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This paper aims to study the corresponding optimal service strategies and pricing in the styled non-preemptive M/M/1 service systems from different objectives, which consider both heterogeneous waiting costs of customers and service values in customer segmentation.

In this paper, the authors consider two service situations where customers cannot leave the service system (i.e. monopoly service system) and customers can leave the service system freely (i.e. non-monopoly service system), respectively. The authors study the following four different perspectives that are revenue, social welfare, social cost and utility of customers. The authors first build up a new model, then propose the related objective functions. Further, the authors optimize the corresponding functions and achieve the optimal results. Later, the authors propose the corresponding optimal strategies. Finally, the authors use a practical numerical case to verify the proposed results.

The results of this paper indicates that the service provider should adopt classification services to gain the maximum revenue, the maximum social welfare and the minimum social costs by charging a priority fee in above two service systems. However, the service provider should cancel customer classification and keep regular customer only to obtain maximum utility. In the monopoly service system, both the optimal proportion priority customers and the revenue decrease with the increasing of the service rate, while in a non-monopoly service system, both of them are increasing with the service rate improving.

This paper first considers both heterogeneity of service values and waiting costs in queuing system, then the author set up a new model based on this assumption. Moreover, the authors draw the corresponding management insights based on the optimal results, which were unavailable before.

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the absence of appointment systems, and construct a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system.

The current study utilizes data envelopment analysis (DEA) to develop a combined queuing–DEA model as applied to evaluate the wait times of patients, within different stages of the outpatients' department at the Combined Military Hospital (CMH) in Lahore, Pakistan, over a period of seven weeks (23rd April to 28th May 2014). The number of doctors/personnel and consultation time were considered as outputs, where consultation time was the non-discretionary output. The two inputs were wait time and length of queue. Additionally, VBA programming in Excel has been utilized to develop the dynamic framework for continuous queue monitoring.

The inadequate availability of personnel was observed as the critical issue for long wait times, along with overcrowding and variable arrival pattern of walk-in patients. The DEA model displayed the “required” number of personnel, corresponding to different wait times, indicating queue build-up.

The current study develops a queue evaluation model for a busy outpatients' department in a public hospital, where “all” patients are walk-in and no appointment systems. This model provides vital information in the form of “required” number of personnel which allows the administrators to control the queue pre-emptively minimizing wait times, with optimal yet dynamic staff allocation. Additionally, the dynamic framework specifically targets practical implementation in resource-poor public hospitals of emerging economies for continuous queue monitoring.

The purpose of this paper is to introduce a reconfigurable model that is a combination of a schedule model and a queuing system M/M/m/K to reduce the duration of the wind turbine construction project closure phase and reduce the project documentation waiting time in the queue.

This research was implemented in a wind farm project. The schedule model deals with reducing the duration of the turbines closure phase by an activity overlapping technique, and the queuing system deals with reducing the turbine documentation waiting time in the queue, as well as reducing the probability of server idleness during the closure phase.

After the implementation of the model, the obtained results were compared to those of similar previously conducted projects in terms of duration, and the model was found effective.

Project closure is an important and mandatory process in all projects. More often than not, this process is faced with problems including prolonged project duration, disputes, lawsuits, and also in projects like the implementation of wind farms, a queue of documents at closing stage may also cause difficulties in project closure phase.

The contributions of this research are twofold: first, a combination of project management and queuing system is presented, and second, a reconfigurable model is introduced to enhance the performance and productivity of the closure phase of the project through reducing the implementation time and reducing the turbine documentation waiting time in the queue, as well as reducing the probability of server idleness during the closure phase of the wind farm project.

Nowadays, e‐queues are built up everywhere where customer online service is necessary such as in banks' e‐service, enterprises' e‐business, etc. In order to enhance quality of service (QoS), active queue management (AQM) algorithms are frequently employed due to their efficiency in congestion avoidance as well as the differentiated forwarding of packets. This paper aims at developing a novel AQM algorithm to better QoS in terms of congestion prediction, queuing delay, packet loss and link utility, etc.

Upon the traditional designs of AQM, this paper establishes a new integrated AQM scheme (RQ‐AQM) by employing input rate and current queue length to calculate the packet dropping/marking probability. In this way, the rate feedback control enables to rapid response to congestion, decreasing the packet loss from buffer overflow. Meanwhile, the queue length feedback control stabilizes the queue length around a given target, achieving predictable queuing delay and lower delay jitter. Thus, the main feature of the design is to use coefficients of both proportional rate control and proportional‐integral queue length control, and to simplify parameter setting, the control parameters were scaled by the link capacity C to normalize the rate and by the bandwidth‐delay product BDP to normalize the queue length, respectively.

The stability performance of RQ‐AQM was tested via simulation under several conditions. The results proved that it is able to maintain the queue length around the given target. Also, the comparison results with other AQM schemes, including RED, ARED, PI controller, AVQ and REM, demonstrated the superiority of RQ‐AQM in low packet loss, faster convergence to target queue length and closest to the target queue length.

The main limitation of this study is that all the simulations were merely under a single bottleneck network topology. Furthermore, the system stability was examined under just a few cases. Other cases like TCP connections mixed with HTTP connections, or UDP flows, etc. can also be tested. Furthermore, the multiple bottleneck scenarios should be covered in the future work with more parameters set to enhance the proved results.

The paper sets clear but ideal conditions for the performance of proposed algorithm; so the simulation results can only be used as a rough reference instead of an exact practical one. But the concepts the paper attempted to advocate could be considered seriously.

The scope of the paper is within the general theory of AQM. So it can be referred to any specific field that employs AQM technology, no matter locally or globally.

There are not much new brand contents in the paper. The main contribution is on some extension of the known related work.

It is very complicated to keep the business processes under control since the business processes change rapidly and thus flexibility is an important attribute which businesses should possess in order to respond to rapid changes in the business environment. The purpose of this paper is to divide the companies' customers into different priority groups to be served according to their payment history and feedback in order to increase the companies' performance and profit and save the time of customers within high priority class which may lead to increase their satisfaction.

The paper proposes a requirements engineering‐based approach for business process modelling to assist businesses maintain their performance in such an environment. The paper proposes a new numerical model to improve customer satisfaction in relation to delivery or service waiting time according to their priority class, particularly customers in the high priority class. A call centre at the selected telecommunication company is used as a case study to validate the proposed numerical model.

The customers' satisfaction in the area of the time to be served according to their priority group classes can be improved using the proposed model.

The paper has some limitations as the paper only tested the numerical model on one real business organisation and one business process service.

To date, no research has been conducted in the area of separating customers into different priority groups to provide services according to their required delivery time, payment history and feedback which will increase the company's performance and profit and provide prompt service to customers in the high priority class which in turn, will increase their satisfaction.

Queuing, a familiar element of most service delivery systems, has the potential for significantly affecting the customer′s overall satisfaction with the service encounter. A customer′s degree of satisfaction with waiting or with the service received in its entirety is dependent on the actual performance of the delivery system, the customer′s expectations regarding that performance and the customer′s perception of the service encounter. The actual operational performance of different queuing configurations has been previously addressed, as have the issues of managing customers′ expectations and perceptions regarding their queuing experiences. This earlier research has identified several factors which can affect a customer′s perception of waiting and consequently his or her satisfaction with that wait. Proposes a taxonomy based on the service manager′s ability to control the customer′s perception of the queuing experience. Defines which queuing factors can be controlled by the firm, which factors can partially be controlled by the firm and which factors are outside the firm′s control, and suggests tactics for managing queues for each category of factors.

This study examined the waiting times to board an attraction at a theme park (Tokyo DisneySea in Japan) using a simulation based on measured values. Park visitors often complain that waiting times are too long; guests (Disney's term for park visitors) must stand in long, slow-moving queues outdoors in all weather, enduring heat, cold, rain and wind. This can undermine their health and reduce customer satisfaction. To date, no research has offered a scientific approach to solve the problem in the context of theme park queues.

The attraction examined two queues: a short waiting queue for guests with priority entry tickets and a long waiting queue for guests without priority entry tickets. The total number of guests with priority entry tickets remained a constant value, as in the current system; however, the author designed the number as a monotonically increasing function to reduce the waiting times for nonpriority entry. It was impractical to analyze queues or try to explain proposed wait time reduction methods using theories and mathematical models alone. Therefore, the author used a simulation study based on real data to demonstrate the proposed method of this study.

The simulation results indicated that the proposed method significantly decreased guests' waiting times in the nonpriority entry queue, without changing the number of guests in both priority and nonpriority entry queues.

Simple queues can be analyzed using theoretical calculations, but complicated queue systems require simulation methods. Therefore, this paper cannot provide a theoretical basis for the method.

The proposed method offers benefits to managers of any event or location seeking to manage queue times and not just theme parks (e.g. exhibitions, concerts, etc.). Advance tickets are equivalent to priority entry tickets, so applying the proposed method can shorten waiting times on the day of the event.

This study has important practical implications for queues management, and the proposed approach is a unique system that reduces waiting times, thus increasing customer satisfaction. The proposed method can be applied to similar types of priority entry systems.

State‐based management (SBM) is a specific form of the game‐play methodology (GPM) – an approach to the application of constraints in humanity activity systems. The theory that supports GPM was developed using a team sports model and promotes a coordination of workers’ efforts, without limiting their individual abilities to be creative in support of corporate goals. This paper reports on research undertaken to prove the applicability of GPM to a business environment. The case studies focused on over‐the‐counter (OTC) banking services. The use of game‐play tactics (SBM) in the banking chamber was highly successful. It provided a mechanism for designing constraints that addressed issues of the incomplete and inaccurate system state knowledge usually held by the tellers. Further, the process promoted the development of a team ethos, and this change in attitudes had positive repercussions on the overall working environment

Companies work hard to reduce queue lengths due to the common belief that queues in general are undesirable. Extant literature mainly has focused on the negative consequences of queues and overlooked the potential positive effects. The purpose of this paper is to address the benefits of queues by examining how consumers of different segments may read into the lines (queues) as well as why and when positive effects occur.

Applying and integrating psychology and marketing theories, the study develops a model with several propositions to identify and explain the mechanism and conditions under which queues have positive effects.

Contrary to conventional belief, queues may serve as positive signs. In certain segments, consumers can perceive a queue as a reflection of superior service/product quality, an opportunity to fulfill the need(s) for self-uniqueness or social inclusion or an avenue to avoid social exclusion. In addition, the benefits of long queues may come from consumers’ joining a line to seek desirable outcomes/gains based on their attribution of the queue, and consumers’ prefactual thinking that regards “not joining” the queue as potential losses. Furthermore, the magnitude of such effects depends on queue distinctiveness, choice heterogeneity, consumption hedonism and performance uncertainty.

This paper explains how, why and when a long queue can be read as positive cues and benefits both the firms and target/potential consumers. The authors demonstrate the psychological mechanisms of joining a queue based on attribution and prefactual thinking, and identify conditions under which positive queue effects are most likely to occur.