Higher Service Rate Research Articles

Impact of childhood traumatic brain injury on fitness for service class, length of service period, and cognitive performance during military service in Finland from 1998 to 2018: A retrospective register-based nationwide cohort study.

Traumatic brain injury (TBI) can cause neuronal damage and cerebrovascular dysfunction, leading to acute brain dysfunction and considerable physical and mental impairment long after initial injury. Our goal was to assess the impact of pediatric TBI (pTBI) on military service, completed by 65-70% of men in Finland. We conducted a retrospective register-based nationwide cohort study. All patients aged 0 to 17 years at the time of TBI, between 1998 and 2018, were included. Operatively and conservatively treated patients with pTBI were analyzed separately. The reference group was comprised of individuals with upper and lower extremity fractures. Information on length of service time, service completion, fitness for service class, and cognitive performance in a basic cognitive test (b-test) was gathered from the Finnish Military Records for both groups. Linear and logistic regression with 95% CI were used in comparisons. Our study group comprised 12 281 patients with pTBI and 20 338 reference group patients who participated in conscription. A total of 8 507 (66.5%) men in the pTBI group and 14 953 (71.2%) men in the reference group completed military service during the follow-up period. Men in the reference group were more likely to complete military service (OR 1.26, CI 1.18-1.34). A total of 31 (23.3%) men with operatively treated pTBI completed the military service. Men with conservatively treated pTBI had a much higher service rate (OR 7.20, CI 4.73-11.1). In the pTBI group, men (OR 1.26, CI 1.18-1.34) and women (OR 2.05, CI 1.27-3.36) were more likely to interrupt military service than the reference group. The PTBI group scored 0.15 points (CI 0.10-0.20) less than the reference group in cognitive b-test. PTBI groups had slightly shorter military service periods and higher interruption rate than our reference-group. There were only minor differences between groups in cognitive b-test.

Scheduling policies to minimize abandonment costs in infomercial call centers

Infomercial call centers answer calls in an order that reduces the number of abandoned calls to minimize lost revenue. We study a multi-class queuing system with a single server and customer abandonment, motivated by the case of infomercial call centers. We fully characterize the structure of the server’s optimal scheduling policy that minimizes the long-run average customer abandonment cost. We show that the optimal service policy is a static priority policy. We derive sufficient conditions under which the so-called b μ –rule is optimal. Under the b μ –rule, it is optimal to give priority to the customer type that has a higher service rate ( μ ) and higher abandonment cost (b), i.e., higher index b μ . We numerically test the robustness of our proposed policy in more general settings, including multi-server infomercial call centers.

Equilibrium and social optimality in queues with service rate and customers’ joining decisions

ABSTRACT This paper treats a single server Markovian queueing system with changeable service rates and customer choice. When a customer arrives at an empty system, he is served by the server at a low service rate. This low service rate continues until the queue length reaches a threshold. Then the server switches to a high service rate to serve customers who join the system. The high service rate continues until the system becomes empty and a low service rate starts again. Under a service reward and waiting cost structure, customers decide to join or to balk based on their net utility of joining the system. The customers’ equilibrium and socially optimal joining strategies are studied under two information scenarios which are observable and unobservable queues, respectively. Such a model fits a stochastic service system where the server can make his service rate decision and customers can make their joining or baking decisions and thus has wide applications in service industries. Our focus is on examining the equilibrium performance of the queue under the interaction between the server’s service rate and customers’ joining decisions. We develop the steady-state distribution and performance measures of the system by formulating two-dimensional continuous-time Markov chains.

Competition and Cooperation of Autonomous Ridepooling Services: Game-Based Simulation of a Broker Concept

With advances in digitization and automation, autonomous mobility on demand services have the potential to disrupt the future mobility system landscape. Ridepooling services in particular can both decrease land consumption by reducing the need for parking and increase transportation efficiency by increasing the average vehicle occupancy. Nevertheless, because ridepooling services require a sufficient user base for pooling to take effect, their performance can suffer if multiple operators offer such a service and must split the demand. This study presents a simulation framework for evaluating the impact of competition and cooperation among multiple ridepooling providers. Two different kinds of interaction via a broker platform are compared with the base cases of a single monopolistic operator and two independent operators with divided demand. In the first, the broker presents trip offers from all operators to customers (similar to a mobility-as-a-service platform), who can then freely choose an operator. In the second, a regulated broker platform can manipulate operator offers with the goal of shifting the customer-operator assignment from a user equilibrium towards a system optimum. To model adoptions of the service design depending on the different interaction scenario, a game setting is introduced. Within alternating turns between operators, operators can adapt parameters of their service (fleet size and objective function) to maximize profit. Results for a case study based on Manhattan taxi data, show that operators generate the highest profit in the broker setting while operating the largest fleet. Additionally, pooling efficiency can nearly be maintained compared to a single operator. The regulated competition benefits not only operators (profit) and cities (increased pooling efficiency), but customers also experience higher service rate, though they need to accept slightly increased waiting and travel time due to increased pooling efficiency. Contrarily, when users can decide freely, the lowest pooling efficiency and operator profit is observed.

Cost-Aware Resource Optimization for Efficient Cloud Application in Smart Cities

In this generation of smart computing environment, every device in the system and multple system are interconnected to each other, which allows users to view, analyze data, and make smart decisions. Smart cities are one example of smart environments where every device is connected and computing is performed on the cloud. In such a situation, the system requires an efficient system to handle huge requests and deliver data. Cloud computing plays an essential role in solving this issue but suffers from resource optimization, cost optimization, and load balancing. This work is aimed at solving the issue of resource and cost optimization in cloud infrastructure to provide a high service rate and sustainable infrastructure to cloud applications in smart cities. The proposed model is inspired by artificial neural networks and nature-inspired algorithm to reduce execution cost, average start time, and finish time and to make the system power efficient at the same time to improve the utilization of the system. The result shows that the proposed model completes more tasks in the least time and execution cost as compared to the existing models. This showcases the smart cloud applications are cost efficient and can complete the tasks in less time.

RETRACTED: Port Terminal Performance Evaluation and Modeling

Background: The efficiency and competitiveness of port supply chain entities are two critical concerns for maritime transport that must constantly be enhanced. This paper presents an approach called ECOGRAISIM for evaluating the performance of the seaport supply chain. The objective is to achieve an effective operational plan for multimodal terminals. Methods: The proposed approach incorporates the ECOGRAI (Graph with Interconnected Results and Activities) technique with ESSIMAS (Evaluation by Simulation of Innovative Solutions for the Development of Mass Transport on the Seine Axis by electric rail coupons). An additional stage was incorporated to accomplish the performance control. A particular focus was put on action variables and procedures for container and massified transfer management. The multimodal terminal at Le Havre seaport was adopted as the case study. Results: Several scenarios of container transfer were defined and investigated based upon specific features, including delays, minimizing expenses and CO2 emissions. The results show that the operational planning method results in a higher service rate and significantly reduces delay, cost and CO2 emissions. Conclusions: The proposed approach is bound to be beneficial for maritime transport planners and decision makers.

Approximations and Optimal Control for State-Dependent Limited Processor Sharing Queues

The paper studies approximations and control of a processor sharing (PS) server where the service rate depends on the number of jobs occupying the server. The control of such a system is implemented by imposing a limit on the number of jobs that can share the server concurrently, with the rest of the jobs waiting in a first-in-first-out (FIFO) buffer. A desirable control scheme should strike the right balance between efficiency (operating at a high service rate) and parallelism (preventing small jobs from getting stuck behind large ones). We use the framework of heavy-traffic diffusion analysis to devise near optimal control heuristics for such a queueing system. However, although the literature on diffusion control of state-dependent queueing systems begins with a sequence of systems and an exogenously defined drift function, we begin with a finite discrete PS server and propose an axiomatic recipe to explicitly construct a sequence of state-dependent PS servers that then yields a drift function. We establish diffusion approximations and use them to obtain insightful and closed-form approximations for the original system under a static concurrency limit control policy. We extend our study to control policies that dynamically adjust the concurrency limit. We provide two novel numerical algorithms to solve the associated diffusion control problem. Our algorithms can be viewed as “average cost” iteration: The first algorithm uses binary-search on the average cost, while the second faster algorithm uses Newton-Raphson method for root finding. Numerical experiments demonstrate the accuracy of our approximation for choosing optimal or near-optimal static and dynamic concurrency control heuristics.

E-Ride: An Adaptive Event-Driven Windowed Matching Framework in Ridesharing

Ridesharing services aim at reducing the users’ travel cost and optimizing the drivers’ routes to satisfy passengers’ expected maximum matching times in practice request dispatching. Existing works can be roughly classified into two types, i.e., online-based and batch-based methods, in which the former mainly focus on responding quickly to the requests and the latter focuses on enumerating request combinations meticulously to improve the service quality. However, online-based methods perform poorly in terms of service quality due to the neglect of the sharing relationship between requests, while batch-based methods fail on efficiency. None of these works can smoothly balance the service quality and matching time cost since the matching window is not sufficiently explored or even neglected. To cope with this problem, we propose a novel framework <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {E}$ </tex-math></inline-formula> -Ride, which comprehensively leverages the matching time window based on the event model. Specifically, an adaptive windowed matching algorithm is proposed to adaptively consider personalized matching time and provide a matching solution with higher service rates at lower latencies. Besides, we maintain the request groups through a mixed graph and further integrate the subsequent arrival requests to optimize the matching results, which can scale to or satisfy online use demands. The extensive experimental results demonstrate the efficiency and effectiveness of our proposed method.

Transient and steady state analysis of M/M^([b])/1 queue with second optional service

ABSTRACT In this paper, the transient and steady state of a single server batch service queueing system with second optional service is analyzed. All incoming units receive essential service, once the essential service is completed, the units may opt the optional service. The study applies the probability generating function, Rouche’s theorem, and Laplace transform techniques to obtain the transient state probabilities. The stationary probabilities are obtained by using the Tauberian property in the Laplace transform expressions. This study contributes to filling the gap on investigation the batch queue with essential and optional services with interesting practical application in health care systems. Furthermore, we have presented numerical results and cost optimization. The results reveal that a higher service rate in the essential service helps the system manager to run the system effectively and emphasize on optimal service rates in order to have a cost benefit and less congestion in the queue.

Linear-quadratic optimal control of multi-modal distribution systems with imperfect channels*

The paper examines the perspectives of linear-quadratic (LQ) optimal control in steering the process of goods distribution in logistic systems with multiple transportation options. In the considered class, the distribution centre governs the stock replenishment process of subordinate depots, from which un uncertain market demand is served. The centre is linked with the depots via shared supply channels with different characteristics regarding delay, reliability, and capacity, e.g. train vs truck delivery. The design objective is a rule of dynamical channel allocation – how many goods to send in a period using a given mode – so that balanced, cost-efficient system performance and high customer service rate are achieved. The received goods are inspected for quality defects and rejected when faulty. Thus, one needs to cope with two major sources of uncertainty: unpredictable demand variations and channel imperfections. A multi-variable LQ optimal controller is designed and presented in closed form for detailed analytical and numerical treatment. It is formally shown that despite perturbations, the controller always establishes a non-negative and upper-bounded replenishment signal, and the stock level does not cross the reference value. Conditions for warehouse space selection and obtaining full demand satisfaction at the depots are specified and formally proved.

Detection of an appropriate pharmaceutical company to get a suitable vaccine against COVID-19 with minimum cost under the quality control process.

Some international pharmaceutical companies have succeeded in producing vaccines against COVID‐19. Countries all over the world have aimed to obtain these vaccines with minimum cost. We consider a set of K‐independent Markovian waiting lists. Each list contains a set of countries, where each one of them has an exponential service time and a Poisson arrival process. These companies differ in some characteristics such as the vaccine production cost and the speed of the required quantity delivery. We present a new detection model that helps in providing an appropriate decision to choose a suitable company. Moreover, the concept of balking and the retention of reneged countries is taken into consideration under the quality control process of each waiting list. Under steady state, we face an interesting and difficult discrete stochastic optimization problem. Its solution gives an optimal distribution of the searching effort, which is bounded by a known probability distribution. A simulation study has been derived to get the minimum value of the paid cost random values. The highest service rate, the total expected profit of each queuing system, and the optimum performance measures, which depend on this cost, have been obtained to show the effectiveness of this model.

Світові тенденції діджиталізації банківського сектора

The article is aimed at identifying global trends in using modern information technologies in the banking sector, which were formed under the influence of digitalization of the economy. Digitalization is one of the main trends in global economy. Digital transformation has become relevant for business in various areas of economic activity, and the banking sector is not any different. The introduction of a bank card is considered to be the initial stage of the banking sector digitalization. At the present stage of development, banks have a wide range of tools to digitalize their activities, from office operations to customer service, to credit portfolio management, and to risk management. Today, the world's leading banks of various ownership forms can create digital currencies. The most widespread digital technologies in the banking sector are: mobile technologies, "big" data, blockchain, "cloud" technologies, artificial intelligence, the Internet of Things. The application of the outlined technologies allows banks to optimize work processes, reduce operating costs, provide high service rate and convenience for customers, and also generate innovations and bring new banking products on the market. Digitalization makes it possible to track the customer base in real time, to form personalized sets of services for consumers, to restructure revenues and manage the bank’s credit risks. The authors conclude that digitalization is a non-alternative way for a modern bank to develop. Digitalization, introduction of information technologies, and the creation of innovative products are the factors determining a bank's competitiveness in the modern market.

Strategic joining and information disclosing in Markovian queues with an unreliable server and working vacations

ABSTRACT We study a queueing system of an unreliable server providing services to customers with two different service rates. When the system is empty of customers, the server takes a vacation and provides a low service rate. Once the vacation is completed, the server is resumed to serve customers, if any, at a higher service rate, or takes another vacation if the server found an empty system. The server is subject to random breakdowns when serving customers and if failed, it enters a repair stage. Under different system information levels, we investigate customers’ equilibrium strategies and follow-up system measures. Numerical comparisons among different information levels reveal that when the traffic load is large enough, the service provider (SP) prefers to disclose the server’s status and queue length information to customers to maximize the system throughput. While when the traffic load is small enough, there exist two cases with regard to the failure rate: (i) If the failure rate is smaller than a certain threshold, the SP tends to conceal the system information. (ii) If the failure rate is greater than the threshold, revealing the information is helpful to maximize the system throughput.

Quality control for a detected an appropriate queue from K‐independent M/M/C/N queueing models

AbstractIn this paper, we consider a system of K‐independent Markovian queues such that each one of them has a Poisson arrival process and exponential service time. We assume that every server has some characteristics such as the speed of the service performance or the service cost. To find an appropriate queue, which meets customer needs for the service performance, we present a new approach that gives a suitable decision to choose an appropriate queue from our system. This allows the customer to deal with minimum cost and faster server under steady state. We solve an interesting discrete stochastic optimization problem where the paid cost by the customer is bounded by a Gaussian distribution. Using these hypotheses, we perform a simulation study by generating the paid cost random values and choosing the minimum value between them. This minimum cost gives the highest service rate, which is used to obtain the optimum values of the system effectiveness measures.

Analysis of retrial queue with heterogeneous servers and Markovian arrival process

Multi-server retrial queueing system with heterogeneous servers is analyzed. Requests arrive to the system according to the Markovian arrival process. Arriving primary requests and requests retrying from orbit occupy an available server with the highest service rate, if there is any available server. Otherwise, the requests move to the orbit having an infinite capacity. The total retrial rate infinitely increases when the number of requests in orbit increases. Service periods have exponential distribution. Behavior of the system is described by multi-dimensional continuous-time Markov chain which belongs to the class of asymptotically quasi-toeplitz Markov chains. This allows to derive simple and transparent ergodicity condition and compute the stationary probabilities distribution of chain states. Presented numerical results illustrate the dynamics of some system effectiveness indicators and the importance of considering of correlation in the requests arrival process.

GIS-based spatial analysis: basic sanitation services in Parana State, Southern Brazil

The objective of this study is to provide a view of the geographic distribution of basic sanitation services offered in Parana, Brazil. Temporal distributions, spatial patterns, and clusters were determined using 2000, 2010, and 2016 data. Tabular data of the percentages of households receiving water supply, waste collection, and sanitary sewage treatments were used as the basic inputs of the study. Time series maps were created using geographic information systems (GIS) for the visualization of the temporal changes in basic sanitation rates. The spatial variation in sanitation services was measured using Moran's Global Index (Moran's I), while the Local Indicators of Spatial Association (LISA) and Getis-Ord Gi*(d) were used to identify the presence of possible clusters and hot spots in the percentage of households with sanitation services. The study showed strong evidence that the service rates for sanitary sewage were significantly lower than those for water supply and waste collection. Waste collection proved to have the highest service rate in the study area. Waste collection was also the service that increased the most in the study period, followed by the water supply service. Identification of the most vulnerable municipalities was performed using LISA and the local Getis-Ord Gi*(d) statistic. Most of the low value clusters and cold spots were detected in the southern and west-central regions, while the high value hot spots and clusters were detected in the northern and northeastern regions of the state of Parana.

Pooling and Balking: Decisions on COVID-19 Testing

The most important issue in the prevention and mitigation of the COVID-19 pandemic is, perhaps, testing. To increase the convenience and efficiency of testing, many countries have set up walk-in testing sites. One fundamental decision for local governments or authorities to make is, should they build fewer centralized walk-in or drive-thru testing site (with more manpower and therefore a higher service rate) or more decentralized testing sites (each with less manpower and therefore a lower service rate)? To make this decision, one needs to understand the benefits and/or drawbacks of pooling incorporating people's balking behavior. In this short paper, we build classic queueing models to analytically characterize how system performance measures such as the expected waiting time, the expected queue length, and the balking probability change with the service rate, under different values of the implied utilization. These performance measures not only indicate the productivity and efficiency of testing operations, but also influence the safety and well-being of people and society.

Analysis and Optimization of Systems with Heterogeneous Servers and Jump Priorities

Markov models of systems with heterogeneous servers, various types of requests, and jump priorities are proposed. It is assumed that there are high and low priority requests; the high priority requests are assigned to the server with the high service rate, and the low priority requests are assigned to the server with the low service rate. Models of two types are studied: with separate queues and the shared queue for requests of different types. Jump priorities determine the rules according to which the type of low priority requests changes depending on the state of the queue. Methods for calculating the distribution of the probability of the system states are developed, formulas for the calculation of the characteristics of the system are derived, and the problem of the optimization of these characteristics is solved. The results of numerical experiments are presented.

Optimal Policy in Single-Server Multi-Class Queuing Systems with Abandonment

We study a multi-class queuing system with a single server and customer abandonment. We fully characterize the structure of the server's optimal scheduling policy that minimizes the long-run average customer abandonment cost. We show that the optimal service policy is a static priority policy. We derive sufficient conditions under which the so-called b.mu-rule is optimal. Under the b.mu-rule, it is optimal to give priority to the customer type that has higher service rate (mu) and higher abandonment cost (b), i.e., higher index b.mu.

Custom Bus optimization of docking stations based on partition path selection

The Custom Bus has advantages on punctuality, direct arrival, and economic benefits. It has a good effect on attracting passengers from private cars commuting in city. Due to the unscientific docking stations, the Custom Bus is always ineffective. There are little studies about the optimization of docking stations. Based on the reference to minimum cost maximum flow problem, using the chain of minimum cost increase flow and the Dijkstra algorithm, choosing points by 0-1 planning model, studying the docking forms on multi-origin to multi-destination. Guaranteeing the attendance rate and limited docking stations, to achieve the goals that shortest single path and the highest service rate by arranging the docking stations reasonably. This algorithm has been demonstrated during the process of optimizing Custom Bus docking stations between Guilin’s new and old urban districts.