Modelización, Simulación y Optimización del personal operativo en la administración de Call/Contact Center

El presente trabajo expone una descripción del proceso de la estimación óptima de los requerimientos de agentes, y se analiza a través de la simulación el problema de la programación de turnos de trabajos, desde la perspectiva de la programación matemática no lineal, para la administración efectiva y eficiente del recurso humano en los Centros de Llamadas o Contactos Telefónicos (Telephone Call/Contact Centers). La simulación que se implementa lleva a cabo dos procesos de optimización: 1) La determinación óptima de los recursos humanos necesarios para la atención de los clientes, que se materializa con la resolución de un modelo de programación lineal. 2) La determinación de la política más adecuada para la asignación de turnos, bajo la restricción de mantener un cierto nivel de servicio. Para éste último, se utiliza el método de las Combinaciones Lineales basadas en función objetivo no lineal y restricciones lineales convexas. El software de simulación que se implementó, está basado en la técnica de simulación de eventos discretos permitiendo un diseño visual de los modelos de análisis. El aporte significativo y novedoso del trabajo, se orienta hacia la utilización de técnicas sencillas de la Programación Matemática No Lineal en la programación de turnos, y el uso de la herramienta de simulación para hacer una predicción precisa de los requerimientos de personal y la asignación de turnos.Sociedad Argentina de Informática e Investigación Operativ

A Data-Driven Approach for Operational Improvement in Emergency Departments

Emergency departments (EDs) in the US are experiencing significant stress from crowding, of which one of the main contributors is the lengthy boarding process, which is the process of to-be-admit patients waiting in the ED for the hospital to ready beds for them. We explored ways to reduce crowding by initiating hospital bed request (BeRT) early on for likely to-be-admit patients. In Chapter 2, we modeled the ED patient flow as a Markov decision process. With the objective of balancing the tradeoff between waiting cost and the cost of false early BeRTs, we found the optimal early BeRT policy to be of threshold type, where the threshold is a function of census and patients probability of admission. Chapter 3 built a fluid model, where patients flow into the ED (a fluid tank) as continuous fluid flowing at a time-dependent deterministic rate. To control the number of false early BeRTs, we imposed a constraint on the length of time for the early BeRT option. The optimal policy that minimizes the fluid level (congestion level) in the ED dictates that when ED is under heavy traffic regime, one should BeRT early as early, and as long, as allowed. In chapter 4, we looked at several early BeRT heuristics that are inspired by the theoretical optimal policies found previously. We tested and compared their performances in terms of length-of-stay and waiting time using a simulation model built for the UNC ED based on 2012 patient data. We observed that as the admission probability distributions of the patient population became less variable, the heuristics that take more information into account performed better. Lastly, we offered a different perspective on ED crowding in Chapter 5, where we explored the association between ED cencus and providers’ triage and admission decisions. We found that the more crowded the ED was, the more conservative providers were, in that nurses tend to triage more patients as critical, and physicians tend to admit more patients into the hospital.Doctor of Philosoph

Analysis of buffer allocations in time-dependent and stochastic flow lines

This thesis reviews and classifies the literature on the Buffer Allocation Problem under steady-state conditions and on performance evaluation approaches for queueing systems with time-dependent parameters. Subsequently, new performance evaluation approaches are developed. Finally, a local search algorithm for the derivation of time-dependent buffer allocations is proposed. The algorithm is based on numerically observed monotonicity properties of the system performance in the time-dependent buffer allocations. Numerical examples illustrate that time-dependent buffer allocations represent an adequate way of minimizing the average WIP in the flow line while achieving a desired service level

Dynamic resource allocation for energy management in data centers

In this dissertation we study the problem of allocating computational resources and managing applications in a data center to serve incoming requests in such a way that the energy usage, reliability and quality of service considerations are balanced. The problem is motivated by the growing energy consumption by data centers in the world and their overall inefficiency. This work is focused on designing flexible and robust strategies to manage the resources in such a way that the system is able to meet the service agreements even when the load conditions change. As a first step, we study the control of a Markovian queueing system with controllable number of servers and service rates (M=Mt=kt ) to minimize effort and holding costs. We present structural properties of the optimal policy and suggest an algorithm to find good performance policies even for large cases. Then we present a reactive/proactive approach, and a tailor-made wavelet-based forecasting procedure to determine the resource allocation in a single application setting; the method is tested by simulation with real web traces. The main feature of this method is its robustness and flexibility to meet QoS goals even when the traffic behavior changes. The system was tested by simulating a system with a time service factor QoS agreement. Finally, we consider the multi-application setting and develop a novel load consolidation strategy (of combining applications that are traditionally hosted on different servers) to reduce the server-load variability and the number of booting cycles in order to obtain a better capacity allocation

Distributed Dynamic Replica Placement and Request Redirection in Content Delivery Networks

Chiara Petrioli, Giancarlo Bongiovanni, Francesco Lo Prest

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

Patient prioritization and resource allocation in mass casualty incidents

Mass-casualty incidents, such as multi-car traffic accidents, plane crashes, and terrorist bombings, create a sudden spike in demand for emergency resources in an area. Providers of emergency medical services must act quickly to make decisions that will affect the lives of injured patients. Particularly important is triage, the process of classifying patients and prioritizing them for transportation from the scene of the incident. The most widely used standard for mass-casualty triage, START, prescribes a fixed priority ordering among the different classes of patients, without explicitly accounting for resource limitations. We develop policies to improve the resource allocation phase of START by explicitly incorporating resource limitations. Next, we develop policies for assigning resources when two or more incidents occur at the same time and demand the same set of resources. Current standards, such as START, do not prescribe how to handle such situations---these decisions are most often made in an ad hoc manner. Finally, we examine the problem of efficiently routing a large number of patients affected by a major disaster, such as a biological, chemical, or nuclear incident, to facilities where they can be treated. We provide insight on how resources can be used effectively to treat patients as quickly as possible. Throughout this work, we focus on policies that are analytically justified, intuitive, broadly applicable, and easy to implement. Using numerical results and simulation, we demonstrate that implementing policies based on quantitative analysis can make a meaningful impact by increasing the expected number of survivors in a mass-casualty incidentDoctor of Philosoph

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing