Search

A sequential clinical scheduling method for patients with general service time distributions is developed in this paper. Patients call a medical clinic to request an appointment with their physician. During the call, the scheduler assigns the patient to an available slot in the physician's schedule. This is communicated to the patient before the call terminates and, thus, the schedule is constructed sequentially. In practice, there is very limited opportunity to adjust the schedule once the complete set of patients is known. Scheduled patients might not attend, that is, they might 'no-show,' and the service times of those attending are random. A myopic scheduling algorithm with an optimal stopping criteria for this problem assuming exponential service times already exists in the literature. This work relaxes this assumption and develops numerical techniques for general service time distributions. A special case in which service times are gamma distributed is considered and it is shown that computation is significantly reduced. Finally, exhaustive experimental results are provided along with discussions that provide insights into the practical aspects of the scheduling approach. Keywords: Overbooking, appointment scheduling, patient no-shows, outpatient clinics, 1. Introduction and literature review Between 80 and 90% of U.S. patient care is provided by approximately 200 000 non-psychiatric outpatient clinics (Bodenheimer and Grumback, 2002; United States Census, 2004). [...]

Condition monitoring uses sensory signals to assess the health of engineering systems. A degradation model is a mathematical characterization of the evolution of a condition signal. Our recent research focuses on using degradation models to compute residual-life distributions for degrading components. Residual-life distributions are important for providing probabilistic estimates of failure time for use in maintenance planning and spare parts inventory management. To obtain residual-life distributions, our earlier work assumed the degradation model's stochastic parameters to be normally distributed. This paper investigates the performance of these residual-life distributions when the underlying normality assumptions are not satisfied. The paper also develops methods for estimating residual-life when the stochastic parameters of the degradation model follow more general distributions. Keywords: Residual life, Bayesian updating, non-symmetric priors, 1. Introduction Condition monitoring uses sensory information from a functioning device to evaluate its health. The evolution of this sensory information often exhibits characteristic patterns that reflect the underlying physical [...]

In this paper a stochastic overbooking model is formulated and an appointment scheduling policy is developed for outpatient clinics. The schedule is constructed for a single service period partitioned into time slots of equal length. A clinic scheduler assigns patients to slots through a sequential patient call-in process where the scheduler must provide each calling patient with an appointment time before the patient's call terminates. Once an appointment is added to the schedule, it cannot be changed. Each calling patient has a no-show probability, and overbooking is used to compensate for patient no-shows. The scheduling objective captures patient waiting time, staff overtime and patient revenue. Conditions under which the objective evolution is unimodal are derived and the behavior of the scheduling policy is investigated under a variety of conditions. Practical observations on the performance of the policy are presented. Keywords: Open access, appointment scheduling, patient no-shows, outpatient clinic operations, 1. Introduction Healthcare currently consumes 15% of the US Gross Domestic Product and is expected to reach 19% within the coming decade (Centers for Medicare and Office of the Actuary [...]

This paper develops a method for allocating a security budget to a water supply network so as to maximize the network's resilience to physical attack. The method integrates max-min linear programming, hydraulic simulation, and genetic algorithms for constraint generation. The objective is to find a security allocation that maximizes an attacker's marginal cost of inflicting damage through the destruction of network components. We illustrate the method on two example networks, one large and one small, and investigate its allocation effectiveness and computational characteristics. Keywords: Disaster planning, water distribution, consequence mitigation, vulnerability assessment, 1. Introduction The President's Commission on Critical Infrastructure Protection identified eight key infrastructures that provide essential services to our society (PPD 63, 1998). They are: (i) energy systems; (ii) telecommunications; [...]

Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation and residual-life models to degradation signals obtained through the accelerated testing of bearings., 1. Introduction Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. The promise of condition [...]

A study reviewing the execution of production schedules in the shop floor in the presence of uncertainties is presented and suggestions are offered for various alternate courses that need to be examined in this area.

Maintenance of mechanical and rotational equipment often includes bearing inspection and/or replacement. Thus, it is important to identify current as well as future conditions of bearings to avoid unexpected failure. Most published research in this area is focused on diagnosing bearing faults. In contrast, this paper develops neural-network-based models for predicting bearing failures. An experimental setup is developed to perform accelerated bearing tests where vibration information is collected from a number of bearings that are run until failure. This information is then used to train neural network models on predicting bearing operating times. Vibration data from a set of validation bearings are then applied to these network models. Resulting predictions are then used to estimate the bearing failure time. These predictions are then compared with the actual lives of the validation bearings and errors are computed to evaluate the effectiveness of each model. For the best model, we find that 64% of predictions are within 10 % of actual bearing life, while 92 % of predictions are within 20% of the actual life. Index Terms--Backpropagation, neural networks, prediction methods, vibrations.

Structural Control Policies (SCPs) are real-time flexible manufacturing system (FMS) operating policies that guarantee deadlockfree operation. SCPs must be correct and scalable. A correct SCP guarantees deadlock-free operation whereas a scalable (polynomial) SCP remains computationally tractable as FMS size increases. The NP-completeness of state safety (is there a sequence of state transitions leading to the empty state?) implies that a correct and scalable SCP will reject some safe states. SCP efficiency is the ratio of policy admissible space to FMS safe state space. This paper, the first of a two-part series, discusses FMS structural control and presents a correct and scalable SCP referred to as the Neighborhood Policy. SCP efficiency issues will be discussed in the second part., 1. Introduction The concept of agile manufacturing is emerging as a key competitive organizational feature and strategic goal for manufacturing enterprises. The rapid configuration of human and technological manufacturing resources [...]

In the first of this pair of papers, the Neighborhood Policy was shown to be a correct and scalable Structural Control Policy (SCP). Operating constraints that guarantee deadlock-free flexible manufacturing system (FMS) operation are generated based on machine capacity. For some systems these constraints appear to be quite conservative. This paper generalizes the Neighborhood Policy so that many different SCPs can be generated for the same FMS configuration. A method of finding permissive neighborhood systems is then developed. Finally, SCP efficiency is defined and investigated for Capacity-based and Optimal Neighborhood Policies., 1. Introduction Agile manufacturing is emerging as an important strategy for manufacturing competitiveness in the 21st century. Rapid configuration of manufacturing and production systems to meet changing market needs will [...]