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This study aimed to develop and evaluate the performance of a service system platform based on the Internet of Things (IoT) for monitoring nutritional deficiencies in plants and providing fertilizer recommendations. There are two distinct differences between this work and previous ones; namely, this service system platform has been developed based on IoT using a system engineering approach and its performance has been evaluated using dependability. We have successfully developed and integrated a service system platform and chlorophyll meter that is based on IoT. We have also successfully tested the performance of the service system platform using the JMeter software. The dependability value measured from the five tested variables (reliability, availability, integrity, maintainability, and safety) showed a value of 0.97 which represents a very good level of system confidence in not failing to deliver services to users under normal operational conditions. From a future perspective, this platform can be used as an alternative service to monitor nutrient deficiencies in plants and provide fertilization recommendations to increase yields, reduce fertilizer costs, and prevent the use of excessive fertilizers, which can cause environmental pollution.

Problem definition: Classical models of queueing systems with rational and strategic customers assume queues to be either fully visible or invisible, while service parameters are known with certainty. In practice, however, people only have “partial information” on the service environment, in the sense that they are not able to fully discern prevalent uncertainties. This is because assessing possible delays and rewards is costly, as it requires time, attention, and cognitive capacity, which are all limited. On the other hand, people are also adaptive and endogenously respond to information frictions. Methodology: We develop an equilibrium model for a single-server queueing system with customers having limited attention. Following the theory of rational inattention, we assume that customers optimize their learning strategies by deciding the type and amount of information to acquire and act accordingly while internalizing the associated costs. Results: We establish the existence and uniqueness of a customer equilibrium when customers allocate their attention to learn uncertain queue lengths and delineate the impact of service characteristics. We provide a complete spectrum of the impact of information costs on throughput and show numerically that throughput might be nonmonotone. This is also reflected in social welfare if the firm’s profit margin is high enough, although customer welfare always suffers from information costs. Managerial implications: We identify service settings where service firms and social planners should be most cautious for customers’ limited attention and translate our results to advisable strategies for information provision and service design. For example, we recommend firms to avoid partial hindrance of queue-length information when a low-demand service is not highly valued by customers. For a popular service that customers value reasonably highly, however, partial hindrance of information is particularly advisable. Academic/practical relevance: We propose a microfounded framework for strategic customer behavior in queues that links beliefs, rewards, and information costs. It offers a holistic perspective on the impact of information prevalence (and information frictions) on operational performance and can be extended to analyze richer customer behavior and complex queue structures, rendering it a valuable tool for service design. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1032 .

Problem definition: Putting customer experience at the heart of service design has become a governing principle of today’s “experience economy.” Echoing this principle, our paper addresses a service designer’s problem of how to select and sequence activities in designing a service package. Academic/practical relevance: Empirical literature shows an ideal sequence often entails an interior peak; that is, the peak (i.e., highest-utility) activity is placed neither at the beginning nor the end of the package. Theoretic literature, by contrast, advocates placing the peak activity either at the beginning or at the end. Our paper bridges this gap by developing a theory accounting for interior peaks. It also provides managerial implications for activity sequencing and selection. Methodology: We model the activity sequencing and selection problem as a nonlinear optimization problem and reformulate its objective as an additive function to generate structural insights. Results: We show that heterogeneity in memory decay explains the phenomenon of interior peaks. The optimal sequence is in either an “IU” or “UI” shape. An interior peak is optimal when the memory decay rate of the peak activity is neither too high nor too low. Managerial implications: Our research sheds light on service sequencing by weighing the phenomenon of interior peaks. In the presence of an interior peak, we show it is optimal to schedule a low point immediately before or after the peak activity, creating a contrast in customer experience. In addition, interior peaks arise partly because the peak activity is more memorable than others. Guided by this logic, as the peak activity becomes even more memorable, one might be tempted to move it to an earlier slot; we show that, counterintuitively, moving it to a later slot can be optimal. Our research also provides implications for activity selection by showing the optimal portfolio may consist of activities with the highest- and lowest-utility values but not those with medium values.

Paying taxes is an example of public service. In the process of serving, the service is often synonymous with the queuing process. Queuing is a condition in which several people or objects from a waiting line to be served are generally caused by the need for services to exceed the service capacity or service facilities so that users of arriving facilities cannot immediately receive service. Therefore, overcoming many complaints due to queues can be done by improving services and maximizing time efficiency using the Petri net model. In this study, a Petri net model of the 5-year tax payment service system for a motor vehicle at SAMSAT Oku Timur 1 was made as many as 17 places, 15 transitions, two operators, and 30 arcs using WOPED 3.2.0 software.

Dynamic Scheduling to Differentiate Delay-Based Service Levels in Multiclass Service Systems

Society 5.0 refers to an advanced society based on big data, artificial intelligence, sensors, and robots to improve many aspects of life in a smart city. The role of sensors in Society 5.0 is critical. Sensors and the Internet of Things can be considered to work as a service system. Specifically, sensors can track millions of objects to support city security. This article considers the competitive sensor networks in terms of Quality of Service, which can be quantified by service price. The sensors are modeled as an $M/M/1/n$ queueing service system with a location on a secured grid area. A competitive admission fee is considered for tracking orders from society, which makes the arriving tracking demands price-sensitive. Moreover, a bi-level nonlinear program is developed wherein the first level maximizes the sensors’ revenue and social benefit revenue while also minimizing the wait time of tracking orders from society; in the second level, the expected damage cost is minimized from a disruptive scenario toward valuable infrastructure. Moreover, the second-level model is linearized, and it solves the problem by a branch and bound and enumeration algorithm. Finally, an illustrative example of the proposed model is presented.

We present a spatiotemporal data set of all out-of-hospital sudden cardiac arrests (OHCA) dispatches for the City of Virginia Beach. We also develop a modular toolkit that can be used to process the data and generate problem instances based on user-defined input. The data were collected from multiple sources, and our analysis process was validated by Virginia Beach officials. The data set consists of detailed information about each dispatch made in response to an OHCA; it includes the time the call for service arrived, the response time of the first unit on scene, the address, and the coordinates of each OHCA incident. It also contains detailed spatial information for all existing first-responder stations and both the great-circle and the road distances between all first-responder stations and OHCA incidents. The raw data files were very large in size and were processed using SAS®, MATLAB, and QGIS. In conjunction with the database, we provide a MATLAB code that allows generating multiple random test instances based on user-defined input. The library of problems can be used in healthcare emergency problems and also for facility location models, bilocation problems, and drone studies. The data set was organized such that it can be readily used by researchers in the field of healthcare operations research and those studying the spatiotemporal distribution of OHCAs. Given the difficulty to access OHCA data at the level of detail we provide, the data set will facilitate the implementation of data-driven models to design emergency medical response networks and to study the distribution of OHCAs. Additionally, the provision of data and the toolkit will be very useful in benchmarking algorithms and solvers, which is valuable to the data-driven optimization community in general. Summary of Contribution: The paper provides a data set of spatiotemporal information out-of-hospital cardiac arrests (OHCAs) for the City of Virginia Beach. The complete data set also includes spatial information about all fire, emergency medical services, and police stations in the city and both the road and haversine distances between each pair of stations and OHCA incident. Additionally, we provide a toolkit to generate random instances based on user input. To the best of our knowledge, it is the first time that an OHCA database is made publicly available in such level of detail, and there is no precedent of such in IJOC. OHCAs are a leading cause of death worldwide, and emergency medical services still encounter difficulties in providing care in a timely manner. Given the criticality of OHCAs, we believe that making this data set publicly available can help the implementation of data-driven models by researchers in the field of operations research.

We consider a large-scale service system where incoming tasks have to be instantaneously dispatched to one out of many parallel server pools. The user-perceived performance degrades with the number of concurrent tasks and the dispatcher aims at maximizing the overall quality-of-service by balancing the load through a simple threshold policy. We demonstrate that such a policy is optimal on the fluid and diffusion scales, while only involving a small communication overhead, which is crucial for large-scale deployments. In order to set the threshold optimally, it is important, however, to learn the load of the system, which may be unknown. For that purpose, we design a control rule for tuning the threshold in an online manner. We derive conditions which guarantee that this adaptive threshold settles at the optimal value, along with estimates for the time until this happens. In addition, we provide numerical experiments which support the theoretical results and further indicate that our policy copes effectively with time-varying demand patterns., Comment: 51 pages, 6 figures

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.

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.

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.

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.

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.

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.

To discuss the application of Internet of Things (IoT) in the agriculture, an agricultural product price prediction model is constructed based on the improved Elman neural network (ENN) of deep learning. Simulation experiment of pest prediction is carried out based on MATLAB, and then the agricultural IoT information service system is combined with the improved ENN prediction model to predict and analyze agricultural product prices. The results show that the agricultural product price prediction model based on the improved ENN has high accuracy, which reaches 0.9241. Through this agricultural product price prediction model, the agricultural IoT information service system can predict the price trend of agricultural products better, and it can also predict the shortcomings in the production and sales of agricultural products, so that corresponding management and control measures can be realized. This work can provide an important reference for the application of deep learning methods in agricultural IoT.

Toko Romeo Jok adalah toko mebel yang menjual berbagai jenis mebel dan menyediakan jasa servis segala jenis mebel yang berada di Kabupaten Pati. Toko Romeo Jok masih menggunakan pendataan data pesanan dan servis secara manual maka terjadi masalah kurang terorganisirnya data pesanan dan servis, dan data mudah untuk hilang. Sehingga diperlukan sebuah sistem pengelolaan data yang dapat menyimpan dan menyajikan data pesanan dan servis secara akurat, efektif, dan efisien. Pembangunan sistem pengelolaan data pesanan dan servis berbasis web ini bisa digunakan Toko Romeo Jok untuk mengelola data pesanan dan servis secara cepat, mudah, dan akurat. Pada tahap perancangan sistem ini memgunakan metode pengembangan Rapid Application Development (RAD), yaitu metode yang menyediakan pengembangan yang cepat dan mendapatkan hasil yang bagus . Sistem pengelolaan data pesanan dan servis jok berbasis web ini di buat menggunakan bahasa pemograman PHP dan mengunakan software xampp dan text editor seperti sublime text, pembuatan desain sistem menggunkan draw.io, basis data nya menggunakan MySQL. Berdasarkan Hasil UAT dari aspek penilaian antar muka mendapat persentase 82% dari aspek penilaian kemudahan pengoperasional mendapatkan presentase 84% dan dari aspek kemanfaatan isi sistem ini mendapat persentase 83%. Sistem ini terbukti layak digunakan karena pada hasil pengujian white box dan black box keduanya mendapat hasil persentase 100%.

This article discusses the profitability of introducing an automated hardware complex into the system of passing pre-flight procedures. Technological solutions are described at the stages: check-in, baggage check-in, boarding, as well as passport and customs control. The complex offers the introduction of automatic check-in, self-baggage check-in, hardware identification of biometric data at passport and border control, boarding pass by scanning a QR code from a smartphone screen. The complex is aimed at improving services and increasing the speed of passenger service on the example of Pulkovo Airport. The experience of other countries and existing technologies are considered.

The driving license administration service system is a special service provided by Nasional Police to people who want to make a SIM. This service system can be done online. The current application has made it a lot easier in the process of making up the SIM card production. However, there are some problems related to internal reports that must be reported because the service is not yet available in an existing application, so some recapitulation processes are still done conventionally and technical constraints on the server network. This study aims to create a service management information system application (SIMPEL SIM) that can provide solutions to internal reporting and server network technical constraints. The method used software development is waterflow. This application handles the printing of bank payment receipts automatically, granting the applicant's registration number automatically, printing the SIM application form on the form provided. Verification of the completeness of the file, validation of the score of the theory test, practice 1 and practice 2, validation of sim issuance and printing of temporary sim numbers if there is a technical problem in the existing network server. The use of the SIMPEL SIM application makes it easy to recapitulate internal reporting and can improve public services.

This paper proposes a modeling framework to design an integrated mobility service system that is composed of a local demand-responsive transportation (DRT) component and a fixed-route transit network component. The region is partitioned into disjoint local zones, and a trip is characterized into an intra- or inter-zonal one based on whether its origin and destination belong to the same zone. The transit network provides backbone line-haul service to inter-zonal trips, while the DRT system targets intra-zonal trips as well as the first-mile and last-mile legs of inter-zonal trips. The system components can be broadly defined, and this paper considers, as examples, conventional non-shared taxi or ride-sharing for the DRT service, and regular bus, bus rapid transit (BRT), or metro for the backbone network. For quantitative analysis of the DRT services, new aspatial queuing network models are developed to capture various DRT operations for serving trips with randomly distributed origins and destinations (i.e., intra-zonal trips) and those with origins or destinations concentrated at a set of fixed transit stations (i.e., first- or last-mile trip legs) at the same time. Each queuing model is integrated with the transit network design model, through proper service region partition, to derive closed-form formulas for the agency and passenger costs. Then, we formulate constrained non-linear programs that simultaneously optimize the zone partition, the fleet size and repositioning operation for the local DRT service, as well as the spacing and headway of a grid transit network. We apply the proposed models to a variety of transit technologies and application scenarios, so as to demonstrate applicability of the models, and to show promising performance of the proposed systems.

We examine a service system that matches jobs and candidates with different ability levels. A candidate’s ability will affect the possibility of obtaining a high return on a job assignment. Based on the property that the jobs to be assigned to a candidate are substituted in an assignment problem, we propose a novel random parameter generating process to demonstrate that the expected reward increases with the ability level of a candidate at a diminishing rate. This observation is consistent with the classic result in process flexibility literature that a little flexibility (i.e., ability level) can capture the most benefit. Our observations can help a company decide the proper level of ability.

Problem definition : We study the problem of designing a dynamic invitation policy for proactive service systems with finite customer patience under scarce capacity. In such systems, prior knowledge regarding customer value or importance is used to decide whether the company should offer service or not. Academic/practical relevance : Proactive service systems are becoming more popular, as data availability and machine-learning techniques are developed to forecast customer needs. However, very little is known about the efficient use of such tools to promote and manage service systems. Methodology : We use fluid approximation and the Filippov convex method to analyze system dynamics and develop approximations for important performance measures. Results : We show that whereas prioritizing customers in descending order of their r-μ ranking (as long as there are idle servers in the system) is optimal on the fluid level, refinements are necessary in the presence of abandonment on the stochastic level. We propose an r-μ-N policy to account for customer patience. Managerial implications : Our policy can be used to promote service effectivenes and allow decision makers the means to trade off service level against costs in such systems explicitly. Using a case study of a transportation service provider, we show that such a policy can triple revenue compared with random arrivals (nonproactive policy).

Problem definition : We consider the development of an efficient and scalable video-analytics approach to measure customer assistance and its mediating role in the relationship between staffing levels and revenues. Academic/practical relevance : Staffing decisions account for a large portion of a retailer’s operational costs. Researchers have studied the extent to which an increase in staffing levels translates into greater revenues, without emphasizing the underlying mechanisms that generate this potential improvement, such as assisting customers. Methodology : We use econometric methods, including survival-analysis techniques, to analyze data gathered from in-store video recordings of customer visits to stores of a women’s apparel chain. Results : We find that, under average store conditions, a 25% increase in labor yields a 16% increase in store revenues. An increase in the assistance of customers while searching, browsing, or trying products mediates approximately 50% of this increase, while the remainder originates from other activities, such as helping customers at the checkout counter. Moreover, we find that this form of assistance has a significant and positive impact on both conversion and ticket size. Managerial implications : Our approach can be used to explain heterogeneity in employee productivity across stores, to monitor and detect unexpected deviations in customer-assistance levels, and to measure the productivity of multitasking agents for the different functions that they perform.

Drones, with the potential to significantly increase the efficiency of the delivery, have received much attention in recent years. Still, there are some bottleneck problems in the application of the long-distance drone delivery, such as the limited flight range and flight safety. Therefore, the article proposes a novel service system, including the battery exchange stations and maintenance checkpoints, to provide long-distance delivery services. Then, with respect to the service system, we construct a drone path programming model, where a special penalty value is proposed as the objective function to simultaneously minimize the path length and number of landing depots for the delivery service. Thereafter, to efficiently find the optimal flight path among huge solution space, we improve the ant colony optimization with the A* algorithm embedded to avoid the nondirectional searching of ants. Finally, we use a case of Shanghai city to study the feasibility and effectiveness of our approaches, which includes the comparison of our algorithm and the other three heuristics on ten random delivery cases, the verification of the effectiveness of our algorithm on the long-distance delivery service, and a sensitive analysis of the effect of the depot number on the optimal solution.

In the fourth industrial revolution to develop new products and processes, the digital twin, virtual copies of the system that can interact with the physical counterparts in a bidirectional way, seem to be promising enablers to replicate production systems in real time and analyze them. They aim to solve insufficient guidance methods in the existing enterprise service remote assistance guidance system. In this paper, a digital twin-driven enterprise service remote assistance guidance system is proposed. The digital twin system is designed to carry out different all-around analyses of the remote internal system. The digital and physical spaces of the enterprise service system are reset according to the data query results. The proposed model achieves the internal data mapping effect of the enterprise service and analyzes the internal data of the system. Based on the realization of real-time mapping and a large amount of twin data generated by virtual and real interaction, the data are visualized and stored in a database for the upper layers. The proposed model has been simulated, and the test results show its potential benefits for enterprise control, optimization, and forecasting and can provide essential support for realizing the twin’s optimized control of entities.

With the development and extension of the Internet of Things technology, many fields have already involved the Internet of Things technology, and its application in dance training has gradually emerged. In order to guide dance training and solve the hidden dangers of dance training, this article mainly introduces the application of posture recognition service system based on information fusion intelligent sensor in dance training. This article uses human body detection, computer interactive Boosting algorithm calculations and establishes standard dance poses. The difference between the trainer’s posture and the standard posture can be clearly seen on the posture recognition service platform. The error is only less than 5%, and it can also help the trainer find their own mistakes in time. Dance and correct your posture in time, and it can reduce the error rate of difficult dances and reduce the error rate of 10%-20%. In addition, through the survey on the satisfaction survey of the security services of the gesture recognition service platform, the satisfaction rate is as high as 90%. Thereby, while ensuring safe training, the dance quality and level of trainers are improved.

Cloud computing is quickly used to run services of information technology by remarkable solutions for multiple welfare such as automatically improves management of resources and new service delivery system. Cloud computing supplier have to deal with reducing energy usage, to meet Service Level Agreement (SLA) demand. To decrease the cost of pay as you go technique of cloud services, resource reservation based facility is provided by cloud owners that allow users to personalize their Virtual Machines (VMs) with given time and physical resource. However, owing to Energy efficiency of Physical Machines (PMs) and efficient management of reserved services are not guaranteed. In this methodology, green cloud computing provides energy efficient data centers for the aim of cost savings, decrease negative impacts on the environment and reduces usage of energy. For better alternative for energy minimization is by exploring an alternative for energy consumption that has potential using the Particle Swarm Optimization (PSO). The PSO must be enhanced for solving the optimization problem due to more energy usage. The Enhanced PSO (E-PSO) is proposed in the research that redefines the operators and parameters of the PSO thereby adapts the energy aware local fitness that designs the coding scheme. The proposed EPSO shows an optimal VM replacement scheme that will be found with the energy consumption at the lowest. The proposed EPSO shows better energy consumption of 22% of Energy consumption was lowered better when compared with the existing methods.

This paper describes an improved model for the monitoring of power used by a party such as household users and different industries in Pakistan. The power theft detection was done using the intelligent internet of things (IoT) service system for calculating the user's power simultaneously. The power meter catches a theft detection device that is instantly transmitted to the central system which compares both the data by means of microcontroller and if there is any difference found, it informs the power utility about the hooking, meter relief or theft activities happen. Information of the theft detection through the global mobile communications system is transmitted and notified theft is displayed on the terminal monitor or won. As a result, although consumers continue to use excess fuel, the customer's power supply is cut in the electricity boards segment. The general radio package module system sends central circuit and meter data via an internet protocol address to a web server. GSM's IoT based perception is used to monitor the power supply and billing information calculated with a microcontroller continuously with the determination of the electricity table area. With this unit, the duplicate user can be located at the rear of the electricity office with the power meter status.

A series of digital marketing transformations have been carried out at Chinese gas stations. From the perspective of user experience, the article discovers the problems in the digital service system of gas stations. Through semi-structured interviews, we collect consumers’ actual usage of digital equipment and service systems. The research adopts grounded theory to encode qualitative data. The behavioral state and psychological needs of consumers in the driving situation are analyzed. The study clarifies the influence of consumer behavior status, service context process, key task difficulty, and service information visualization on service experience. The discovery of service problems in turn help to further optimize the digital service system. The constructed SITE model clarifies the key factors of user experience in the gas station scenario. The research findings can provide a theoretical reference and practical guidance for gas station business owners, managers, and designers.

To pursue business innovation with PSS, many different PSS concepts are designed and evaluated. Various business models of a PSS design concept are devised and evaluated as well. Evaluation of the economic sustainability of PSS business models is critical. This paper presents a systematic method to evaluate the economic sustainability of PSS business models using a system dynamics modelling template. System dynamics modelling task is challenging for practitioners due to the variety of variables comprising business model strategies and their complex interrelationships. To enable the modelling task, a system dynamics modelling template composed of six modules of customer acquisition, channel acquisition, profit creation, resource acquisition, PSS provision, and partnership pattern has been devised. The PSS business model evaluation method has been illustrated using a smart study experience management service system design case to demonstrate the proposed system dynamics modelling template can reflect the case-specific business model which consists of the particular business model strategies.

The Puskesmas Information Reporting System (SIP) has been in since 1981. In its implementation, SIP was still limited to data which was the result of interactions between the community and health facilities. Methods used Research and Development (R&D). Collecting data by interview, observation, and documentation. The sample contained sample I amounted were 6 people, sample II were 25 people, and sample III were 16 people. Sampling technique with non probability sampling. Data analyswas used univariate and bivariate analyswas. Result that designed and built after being tested by experts, it was feasible to apply the Digital-Based Dental Health Recording and Reporting Service System Development Model. The Development Model of the Digital-Based Dental Health Recording and Reporting Service System after being tested has been proven to be relevant/suitable for use in the implementation of the digital-based dental health recording and reporting service system at the Puskesmas in Boyolali Regency. The Development Model of the Digital-Based Dental Health Recording and Reporting Service System has effective for the substance of the application system and improves the quality of the measurement before and after the development of a dental health recording and reporting service system based at the Puskesmas in Boyolali District with p value =0.001. Conclusion that Development Model of the Digital-Based Dental Health Recording and Reporting Service System has effective for the substance of the application system and improves the quality dental health recording and reporting service system based at the Puskesmas.

Cloud/edge computing and deep learning greatly improve performance of semantic understanding systems, where cloud/edge computing provides flexible, pervasive computation and storage capabilities to support variant applications, and deep learning models could comprehend text inputs by consuming computing and storage resource. Therefore, we propose to implement an intelligent online custom service system with power of both technologies. Essentially, task of semantic understanding consists of two subtasks, i.e., intent recognition and slot filling. To prevent error accumulation caused by modeling two subtasks independently, we propose to jointly model both subtasks in an end-to-end neural network. Specifically, the proposed method firstly extracts distinctive features with a dual structure to take full advantage of interactive and level information between two sub-tasks. Afterwards, we introduce attention scheme to enhance feature representation by involving sentence-level context information. With the support of cloud/edge computing infrastructure, we deploy the proposed network to work as an intelligent dialogue system for electrical customer service. During experiments, we test the proposed method and several comparative studies on public ATIS and our collected PSCF dataset. Experiment results prove the effectiveness of the proposed method by obtaining accurate and promising results.

Self-sovereign identity (SSI) is a new paradigm, which puts users back in control of their own digital identity. This does not only strengthen the position of the users but implies new interaction schemes that may improve interoperability and usability. Smart services systems enable the integration of resources and activities and use smart products as boundary objects. As such systems typically involve digital interactions between multiple actors, it can be assumed that utilising SSI has a positive impact on them. To investigate how these potential improvements manifest themselves, we investigate electric vehicle charging as example of a smart service system. At the core of our conceptual analysis is the service process, which we extract from a reference model. Based on a SWOT analysis, we identify areas for transformation and derive an SSI-enabled interaction model for an electric vehicle charging service. The evaluation of the new process shows that SSI can reduce complexity of integration with partners and can provide a better customer experience through simplified registration and authentication. Moreover, SSI might even lead to the disintermediation of actors in the service system. Although SSI is still emerging, our findings underline its relevance as a mechanism to establish trust in smart service systems through the seamless and standardised integration of digital identities for humans, organisations, and things.

Problem Definition:In service operations settings where the difficulty of jobs is unpredictable, workerscan encounter critical incidents (CIs)—jobs which are sufficiently disturbing to challenge workers’ copingmechanisms. We examine the impact of encountering CIs on subsequent operational performance of workers.Academic / Practical Relevance:Prior work has examined the effects of CIs on the long-term psycho-logical health of workers. We demonstrate that encountering CIs has a practically meaningful impact onoperational performance. We also examine the time-dependency and process-dependency of the effect, andanalyze whether it is mitigated by individual characteristics such as age or experience.Methodology:We use data on 902,002 ambulance activations conducted by paramedics at the LondonAmbulance Service (LAS). We define CIs as incidents where patients have a high probability of dying at thescene, and examine the impact of such events on the paramedics’ performance for the remainder of theirshifts. Our outcomes are the completion time of the ambulance activation and each of its five sub-processes.The exogenous assignment of CIs to paramedic crews allows a clean identification of our effect using ashift-level difference-in-differences specification.Results:Crews who have encountered one prior CI (two prior CIs) spend on average 2.6% (7.5%) moretime completing each remaining ambulance activation in the shift. The impact is strongest for the jobsimmediately following a CI but persists throughout the shift. The largest effects come from the sub-processeswhich are least standardized and where paramedics cannot rely on standard operating procedures. Theduration effect is larger for teams of older paramedics, but is simultaneously mitigated by experience.Managerial Implications:Our results show that CIs increase subsequent job duration and that more thanone CIs have a compounding, negative effect on operational performance. As a result, managers in settingswhere performance consistency is key would be advised not to assign new jobs to teams with recent CI experiences.

Problem definition: We consider a service system in which customers must travel to the queue to be served. In our base model, customers observe the queue length and then decide whether to travel. We also consider alternative information models and investigate how the availability of queue-length information affects customer-equilibrium strategies, throughput, and social welfare. Academic/practical relevance: A common assumption in queueing models is that once a customer decides to join the queue, joining is instantaneous. This assumption does not fit real-life settings, where customers possess online information about the current wait time at the service, but while traveling to the service, its queue length may change. Motivated by this realistic setting, we study how queue-length information prior to traveling affects customers’ decision to travel. Methodology: We prove that a symmetric equilibrium exists in our base model. We perform the calculation numerically as a result of the model complexity, which is due to the fact that the arrival rate to the traveling queue depends on the current state of the service queue, and vice versa. The alternative models are tractable, and we present their analytical solution. Results: When customers can observe the service-queue length prior to traveling, their probability of traveling is monotonically nonincreasing with the observed queue length. We find that customers may adopt a generalized mixed-threshold equilibrium strategy: Travel when observing short queue lengths, avoid traveling when observing long queue lengths, and mix between traveling and not traveling when observing intermediate queue lengths, with a decreasing probability of traveling. Managerial implications: Our results imply that when system congestion is high, the provider can increase throughput by disclosing the queue-length information, whereas at low congestion, the provider benefits from concealing the information. With respect to social welfare, queue-length information prior to departure is beneficial when congestion is at intermediate to high levels and yields the same social welfare otherwise.

We study an emerging computer network model of delayed observations in which the system is unobservable for the customers at their arrival instants, but after a while, they are informed about their current positions and they may renege. We develop a queueing-game-theoretic vacation model to explore customers’ equilibrium strategy, stationary system behavior and social welfare based on a reward-cost structure. Our main results are as follows. First, we determine a closed form of the customers’ equilibrium strategy, the expected net benefit of a customer and social welfare in the service system. Second, extensive numerical experiments that demonstrate the effect of vacation rate θ and system announcement rate δ on the equilibrium strategy and social welfare. We find that the impact of announcement rate δ is greater than vacation rate θ in some cases. Finally, we show that the equilibrium strategy can give customers more information and reduce the cost of waiting. Moreover, our results can also provide more precise information to the system administrators.

Jhon Motor is a workshop engaged in the sale of spare parts and also service. So far, the service system at Jhon Motor is still done manually, by means of consumers coming directly to the service area or calling Customer Service to set a booking service schedule. However, the problem that often occurs is when the service schedule is full or full, consumers who come directly cannot do service on the same day because they have to reschedule for service. In addition, there are some consumers who cannot contact Customer Service, because of the many incoming calls to make a booking service. The Booking Service application at Jhon Motor is made using the PHP and Java programming languages, with a MySQL database. The software used for making the application is Adobe Dreamweaver CC 2019, Android Studio, and XAMPP. The Booking Service application at Jhon Motor is a medium or container that can be used to assist the booking service process at Jhon Motor, which is still done conventionally. The Booking Service application at Jhon Motor has 2 interfaces, namely web-based (accessed by the Jhon Motor administrator) and Android-based (accessed by consumers who want to book service). The Booking Service application at Jhon Motor also has 1 (one) database server, so that whatever is processed in a web-based application, the information on the Android application automatically changes. Based on the results of tests carried out both offline and online, web-based applications can process data properly and smoothly, and Android-based applications successfully display service fee information and can make booking services.

We present an expanded framework for the use of business analytics in projects. To the commonly used descriptive, predictive, and prescriptive analytics, we add comparative analytics, wherein we compare the performance of systems under different interventions. This framework provides a conceptual roadmap for the implementation of business analytics projects. We then demonstrate this framework using recent operations research literature on analytics in healthcare, summarizing papers focusing on one of these aspects. Next, we discuss queue mining as an example of theory and practice illustrative of these aspects. We conclude there is room for further work by operations researchers and management scientists within business analytics projects generally and the healthcare industry more specifically. We argue future work should consider both theory and practice, especially within prescriptive analytics projects, where analysis through the lens of operations research and management science is imperative. We provide some thoughts on the current and future state of operations research and management science in business analytics.

Problem of structural optimization in a distributed service system is solved by the example of system "Production - delivery - consumption" for mass market product. In this regard, the purpose of work is to develop a method for structural optimization of "Production - delivery - mass consumption" system, by introducing and rational placement of intermediate production points based on solving clustering problems with taking into account the peculiarities of calculating distances between city objects. To achieve the goal of the work, it is necessary to solve the following tasks: clustering of city objects, using the metric of city blocks, for a given number of groups for selected location of production and grouping centers; finding the best location for a given number of clustering centers; determination of a rational number of clustering centers. Task was solved in three stages. First stage - clustering a set of consumption objects for given intermediate delivery centers locations. The second stage - finding the best locations for a given number of intermediate delivery centers. The third stage - determination of the rational number of intermediate centers. Formulated problem is solved according to two criteria: combined length of delivery routes product consumers and the probability that a random delivery time exceeds the critical value. The numerical value of the second criterion is calculated on the assumption that for each path may be estimated value of the mean and variance delivery time. The appropriate number of production centers is determined by a simple comparison of system efficiency for several realistically possible options. An example of clustering problem solving in the metric of "city blocks" on a directed graph by both criteria is given.

BPPW JABAR (Balai Prasarana Permukiman Wilayah Jawa Barat) under the Ministry of Public Works and Public Housing Directorate General Cipta Karya, is a representative of the Central Government to support the Coordination function with the Regional Government, In the BPPW JABAR organizational structure, there is an Individual Consultant who is placed specifically to deal with obstacles experienced by the organization in the use of IT services, but in its application, there has never been an evaluation for any process of repairing the constraints experienced and documentation for the process of repairing IT service constraints, resulting in every obstacle never experienced repeated with the same case. That is because the number of people who handle IT problems is only 1 person and there is no IT work structure so that work is carried out based on continuous orders, if it continues to occur it will result in obstruction of existing business processes and processes, due to problem handling IT services are not completed quickly and precisely because of the limitations of people and lack of documentation for any problems that have been experienced and resolved. Not just repairing IT service constraints. In the BPPW JABAR organization the dependence on centralized data and secure data storage becomes a high need because every existing data is important and confidential data because it is related to planning data and the implementation of development in the field of Cipta Karya, so the need for an IT service for data storage centralized and can be shared specifically for the work environment of the BPPW JABAR organization and people who have access to the data. The steps taken are evaluating IT services in service operations to assess the level of readiness of the IT problem improvement process experienced by the organization while using IT services and to conduct a GAP analysis between the existing conditions of IT service improvement with the level of maturity expected by the organization in the process of improving IT services and design service strategy for related centralized data and can be shared.

Smart medical care is user-centric, medical information is the main line, and big data, Internet of Things, cloud computing, artificial intelligence, and other technologies are used to establish scientific and accurate information, as well as an efficient and reasonable medical service system. Smart medical plays an important role in alleviating doctor-patient conflicts caused by information asymmetry, regional health differences caused by irrational allocation of medical resources, and improving medical service levels. This article mainly introduces the remote care assistance system of emergency department based on smart medical and intends to provide some ideas and directions for the technical research of patients in emergency department receiving remote care. This paper proposes a research method for remote care assistance in emergency departments based on smart medical, including an overview of remote care based on smart medical, remote care sensor real-time monitoring algorithms based on smart medical, signal detection algorithms, and signal clustering algorithms for smart medical. Remote care in the emergency department assisted in research experiments. The experimental results show that 86.0% of patients like the remote care system based on smart medical studied in this paper.

Aiming at the challenges of mobile learning in the current big data environment and the current research status of teaching intelligent services(TIS), the article first explores the construction of mobile learning in the teaching intelligent service system(TISS) of colleges and universi-ties(CU secondly, the business process of mobile learn-ing for support and guidance, big-data-driven mobile learning intelligent service framework system is constructed, which mainly includes three levels: infrastructure layer, data resource layer, and service application layer. The functions and characteristics of each layers are dis-cussed in detail. Use big data to analyze the application status of mobile learning in college education from three aspects: overall development, resource construction, and utilization. It is proposed to increase courseware resources, reform teaching models, and use big data analysis to improve the level of teaching management continuously comments and suggestions. Re-searchers believe that the system can integrate mobile learning infrastructure, data resources, service applications and students, reconstruct the business process of mobile learning intelli-gent services, provide decision support for mobile learning services, and at the same time, it can improve learning capabilities and provide personalized the service realizes teaching stu-dents by their aptitude.

The article analyzes the positive foreign experience of implementing augmented reality (AR) library projects. It has been found out that the most common uses of augmented reality in libraries today are providing access to information products with augmented reality and their promotion, navigation of library space and resources, promoting user education through the creation of appropriate applications with learning materials and resources and simplification for the user assimilation of information due to the diversity and interactivity of its visual presentation, informing and providing access to cultural heritage sites, organizing tours and exhibitions. Given that the use of AR in libraries not only expands the range of services and resources but also positively affects its reputation as an interactive creative institution, open to users and innovation, AR technology is effective not only in the library service system but also in the marketing activities of the institution, helping to attract new users. Current approaches to the implementation of library projects using augmented reality technology are highlighted. Their assistance in strengthening corporate culture, inter-institutional ties, and civil society is substantiated. It has been proved that the AR applications created as a result of project implementation operate on the principle of integrated information products, which allow the user to access distributed information from one access point via mobile phone, integrating not only information of the virtual space of the Internet environment but also real, physical, space. The advantages of AR implementation in the library have been determined, among which: budget realization, easy integration with available technologies, and promotion of interactivity of library service. The need for professional development of librarians and the organization of training courses on the use of immersive technologies in library activities is emphasized.

Tourism safety is the lifeblood of national tourism development, and tourism safety information monitoring is an important foundation for tourism safety management. The maturity of Internet information technology has led to the rapid rise of blockchain and the Internet of Things. In essence, the blockchain is a shared database. The Internet of Things can access all kinds of necessary information through various networks, such as heat, electricity, dynamics, chemistry, biology, realize the ubiquitous connection between things and people, and realize the recognition and intelligent perception of objects. This article aims to study a tourism safety monitoring information service system based on the Internet of Things and blockchain. This paper uses the principle of sensor ranging and electromagnetic induction, acoustic calculation, sensor calculation, and RFID tag distance to measure the real-time position of the person’s RFID tag information and continuous monitoring information of infrasound intensity to monitor and pre-warn natural disasters in the scenic area. Research results, it shows that this system can monitor disaster information in time and can effectively reduce the risk of injury by 10% –20% of tourists.

With the development of National Platform for Geospatial Information Services in China, it accumulated massive image data with multi-source, multi-temporal and multi-scale. Displaying and taking full use of these historical data can be meaningful to the users. This paper provided design and implementation of multi-temporal image service system based on national platform, which meets the requirements of different users. In this paper, the design of the overall system architecture was introduced, and different system levels stated respectively. The Key technologies presented in the third part, and the implementation displayed parts of functions of this system. Applications, that is typical, recommended in the following part. Also, future efforts and discussions were put forward in the end, which could be guided following work. The research also looks forward to the development of national platform, and the direction of services that should be provided by multi-temporal image service is already under development.

PurposeCustomer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.Design/methodology/approachThe authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.FindingsAs a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.Originality/valueThe authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

The ubiquitous power Internet of Things (UPIoT) is an intelligent service system with comprehensive state perception, efficient processing, and flexible application of information. It focuses on each link of the power system and makes full use of the mobile internet, artificial intelligence, and other advanced information and communication technologies in order to realize the inter-human interaction of all things in all links of the power system. This article systematically presents to the national and international organizations and agencies in charge of UPIoT layer standardization the status quo of the research on the Internet of Things (IoT)-related industry standards system. It briefly describes the generic standard classification methods, layered architecture, conceptual model, and system tables in the UPIoT application layer. Based on the principles of inheritance, innovation, and practicability, this study divides the application layer into customer service, power grid operation, integrated energy, and enterprise operation, emerging business and analyzes the standard requirements of these five fields. This study also proposes a standard plan. Finally, it summarizes the research report and provides suggestions for a follow-up work.

Student service corner is an application designed as a student service system by utilizing a laravel framework in the Informatics Engineering study program, University of Lampung. The choice of Laravel as an application development framework at this time is because this application will continue to be developed with several programmers and will be integrated with other systems into a larger informatics study program. Data were collected by conducting interviews and literature studies. Data modeling in this system uses an entitty relationship diagram / ERD. The test result of this system is that the level of student satisfaction with services in the Information Engineering study program increases because it is always available because it is online. The admin of the study program can manage and request services for follow up. The results of this test show that the student service corner application can meet the system requirements as a communication between the the administration and students