A rapidez na realização do atendimento às vítimas é uma das maiores necessidades de serviços de atendimento médico de urgência, e o tempo decorrido entre o instante da ocorrência da solicitação pelo serviço e o início do atendimento, denominado tempo de resposta, é um dos principais fatores que influenciam o desempenho do sistema. Este tempo depende de uma reunião de fatores como condições de tráfego, dia e período do dia, número de veículos disponíveis e suas localizações, capacitação profissional da equipe, etc. Apresenta-se neste trabalho uma análise do desempenho do serviço oferecido na cidade de Campinas-SP, tratando o problema por meio do modelo hipercubo de filas, que considera as variações aleatórias dos processos de chegadas e atendimento dos chamados. Sua aplicação produz uma ampla variedade de indicadores de desempenho para o sistema, que são comparados com os valores reais observados, para validar a hipótese de aplicação do modelo. Os resultados de sua aplicação para configurações operacionais alternativas, tais como descentralização e aumento do número de ambulâncias, mostraram uma elevação significativa do nível de serviço oferecido ao usuário. Conclui-se que o modelo constitui uma importante ferramenta de análise para este tipo de sistema, auxiliando na tomada de decisões estratégicas e operacionais do sistema. / One of the major concerns of medical emergency systems is to provide the fastest possible medical attention for the victims. The time elapsed between the emergency call and the assistance, called the response time, is one of the main factors that influence the system\'s performance. This time lapse depends on traffic conditions, the day of the week and time of day, the number of available vehicles and their location, the rescue team\'s professional qualifications, etc. This work consists of an analysis of the performance of the emergency service available in Campinas, SP, and deals with the problem using the hypercube queuing model, which considers stochastic variations of the arrival and assistance processes. The application of this model produces a wide variety of system performance indicators, which are compared with the real observed values to validate the model\'s hypothetical application. Application of the model in alternative operational scenarios, such as decentralization and a greater number of ambulances, showed a significant increase in the quality of the service offered to the user. It was concluded that the model constitutes an important analytical tool for this type of system, serving as an aid for strategic and operational decision-making.

As ever more devices are connected to the internet, and applications turn ever more interactive, it becomes more important that the network can be counted on to respond reliably and without unnecessary delay. However, this is far from always the case today, as there can be many potential sources of unnecessary delay. In this thesis we focus on one of them: Excess queueing delay in network routers along the path, also known as bufferbloat. We focus on the home network, and treat the issue in three stages. We examine latency variation and queueing delay on the public internet and show that significant excess delay is often present. Then, we evaluate several modern AQM algorithms and packet schedulers in a residential setting, and show that modern AQMs can almost entirely eliminate bufferbloat and extra queueing latency for wired connections, but that they are not as effective for WiFi links. Finally, we go on to design and implement a solution for bufferbloat at the WiFi link, and also design a workable scheduler-based solution for realising airtime fairness in WiFi. Also included in this thesis is a description of Flent, a measurement tool used to perform most of the experiments in the other papers, and also used widely in the bufferbloat community. / HITS, 4707

Motivated by the management of sales representatives who visit customers to develop customer relationships, we present a stochastic orienteering problem on a network of queues, in which a hard time window is associated with each customer and the representative may experience uncertain wait time resulting from a queueing process at the customer. In general, given a list of potential customers and a time horizon consisting of several periods, the sales representative needs to decide which customers to visit in each period and how to visit customers within the period, with an objective to maximize the total reward collected by the end of the horizon. We start our study with a daily orienteering problem, which is a subproblem of the general problem. We focus on developing a priori and dynamic routing strategies for the salesperson to implement during a day. In the a priori routing case, the salesperson visits customers in a pre-planned order, and we seek to construct a static sequence of customers that maximizes the expected value collected. We consider two types of recourse actions. One is to skip a customer specified by an a priori route if the representative will arrive late in the customer's time window. The other type is to leave a customer immediately after arriving if observing a sufficiently long queue (balking) or to leave after waiting in queue for a period of time without meeting with the customer (reneging). We propose customer-specific decision rules to facilitate the execution of recourse actions and derive an analytical formula to compute the expected sales from the a priori route. We tailor a variable neighborhood search (VNS) heuristic to find a priori routes. In the dynamic routing case, the salesperson decides which customer to visit and how long to wait at each customer based on realized events. To seek dynamic routing policies, we propose an approximate dynamic programming approach based on rollout algorithms. The method introduces a two-stage heuristic estimation that we refer to as compound rollout. In the first stage, the algorithm decides whether to stay at the current customer or go to another customer. If departing the current customer, it chooses the customer to whom to go in the second stage. We demonstrate the value of our modeling and solution approaches by comparing the dynamic policies to a priori solutions with recourse actions. Finally, we address the multi-period orienteering problem. We consider that each customer's likelihood of adopting the representative's product stochastically evolves over time and is not fully observed by the representative. The representative can only estimate the adoption likelihood by meeting with the customer and the estimation may not be accurate. We model the problem as a partially observed Markov decision process with an objective to maximize the expected sales at the end of the horizon. We propose a heuristic that decomposes the problem into an assignment problem to schedule customers for a period and a routing problem to decide how to visit the scheduled customers within the period.

Processing user requests quickly requires not only fast servers, but also demands methods to quickly locate idle servers to process those requests. Methods of finding idle servers are analogous to open addressing in hash tables, but with the key difference that servers may return to an idle state after having been busy rather than staying busy. Probing sequences for open addressing are well-studied, but algorithms for locating idle servers are less understood. We investigate sequential probing with a random start as a method for finding idle servers, especially in cases of heavy traffic. We present a procedure for finding the distribution of the number of probes required for finding an idle server by using a Markov chain and ideas from enumerative combinatorics, then present numerical simulation results in lieu of a general analytic solution.

Hospitals stand to lose millions of dollars in revenue due to patients who leave without treatment (LWT). Grounded in queueing theory, the purpose of this correlational study was to examine the relationship between daily arrivals, daily staffing, triage time, emergency severity index (ESI), rooming time, door-to-provider time (DTPT), and LWT rates. The target population comprised patients who visited a Connecticut emergency room between October 1, 2017, and May 31, 2018. Archival records (N = 154) were analyzed using multiple linear regression analysis. The results of the multiple linear regression were statistically significant, with F(9,144) = 2902.49, p < .001, and R2 = 0.99, indicating 99% of the variation in LWT was accounted for by the predictor variables. ESI levels were the only variables making a significant contribution to the regression model. The implications for positive social change include the potential for patients to experience increased satisfaction due to the high quality of care and overall improvement in public health outcomes. Hospital leaders might use the information from this study to mitigate LWT rates and modify or manage staffing levels, time that patients must wait for triage, room placement, and DTPT to decrease the rate of LWT in the emergency room.

The determination of the right number of servers in a multi-server queueing system is one of the most important problems in applied queueing theory. The problem becomes more complex in a system that consists of both cross-trained and specialized servers. Such queueing systems are readily found in the call centres (also called contact centres) of financial institutions, telemarketing companies and other organizations that provide services to customers in multiple languages. They are also found in computer network systems where some servers are dedicated and others are flexible enough to handle various clients' requests. Over-staffing of these systems causes increased labour costs for the underutilized pool of agents on duty, while under-staffing results in reduced revenue from lost customers and an increase in queue times. The efficient design and analysis of these systems helps management in making better staffing decisions. This thesis aims to develop models for establishing agent staffing levels in organizations with cross-trained and specialized staff with a view to minimizing cost and maintaining a desirable customer satisfaction. The work investigates the effect of various traffic loads on the number of agents required and the cost. It also considers how using specialized agents, flexible agents and a combination of both categories of agents affects the system. It uses a contact centre that has agents with monolingual, bilingual and trilingual (English, French and Spanish) capabilities to do the study.

In this thesis, we study the repair shop scheduling problem(repair-to-stock) and the production/inventory system pricing and production scheduling problem(make-to-stock). For both types of problems, we compare the performance of different scheduling policies. For the make-to-stock type problem, we also study the performance of different pricing strategies. The optimal repair/production scheduling policy of both problems is difficult to characterize, and, therefore, is only formulated as a Markov Decision Process to numerically compute the optimal cost/profit. As an alternative, we propose the dynamic Myopic policy, which is easy to implement. The numerical study we have conducted demonstrates that the performance of Myopic policy is superior compared to the alternative policies and yields costs very close to the optimal for the repair-to-stock type problem. On the other hand, for the make-to-stock type problems, the performance of Myopic policy is not superior compared to the alternative policies when dynamic pricing strategy is implemented.

In this thesis, we study the repair shop scheduling problem(repair-to-stock) and the production/inventory system pricing and production scheduling problem(make-to-stock). For both types of problems, we compare the performance of different scheduling policies. For the make-to-stock type problem, we also study the performance of different pricing strategies. The optimal repair/production scheduling policy of both problems is difficult to characterize, and, therefore, is only formulated as a Markov Decision Process to numerically compute the optimal cost/profit. As an alternative, we propose the dynamic Myopic policy, which is easy to implement. The numerical study we have conducted demonstrates that the performance of Myopic policy is superior compared to the alternative policies and yields costs very close to the optimal for the repair-to-stock type problem. On the other hand, for the make-to-stock type problems, the performance of Myopic policy is not superior compared to the alternative policies when dynamic pricing strategy is implemented.

Although healthcare operations management has been an active and popular research direction over the past few years, there is a lack of formal quantitative models to analyze the ambulance o oad delay problem. O oad delays occur when an ambulance arriving at a hospital Emergency Department (ED) is forced to remain in front of the ED until a bed is available for the patient. Thus, the ambulance and the paramedic team are responsible to care for the patient until a bed becomes available inside the ED. But it is not as simple as waiting for a bed, as EDs also admit patients based on acuity levels. While the main cause of this problem is the lack of capacity to treat patients inside the EDs, Emergency Medical Services (EMS) coverage and availability are signi cantly a ected. In this thesis, we develop three network queueing models to analyze the o oad delay problem. In order to capture the main cause of those delays, we construct queueing network models that include both EMS and EDs. In addition, we consider patients arriving to the EDs by themselves (walk-in patients) since they consume ED capacity as well. In the rst model, ED capacity is modeled as the combination of bed, nurse, and doctor. If a patient with higher acuity level arrives to the ED, the current patient's service is interrupted. Thus, the service discipline at the EDs is preemptive resume. We also assume that the time the ambulance needs to reach the patient, upload him into the ambulance, and transfer him to the ED (transit time) is negligible. We develop e cient algorithms to construct the model Markov chain and solve for its steady state probability distribution using Matrix Analytic Methods. Moreover, we derive di erent performance measures to evaluate the system performance under di erent settings in terms of the number of beds at each ED, Length Of Stay (LOS) of patients at an ED, and the number of ambulances available to serve a region. Although capacity limitations and increasing demand are the main drivers for this problem, our computational analysis show that ambulance dispatching decisions have a substantial impact on the total o oad delays incurred. In the second model, the number of beds at each ED is used to model the service capacity. As a result of this modeling approach, the service discipline of patients is assumed to be nonpreemptive priority. We also assume that transit times of ambulances are negligible. To analyze the queueing network, we develop a novel algorithm to construct the system Markov chain by de ning a layer for each ED in a region. We combine the Markov chain layers based on the fact that regional EDs are only connected by the number of available ambulances to serve the region. Using Matrix Analytic Methods, we nd the limiting probabilities and use the results to derive di erent system performance measures. Since each ED's patients are included in the model simultaneously, we solve only for small instances with our current computational resources. In the third model, we decompose the regional network into multiple single EDs. We also assume that patients arriving by ambulances have higher nonpreemptive priority discipline over walk-in patients. Unlike the rst two models, we assume that transit times of ambulances are exponentially distributed. To analyze the decomposed queueing network performance, we construct a Markov chain and solve for its limiting probabilities using Matrix Analytic Methods. While the main objective for the rst two models is performance evaluation, in this model we optimize the steady state dispatching decisions for ambulance patients. To achieve this goal, we develop an approximation scheme for the expected o oad delays and expected waiting times of patients. Computational analysis conducted suggest that larger EDs should be loaded more heavily in order to keep the total o oad delays at minimal levels.

The determination of the right number of servers in a multi-server queueing system is one of the most important problems in applied queueing theory. The problem becomes more complex in a system that consists of both cross-trained and specialized servers. Such queueing systems are readily found in the call centres (also called contact centres) of financial institutions, telemarketing companies and other organizations that provide services to customers in multiple languages. They are also found in computer network systems where some servers are dedicated and others are flexible enough to handle various clients' requests. Over-staffing of these systems causes increased labour costs for the underutilized pool of agents on duty, while under-staffing results in reduced revenue from lost customers and an increase in queue times. The efficient design and analysis of these systems helps management in making better staffing decisions. This thesis aims to develop models for establishing agent staffing levels in organizations with cross-trained and specialized staff with a view to minimizing cost and maintaining a desirable customer satisfaction. The work investigates the effect of various traffic loads on the number of agents required and the cost. It also considers how using specialized agents, flexible agents and a combination of both categories of agents affects the system. It uses a contact centre that has agents with monolingual, bilingual and trilingual (English, French and Spanish) capabilities to do the study.