Here are two books on a topic new to Technometrics: statistical and mathematical demography. The first author of Applied Mathematical Demography wrote the first two editions of this book alone. The second edition was published in 1985. Professor Keyfritz noted in the Preface (p. vii) that at age 90 he had no interest in doing another edition; however, the publisher encouraged him to find a coauthor. The result is an additional focus for the book in the world of biology that makes it much more relevant for the sciences. The book is now part of the publisher’s series on Statistics for Biology and Health. Much of it, of course, focuses on the many aspects of human populations. The new material focuses on mature population models, the particular focus of the new author (see, e.g., Caswell 2000). As one might expect from a book that was originally written in the 1970s, it does not include a lot of information on statistical computing. The new book by Alho and Spencer is focused on putting a better emphasis on statistics in the discipline of demography (Preface, p. vii). It is part of the publisher’s Series in Statistics. The authors are both statisticians, so the focus is on statistics as used for demographic problems. The authors are targeting human applications, so their perspective on science does not extend any further than epidemiology. The book actually strikes a good balance between statistical tools and demographic applications. The authors use the first two chapters to teach statisticians about the concepts of demography. The next four chapters are very similar to the statistics content found in introductory books on survival analysis, such as the recent book by Kleinbaum and Klein (2005), reported by Ziegel (2006). The next three chapters are focused on various aspects of forecasting demographic rates. The book concludes with chapters focusing on three areas of applications: errors in census numbers, financial applications, and small-area estimates.

Univariate Discrete Distributions

This study presents an integrated methodology that considers renewable distributed generation (RDG) and demand responses (DR) as options for planning distribution systems in a transition towards low-carbon sustainability. It is assumed that demand responsiveness is enabled by real-time pricing (RTP), and the problem has been formulated as a dynamic two-stage model. It co-optimizes the allocation of renewables [including wind and solar photovoltaic (PV)], non-renewable DG units (gas turbines) and smart metering (SM) simultaneously with network reinforcement for minimizing the total economic and carbon-emission costs over planning horizons. The behavior compliance to RTP is described through a nodal-based DR model, in which the fading effect attended during the load recovery is highlighted. Besides, uncertainties associated with renewable energy generation and price-responsiveness of customers are also taken into account and represented by multiple probabilistic scenarios. The proposed methodology is implemented by employing an efficient hybrid algorithm and applied to a typical distribution test system. The results demonstrate the effectiveness in improving the efficiency of RDG operations and mitigating CO2 footprint of distribution systems, when compared with the conventional planning paradigms.

Integrated Planning for Transition to Low-Carbon Distribution System With Renewable Energy Generation and Demand Response

To extract the maximum potential advantages in light of environmental, economical and technical aspects, the optimum installation and sizing of Distributed Generation (DG) in distribution network has always been challenging for utilities as well as customers. The installation of DG would be of maximum benefit where setting up of central power generating units are not practical, or in remote and small areas where the installation of transmission lines or availability of unused land is out of question. The objective of optimal installation of DG in distribution system is to achieve proper operation of distribution networks with minimization of the system losses, improvement of the voltage profile, enhanced system reliability, stability and loadability etc. In this respect analytical (classical) methods, although well-matched for small systems, perform adversely for large and complex objective functions. Unlike the analytical (classical) methods, the intelligent techniques for optimal sizing and siting of DGs are speedy, possess good convergence characteristics, and are well suited for large and complex systems. However, to find a global optimal solution of complex multi-objective problems, a hybrid of two or more meta-heuristic optimization techniques give more effective and reliable solution. This paper presents the fundamentals of DG and DG technologies review the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers. An attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal sizing and siting of DG in distribution networks. The present study can contribute meaningful knowledge and assist as a reference for investigators and utility engineers on issues to be considered for optimal sizing and siting of DG units in distribution systems.

Optimal sizing and siting techniques for distributed generation in distribution systems: A review

Distributed generation, with respect to its ability in utilizing the alternative resources of energy, provides a promising future for power generation in electric networks. Distributed generators contribution to power systems include improvement in energy efficiency and power quality to reliability and security. These benefits are only achievable with optimal allocation of distributed resources that considers the objective function, constraints, and employs suitable optimization algorithm. In this paper, a comprehensive review on the optimal allocation of distributed generators was carried out for different objectives, constraints, and algorithms. Current review highlights how the methods and algorithms for optimal distributed generation allocation play an important role in improving the accuracy and efficiency of the results.

A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms

Purpose – This paper aims to clarify emerging aspects and trends of Six Sigma literature over 17 years, from 1992 to 2008Design/methodology/approach – The literature on Six Sigma from 417 referred journal articles in business and management disciplines, information systems and computer science, engineering, healthcare, etc were systematically analyzed based on a scheme that consists of four distinct dimensions: publication year and journal, major themes, research type, and application sector (ie manufacturing vs service)Findings – A number of key findings emerged: Six Sigma research is growing rapidly, covering various disciplines and domains with a great focus on Six Sigma tools and techniques; empirical research is dominant with more emphasis on case study approach; and the growing gap between manufacturing‐ and service‐focused articles implies the return of Six Sigma to manufacturing as its initial base Although a large volume of literature is available on Six Sigma, the topic is still under deve

Six Sigma quality: a structured review and implications for future research

In this paper, control charts for monitoring exponentially distributed time between events (TBE) are studied. In particular, a Gamma chart which monitors the time until the rth event is proposed and investigated. A new method based on a random-shift model for calculating the out-of-control average time to signal (ATS) of the Gamma chart is developed. It is shown to be much more accurate than the conventional method based on a fixed-shift model through comparing with Monte Carlo simulation. A comparison is also made among the exponential, the Gamma and the exponential CUSUM charts, which shows that the Gamma chart is more sensitive than the exponential chart and the performance of a Gamma chart with r = 4 is comparable with that of an exponential CUSUM optimally designed. However, the advantage of the Gamma chart is the ease involved in the design, evaluation and implementation. The use of the Gamma chart is illustrated with two real and one simulated examples.

A control chart for the Gamma distribution as a model of time between events

Six Sigma as a quality improvement framework cannot remain static if it is to sustain its value for businesses beyond the first waves of applications. This paper explores the possibilities of enhancing the usefulness and effectiveness of Six Sigma by the integration of established Operations Research/Management Science (OR/MS) techniques. In this paper, we elucidate the needs for OR/MS techniques to enhance Six Sigma deployment in operational and transactional environments and propose a new training roadmap for core Six Sigma professionals (Six Sigma Black Belts) which incorporates these techniques. A matrix relating the components of the proposed training curriculum to the actual deliverables during implementation for a hybrid of operational and transactional environments is also presented. A practical case study is also presented to demonstrate the usefulness of the OR/MS tools in a typical transactional environment. Copyright © 2007 John Wiley & Sons, Ltd.

Fortification of six sigma: expanding the dmaic toolset

Control charts for monitoring the time between events can be applied in various areas. In this study, we focus on the exponential control chart and consider the phase II problem (when process parameters are known) as well as the phase I problem (when process parameters are unknown). An exponential chart designed with the conventional approach has the disadvantage that the Average Run Length (ARL) value may increase when the process deviates from the nominal state. An ARL-unbiased design approach is therefore proposed for both phase II and phase I exponential charts. A sequential sampling scheme is adopted for the phase I exponential chart. The proposed ARL-unbiased design approach has several advantages over the conventional one, as it provides a self-starting feature and can significantly improve the ARL performance. Specific guidelines are suggested regarding the time to stop updating the estimates of parameters and control limits based on the actual false alarm rate. The phase I exponential chart can b...

Design of exponential control charts using a sequential sampling scheme

Power intermittency and maintenance cost are the major challenges in harvesting wind energy This paper proposes a multicriteria optimization model to design and operate a wind-based distributed generation (DG) system The goal is to determine the equipment sizing, siting, and maintenance schedules so that the system cost is minimized while the turbine reliability is maximized System cost comprises initial capital, operations, maintenance, downtime losses, and environmental penalty The study makes an early attempt to incorporate the maintenance policy of generating units into the planning model The moment method and the central limit theorem are used to characterize the power intermittency and the load uncertainty A genetic algorithm is developed to search the nondominant solution set for the equipment siting, sizing, and maintenance intervals The proposed model is demonstrated on the IEEE 37-node distribution network considering independent and correlated wind speed scenarios

Multicriteria Planning for Distributed Wind Generation Under Strategic Maintenance

https://www.researchgate.net/profile/Min_Xie6/publication/263501043_Economic_design_of_exponential_charts_for_time_between_events_monitoring/links/54b2105c0cf220c63cd12b34.pdf

Cai Wen Zhang

Papers

A control chart for the Gamma distribution as a model of time between events

Fortification of six sigma: expanding the dmaic toolset

Design of exponential control charts using a sequential sampling scheme

Multicriteria Planning for Distributed Wind Generation Under Strategic Maintenance

Economic design of cumulative count of conforming charts under inspection by samples