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This paper aims to propose a systematic approach to facilitate passenger flow in high speed rail (HSR) stations. A case study is conducted to validate the approach.

This paper argues that HSR station management consists of train management, station capacity management, waiting room management and passenger flow management, and that two-way and dynamic information exchange exists among the components. Accordingly, the proposed approach integrates the components in HSR station management based on information exchange and heuristic algorithms. A HSR station in China with four regular waiting rooms, one mother-and-infant waiting room and one VIP waiting room located on two floors are chosen for a case study. An arrangement plan for waiting zones and waiting rooms is generated by following the proposed approach.

The result of the case study indicates that the proposed approach is capable of arranging waiting zones and waiting rooms quickly and effectively to maximize utilization of waiting room capacity and to minimize passenger walking distance from waiting rooms to platforms.

The management problem of railway station waiting room also relates to some other factors such as platforms, tracks adjustment and delay of the trains. As an emerging technology, the Internet of Things (IoT) is expected to offer promising solutions to transform the operation and role of many existing industrial systems such as transportation systems including railway systems. The future study of the authors will focus on IoT intelligent railway management system.

The result indicates that the proposed method is an effective solution to the management of railway station waiting room, which can be applied to railway station management system.

China Railway System provides service to billions of passengers. This research has significant social-economic impacts.

This paper fulfills an information integrated approach for improving waiting room management in high speed railway stations and a case study testing the approach.

The purpose of this study is that service sectors sectors create queues intentionally as a promotional strategy. Potential buyers might become actual customers after witnessing and joining queues. However, the effectiveness of company promotional activities involving queues remains unclear. Despite the innovativeness of this marketing strategy, few companies have adopted this approach, owing to the lack of research on how waiting influences customer behaviors toward waiting in queues.

Therefore, this study identifies four factors of customer willingness to stand in a queue using questionnaire survey: company promotional activities, improvement of waiting environment, company’ reactions to the queue and customers’ perceptions regarding waiting time.

This study identifies causal relationships among the aforementioned factors. The results of this investigation reveal that a company’s promotional activities significantly and indirectly reduce customers’ perceived waiting time by improving the waiting environment. Analytical results also show that a company’s queuing management can indirectly reduce customers’ perceived waiting time by improving the waiting environment.

Based on the analytical results concerning causal relationships, improving the waiting environment is critical to affecting positively customers’ perceptions regarding waiting time. Queuing management can indirectly reduce customers’ perceived waiting time by improving the waiting environment. A company’s promotional activities can indirectly reduce customers’ perceived waiting time by improving the waiting environment. Customers who enjoy both the waiting environment and the promotional activities experience much shorter perceived waiting time.

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.

Hospital waiting lists are a feature of publicly funded health services that result when demand appears to exceed supply. While much has been written about surgical waiting lists, little is known about the dynamics of radiology waiting lists, which is surprising given that rational treatment, and indeed the medical profession's claim to expertise, rests on establishing a diagnosis. This paper reports the findings of a case study of a problematic ultrasound waiting list. In particular, this paper highlights how the management of the ultrasound waiting list served to subordinate the needs of waiting patients and their general practitioners to the interests and values of radiologists. Radiologist concern to protect specialist expertise from encroachment by outpatient clinicians and sonographers is implicated in the growth of the ultrasound waiting list. It is argued that an adequate understanding of ultrasound waiting lists depends on grasping how radiologists are successful in structuring problems of access in ways that enhance radiologist control over ultrasound imaging. The case study reported helps to shed light on why increasing funding to clear waiting lists proves ineffective.

The purpose of this paper is to explore healthcare waiting time and the negative and positive effects (i.e. the dual effects) it has on outpatient satisfaction.

Self-administered surveys with 334 outpatients and follow-up interviews with 20 outpatients in three large hospitals in Taiwan were conducted to collect data.

Quantitative surveys demonstrated that perceived waiting time correlated with satisfaction negatively first but then positively. Satisfaction also correlated with doctor reputation and patient sociability. Follow-up qualitative interviews further revealed that, for some patients, waiting contributed positively to patient evaluations through signaling better healthcare quality and facilitating social interaction.

This research demonstrated the possibility that waiting might have positive effects on healthcare satisfaction. It also identified variables that could produce greater positive perceptions during hospital waiting and underlying mechanisms that could explain how the positive effects work. This research may potentially help hospitals with a better understanding of how they can improve patients’ waiting experiences and increase satisfaction.

A study comprising both questionnaire and interview of patients waiting for general surgery revealed that the majority of patients suffer from relatively few clinical conditions, that more than three‐quarters had been on the in‐patient list for over a year and that they suffered significant pain while waiting. Patients added to long lists with initially non‐serious (non‐life‐threatening) conditions may be overlooked, yet their clinical and associated social conditions may deteriorate. Since treatment for these conditions is generally effective, many patients would benefit from more organised queue management and earlier surgery.

The purpose of this research is to focus on a hospital Division of Diagnostics and Clinical Support (150 medical, 1,975 non‐medical staff) and how systems were enhanced through lean principles application to facilitate quality and performance improvement.

An action research methodology was adopted. The research involved: review of available performance and quality improvement literature; identification of the systems that required improvement; adoption and implementation of new working methods.

The results were recognised as being beneficial to all parties, especially the patients! Staff recognised the need for change; the process transformation was actually welcomed. Patient waiting times reduced from 26 to 13 weeks. Fast‐track/“query cancer” service for out‐patients now within ten days; the majority of in‐patients receive imaging within 72 hours. Ultimately, patients are diagnosed faster and treatment commences earlier. Departmental managers can effectively manage capacity to meet demand because they now understand the waiting “profile”.

The methodology applied was appropriate, generating data to facilitate discussion and from which to draw conclusions. A perceived limitation is the single case approach; however, Remenyi et al. argue that this can be enough to add to the body of knowledge.

Guidelines indicating “What went well?” and “What could have gone better?” were produced. These centred on the practical application aspects. The implementation methodology developed is being used elsewhere within the same hospital group.

The paper demonstrates that the application of improvement techniques, such as “Lean”, can focus efforts to improve performance. This is of value to those working in the UK healthcare and wider public sector.

Short delivery time is a feature that can influence consumers’ purchasing decisions and that retailers compete over fiercely. Accordingly, evaluating the effect of delivery time on demand and identifying marketing-mix variables that alter this relationship may influence retailers’ strategies and impact supply chain (SC) performance. The paper aims to discuss these issues.

This study was performed in collaboration with the largest furniture retailer in Italy, which provided its sales and inventory data for 19,000 units sold over a six-month period in 32 stores throughout Italy. Data were analysed using logistic regression with fixed effects.

The value of delivery time for consumers, even in an industry generally characterised by long delivery lead times, is surprisingly high. The evidence reveals that when the delivery time changes from two days to seven days, demand is reduced by 37.5 per cent, although variables related to location and the marketing mix moderate this relationship.

Retailers can use the findings presented herein to drive their inventory and facility planning decisions and support investments in SC integration.

Supply chain management (SCM) studies consider the value of delivery time anecdotally and have neglected empirical estimations of the magnitude of the effects of delivery time on consumer demand. Further, SCM studies have not explored the factors moderating this relationship, although intertemporal choice and service management studies have demonstrated the existence of such factors.

The purpose of this paper is to examine Canadian organizational and systemic factors that made it possible to keep wait times within federally established limits for at least 18 months.

The research design is a multiple cases study. The paper selected three cases: Case 1 – staff were able to maintain compliance with requirements for more than 18 months; Case 2 – staff were able to meet requirements for 18 months, but unable to sustain this level; Case 3 – staff were never able to meet the requirements. For each case the authors interviewed persons involved in the strategies and collected documents. The paper analysed systemic and organizational-level factors; including governance and leadership, culture, resources, methods and tools.

Findings indicate that the hospital that was able to maintain compliance with the wait time requirements had specific characteristics: an exclusive mandate to do only hip and knee replacement surgery; motivated staff who were not distracted by other concerns; and a strong team spirit.

The authors’ research highlights an important gradient between three cases regarding the factors that sustain waiting times. The paper show that the hospital factory model seems attractive in a super-specialized surgery context. However, patients are selected for simple surgeries, without complications, and so this cannot be considered a unique model.

National Health Service (NHS) waiting list problems have many complex causes. The results of research conducted to understand the complexities of patient need and demand, factors influencing general practitioner referral decisions, factors determining the numbers of patients seen by consultants, and the criteria used for selecting patients from the waiting lists are described.