UNCERTAINTY QUANTIFICATION OF SELF-PROPULSION ANALYSES WITH RANS-CFD AND COMPARISON WITH FULL-SCALE SHIP TRIALS

RANS-CFD is a well-established tool with widespread use in maritime industry and research. Valuable information might be extracted from the results of such simulations in terms of ship resistance and flow field variables. With recent advancements in computational power, it became possible to investigate the performance of ships in self-propulsion conditions with RANS method. This paper presents the results of a study in which self-propulsion analyses of a small size product/oil tanker has been carried out at ship scale. The methodology proposed in this study makes use of open water propeller performance predictions, resistance analyses at model scale and self-propulsion computations at ship scale for a minimum of 2 different propeller loadings to obtain the self-propulsion point and respective performance parameters. In order to speed up the time-consuming self-propulsion computations, these cases have been solved with a single-phase approach. Resistance predictions have been compared with experimental findings. Uncertainty associated with prediction of resistance and thrust has been quantified. Additionally, sea trials have been conducted on the subject vessel and its two sisters and measured delivered power data have been used for evaluating the capability of the numerical method in self-propulsion predictions. Comparison of results indicate that the proposed self-propulsion computation methodology with RANS CFD at ship scale is capable of predicting delivered power with sufficient accuracy at an acceptable computational cost

A Spoonful of Math Helps the Medicine Go Down: An Illustration of How Healthcare can Benefit from Mathematical Modeling and Analysis

<p>Abstract</p> <p>Objectives</p> <p>A recent joint report from the Institute of Medicine and the National Academy of Engineering, highlights the benefits of--indeed, the need for--mathematical analysis of healthcare delivery. Tools for such analysis have been developed over decades by researchers in Operations Research (OR). An OR perspective typically frames a complex problem in terms of its essential mathematical structure. This article illustrates the use and value of the tools of operations research in healthcare. It reviews one OR tool, queueing theory, and provides an illustration involving a hypothetical drug treatment facility.</p> <p>Method</p> <p>Queueing Theory (QT) is the study of waiting lines. The theory is useful in that it provides solutions to problems of waiting and its relationship to key characteristics of healthcare systems. More generally, it illustrates the strengths of modeling in healthcare and service delivery.</p> <p>Queueing theory offers insights that initially may be hidden. For example, a queueing model allows one to incorporate randomness, which is inherent in the actual system, into the mathematical analysis. As a result of this randomness, these systems often perform much worse than one might have guessed based on deterministic conditions. Poor performance is reflected in longer lines, longer waits, and lower levels of server utilization.</p> <p>As an illustration, we specify a queueing model of a representative drug treatment facility. The analysis of this model provides mathematical expressions for some of the key performance measures, such as average waiting time for admission.</p> <p>Results</p> <p>We calculate average occupancy in the facility and its relationship to system characteristics. For example, when the facility has 28 beds, the average wait for admission is 4 days. We also explore the relationship between arrival rate at the facility, the capacity of the facility, and waiting times.</p> <p>Conclusions</p> <p>One key aspect of the healthcare system is its complexity, and policy makers want to design and reform the system in a way that affects competing goals. OR methodologies, particularly queueing theory, can be very useful in gaining deeper understanding of this complexity and exploring the potential effects of proposed changes on the system without making any actual changes.</p

Uniform and precision pricing for a service facility

Ph.D.Hayriye Ayha

SCHEDULING IMPATIENT JOBS IN A CLEARING SYSTEM WITH INSIGHTS ON PATIENT TRIAGE IN MASS CASUALTY INCIDENTS

Motivated by the patient triage problem in emergency response, we consider a single-server clearing system in which jobs may abandon the system if they are not taken into service within their “lifetime. ” In this system, jobs are characterized by their lifetime and service time distributions. Our objective is to dynamically determine the optimal or near-optimal order of service for jobs so as to minimize the total number of abandonments. We first show that if the jobs can be ordered in such a way that the job with the shortest lifetime (in the sense of hazard rate ordering) also has the shortest service time (in the sense of likelihood ratio ordering), then the optimal policy gives the highest priority to this “time-critical ” job independently of the system state. For the case where the jobs with shorter lifetimes have longer service times, we observed that the optimal policy generally has a complex structure that may depend on the type and number of jobs available. For this case, we provide partial characterizations of the optimal policy and obtain sufficient conditions under which a state-independent policy is optimal. Furthermore, we develop two state-dependent heuristic policies, and by means of a numerical study, show that these heuristics perform well, especially when jobs abandon the system at a relatively faster rate when compared to service rates

Optimal prices for finite capacity queueing systems

We prove a lower bound on the optimal price for a fairly large class of blocking systems with general arrival and service processes, determine optimal price expressions for M/M/1/m and M/GI/s/s systems, and investigate how optimal prices change with changes in the size of the waiting room and service capacity

Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations

This paper develops a framework and proposes heuristic dynamic policies for scheduling patient appointments, taking into account the fact that patients may cancel or not show up for their appointments. In a simulation study that considers a model clinic, which is created using data obtained from an actual clinic, we find that the heuristics proposed outperform all the other benchmark policies, particularly when the patient load is high compared with the regular capacity. Supporting earlier findings in the literature, we find that the open access policy, a recently proposed popular scheduling paradigm that calls for "meeting today's demand today," can be a reasonable choice when the patient load is relatively low.service operations, health-care management, stochastic methods

Optimal Pricing for a Service Facility

This paper investigates optimal pricing policies for a service facility modeled as a queueing system. Arriving customers are accepted if they are willing to pay the price charged by the service provider and if there is room in the waiting area. Capacity of the waiting area can be either finite or infinite. We determine expressions for the optimal prices that maximize the service provider&apos;s long-run average profit and we prove some structural results on the optimal policies exploring their relationships with the customers&apos; willingness to pay and system parameters such as service speed and waiting room capacity