Customer Behavior Analysis in Ticket Windows at Scenic Spots from Game-Theoretic Perspective

In service industries, especially in some ticket windows at scenic spots, affected by congestion, some customers often find excuses or ask the acquaintances in the queue so as to jump the queue and complete their corresponding service as early as possible. In this paper, we model the queueing phenomenon in the ticket windows at scenic spots as a special queue, namely, the so-called “team queue.” Although this phenomenon often happens in daily life, it is less well known to people. In a team queue, an arriving customer first searches the queue from the top to bottom to see if some of his teammates are already in the queue. If yes, he would join the queue and be served with his teammates; otherwise, he would join the queue at the tail. To this end, arising from the customers’ perspective, the strategic behavior of queueing customers in the ticket windows at scenic spots is analyzed. On the basis of considering the waiting cost and reward, the individual strategies and social optimal strategies of queueing customers are derived, regarding the joining or balking dilemma and for the observable and unobservable cases. Finally, to demonstrate how various parameters affect the joining strategies, some numerical examples are provided.

Statistical analysis of waiting time of patients by queuing techniques: case study of large hospital in Pakistan

<p class="JARTEAbstractHeader">The purpose of this empirical research was to analyze the comfortable waiting time (CWT) of patients at the outpatient department (OPD) of Gastrology of ABC hospital of Karachi. It is based on the analysis of CWT of patients who were being served at the OPD of Gastrology of ABC hospital of Karachi. The data was collected by the help of questionnaire. Altogether 250 questionnaires were distributed among the patients, 210 of them were collected back and 10 of them were incompletely filled. Data was analysed in the statistical package for social sciences (SPSS) version 22. Data analysis included frequency distribution of various demographics;stratification tables were made for the comparison of CWT across various demographics. Results indicated that more females (old aged) had greater CWT in the comparison of males. It is found that the mean CWT of patients decreased with decreasing age, increasing OPD visiting time and increasing income. It is also found that he mean CWT for the patients from Afghanistan was greater than the patients from other regions i.e. Baluchistan, interior Sindh and Karachi. The authors highlighted that when patients arrive at the hospital and wait for their service, in this scenario, waiting cost is associated with their waiting time; since it is the matter of cost, thus it should be known to the hospital that if patients are made to wait longer, it can lead to the customer dissatisfaction. In this regard, analysis of comfortable waiting time of patients was extremely needed. Since, Karachi is the biggest city of Pakistan and targeted hospital is one the biggest private hospitals of Karachi and in the analysis of this paper. Only 200 patients were approached for data collection which is the main limitation of the paper. In future, the researchers should also focus on the same OPD for more responses and at the same time, other departments can also be targeted for conclude better and precise results. The authors have tried to focus on the CWT of patients so that the waiting capacity of patients could be highlighted. At the same time, detailed analysis was conducted across demographics so that their influence on CWT could be analysed.</p><p class="JARTEKeywords"> </p>

Demand Pooling in Omnichannel Operations

Both traditional retailers and e-tailers have been implementing omnichannel strategies such as buy online, pick up at store (BOPS). We build a stylized model to investigate the impact of the BOPS initiative on store operations from an inventory perspective. We consider two segments of customers, namely store-only customers who only make purchases offline and omni-customers who strategically choose between offline and online channels. We show that BOPS may either benefit or hurt the retailer depending on two fundamental system primitives: the store visiting cost and the online waiting cost. If the online waiting cost is relatively low and the store visiting cost is even lower, BOPS can induce omni-customers to migrate from online buying to BOPS, leading to demand pooling at the brick-and-mortar (B&M) store. Such demand pooling provides two benefits for the retailer: it reduces the overstocking cost, and after inventory reoptimization, it results in a higher fill rate at the B&M store, which benefits existing customers and potentially attracts more customers to the store. In contrast, if both store visiting and online waiting costs are relatively high with the latter even higher, introducing BOPS can result in demand depooling as a result of the migration of the omni-customers from offline purchasing to BOPS. This leads to a lower fill rate after inventory reoptimization, likely the result of a lower profit margin under BOPS, which turns away store-only customers and hurts the retailer. This paper was accepted by Charles Corbett, operations management.

The Reference Effect of Delay Announcements: A Field Experiment

We explore whether customers are loss averse in time and how delay information may impact such reference-dependent behavior using observational and field experiment data from two call centers of an Israeli bank. We consider settings with no announcements and announcements of different accuracy levels. We face two key challenges: (1) we do not observe the reference points customers use in our data, as any other field studies, and (2) it is difficult to separate the reference-dependent behavior from the potential nonlinear waiting cost of customers. To address these challenges, we develop a dynamic decision model with consumer learning, through which we infer the reference point each customer used during any given call. The reference points may be different across different customers and evolve across different calls of the same customers. We also exclude the alternative explanation by showing that our main reference-dependent models better explain the observed customer abandonment than models where customers have nonlinear waiting cost. Our results indicate that customers are loss averse regardless of the availability or accuracy of the announcements when their waiting time is relatively long (≥ 90s). Although delay announcements do not alter the nature that customers are loss averse, accurate announcements may affect customers’ belief about the offered waiting time and thus, impact the reference points. Through counterfactual studies, we demonstrate that providing delay announcements improves the call center performance given the loss aversion behavior observed in our data. Interestingly, as customers become more loss averse, the value of providing delay announcements decreases. This paper was accepted by Terry Taylor, operations management.

Reimbursement policy in a healthcare system with priorities: fee for priority versus bundled priority

This paper studies a healthcare system under two priority charging schemes. The first is a fee-for-priority (FFP) scheme under which the patients can choose to buy the priorities or not at their admission or readmission to the healthcare provider (HCP). The second is a bundled priority (BP) scheme under which the HCP receives a lump sum priority payment from patients for the entire episode of priority visits (regardless of the number of readmissions). Under both schemes, a certain proportion of revenue of the HCP (the reimbursement) is used to improve the service quality for patients. A two-stage Stackelberg game is established that captures the non-cooperative interactions between the HCP and patients, where the HCP is the leader who sets a price of priority and patients are the followers who decide whether to buy the priorities or not. We first characterize the Pareto-dominant equilibrium strategy of patients under the FFP and BP schemes, and we find that the total cost (including delay disutility and priority fee) of patients can be reduced if the patient pool is large. Although patients' minimum cost, including waiting cost and priority payment, is obtained under both the FFP and BP schemes, the prices are more flexible under the former than the latter. Finally, we demonstrate that implementing the FFP scheme can induce higher social welfare than the BP scheme.

A Dynamic Shelter Location and Victim Resettlement Model Considering Equitable Waiting Costs

Catastrophic natural disasters cause devastating damage and leave a huge number of homeless people. Waiting for resettlement in a post-disaster environment brings human suffering, which is defined by waiting cost in this paper. Taking into account waiting cost and fairness consideration simultaneously, a mixed integer linear programming model is constructed for the multiperiod location-allocation process. Two fairness indicators are incorporated to guarantee both the whole-process equity and the periodic equity. The model is implemented in the General Algebraic Modeling System (GAMS) and solved by the CPLEX solver. An illustrative example is provided to explain the model characteristics. Furthermore, a case study of the Yushu earthquake is conducted to demonstrate the applicability of the model to practical problems.