الصفحة 1
الصفحة 1
Patient Flow : Reducing Delay in Healthcare Delivery
Patient Flow: Reducing Delay in Healthcare Delivery is dedicated to improving healthcare through reducing the delays experienced by patients. One aspect of this goal is to improve the flow of patients, so that they do not experience unnecessary waits as they flow through a healthcare system. Another aspect is ensuring that services are closely synchronized with patterns of patient demand. Still another aspect is ensuring that ancillary services, such as housekeeping and transportation, are fully coordinate with direct patient care. It is the first book treatment to have reduction in patient delay as its sole focus, and therefore, provides the foundation by which hospitals can implement change. Reflecting the highly interdisciplinary and practitioner nature of this book, the chapters have been written by doctors, nurses, industrial engineers, system engineers and geographers, and thus, these perspectives provide the comprehensive view needed to address the problem of patient delay.
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems is a comprehensive introduction to the field of discrete event systems, offering a breadth of coverage that makes the material accessible to readers of varied backgrounds. The book emphasizes a unified modeling framework that transcends specific application areas, linking the following topics in a coherent manner: language and automata theory, supervisory control, Petri net theory, Markov chains and queueing theory, discrete-event simulation, and concurrent estimation techniques
Design and Analysis of Simulation Experiments
This is an advanced expository book on statistical methods for the Design and Analysis of Simulation Experiments (DASE). Though the book focuses on DASE for discrete-event simulation (such as queuing and inventory simulations), it also discusses DASE for deterministic simulation (such as engineering and physics simulations). The text presents both classic and modern statistical designs. Classic designs (e.g., fractional factorials) assume only a few factors with a few values per factor. The resulting input/output data of the simulation experiment are analyzed through low-order polynomials, which are linear regression (meta)models. Modern designs allow many more factors, possible with many values per factor. These designs include group screening (e.g., Sequential Bifurcation, SB) and space filling designs (e.g., Latin Hypercube Sampling, LHS). The data resulting from these modern designs may be analyzed through low-order polynomials for group screening and various metamodel types (e.g., Kriging) for LHS.
