Server Idle Time Research Articles

Estimating the Implied Value of the Customer's Waiting Time

Almost all research in appointment scheduling has focused on the trade-off between customer waiting times and server idle times. In this paper, we present an observation-based method for estimating the relative cost of the customer waiting time, which is a critical parameter for finding the optimal appointment schedule.

Simulation analysis for reducing queues in mixed-patients' outpatient department

Customers desire short waiting times whereas service providers want to maximize resources utilization. Long waiting time is not uncommon in many service organizations and it is familiar especially in outpatient departments. To make the study more realistic, some assumptions were removed and uses an animated simulation model for a mixed-patient type environment in an outpatient department. A special purpose data generator is designed to explore bottlenecks in consultation rooms. Four appointment scheduling rules and their possible combinations are evaluated in two steps. First, experimentation concentrates on appointed and non-appointed patients. Second, it considers new patients in addition to those two categories. It is revealed that the rule which records the lowest waiting time is not feasible due to the high portion of server idle time. The rule that shows the lowest server idle time is not viable due to increased waiting time. It is possible to find the best rule which lies between these two times in a mixed-patient type environment.

Control of mobile communications with time-varying channels in heavy traffic

Consider the forward link of a system with K remote units and a single base transmitter with time varying connecting channels. Data to be transmitted to the remote units arrives according to some random process, and is queued according to its destination. Power is to be allocated to the K channels in a queue and channel state depend on way to minimize some cost criterion. The channel fading rate is fast and the bandwidth and data arrival rates are high. Owing to the high speed of the fading, arrival, and service rates, an asymptotic or averaging of the heavy traffic type method is promising. By heavy traffic, we mean that on the there is little server idle time and spare power over the average requirements. Heavy traffic analysis eliminates inessential details and focuses on the fundamental issues of scaling and parametric dependencies. To illustrate the scope of the method, several models are considered, and more complicated systems are also treated.

OPTIMAL MAINTENANCE AND OPERATION OF A SYSTEM WITH BACKUP COMPONENTS

We consider a system with heterogeneous unreliable components that requires only one component to be turned on in order for it to operate. Repair workers may have different skills and may be unavailable for random periods of time. The problem is to determine a usage and repair policy to maximize system availability. We give conditions under which the optimal usage policy is to always use, or turn on, the component with the shortest repair time, and the optimal repair policy is to always repair the most reliable component (with the smallest failure rate). We fully characterize the optimal policy when there are only two components. Our system is equivalent to a closed system with multiple single-server queues, where the objective is to minimize server idle time at one of the queues.

Using client-variance information to improve dynamic appointment scheduling performance

Clients of services expect short waiting times and servers desire short periods of non-productive time. One of the areas where this is most important is appointment scheduling systems. Recent research has indicated that using information about clients’ service time variability can simultaneously reduce waiting times and server idle time. In this study, a more realistic, dynamic appointment-scheduling environment is developed and used to analyze several scheduling rules. Additional complexities considered in this study include: continuously distributed service-time variances, special client appointment requests, and appointment-scheduler uncertainty. Results show that rules using client-variance information are still best at minimizing waiting time and idle time with the additional complexities. In fact, these rules perform best when client variance is large. However, on measures related to clients requesting specific appointment slots the results are not as clear cut. A key factor for these measures is the distribution of the desired slots. When the desired slots are near the end of the appointment scheduling period, traditional rules like first-call-first-appointment perform better on client appointment request measures.

A single server queue with random arrivals and balking: Confidence interval estimation

This paper investigates a queueing system, which consists of Poisson input of customers, some of whom are lost to balking, and a single server working a shift of lengthL and providing a service whose duration can vary from customer to customer. If a service is in progress at the end of a shift, the server works overtime to complete the service. This process was motivated by the behavior of fishermen interviewed in the NY Great Lakes Creel Survey.We derive the distributions of the number of services (X), overtime and total server idle time (T), both unconditionally (for Poisson arrivals) and conditionally on the number (n) of arrivals per shift, assuming that the arrival times are not recorded in the data. These distributions provide the basis for estimation of the parameters from asingle realization of the queueing process during [0,L]. The conditional distributions also can be used to estimate common service time,w, when (n, X) or (n, T) are observed. Confidence intervals based onT are of shorter length, for all confidence coefficients, than the corresponding intervals based onX.

Adaptive optimal load balancing in a nonhomogeneous multiserver system with a central job scheduler

A model comprising several servers, each equipped with its own queue and with possibly different service speeds, is considered. Each server receives a dedicated arrival stream of jobs; there is also a stream of generic jobs that arrive to a job scheduler and can be individually allocated to any of the servers. It is shown that if the arrival streams are all Poisson and all jobs have the same exponentially distributed service requirements, the probabilistic splitting of the generic stream that minimizes the average job response time is such that it balances the server idle times in a weighted least-squares sense, where the weighting coefficients are related to the service speeds of the servers. The corresponding result holds for nonexponentially distributed service times if the service speeds are all equal. This result is used to develop adaptive quasi-static algorithms for allocating jobs in the generic arrival stream when the load parameters are unknown. The algorithms utilize server idle-time measurements which are sent periodically to the central job scheduler. A model is developed for these measurements, and the result mentioned is used to cast the problem into one of finding a projection of the root of an affine function, when only noisy values of the function can be observed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>