Dr. Farhed Moeeni – Prof. of Computer & Information Technology, Arkansas State University
GUEST BLOGGER – Dr. Farhed Moeeni
In an earlier blog I discussed some of the issues related to modeling customers’ behavior; such as impatience, especially the balking behavior. Customers’ impatience can lead to certain behaviors such as balking, reneging and jockeying. Ignoring the case of balking at arrival caused by queue capacity, balking happens when customers assess the waiting line, develop some opinion about the required waiting time (likely by scanning the queue length) and decide whether to join the queue or not. In short, balking happens when the person’s tolerance for waiting is less than the anticipated waiting time at arrival. Jockeying happens when two or more service channels provide identical services, independently and with distinct waiting lines. A customer who is presently waiting in one of the queues chooses to leave the current line and join another queue in the hopes of less waiting time.
Reneging, however happens after a person joins a queue. In other words, the initial anticipated delay was tolerable. But later, as time passes, the person becomes impatient and abandons the queue without receiving the service because the prospect of being served within the initially anticipated time diminishes or the predicted remaining waiting time exceeds the customer’s tolerance threshold for further waiting. Other incidents such as receiving an urgent call may also cause abandoning of the queue. However, this or similar scenarios are not caused by impatience.
Researchers believe the tolerance threshold is not a constant and can change over the course of the waiting period. For example, studies indicate that the probability of reneging decreases as the person continues to wait, but only for a certain amount of waiting time after which the customer starts losing patience and the probability of reneging increases. Another interesting conjecture proposed in the literature is that as the number of people behind a waiting customer increases, the likelihood of the customer reneging decreases. The decision to renege is also influenced by other factors, for example, personal differences, prior experiences with similar situations, the critical nature of the service to be received, attributes and atmosphere of the waiting area’s surrounding (service-scape), and whether or not an alternative date or time for the same service or a similar service is available. Thus, the decision process for reneging is more complex than balking and modeling and simulating reneging is also more complex.
Obviously, it is not possible to directly characterize the complex behavioral aspects of customers into the simulation model. Each reneging incident is the result of complex interactions among many factors and the result of a thought process that eventually causes a customer to become impatient and abandon the queue. In other words, reneging cases are not random events; however, from the perspective of a simulation modeler or an observer who is watching the queue, the incidents may look like a random process.
In discrete-event simulation, the modeler has a number of mechanisms or tools available. For example, the modeler may apply some probability distribution to characterize the uncertain behavior of customers or formulate some rules to trigger reneging during the simulation runs. In addition to probability distributions, the modeler may also identify and apply a number of observable predictor variables to fine tune the reneging behavior. These variables may include the waiting time of each customer in queue before reneging incidents, the position of each customer in queue just before reneging, the number of customers in queue behind the reneging customer at the moment of abandoning the queue, etc.
Incorporating the above mechanisms is not too complex from the modeling and coding perspective. On the other hand, it is difficult to collect relevant data from the field if the required information is not available. A number of obstacles can contribute to these difficulties. First, sufficient observations are needed to estimate the probability distributions, the shapes and parameters with adequate precision. Unfortunately, in many instances, reneging does not happen often enough to yield sufficient observational data from the field within a reasonable amount of time.
Second, accurate data collection for events such as reneging may need sophisticated data collection procedures because each customer should be tracked while in the system and the values of relevant state variables along with other important information such as the “waiting time in queue before reneging” and “the position of the customer in queue” before abandoning the queue should also be recorded. Only the information related to those customers who renege is needed. However, those who eventually renege are not known in advance or at the time of joining the queue; thus everyone should be tracked. Such elaborate data collection system may be costly to implement, intrusive to customers, or may need consent and collaboration from people. The latter may also influence the behavior and contaminate the collected data.
I discussed some of the important issues surrounding customer’s reneging behavior in simulation. The focus of the discussion has so far been on physical queues or waiting lines in which customers should be physically present to receive the service. Some of the complex characteristics and thought processes associated with physical queues may or may not apply to virtual queuing systems such as call centers, web servers, e-commerce sites and the like. One simplifying advantage of modeling human impatience in virtual queues is that much of the data needed to model reneging behavior may readily be collected electronically without facing the obstacles explained above. Virtual queuing systems have unique characteristics and deserve a separate discussion.
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