights Reserved.

We consider a queueing system with multiple stations serving a single class of customers. Each station has many servers, and its own dedicated queue. The system manager must decide to which station each new customer is routed. We propose and analyze a minimum-expected-delay faster-server-first with ξ -choice ( MED-FSF ( ξ ) ) routing policy, under which an integer ξ , with P { ξ ≥ 2 } > 0 , is randomly generated for each new customer. Then, a subset of ξ stations will be randomly collected, with state information being retrieved. Among these collected stations, the system manager assigns the new customer to: (i) the one with minimum expected delay if all these ξ stations are fully occupied; or (ii) the one with fastest available server. We prove that in the Halfin–Whitt regime this policy is asymptotically equivalent to the MED-FSF policy, which only works when full information of the system state is available. Moreover, we compare the MED-FSF ( ξ ) policy with the random routing policy, which uses no information and routes each incoming customer to a station in a random manner. Using simulation experiments, we validate the theoretical results, and find that our proposed policy significantly outperforms the random routing policy. This suggests that a slight increase in information at each routing epoch can substantially improve system performance.

Research on Arctic passages has mainly focused on navigation policies, sea ice extraction models, and navigation of Arctic sea routes. It is difficult to quantitatively address the specific problems encountered by ships sailing in the Arctic in real time through traditional manual approaches. Additionally, existing sea ice information service systems focus on data sharing and lack online calculation and analysis capabilities, making it difficult for decision-makers to derive valuable information from massive amounts of data. To improve navigation analysis through intelligent information service, we built an advanced Ship Navigation Information Service System (SNISS) using a 3D geographic information system (GIS) based on big Earth data. The SNISS includes two main features: (1) heuristic algorithms were developed to identify the optimal navigation route of the Arctic Northeast Passage (NEP) from a macroscale perspective for the past 10 years to the next 100 years, and (2) for key sea straits along the NEP, online local sea-ice images can be retrieved to provide a fully automatic sea ice data processing workflow, solving the problems of poor flexibility and low availability of real sea ice remote sensing data extraction. This work can potentially enhance the safety of shipping navigation along the NEP.

Coproductive services involve the active participation of customers by exerting physical/mental effort as a part of the service process to co-create value. While the demand for coproduction models is surging, there are complaints about customer experience and service system design. Owing to these dissatisfaction issues, providers are now shifting away from self-service models, towards the notion of smart service; where service tasks are efficiently divided between the provider and the customer. This study addresses the business problem of the configuration and pricing of such a smart coproduction service channel targeted at a segment within an incumbent provider's captive customer base. We analyze the relative preference of the popular uniform pricing policy against a new proposed policy that accounts for the strategic behaviour of customers. This study uses a suite of analytical modelling tools to address this problem. We analyze the applicability of the alternate pricing regimes when providers are pursuing certain prevalent marketing strategies. We also analyze the influence of relative co-creation productivity between the provider and customer on the choice of pricing regime. We find that the popularity of the uniform pricing model can be explained with its simplicity in configuration and applicability. On the other hand, we find that the proposed strategic pricing regime not only induces customers to adopt coproduction channels but also enables providers to charge a price premium where possible. Furthermore, our analysis explains several observations in practice w.r.t. uptake and proliferation of coproduction channels. We also present useful guidelines to managers for configuring and pricing coproduction service channels.

The public service system serves population spread over a geographical area from a given number of service centers. One of the possible approaches to the problem with two or more simultaneously applied contradicting objectives is determination of the so-called Pareto front, i.e. set of all the feasible non-dominated solutions. The Pareto front determination represents a crucial computational deal, when a large public service system is designed using an exact method. This process complexity evoked an idea to use an evolutionary metaheuristic, which can build up a set of non-dominated solution continuously in the form of an elite set. Nevertheless, the latter approach does not assure that the resulting set of solutions represents the true Pareto front of the multi-objective problem solutions. Within this paper, authors deal with both approaches to evaluate the difference between the exact and heuristic approaches.

Problem definition: We study the effects of emergency department (ED) physician workload on healthcare system utilization after the patient leaves the ED. Further, we explore the mediating effects of care intensity in the ED on post-ED care use. Academic/practical relevance: ED crowding has been a pressing concern in healthcare systems in the United States and other developed countries. As such, many researchers have studied its effects on outcomes within the ED. In contrast, we present novel results regarding the impacts of ED crowding on system performance outside the ED—specifically, on post-ED care utilization. Methodology: We utilize a data set assembled from more than four years of microdata from a large U.S. hospital and exhaustive billing data in an integrated health system. We use count models and instrumental variable analyses to answer the proposed research questions. Results: We find that there is an increasing concave relationship between ED physician workload and post-ED care use. When ED workload increases from its fifth percentile to the median, the number of post-discharge care events (i.e., medical services) for patients who are discharged home from the ED increases by 5%, and it is stable afterward. Further, we identify physician test-ordering behavior as a mechanism for this effect; when the physician is busier, she responds by ordering more tests for less severe patients. We document that this “extra” testing generates “extra” post-ED care utilization for these patients. Managerial implications: This paper contributes new insights on how physician and patient behaviors under ED crowding impact a previously unstudied system performance measure: post-ED care utilization. Our findings suggest that prior studies estimating the cost of ED crowding underestimate the true effect, as they do not consider the “extra” post-ED care utilization. Funding: Support for this research was provided by the University of Alberta Endowment Fund for the Future: Support for the Advancement of Scholarship, Canadian Utilities Faculty Fellowship, and the University of Wisconsin–Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation. Brian Patterson’s contribution to this research was supported by funding from the Agency for Healthcare Research and Quality (AHRQ) [Grant K08HS024558]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1110 .

We consider a price-based revenue management problem with finite reusable resources over a finite time horizon. Stochastically arrived customers request an exponentially distributed service time and may balk and renege given insufficient resource. The resource unit, upon completion of serving one customer, can be released to serve the next customer immediately. The arrival, service, balking, and reneging rates all depend on the price being offered. In this paper, we assume that the firm does not know the mappings between these rates and prices, and thus it makes adaptive pricing decisions in each period based only on past sales to maximize the cumulative revenue. We propose two new multi-armed bandit (MAB) based learning algorithms, termed Batch Upper Confidence Bound (BUCB) algorithm and Batch Thompson Sampling (BTS) algorithm, for finding near-optimal pricing policies. Compared with prior pricing and MAB literature, the salient difficulties of this problem lie in (i) the unknown rate-and-price mapping information, (ii) the dynamic nature of reusable resources being committed over time, (iii) the transient behavior of the service system when the price changes, and (iv) unbounded and heavy-tailed distributions of observed random variables. Our proposed algorithms contain a Warm-up Phase to eliminate the heavy-tail effects and a Learning Phase to identify the optimal price. Our algorithms separate the Learning Phase into successive operational batches and select a price from a prescribed set in each batch using past sales collected in previous batches. The performance measure is cumulative regret, which is the difference between the revenue attained by our approach and by a clairvoyant optimal pricing policy under full distributional information. We prove that the cumulative regret is $O(\sqrt{PT\log (T)})$, where $T$ is the total number of time periods and $P$ is the cardinality of the feasible price set, and the result matches the lower bound up to a logarithmic factor. As an intermediate step, we also develop a coupling analysis for analyzing the time for a queue to reach the steady state from an empty state or from a steady state under another set of system parameters. Our numerical experiments demonstrate and confirm the efficacy of the proposed BUCB and BTS algorithms.