Finish Them!: Pricing Algorithms for Human Computation

Given a batch of human computation tasks, a commonly ignored aspect is how the price (i.e., the reward paid to human workers) of these tasks must be set or varied in order to meet latency or cost constraints. Often, the price is set up-front and not modified, leading to either a much higher monetary cost than needed (if the price is set too high), or to a much larger latency than expected (if the price is set too low). Leveraging a pricing model from prior work, we develop algorithms to optimally set and then vary price over time in order to meet a (a) user-specified deadline while minimizing total monetary cost (b) user-specified monetary budget constraint while minimizing total elapsed time. We leverage techniques from decision theory (specifically, Markov Decision Processes) for both these problems, and demonstrate that our techniques lead to upto 30\% reduction in cost over schemes proposed in prior work. Furthermore, we develop techniques to speed-up the computation, enabling users to leverage the price setting algorithms on-the-fly

Topics in queueing theory

There are three topics in the thesis. In the first topic, we addressed a control problem for a queueing system, known as the ``$N$-system\u27\u27, under the Halfin-Whitt heavy traffic regime and a static priority policy was proposed and is shown to be asymptotically optimal, using weak convergence techniques. In the second topic, we focused on the hospitals, where faster servers(nurses), though work more efficiently, have the heavier workload, and the Randomized Most-Idle (RMI) routing policy was proposed to tackle this unfairness issue, trying to reward faster servers who serve more with less workload. we extended the existing result to show that this desirable property of the RMI policy holds under a system with multiple customer classes using theoretical exact analysis as well as numerical simulations. In the third topic, the problem was to decide an appropriate number of representatives over time according to the prescribed service quality level in the call center. We examined the stability of two methods which were designed to generate appropriate staffing functions on a simulated data and real call center data from an actual bank

Response times in healthcare systems

It is a goal universally acknowledged that a healthcare system should treat its patients – and especially those in need of critical care – in a timely manner. However, this is often not achieved in practice, particularly in state-run public healthcare systems that suffer from high patient demand and limited resources. In particular, Accident and Emergency (A&E) departments in England have been placed under increasing pressure, with attendances rising year on year, and a national government target whereby 98% of patients should spend 4 hours or less in an A&E department from arrival to admission, transfer or discharge. This thesis presents techniques and tools to characterise and forecast patient arrivals, to model patient flow and to assess the response-time impact of different resource allocations, patient treatment schemes and workload scenarios. Having obtained ethical approval to access five years of pseudonymised patient timing data from a large case study A&E department, we present a number of time series models that characterise and forecast daily A&E patient arrivals. Patient arrivals are classified as one of two arrival streams (walk-in and ambulance) by mode of arrival. Using power spectrum analysis, we find the two arrival streams exhibit different statistical properties and hence require separate time series models. We find that structural time series models best characterise and forecast walk-in arrivals, but that time series analysis may not be appropriate for ambulance arrivals; this prompts us to investigate characterisation by a non-homogeneous Poisson process. Next we present a hierarchical multiclass queueing network model of patient flow in our case study A&E department. We investigate via a discrete-event simulation the impact of class and time-based priority treatment of patients, and compare the resulting service-time densities and moments with actual data. Then, by performing bottleneck analysis and investigating various workload and resource scenarios, we pinpoint the resources that have the greatest impact on mean service times. Finally we describe an approximate generating function analysis technique which efficiently approximates the first two moments of customer response time in class-dependent priority queueing networks with population constraints. This technique is applied to the model of A&E and the results compared with those from simulation. We find good agreement for mean service times especially when minors patients are given priority

Dynamic Formation and Strategic Management of Web Services Communities

In the last few years, communities of services have been studied in a certain numbers of proposals as virtual pockets of similar expertise. The motivation is to provide these services with high chance of discovery through better visibility, and to enhance their capabilities when it comes to provide requested functionalities. There are some proposed mechanisms and models on aggregating web services and making them cooperate within their communities. However, forming optimal and stable communities as coalitions to maximize individual and group efficiency and income for all the involved parties has not been addressed yet. Moreover, in the proposed frameworks of these communities, a common assumption is that residing services, which are supposed to be autonomous and intelligent, are competing over received requests. However, those services can also exhibit cooperative behaviors, for instance in terms of substituting each other. When competitive and cooperative behaviors and strategies are combined, autonomous services are said to be "coopetitive". Deciding to compete or cooperate inside communities is a problem yet to be investigated. In this thesis, we first identify the problem of defining efficient algorithms for coalition formation mechanisms. We study the community formation problem in two different settings: 1) communities with centralized manager having complete information using cooperative game-theoretic techniques; and 2) communities with distributed decision making mechanisms having incomplete information using training methods. We propose mechanisms for community membership requests and selections of web services in the scenarios where there is interaction between one community and many web services and scenarios where web services can join multiple established communities. Then in order to address the coopetitive relation within communities of web services, we propose a decision making mechanism for our web services to efficiently choose competition or cooperation strategies to maximize their payoffs. We prove that the proposed decision mechanism is efficient and can be implemented in time linear in the length of the time period considered for the analysis and the number of services in the community. Moreover, we conduct extensive simulations, analyze various scenarios, and confirm the obtained theoretical results using parameters from a real web services dataset

Empirical study of the effect of offramp queues on freeway mainline traffic flow

The dissertation examines the relationship between the number of lane changes, the speed of the ramp lane, and the location upstream of the ramp split. Analyses indicate the number of lane changes exhibits a parabolic relationship with respect to the ramp lane speed, and the number of lane changes exhibits gamma-distributed relationship with respect to the distance upstream of the ramp. The macroscopic lane changing model presented is best characterized as the development of generalized lane-changing relationships, and provides a starting point from which more complex corridor-level models can be developed. This study also identifies an unusual car-following behavior exhibited by certain lane-changing drivers. When the target lane is moving slowly, some lane-changing drivers will slow down, causing a disruption in their initial lane. Regression analysis is used to estimate the speed upstream of the initial lane to indicate the disruption is responsible for the lateral propagation of congestion. The lane choice of exiting vehicles is also studied. Lane choice appears to be a function of origin/destination, and freeway speed. As speeds in the general purpose lanes decrease, exiting vehicles are more likely to wait longer to move into the exit ramp lanes, resulting in an increased lane changing density. Results from this study are expected to have the greatest impact on microscopic lane-change model validation. Additionally, results have implications for design and safety issues associated with freeway ramps. As data collection technologies improve and data becomes increasingly available, this research provides the basis for the further development of more elaborate lane-changing models.Ph.D