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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.

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.

Service experiences and waiting lines are often – unfortunately – seen to go hand in hand. This chapter explains why this is the case. Beginning with an exploration of capacity and operating constraints, discussion then delves into both the mathematical origins and psychological implications of waiting lines. The final section offers hope to managers and guests alike, with a survey of different operations strategies and tactics that can eliminate or abate the need to wait.

Relief item distribution to victims is a key activity during disaster response. Currently many humanitarian organizations follow simple guidelines based on experience to assess need and distribute relief supplies. However, the interviews with practitioners suggest a problem in efficiency in relief distribution efforts. The purpose of this paper is to develop a model and solution methodology that can estimate relief center (RC) performance, measured by waiting time for victims and throughput, for any RC design and analyze the impact of key design decisions on these performance measures.

Interviews with practitioners and current practice guidelines are used to understand relief distribution and a queuing network model is used to represent the relief distribution. Finally, the model is applied to data from the 2015 Nepal earthquake.

The findings identify that dissipating congestion created by crowds, varying item assignment decisions to points of distribution, limiting the physical RC capacity to control congestion and using triage queue to balance distribution times, are effective strategies that can improve RC performance.

This research bases the RC designs on Federal Emergency Management Agency guidelines and assumes a certain area and volunteer availability.

This paper contributes to humanitarian logistics by discussing useful insights that can impact how relief agencies set up and operate RCs. It also contributes to the queuing literature by deriving analytic solutions for the steady state probabilities of finite capacity, state dependent queues with blocking.

Consumers are inclined to join longer queues due to social interactions in service consumptions. This purchase behavior brings in operational challenges in terms of capacity planning, which affects consumers’ demand, leading to an unstable and fluctuated arrival process. This paper aims to investigate the dynamic characteristics of the arrival process of a service system with boundedly rational consumers whose purchase decisions are influenced by the queue length under social interactions.

Consumers’ bounded rationality is modeled based on the random utility theory. Due to social interactions, the equilibrium queue length and its interaction with the expected waiting time affect consumers’ value perception. The authors first analyze the optimal service capacity decision with or without considering the influence of social interactions in a static setting. They then focus on the dynamic characteristics of the arrival process by a one-dimensional dynamical model in terms of the arrival rate.

This paper finds that the service system can behave chaotic in terms of arrival rate dynamics under social interactions. The results highlight the dynamical complexity of a simple service system due to consumers’ behavioral factors and the influence of social interactions, which may be the critical drivers leading to fluctuated and uneven demand.

The findings demonstrate that due to consumers’ limited cognitive ability and the influence of social interactions, the demand to a service system can be stable, periodic or even chaotic in terms of the arrival process. This study provides an alternative explanation to the observed demand fluctuations in various service processes under the influence of social interactions, which is important for service providers to effectively manage service capacity to achieve a stable service process and improve operational efficiency.

The purpose of this paper is to provide simulation modelling for bulk arrival bulk service queueing system involved in a textile industry and analyze the performance metrics.

This paper describes the simulation modelling of a bulk queueing system with limited number of admissions and multiple vacations. The model is developed for the proposed queueing system using Flexsim 2017, and it is explained through an application observed in a textile industry involving the process of cone winding.

In this paper, the simulation model has been developed to study the behaviour of queues at different resources in a production system. Various performance measures such as average components, average waiting time, total number of inputs and outputs, processing time and idle time involved in a textile industry are evaluated using simulation and justified through numerical illustration.

The proposed simulation model may be used in various scenarios wherever a real time situation exists related to bulk queueing system. The results produced in this paper can be used by the manufacturing industries to enhance the need-based accuracy. It is worth pointing out that the findings are of direct practical relevance and can be successfully used for a number of industrial applications.

The approach suggested in this paper attempts to deal with the queueing system involved in a textile industry and provides numerical results in less time with less computer resources. It provides a reasonably good approximation for simple and complex queueing models where it is difficult to find closed form of theoretical results.

The purpose of the paper is to analyze reliability characteristics of batch service queuing system with a single server model that envisages Poisson input process and exponential service times under first come, first served (FCFS) queue discipline.

With the help of renewal theory and stochastic processes, a model has been designed to discuss the reliability and its characteristics.

The instantaneous and steady-state availability along with the maintenance model of the systems subject to generalized M/M_b/1 queuing model is derived, and a few particular cases for availability are obtained as well. For supporting the developed model, a case study on electrical distribution system (EDS) has been illustrated, which also includes a comparison for the system subject to M/Mb/1 queuing model and the system without any queue (delay).

It is a quite realistic model that may aid to remove congestion in the system while repairing.

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.

Extant literature on queuing has identified service queues as social systems where social justice is an important factor for service evaluation. First-order justice, defined as a first-come first-served (FCFS) process, has been found to be a necessary condition of social justice and positive evaluation. Second-order justice, defined as equal waiting time, has been found to be an additional factor which comes into play only when first-order justice is met. This paper aims to show that in the emerging market situation, the above definitions of justice and the order mentioned above does not work.

Instead of equal wait, the study has focused on equitable wait, i.e. waiting duration is in sync with the service needs. Three experiments have been performed to establish the hypotheses suggested.

FCFS is found not to be the necessary condition as it was in the extant literature and can be relaxed sometimes to get higher service evaluation by ensuring justice from the equitable wait. The study also portrays the interaction effects of the two types of social justice on service evaluation. Moreover, the impact of justice from equitable wait on service evaluations is found to be moderated by perceived personal connect of the service provider and the consumer, perceived importance of system and process and perceived ability of the service provider of capacity improvement and mediated by perceived control of service provider on providing the justice of equitable wait.

The study contributes toward the understanding of social justice in service queues. It also contributes to the literature of attribution theory and consumer betrayal.

The study provides suggestions to retail managers in emerging markets to choose queue management strategies depending on the size of the retail shops and consumers’ expectations from them.

The study introduces the concept of justice from the equitable wait, which is original in the queuing literature to the best of the author’s knowledge. The study also finds a new order of justice in the emerging market scenario.

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.