Simulation and optimization methods to improve the management of resources and patients in health services. Application to emergency departments

The aim of this thesis is to contribute to the sustainability of public health services by means of data analysis and through the development and application of Operational Research methods and techniques for modeling and analyzing real planning and management problems generally affecting the public health sector and Emergency Departments (EDs) in particular. The focus of the research is on the development of methods of analysis that will yield practicable solutions to improve the efficiency and quality of patient care and working conditions of the health staff. A hospital ED provides medical and/or surgical care to patients arriving in need of immediate attention. The highly stochastic environment of these departments is especially difficult to manage due to the variability of the patient arrival rate, patient severity, and (material and human) health resource requirements. They also have to provide a 24/7 service, where physicians are required to work night, day and weekend shifts, and take on different assignments.reflecting the resource consumption (including the medical staff) required for treatment. A guideline is provided for the construction of a mathematical model of the ED designed to overcome some of the shortcomings of oversimplified queuing theorymodels and capture some important issues that previous simulation models have overlooked. The first part of the thesis addresses the problem of patient-to-physician allocation following triage. It offers a proposal for new allocation rules which prove to outperform the common cyclic allocation approach by taking into account a factor usually neglected by patient-flow management policies: i.e., the workload stress experienced by physicians, which is measured in real time using a method proposed and analyzed in this thesis. The stress score is used as the KPI to assess the performance of current patient-flow management policies and as a criterion for designing new ones. This thesis also illustrates the successful implementation of one of the proposed rules, from initial concept to practical application in the hospital. The tested allocation rule outperforms the current cyclic one, as demonstrated by using the simulation model and analysis of the real data gathered during the pilot test. The second part of the thesis addresses the physician scheduling problem, which is a combinatorial optimization problem posing particular difficulty when all the constraints and objectives observed in practice are considered. The problem is modeled by means of mathematical programming, and thus cannot be solved in practice by commercial software. This leads to the development of a new solution heuristic. A key feature of this algorithm is the greedy constructive phase, which is guided by solving a linear problem in combination with a memory structure. Initial good solutions are very quickly obtained, but they can be unfeasible in heavily constrained cases. The subsequent improvement phase combines a repair strategy based on variable neighborhood search with network optimization. This is the first proposal for such a strategy. A computational analysis and a real-case solution demonstrate the quality of the solutions and the good behavior of the methodology. The research presented in this thesis fulfills the following objectives: to propose a quantitative framework (based on simulation models and their combination with optimization procedures) for the analysis of problems involved in the dimensioning and assessment of management policies in hospital emergency services; to develop a methodology for the real-time assessment of pending workload stress in physicians; to provide new patient-to-physician allocation methods with criteria including the workload and stress balancing across physicians, and patient service quality; to analyze alternatives to pure priority rules for managing the queue of patients awaiting initial emergency assessment by a physician or reevaluation following tests and/or diagnosis; to design efficient algorithms for solving the physician work-shift assignment problem taking into account all real ergonomic constraints while balancing the workload.Funded by Government of Navarre with a PhD grantPrograma de Doctorado en Tecnologías de las Comunicaciones, Bioingeniería y de las Energías Renovables (RD 99/2011)Bioingeniaritzako eta Komunikazioen eta Energia Berriztagarrien Teknologietako Doktoretza Programa (ED 99/2011

Scheduling techniques for efficient execution of stream workflows in cloud environments

Advancements in Internet of Things (IoT) technology have led to the development of advanced applications and services that rely on data generated from enormous amounts of connected devices such as sensors, mobile devices and smart cars. These applications process and analyse such data as it arrives to unleash the potential of live analytics. Considering that our future world will be fully automated, current IoT applications and services are categorised as data-driven workflows, which integrate multiple analytical components. Examples of these workflow applications are smart farming, smart retail and smart transportation. This work flow application also known as a stream work flow is one type of big data workflow application and is becoming gradually viable for solving real-time data computation problems that are more complex. The use of cloud computing technology which can provide on demand and elastic resources to execute stream workflow applications is ideal, but additional challenges are raised due to the location of data sources and end users' requirements in terms of data processing and deadline for decision making. The focus of existing research works in this domain is on the streaming operator graph generated by streaming data platforms, where this graph differs from a stream workflow as there is a single source of data for the whole operator graph and one end operator, while stream workflow has multiple input data sources and multiple output streams. Moreover, the majority of those works investigated one type of runtime change for the streaming graph operator, which is the fluctuation of data. This means that the structural changes that may happen at runtime are not studied. Considering the heterogeneity and dynamic behaviour of stream workflows, these workflow applications have unique features that make the scheduling problem have different assumptions and optimisation goals compared with the placement problem of streaming graph operators. As a consequence, the execution of stream workflow applications on the cloud environment requires advanced scheduling techniques to address the aforementioned challenges as well as handling different runtime changes that may occur during the execution of these applications. To this end, the Multicloud environment approach opens the door toward enhancing the execution of workflow applications by leveraging various clouds to utilise data locality and exploit deployment flexibility. Thus, the problem of scheduling a stream workflow in a Multicloud environment while meeting user real-time data analysis requirements needs to be investigated. In this thesis, we leverage the Multicloud environment approach to design novel scheduling techniques to efficiently schedule outsourcing stream workflow applications over various cloud infrastructures while minimising the execution cost. We also design dynamic scheduling techniques to continuously manage resources to handle structural and non-structural changes at runtime in order to maintain user-defined performance requirements at minimal execution cost. In summary, this thesis makes the following concrete contributions: • Comprehensive state of the art survey that analyses various big data workflow orchestration issues span over three different levels (workflow, data and cloud) by providing a research taxonomy of core requirements, challenges, and current tools, techniques and research prototypes. • Simulation toolkit named IoTSim-Stream to model and simulate stream workflow applications in cloud computing environments. • Two scheduling algorithms that generate scheduling plans at deployment time to execute stream workflow efficiently on cloud infrastructures with minimal monetary cost. • Two-phase adaptive scheduling technique that considers the problem of scheduling stream workflows to support runtime data fluctuations while guaranteeing real-time performance requirements and minimising monetary cost. • Pluggable dynamic scheduling technique that manages cloud resources over time to handle structural changes of stream workflow at runtime in a cost-effective manner, along with three plugin scheduling methods