THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

Theory and practice of uncertain programming

Product family design and platform-based product development has received much attention over the last decade. This paper provides a comprehensive review of the state-of-the-art research in this field. A decision framework is introduced to reveal a holistic view of product family design and platform-based product development, encompassing both front-end and back-end issues. The review is organized according to various topics in relation to product families, including fundamental issues and definitions, product portfolio and product family positioning, platform-based product family design, manufacturing and production, as well as supply chain management. Major challenges and future research directions are also discussed.

Product family design and platform-based product development: a state-of-the-art review

Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, without reducing SVM classification accuracy. A particle swarm optimization (PSO) based approach for parameter determination and feature selection of the SVM, termed PSO+SVM, is developed. Several public datasets are employed to calculate the classification accuracy rate in order to evaluate the developed PSO+SVM approach. The developed approach was compared with grid search, which is a conventional method of searching parameter values, and other approaches. Experimental results demonstrate that the classification accuracy rates of the developed approach surpass those of grid search and many other approaches, and that the developed PSO+SVM approach has a similar result to GA+SVM. Therefore, the PSO+SVM approach is valuable for parameter determination and feature selection in an SVM.

https://romisatriawahono.net/lecture/rm/paper/Lin%20-%20Parameter%20determination%20and%20feature%20selection%20for%20SVM%20by%20PSO%20-%202009.pdf

Particle swarm optimization for parameter determination and feature selection of support vector machines

Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in systems, processes, designs or services and has been used in a wide range of industries. The conventional risk priority number (RPN) method has been criticized to have many deficiencies and various risk priority models have been proposed in the literature to enhance the performance of FMEA. However, there has been no literature review on this topic. In this study, we reviewed 75 FMEA papers published between 1992 and 2012 in the international journals and categorized them according to the approaches used to overcome the limitations of the conventional RPN method. The intention of this review is to address the following three questions: (i) Which shortcomings attract the most attention? (ii) Which approaches are the most popular? (iii) Is there any inadequacy of the approaches? The answers to these questions will give an indication of current trends in research and the best direction for future research in order to further address the known deficiencies associated with the traditional FMEA.

Review: Risk evaluation approaches in failure mode and effects analysis: A literature review

Fuzzy data envelopment analysis (DEA): a possibility approach☆

We examine the effects of component commonality on optimal safety stock levels in a two-product, two-level inventory model. The criterion is to minimize system safety stock subject to a service level constraint. Although our model is specialized, its analysis provides insights not available in other multilevel inventory models.

The Effect of Commonality on Safety Stock in a Simple Inventory Model

This paper describes a model and associated algorithm for generating maximum throughput cyclic schedules for the movements of a hoist in a PCB electroplating facility. The algorithm is enumerative in nature and involves the solution of linear programming subproblems. Computational experience with schedules for real systems is presented.

Hoist Scheduling For A PCB Electroplating Facility

Fuzzy Data Envelopment Analysis (FDEA) is a tool for comparing the performance of a set of activities or organizations under uncertainty environment. Imprecise data in FDEA models is represented by fuzzy sets and FDEA models take the form of fuzzy linear programming models. Previous research focused on solving the FDEA model of the CCR (named after Charnes, Cooper, and Rhodes) type (FCCR). In this paper, the FDEA model of the BCC (named after Banker, Charnes, and Cooper) type (FBCC) is studied. Possibility and Credibility approaches are provided and compared with an α-level based approach for solving the FDEA models. Using the possibility approach, the relationship between the primal and dual models of FBCC models is revealed and fuzzy efficiency can be constructed. Using the credibility approach, an efficiency value for each DMU (Decision Making Unit) is obtained as a representative of its possible range. A numerical example is given to illustrate the proposed approaches and results are compared with those obtained with the α-level based approach.

Henry L. W. Nuttle

Papers

Fuzzy data envelopment analysis (DEA): a possibility approach☆

The Effect of Commonality on Safety Stock in a Simple Inventory Model

Hoist Scheduling For A PCB Electroplating Facility

Fuzzy BCC Model for Data Envelopment Analysis

Relaxed conditions for radial-basis function networks to be universal approximators