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Recently, public and private providers within two-tier healthcare markets have engaged in a fierce competition for patients, which is significantly influenced by patients' multifaceted sensitivities to delays and payments, particularly when patients' potential balking is considered. However, existing literature studying the two-tier healthcare market often presupposes patients as homogeneous or one-dimensionally heterogeneous, and neglects their balking behavior. In stark contrast, this paper delves into the combined effects of patients’ delay- and payment-sensitive heterogeneities, alongside their joining or balking decisions, on the service competition dynamics within the two-tier healthcare market. Leveraging a game-theoretic framework, we initially derive subgame equilibria, both inclusive and exclusive of patient balking, uncovering that patient balking can lead to a subgame perfect Nash equilibrium in the presence of two-dimensional patient heterogeneity. Intriguingly, our findings further suggest that the non-profit public provider may, counterintuitively, curtail its service capacity despite budgetary adequacy, contradicting the conventional assumption that such providers would fully allocate funds towards patient care. Ultimately, from a patient welfare standpoint, we find heightened payment heterogeneity discrimination proves detrimental to patients, whereas increased differentiation in delay heterogeneity benefits them. This revelation underscores a pressing social issue inherent in the two-tier healthcare market model: It has the potential to exacerbate healthcare disparities rooted in economic inequalities within the population.

With the enhancement of environmental protection and the incentive policies issued by governments, the choice of electric vehicles as a means of transportation has gradually become a new mode, so the establishment of a perfect charging station service network is a key link and an critical guarantee for accelerating the adoption of electric vehicles (EVs). In the early stage of EV charging station construction, reasonable and scientific planning is not only related to the convenient travel of users, but also related to the effective utilization of resources. To promote the development of low-carbon economy, this paper first propose a Voronoi diagram based charging station service division approach, followed by a M/G/K queuing model based approach for evaluating the charging capacity of the EV charging station. Finally, a location and volume model is constructed and solved to determine the optimal construction plan of the EV charging station with minimum total cost for the whole society. The experimental results show that the charging station planning method proposed in this paper is economical and effectively improves the charging convenience of electric vehicle users. The reasonable location setting and capacity configuration of charging stations promote the sustainable development of the power grid in the planning area, which has certain practical significance.

ABSTRACTIn this era of global supply chains and just-in-time logistics, the speed with which shipping companies can deliver goods to their customers is an economic competitive advantage. The growth of international trade means more ships and more voyages which will contribute to the increasing global emissions inventory. Nonetheless, developments in ship environmental performance have not matched the increase in shipping activities. Several environmental protection and emissions reduction measures have been identified but are yet to be implemented globally due to technological gaps and their capital-intensiveness. This paper focused on addressing the challenges of applying “slow steaming” as a measure of cutting emissions. It demonstrated that by applying analytical models, the optimal berthing capacity can be defined and that by implementing the model, ships can employ slow steaming to reduce emissions. The paper also proposed a collaboration between port and ship owners/companies where real-time information is exchanged to facilitate access and swift cargo operations at ports so that ships can employ speed reduction techniques during a sail.

With electric vehicles getting increasingly popular, there has been a lot of interest in encouraging future electric vehicle development in terms of greater charging efficiency, greater benefits to the environment, and greater reliability. One of the most prominent research is on the scheduling of electric vehicle charging and navigation. High-quality navigation and charging scheduling strategy must depend on the state of the coupled network between the transportation and power networks, while the outcomes of scheduling can also have a significant impact on these networks. In this paper, we propose a novel dynamic navigation and charging strategy that fully considers the coupled traffic and power distribution network. Firstly, electric vehicles with similar travel and charging requirements are classified. Secondly, an elastic scheme including fixed route navigation and flexible charging scheduling at charging stations is provided. We also propose an information integration framework for the implementation of electric vehicle routing and charging scheduling, information interaction between sectors including the status of heterogeneous electric vehicles, day-ahead power scheduling, and base load demands. The novel strategy aims at optimal electric vehicle navigation and charging at the system level achieving a lower overall travel cost, charging cost, carbon emission, and load variance of the power distribution network. Classification algorithms, stochastic dynamic programming, and queuing theory are used for mathematical modeling. Numerical results demonstrate the effectiveness of the proposed novel strategy for the navigation and charging scheduling of electric vehicles.

One of the important applications of molecular communication is the targeted drug delivery process in which the drug molecules are released toward the target (receiver) in a way that the side effects are minimized in the human body. As the total number of released molecules increases, the receiver cannot receive all of the transmitted drug molecules. Therefore, the molecules accumulate in the system which results in side effects in the body. In order to diagnose the appropriate transmission rate of the drug molecules, it is important to investigate the reception process of the receiver. In this paper, a reception model is studied using queuing theory. In the proposed model, the rejection rate of the drug molecules due to different reasons, such as random movement of the molecules, as well as the rejection rate due to active receptors are taken into account. Moreover, an interval consisting of the lower and upper bounds for the number of released molecules is presented based on the proposed model in order to determine the range of allowable dosage of the drug molecules. It is shown that queuing theory can be successfully employed in accurate modeling of the reception process of the receiver in drug delivery applications.

The toll road service is planned and built to fund road construction and management of traffic operations. Although the toll service has several benefits, it also has challenges by creating a queue in the event of high traffic. Queues at a toll service in addition to pollution and waiting time costs also expose drivers to road traffic accidents and affect the economy of the country. Queuing problems at highway gates and exits due to toll service have become the main concern for transport managers and planners because of the randomness of inter-arrival and service time. The critical point in the management of toll road services is finding the optimal number of servers. This paper aims to analyze the queue feature to optimize the toll service using analytical and simulation models by considering the Addis-Adama expressway toll service in Ethiopia. The paper used a mathematical model and a simulation model using the CPN tools to investigate the queuing parameters. Then the model results helped us to get the optimum output by using economic analysis and identifying the minimum waiting time and operating cost without expense. The performance of the toll service highly depends on the number of servers, the number of vehicles in the system and queue, waiting time in the queue, service time, and inter-arrival time.

The paper presents a proposed queuing model based on Kendall’s notation for the intersection of two streets in Panama City (53 East and 56 East). The proposed model is based on a set of traffic lights that controls the flow of vehicles at the intersection according to a predetermined schedule. The model analyzes the stability of the system and simulations are performed to evaluate its performance. The main objective of the paper is to optimize the vehicle flow by minimizing the waiting time for passage. In the study, it was observed that the current traffic light system on Calle 50 (50th Street) is unstable and oversaturated during weekdays, which generates large vehicle queues with no estimated exit times. It was proposed to increase the system capacity to 1300 vehicles per hour to achieve reasonable stability and provide a solution to improve traffic signal timing on 50th Street. The need to increase the system capacity has been demonstrated and an optimal value has been suggested. The evaluation of other models and the use of AI can further strengthen the system and improve the prediction accuracy in different traffic scenarios.

The manufacturing industry, with globalization and information technology, is facing cost pressure, low profits, technology difficulties and many other challenges. Enterprises need to strengthen cost management, improve production efficiency, and meet customer demand to cope with the increasingly complex competitive market. Here, our research in this area becomes important. This paper presents a new approach for building a smart decision-making process specifically in manufacturing organizations in terms of profitability. It emphasizes the importance of profitability-based decision making. The basic steps and modules required to build a profitability-based decision process are described. The advantages of this decision-making system are that it can help companies maximize profits, increase productivity, and meet customer demand for order adjustments.

The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper’s model can save the distribution cost of electric vehicles.

In the paper, a queueing system with an unlimited number of servers and two phases of service with degradation in the service rate is studied. The problem of service rate degradation emerges in cloud nodes, where there is contention for hardware resources including computational resources such as CPU cores. In a node, we have a limited number of CPU cores that should execute potentially an unlimited number of processes (requests) in parallel. In our model, the term “server” means a process allocated in the node for execution. So, the number of “servers” is unlimited but their individual performances decrease because CPUs should switch between them during the execution. We consider processes executed in the node with two phases of life cycle that reflects periods with different activity of a process; e.g., in the first phase, the process may require intensive usage of CPU cores but low usage in the second one. Our model distinguishes the phases using different service parameters for them as well as different influence on the service rate degradation in the node. In the paper, two analytical methods are proposed: exact solving of the system of the local balance equation and the asymptotic analysis of the global balance equations. Formulas for the stationary probability distribution of the number of customers in the phases are obtained for both cases. Several numerical examples are provided that illustrate some properties and applicability of the obtained results.

A reasonable lane work-schedule in each time period can not only guarantee the traffic efficiency of toll stations, but also reduce the operating cost of toll stations. This paper proposes a comprehensive solution for lane work plan. Firstly, the average queue length is selected as a good index for measuring the congestion of toll station. And then, based on the queuing theory, the service level of toll station is divided into four levels according to the relationship between the average queue length and traffic capacity. Secondly, based on the toll data, a toll station congestion prediction model is established with the Long Short-Term Memory model (LSTM) and the particle swarm optimization (PSO) algorithm. In this model, the average queue length, service time and traffic volume are selected as three inputs, the average queue length value of the next hour is the output. Thirdly, on the basis of meeting the secondary level service level of toll stations, the lane work-schedule model is established. Then, the number of lanes opened in each time period can be calculated by using this model and congestion prediction results. Fourthly, considering the two scenarios of weekday and weekend, the effectiveness of the methods proposed in this paper is analyzed and verified with the toll data of the Dongshe, Changfeng, and Linfen toll stations in Shanxi Province. Finally, based on operating costs analysis, the results show that the proposed solution could effectively realize the reasonable work-schedule of the toll station.

Wireless powered communication network (WPCN) is a novel structure of the network that integrates wireless information communication (WIT) and wireless energy transfer(WET). The scheduling design is a key factor for performance improvement for WPCN. As an effective scheduling design approach, “Harvest-then-Access” has been proposed. This approach requires optimizing control parameters in the scheduling. Therefore, some approaches, such as machine learning or simulation, have been used. This paper proposes a Markov-chain model-based analytical model of Harvest-then-Access scheduling for WPCN. We propose a new Markov-chain model, which includes the essential operation in WPCN with Harvest-then-Access. From the analytical model, we derive the optimal time interval of WET in the network with Harvest-then-Access, which simultaneously provides throughput improvement and fairness among devices. The validity of the proposed analytical model is demonstrated by comparing the analytical and simulation result. Then, we evaluate the performance of Harvest-then-Access scheduling through comparison to the other scheduling.

Queues or waiting lines are a natural occurrence in the everyday lives of consumers and the process of every business. Being customers' first point of contact with the business, a customer’s experience in the queue becomes a determining factor of their first impression of the business. Queuing provides the cornerstone of efficiency to businesses as they assist employees and managers in tracking, prioritising, and ensuring the delivery of services and transactions. Inefficiencies in queues are undesired as they can result in substantial losses to a business, such as bad reputation and loss of customers due to balking or reneging behaviour.Previous research has shown that the application of queuing theory enables businesses to analyse their queuing system and the trends of demand for their services. This allows the business to effectively identify measures to improve their queuing system and serve demand at the desired level of service.In this paper, where it examined Cairo International Airport (CAI)’s existing departure queue system and benchmarked it against the optimum wait time suggested in the International Air Transport Association’s (IATA) Level of Service (LoS) concept. The application of Kendall-Lee’s Notation is applied to describe the existing queuing system of the airport. It also suggested areas of improvement after the analysis and recommended that the CAI focus on improving service time for its Check-in, Security, and Boarding process. Although the Immigration process has met IATA’s recommended optimal wait time, training could be provided to employees to enable them to progressively work towards a better service time and prepare for the future higher traffic volume. The team has also suggested for CAI to introduce autonomous technology for the departure process and software which analyses passenger flow and projects a forecast value based on the airport growth trends.

The motor-driven intracellular transport of vesicles to synaptic targets in the axons and dendrites of neurons plays a crucial role in normal cell function. Moreover, stimulus-dependent regulation of active transport is an important component of long-term synaptic plasticity, whereas the disruption of vesicular transport can lead to the onset of various neurodegenerative diseases. In this paper we investigate how the discrete and stochastic nature of vesicular transport in axons contributes to fluctuations in the accumulation of resources within synaptic targets. We begin by solving the first passage time problem of a single motor-cargo complex (particle) searching for synaptic targets distributed along a one-dimensional axonal cable. We then use queuing theory to analyze the accumulation of synaptic resources under the combined effects of multiple search-and-capture events and degradation. In particular, we determine the steady-state mean and variance of the distribution of synaptic resources along the axon in response to the periodic insertion of particles. The mean distribution recovers the spatially decaying distribution of resources familiar from deterministic population models. However, the discrete nature of vesicular transport can lead to Fano factors that are greater than unity (non-Poissonian) across the array of synapses, resulting in significant fluctuation bursts. We also find that each synaptic Fano factor is independent of the rate of particle insertion but increases monotonically with the amount of protein cargo in each vesicle. This implies that fluctuations can be reduced by increasing the injection rate while decreasing the cargo load of each vesicle. Statement of Significance: The motor-driven intracellular transport of vesicles to synaptic targets in the axons and dendrites of neurons plays a crucial role in normal cell function. Moreover, stimulus-dependent regulation of active transport is an important component of long-term synaptic plasticity, whereas the disruption of vesicular transport can lead to the onset of various neurodegenerative diseases. In this paper we investigate how the discrete and stochastic nature of vesicular transport in axons contributes to fluctuations in the accumulation of resources within synaptic targets. Almost all previous studies of axonal transport have focused on deterministic population models, or on stochastic models of a single motor particle searching for a target. The novel feature of the current paper is to take a target-centric viewpoint, which focuses on the accumulation of resources within synaptic targets under multiple search-and-capture events. In particular, building on previous work by the author on cytoneme-based morphogenesis, he develops a general framework for studying synaptic resource accumulation based on queuing theory. Two major results of the paper are as follows: (i) the discrete nature of vesicular transport can lead to Fano factors that are greater than unity (non-Poissonian) across the array of synapses, resulting in significant fluctuation bursts. (ii) Each synaptic Fano factor is independent of the rate of particle insertion but increases monotonically with the amount of protein cargo in each vesicle. This implies that fluctuations can be reduced by increasing the injection rate while decreasing the cargo load of each vesicle. A wide range of possible extensions of the basic model are highlighted in the discussion.

Equipping hospital beds uses a great deal of a hospital''''s resources. Therefore, it is essential to consider the hospital beds'''' efficiency. To increase its efficiency, a fuzzy unrestricted model for managing hospital expenses is presented in this paper. The lack of beds in hospitals leads to patients’ admission loss and consecutively profit loss. On the other hand, increasing the bed count leads to an increase in equipment expenses. Therefore, in order to determine optimal bed capacity, it is of utmost importance to consider these two costs simultaneously. In our paper, hospital admission system is modeled with a multi-server queuing system (M/M/K). Therefore, to calculate the total cost function, limiting probabilities of multi-server queueing model is used. Furthermore, due to uncertain nature of parameters, such as interest rate and hospitalization profit in various future time periods, these uncertainties are covered by fuzzy logic. Finally, to determine the optimal bed count, Lee and Li''''s fuzzy ranking method is used. This model is implemented ona case study. Its goal is to determine the optimal bed count for emergency unit of Razi hospital in Torbat Heydarieh. Considering the high capability of Markovian chains in modeling different circumstances and the various queueing models, the proposed model can be extended for various hospital units.

Leveraging cloud computing services to improve the flexibility and scalability of the IT services delivered to end-users is becoming increasingly important. However, organizations using cloud computing services face many challenges: decreased visibility into the performance of services being delivered to their end-users. Organizations using cloud computing services need to have visibility not only into the performance of applications that have moved to the cloud, but also into the different computing resources on which these applications depend. The monitoring system monitors the running state of virtual machines, response time and throughput. By analysing this data, you can identify the causes of service delays. During the operation of the monitoring system, virtual machines are suspended, a service delay occurs in order to avoid this delay. Therefore in this paper, a cloud computing model is proposed that takes into account the time spent for monitoring, calculations and probabilities of delay and waiting time are made, blocking probabilities are also put to account and results are shown in diagrams and the effects are discussed.

The outbreak of COVID-19 disrupted our everyday life. Many local authorities enforced a cordon sanitaire for the protection of sensitive areas. Travellers can only pass the cordon after tested. This paper aims to propose a method to design an on-ramp control scheme to maximise urban freeway network throughput with a predetermined queuing delay constraint at all off-ramps around cordon sanitaire. A bi-level programming model is formulated where the lower-level is a transportation system equilibrium to predict traffic flow, and the upper-level is onramp metering optimisation that is nonlinear programming. A stochastic queuing model is used to represent the waiting phenomenon at each off-ramp where testing is conducted, and a heuristic algorithm is designed to solve the proposed bi-level model where a method of successive averages (MSA) is adopted for the lower-level model; A genetic algorithm (GA) with elite strategy is adopted for the upper-level model. An experimental study is conducted to demonstrate the effectiveness of the proposed method and algorithm. The results show that the methods can find a good heuristic optimal solution. These methods are useful for freeway operators to determine the optimal on-ramp control for disease control and prevention.

The rapid expansion of electric vehicle charging networks (EVCNs) makes them critical infrastructures bridging power and traffic systems. The EVCN could be vulnerable during power outages at fast charging stations (FCSs), which are induced by planned maintenance or emergency load shedding. This paper proposes an approach to assess the impact of power outages on the Quality-of-service of the EVCN. The Markov decision process is utilized to model the spatial–temporal randomness of EV movement in a graph-based EVCN. The decision of charging by EV drivers is estimated by a fuzzy logic inference system. The spatial–temporal EV charging load at FCSs is formulated by a queuing-based non-linear optimization problem. Yen’s algorithm is adopted to simulate the EV redistribution phenomenon of searching adjacent healthy FCSs in response to the power outage. Quality-of-service (QoS) indices are derived to assess the potential congestions in the adjacent healthy FCSs. The case studies demonstrate that power outages may cause congestion at peripheral FCSs, exacerbating the QoS of the EVCN. Partial charging may alleviate the QoS deterioration in the event of FCS outages.

The offloading of computing tasks to the fog computing system is a promising approach to reduce the response time of resource-greedy real-time mobile applications. Besides the decreasing of the response time, the offloading mechanisms may reduce the energy consumption of mobile devices. In the paper, we focused on the analysis of the energy consumption of mobile devices that use fog computing infrastructure to increase the overall system performance and to improve the battery life. We consider a three-layer computing architecture, which consists of the mobile device itself, a fog node, and a remote cloud. The tasks are processed locally or offloaded according to the threshold-based offloading criterion. We have formulated an optimization problem that minimizes the energy consumption under the constraints on the average response time and the probability that the response time is lower than a certain threshold. We also provide the numerical solution to the optimization problem and discuss the numerical results.

The paper is a follow up to the performance modelling of dominant parallel computers (NOW, GRID) with analytical hybrid models (mixed) based on combinations of a more precisely developed corrected standard analytical model (M/M/1and M/M/m) and on an improved analytical model (M/D/1and M/D/m). Firstly, the paper briefly describes the steps of development of parallel computer architectures and then it summarises the basic concepts of performance evaluation. In the case of using SMP parallel system as node computer, the suggested models use for all the activities of node combinations of M/M/m and M/D/1 or M/D/m and M/M/1different (hybrid) queuing theory systems. The achieved results of both developed analytical models have been compared with the results of other alternative evaluation method, based on simulation, to verify the accuracy of developed analytical models. All developed and presented analytical models could be used for various real ranges of input parameters which influence the final performance of analysed connected computing nodes for the practice.

In this paper, we present a vacation queue model in which the service rate of the server deteriorates during the service period (e.g., due to the fatigue of a human server or the wear and ageing of machinery) and recovers during the vacation period (e.g., following a recuperation period for a human sever or the servicing of a machine). During the recuperation period, the main server is replaced with a temporary server with inferior capabilities. Using the multi-dimensional Markov process, we analyze the effects of different vacation policies on the target function and focus on the scheduling of the vacation period as a function of the deterioration and recovery rates. It is shown that the use of vacations to allow the server to rest and regain efficiency has a strong and valuable effect on the mean customer waiting time, to the extent that switching servers may be beneficial for the system, even when implemented at a point in time when the main server’s service rate is still much higher than that of the temporary server.

Approximately 20% of global container transportation activities are used for empty container repositioning, which does not generate profit margins. For container supply ports, in addition to periodically transporting excess empty containers to container shortage ports to reduce their inventory costs and meet empty container demand of container shortage ports, they also need to maintain a certain amount of empty container inventory to meet their uncertain needs in the future. This paper considers the container supply port and container shortage port as a whole, so that the container supply port maintains a certain amount of empty container inventory to meet its own uncertain needs, and empty container repositioning is carried out based on this. Based on queuing theory, an empty container repositioning model was established with the objective function of minimizing the total cost of the empty container inventory system and the empty container repositioning system and designing genetic algorithms to solve the model. The results indicate that considering empty container inventory and repositioning as a whole resulted in a total cost reduction of approximately 7.2%. Finally, a sensitivity analysis was conducted by changing the parameters in the model, and the results of the sensitivity analysis provided support for the decision-making of shipping lines in different situations.

This paper constructs a berth-quay crane capacity planning model with the lowest average daily cost in the container terminal, and analyzes the influence of the number of berths and quay cranes on the terminal operation. The object of berth-quay crane capacity planning is to optimize the number of berths and quay cranes to maximize the benefits of the container terminal. A steady state probability transfer model based on Markov chain for container terminal is constructed by the historical time series of the queuing process. The current minimum time operation principle (MTOP) strategy is proposed to correct the state transition probability of the Markov chain due to the characteristics of the quay crane movement to change the service capacity of a single berth. The solution error is reduced from 7.03% to 0.65% compared to the queuing theory without considering the quay crane movement, which provides a basis for the accurate solution of the berth-quay crane capacity planning model. The proposed berth-quay crane capacity planning model is validated by two container terminal examples, and the results show that the model can greatly guide the container terminal berth-quay crane planning.

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand.

Omnichannel catering firms need to pay attention to the consumer behavior of customers in both online and offline channels to make the corresponding capacity decisions. However, customers’ consumption behavior is not only affected by base utility, but also by reference utility, which indicates that customers have reference dependence when making consumption decisions. In order to better help omnichannel catering firms to make capacity decisions more in line with customer consumption behavior in their actual operations, this paper adopts the queuing theory to construct an optimal capacity decision-making model for omnichannel catering firms. We also analyzed the impact of channel information availability on customer reference behavior and the subsequent impact on the firm’s optimal decision-making. The findings were as follows: when the channel information is unavailable, customers in each channel take their expectation of waiting time as the reference point. With the improvement of the sensitivity of customers in different channels to the reference point, firms should improve their safety capacity to meet customers’ needs. Moreover, at a higher reference point, the increase in customer sensitivity can make the firm obtain higher profits. When channel information is available, customers take the waiting time of customers in different channels as the reference point, and the increased sensitivity of online customers to the reference point will prompt firms to reduce safety capacity. Comparing the two scenarios, we also found that omnichannel catering firms could develop lower safety capacities and obtain higher profits more easily when channel information is available. This not only contributes to the development of omnichannel catering firms, but also expands the application scope of reference theory.

Big data refers to numerous forms of complex and large datasets which need distinctive computational platforms in order to be analyzed. Hadoop is one of the popular frameworks for analytics of big data. In Hadoop, a big job is split into multiple small tasks and then they are distributed to worker nodes in a parallel way using MapReduce to speed up computational processes. In this aspect, it is important how to improve throughput performance. MapReduce jobs require quick responses from the worker nodes to complete them under their deadlines. The existing scheduling schemes for Hadoop such as FIFO, fair, and capacity schedulers cannot guarantee the quick response requirement satisfying a prior deadline. Thus, Hadoop system needs to improve response time and completion time for the heterogeneous MapReduce jobs. In this paper, we propose an efficient preemptive deadline constraint scheduler based on least slack time and data locality. In order for better allocation of tasks and load balancing, we first analyze the task scheduling behaviors of the Hadoop platform. Based on that, we propose a novel preemptive approach which considers the remaining execution time of the job being executed in deciding preemption. The experimental results show that the proposed scheme significantly reduces the job execution time and queue waiting time, compared to existing schemes.

In this paper, we carry out queuing analysis to examine and integrate a quality control (inspection) process into a multi-stage flexible ow shop configured in an M=M=1 queue system. Our main concern is to improve the performance of multi-stage exible ow shop production. Besides, this work aims to help managers in improving their effeciency and effectiveness, and in selecting a joint inspection, being the most suitable policy for minimizing inspection and queueing related costs. We adopt an analytical approach based on real-life data from a Kach company that produces disposable appliances. Queuing analysis reported in this work provides a basis for estimating and analyzing production systems by measures such as utilization, the percentage of the idle workstation, number of batches in the system, number of batches in the queue, expected time spent in the queue, and expected time spent in the system. The results indicate the performance of the company in relation to inspection and expected time.

With the depletion of oil resources and the aggravation of environmental pollution, electric vehicles have a growing future and will be more popular as the main force of new energy consumption. They have attracted greater attention from various countries. The sizing and location problem for charging stations has been a hot point of global research, and these issues are important for government planning for electric vehicles. In this paper, we first built a BASS model to predict the total number of electric vehicles and calculate the size of charging stations in the coming years. Moreover, we also developed a queuing model to optimize the location of charging stations and solved this issue by using the exhaustion method, which regards minimum cost as the objective function. After that, the model was tested using data from a city in China. The results show that the model in this paper is good at predicting the number of electric vehicles in the coming years and calculating the size of charging stations. At the same time, it can also optimize the distribution of charging stations and make them more balanceable. Thus, this model is beneficial for the government in planning the development of electric vehicles in the future.

In this paper, we analyze backoff protocols, such as the one used in Ethernet. We examine a general backoff function(GBF) rather than just the binary exponential backoff (BEB) used by Ethernet. Under some mild assumptions we find stability and optimality conditions for a wide class of backoff protocols with GBF. In particular, it is proved that the maximal throughput rate over the class of backoff protocols is a fixed function of the number of stations (N) and the optimal average service time is about Ne for large N. The reasons of the instability of the BEB protocol (for a big enough input rate) are explained. Additionally, the paper introduces novel procedure for analyzing bounded backoff protocols, which is useful for creating new protocols or improving existing, as no protocol can use unbounded counters.

Information transition and sharing is necessary for business process management and multi-source information may cause information conflict and reduce activity efficiency. This paper proposes a novel activity efficiency model in business process under conflict information. First, the evidence theory and Pignistic probability conversion are used to measure the conflict information by the difference sum of the probability conversion function based on the relations among the evidences. Second, the M/M/1 queuing system and the mapping between the conflict coefficient and service time are adopted to set up the single activity efficiency model, which is followed by the key activity efficiency models with respect to the four typical process structures, such as the Sequence structure, OR-Join structure, AND-Join structure and loop structure. Then, for a validation experiment, the road-rail intermodal transport business process is introduced and the container preparation activity of the operator sub-business process is demonstrated. As a result, the conflict information has a clear impact on the container preparation activity efficiency. Finally, it is concluded that the models here achieve to quantify the impact mechanism of conflict information on one activity efficiency, which indicates the activity service optimization direction from the view of information sharing.

In this paper, we investigate the air-to-ground multi-traffic backhaul enabled by unmanned aerial vehicle (UAV) equipped with multiple radio access networks (RANs). Faced with the challenging traffic designation problem over diverse RANs, an efficient packet delivery scheme based on multi-priority batch arrival queuing theory is proposed to match various traffics with multiple RANs. Specifically, our contributions are three-fold: 1) The average queuing time of UAV-enabled multi-RAN access is theoretically derived according to multi-priority batch arrival queuing theory. 2) The priority factor and z-type utility function are well tailored by leveraging the derived queuing time. 3) A multiple traffic backhaul strategy is further proposed by exploiting the priority factor and z-type utility function to optimize the packet delivery, i.e., maximizing the delay utility via joint network selection and power allocation for diverse RANs. The simulation results show that the average delay of delay-sensitive packets is reduced by 15%, and the overall packet delivery quality is improved considerably.

The paper deals with possibilities of optimizing the process of check-in and security check at airport terminals using simulation program. Proposed model is based on real parameters and simulated in the environment of the program Tecnomatix Plant Simulation. This program simulates the process of check-in and security check. Queuing theory will be used for simulation. Principally it means queuing customers, i.e. accumulation of requests, in front of a service centre. Limiting criteria for optimization include operating conditions of airport terminals, financial means connected with personnel resources, legal standards related to safety in air transport and level of service indicators. The aim of the paper is to create a model, which analyses the passenger flow processes at the airport terminal and could be used for evaluating potential changes of the check-in and security check system at airport terminals.

Customers often get attracted by lucrative deals and discounts offered by firms. These, attracted customers are termed as encouraged arrivals. In this paper, we developed a multi-server Feedback Markovian queuing model with encouraged arrivals, customer impatience, and retention of impatient customers. The stationary system size probabilities are obtained recursively. Also, we presented the necessary measures of performance and gave numerical illustrations. Some particular, and special cases of the model are discussed.

Recently, urbanization has been expanded rapidly in the world and a number of metropolitan areas have been appeared with a population of more than 10 million people. Because of dense population in metropolitan and consequently increasing the delivery of goods and services, there has been a lot of problems including traffic congestion, air pollution, accidents and high energy consumption. This made some complexities in distribution of urban goods; Therefore, it is essential to provide creative solutions to overcome these complexities. City logistics models can be effective in solving these complexities. In this paper, concepts and definitions related to city logistics are explained to provide a mathematical model in order to design city logistics distribution network aim at minimizing response times. This objective is effective for goods and emergency services, especially in times of crisis and also for goods that are delivered as soon as possible. This is a three-level network and has been used in modeling of queuing theory. To validate the model, a numerical example has been established and results of the model have been explained using BARON solver in Gams software. Finally, conclusions and recommendations for future research are presented.

Size-based routing policies are known to perform well when the variance of the distribution of the job size is very high. We consider two size-based policies in this paper: Task Assignment with Guessing Size (TAGS) and Size Interval Task Assignment (SITA). The latter assumes that the size of jobs is known, whereas the former does not. Recently, it has been shown by our previous work that when the ratio of the largest to shortest job tends to infinity and the system load is fixed and low, the average waiting time of SITA is, at most, two times less than that of TAGS. In this article, we first analyze the ratio between the mean waiting time of TAGS and the mean waiting time of SITA in a non-asymptotic regime, and we show that for two servers, and when the job size distribution is Bounded Pareto with parameter α=1, this ratio is unbounded from above. We then consider a system with an arbitrary number of servers and we compare the mean waiting time of TAGS with that of Size Interval Task Assignment with Equal load (SITA-E), which is a SITA policy where the load of all the servers are equal. We show that in the light traffic regime, the performance ratio under consideration is unbounded from above when (i) the job size distribution is Bounded Pareto with parameter α=1 and an arbitrary number of servers as well as (ii) for Bounded Pareto distributed job sizes with α∈(0,2)\{1} and the number of servers tends to infinity. Finally, we use the result of our previous work to show how to design decentralized systems with quality of service constraints.

Among the micro-logistic transport systems, railway stations should be highlighted, such as one of the most important transport infrastructure elements. The efficiency of the transport industry as a whole depends on the quality of their operation. Such systems have a complex multi-level structure, and the incoming traffic flow often has a stochastic character. It is known that the most effective approach to study the operation of such systems is mathematical modeling. Earlier, we proposed an approach to transport hub modeling using multiphase queuing systems with a batch Markovian arrival process (BMAP) as an incoming flow. In this paper, we develop the method by applying more complex models based on queuing networks that allow us to describe in detail the route of requests within an object with a non-linear hierarchical structure. This allows us to increase the adequacy of modeling and explore a new class of objects—freight railway stations and marshalling yards. Here we present mathematical models of two railway stations, one of which is a freight railway station located in Russia, and the other is a marshalling yard in the USA. The models have the form of queuing networks with BMAP flow. They are implemented as simulation software, and a numerical experiment is carried out. Based on the numerical results, some “bottlenecks” in the structure of the studied stations are determined. Moreover, the risk of switching to an irregular mode of operation is assessed. The proposed method is suitable for describing a wide range of cargo and passenger transport systems, including river ports, seaports, airports, and multimodal transport hubs. It allows a primary analysis of the hub operation and does not need large statistical information for parametric identification.

Electric vehicle as the main development direction of the future automotive industry, has gained attention worldwide. The rationality of the planning and construction of the power station, as the foundation of energy supply, is an important premise for the development of electric vehicles. In full consideration of the electric demand and electricity consumption, this paper proposes a new construction mode in which charging station and centralized charging station are appropriately combined and presents a location optimization model. Not only can this model be applied to determine the appropriate location for the power station, but it can use the queuing theory to determine the optimal number of power equipment, with which we can achieve the minimum costs. Finally, taking a certain city as an example, the optimum plan for power station is calculated by using this model, which provides an important reference for the study of electric vehicle infrastructure planning.

The paper deals with the problems of optimal partition of shared memory for two and arbitrary number of queues in discrete time. Mathematical models and algorithms for the optimal partition of memory between queues, minimizing data loss, are presented.

In this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. Since cell formation problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the problem. For generating initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Moreover, full factorial and Taguchi methods are implemented to set crucial parameters in the solutions procedures. Deterministic method of branch and bound (B&B) algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm. The results indicate that proposed algorithms have better performance in quality of the metaheurstic algorithms final answer and solving time compared with the method of Lingo software’s B&B algorithm. The solution of two metaheurstic algorithms is compared by t test. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.

Introduction: The queue is one of the most complex social systems in which human lives are inevitably ‎associated with the activities of organizations. Due to the characteristics of healthcare ‎organizations and the critical role that planning plays in such organizations, research in this regard ‎can be of great help. This paper was an attempt to demonstrate the role and use of the branches ‎of the operations research discipline in healthcare centers. Methods: The current research was a cross-sectional study conducted on‏ ‏‎2614‎‏ ‏patients referred to the ‎paraclinic ward of Hafez Hospital, Shiraz, Iran. In order to collect data, the patient code, time of arrival, work start time, serving start and end time were recorded with the help of staff and two assistants at the time of arrival of the patients. Then, modeling and optimization of patient flow were conducted using queues and simulation techniques. The data was analyzed using SPSS software and Input Analyzer ‎Software and the system was simulated using Arena software. Results: The results showed that 30% of the patients no longer wished to be referred back to the center. ‎The findings also revealed that there was a significant difference between the standard waiting ‎time and the time spent in the hospital to recive a service. Among the 6 different scenarios, the ‎mixed scenario reduced waiting time of the first service for‏ ‏sonography patients to 75.04 minutes ‎and for radiology patients to 65.573 minutes. Conclusion: The results showed that the suggested model, in comparison to the current model of health ‎centers, can improve the‏ ‏quality of services and reduce the duration of time patients must wait ‎until receiving the first service‏.

This paper addresses the problem of estimating the maximum part-flow or part feed rate for a pick-and-place task with a multi-robot system. An appropriate part-flow is important to guarantee productivity and robustness in the presence of pattern variation, that is, when the timing and position of the parts on the conveyor are presented in random. To determine the appropriate part-flow for a multi-robot system quickly, we divided the multi-robot system into several single-robot systems and estimated the part-flow for each single-robot system based on M/M/s queuing model with impatient customers. Then, the task completion rate for the ensemble of robots was computed. Simulations were used to compare the proposed method to a method based on a Monte Carlo Strategy (MCS) and a method based on an M/M/1 queuing model. The simulation results show that the proposed method can guarantee the required productivity and the task-completion success rate. The task-completion success rate determined using the proposed method reached 98.9% for 10000 randomly generated patterns. The computation time for the proposed method satisfies computation time constraints and is less than 1/10 of that for the MCS method.

In this paper, we proposed a method on the tradeoff between packet transmission speed and battery life in wireless sensor networks (WSN). The data obtained by sensor nodes comprised various types of information with various levels of importance. A misjudgment of the priority of packets according to the importance of data may delay the transmission of important information. The energy consumed by sensor nodes varies according to system states. Therefore, this research used queuing theory and the birth-death process to build a mathematical model capable of quantifying various energy consumption parameters. MATLAB was used to calculate the distribution of system state probability for sensor nodes with two queues. Packet delay equation was then applied to obtain the expected values of delay time for packets of various priorities. We adjust various system parameters and observe how these changes influenced system state probability. Finally, we use different N values to obtain the minimal value of system energy consumption. Obtaining this value helps to overcome the energy hole problem (EHP), which in turn extends the lifespan of the overall WSN. Prioritizing packets can ensure the transmission of important data within a shorter time span. Experimental results show the energy consumption is reduced and important data can be transmitted with less packet delay time.

This paper presents a new nonlinear mathematical model to solve a cell formation problem which assumes that processing time and inter-arrival time of parts are random variables. In this research, cells are defined as a queue system which will be optimized via queuing theory. In this queue system, each machine is assumed as a server and each part as a customer. The grouping of machines and parts are optimized based on the mean waiting time. For solving exactly, the proposed model is linearized. Since the cell formation problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the problem. For generating of initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Also, full factorial and Taguchi methods are used to set the crucial parameters in the solutions procedures. Numerical experiments are used to evaluate the performance of the proposed algorithms. The results of the study show that the proposed algorithms are capable of generating better quality solutions in much less time. Finally, a statistical method is used which confirmed that the MPSO algorithm generates higher quality solutions in comparison with the genetic algorithm (GA).

In recent years, the Chinese government and State Grid Corporation of China (SGCC) have paid great attention to the technical development and infrastructure construction for electric vehicles (EVs). This paper focuses on the mechanism of Smart Battery Charging and Swapping Operation Service Network for EVs including its overall architecture and operational mode. The overall architecture based on the internet of things is analyzed and clarified through terminal device, station management and management center layers. Then two different types of demonstration projects are presented which expound on the condition of EV's infrastructure construction. Lastly, performance analysis of the charging behaviors of electric taxis in fast charging station based on the Queuing theory is proposed. The simulation results show that the service time and the number of generators has an influence on the average waiting time and the length of queue.

This paper investigates the connection between patient satisfaction, waiting time, staff satisfaction, and service time. It uses a variety of models to enable improvement against experiential and operational health service goals. Patient satisfaction levels are estimated using a model based on waiting (waiting times). Staff satisfaction levels are estimated using a model based on the time spent with patients (service time). An integrated model of patient and staff satisfaction, the effective satisfaction level model, is then proposed (using queuing theory). This links patient satisfaction, waiting time, staff satisfaction, and service time, connecting two important concepts, namely, experience and efficiency in care delivery and leading to a more holistic approach in designing and managing health services. The proposed model will enable healthcare systems analysts to objectively and directly relate elements of service quality to capacity planning. Moreover, as an instrument used jointly by healthcare commissioners and providers, it affords the prospect of better resource allocation.

This paper proposes data-driven queuing models and solutions to reduce arrival time delays originating from aircraft arrival processing bottlenecks at Tokyo International Airport. A data-driven analysis was conducted using two years of radar tracks and flight plans from 2016 and 2017. This analysis helps not only to understand the bottlenecks and operational strategies of air traffic controllers, but also to develop mathematical models to predict arrival delays resulting from increased, future aircraft traffic. The queue-based modeling approach suggests that one potential solution is to expand the realization of time-based operations, efficiently shifting from traffic flow control to time-based arrival management. Furthermore, the proposed approach estimates the most effective range of transition points, which is a key requirement for designing extended arrival management systems while offering automation support to air traffic controllers.

The emergency department has an irreplaceable role in the hospital service system because of the characteristics of its emergency services. In this paper, a new patient queuing model with priority weight is proposed to optimize the management of emergency department services. Compared with classical queuing rules, the proposed model takes into consideration the key factors of service and the first-come-first-served queuing rule in emergency services. According to some related queuing indicators, the optimization of emergency services is discussed. Finally, a case study and some compared analysis are conducted to illustrate the practicability of the proposed model.

In this paper, a performance analytical strategy is proposed for Network-on-Chip router with input buffer architecture. First, an analytical model is developed based on semi-Markov process. For the non-work-conserving router with small buffer size, the model can be used to analyze the schedule delay and the average service time for each buffer when given the related parameters. Then, the packet average delay in router is calculated by using the model. Finally, we validate the effectiveness of our strategy by simulation. By comparing our analytical results to simulation results, we show that our strategy successfully captures the Network-on-Chip router performance and it performs better than the state-of-art technology. Therefore, our strategy can be used as an efficiency performance analytical tool for Network-on-Chip design.

In this paper the application of the queuing the01y in optimalcapacity planning for general cargo seaport is presented.The seaport as a queuing syslem is defined and tlws, on the basisof the arrival and serviced number of ships in an obsen•edtime unit, the appropriate operating indicators of a port systemare calculated. Using the model of total port costs, the munberof berths and cranes on the berth can be determined wherebythe optimal port system functioning is achieved.

In this paper, a multi-product continues review inventory control problem within batch arrival queuing approach (MQr/M/1) is modeled to find the optimal quantities of maximum inventory. The objective function is to minimize summation of ordering, holding and shortage costs under warehouse space, service level, and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Np-Hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, a simulated annealing algorithm has been utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analyzed using some numerical illustrations.