Robots,CAM and O.R.

Microelectronics have greatly influenced the nature of manufacturing technology and systems. New factories make growing use of microcomputers and microprocessors in robots, C.N.C. machine tools and flexible manufacturing systems. The result is an increasing trend towards fully integrated computer-based manufacturing systems. As a consequence, O.R. software has a newer, more direct role in the modern factory than ever before. Fully integrated CAM systems are implemented by multi-disciplinary teams of engineers and management scientists. The O.R. practitioner can make a major contribution, for example in layout optimisation and plant simulations.     

How Relevant is Operational Research to Development? The Case of Kenyan Industry

This paper proposes that the debate over O.R.'s role in development can be advanced by looking at a specific industrial environment. The Kenyan example suggests that for O.R. to make a contribution to this developing society it must: first, be defined as broadly as possible so as to include the most basic problem-solving techniques, and second, contribute to the diffusing of these techniques throughout the economy. O.R. should be viewed less as the province of an elite group of specialists— and more as a common resource available to managers at any level of industry. O.R. educators in developing nations can "democratise" quantitative techniques by incorporating them into such traditional business disciplines as accounting, marketing, and production. These courses should stress problem-solving and rely on cases drawn from local experiences. The paper ends by describing a pilot project, the Case Research and Documentation Centre, which is designed to promote more pragmatic management education and research in Kenya.     

Prospects and challenges in R package development

R, a software package for statistical computing and graphics, has evolved into the lingua franca of (computational) statistics. One of the cornerstones of R’s success is the decentralized and modularized way of creating software using a multi-tiered development model: The R Development Core Team provides the “base system”, which delivers basic statistical functionality, and many other developers contribute code in the form of extensions in a standardized format via so-called packages. In order to be accessible by a broader audience, packages are made available via standardized source code repositories. To support such a loosely coupled development model, repositories should be able to verify that the provided packages meet certain formal quality criteria and “work”: both relative to the development of the base R system as well as with other packages (interoperability). However, established quality assurance systems and collaborative infrastructures typically face several challenges, some of which we will discuss in this paper.     

Operational Research and Computers: A Personal View

Lord Blackett played a vital part in the development of O.R. as an identifiable discipline. He was also influential in promoting the early development of computers. I hope that he would have approved of current efforts to make computational O.R. easier to use. Some developments of the last decade are reviewed, with particular reference to optimization methodology. Comments are also made on current trends, and the relationship between simulation and analytical models.     

O.R. Education and Developing Economies with Special Reference to India

The paper reviews the current state of application of O.R. in general and in developed economies in particular. It then analyses the causes for unsatisfactory application of O.R. in developing economies. Based on the analysis, the paper proceeds to review the existing O.R. education and training and develops an integrated design of O.R. education and training for developing economies. The paper outlines the role that international agencies should play in implementing the proposed scheme of O.R. education and training for developing economies.     

“Decisions,Decisions.....”

The decision analyst can provide valuable support to the decisionmaking process and, increasingly, the impact of this support is becoming more forceful through the use of decision support software packages available on microcomputers. What are the opportunities for using decision support software and what software is currently available? This paper discusses these questions and outlines the part decision support software can play, its value to the decisionmaker, and some of the pitfalls in practical application.     

Bemerkungen zum Isotropen analogon der Kubik von Cazamian

The isotropic analogue of the cubic of Cazamian [2] was studied by O. R?schel [4]. We prove two additional results. These are similar to those of H. Sachs [5] and O. R?schel [4, Theorem 4] for the isotropic ABRAMESCU-circle. We make use of the projective differential geometry [1,3]. Received 20 November 2000.     

Rank Conditional Coverage and Confidence Intervals in High-Dimensional Problems

Confidence interval procedures used in low-dimensional settings are often inappropriate for high-dimensional applications. When many parameters are estimated, marginal confidence intervals associated with the most significant estimates have very low coverage rates: They are too small and centered at biased estimates. The problem of forming confidence intervals in high-dimensional settings has previously been studied through the lens of selection adjustment. In that framework, the goal is to control the proportion of noncovering intervals formed for selected parameters. In this article, we approach the problem by considering the relationship between rank and coverage probability. Marginal confidence intervals have very low coverage rates for the most significant parameters and high rates for parameters with more boring estimates. Many selection adjusted intervals have the same behavior despite controlling the coverage rate within a selected set. This relationship between rank and coverage rate means that the parameters most likely to be pursued further in follow-up or replication studies are the least likely to be covered by the constructed intervals. In this article, we propose rank conditional coverage (RCC) as a new coverage criterion for confidence intervals in multiple testing/covering problems. The RCC is the expected coverage rate of an interval given the significance ranking for the associated estimator. We also propose two methods that use bootstrapping to construct confidence intervals that control the RCC. Because these methods make use of additional information captured by the ranks of the parameter estimates, they often produce smaller intervals than marginal or selection adjusted methods. These methods are implemented in R (R Core Team, 2017 R Core Team (2017), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing. [Google Scholar]) in the package rcc available on CRAN at https://cran.r-project.org/web/packages/rcc/index.html. Supplementary material for this article is available online.     

Introducing the Microcomputer into the O.R. Department

This paper reviews the typical main-frame computer services that are currently available and by accepting the alleged advantages of microcomputers postulates the ideal computing requirements of O.R. departments. Experience of microcomputers in O.R. departments, within the N.C.B. and elsewhere, is considered in relation to these ideal requirements. It is concluded that although some main-frame computer services will remain essential to O.R. and accepting that the main-frame can give a reasonable general computing service, the micro offers additional advantages. These include: lower cost and/or better service, the ability to configure the equipment exactly to meet O.R. needs; full control over applications and priorities; and portability - which means that computing power can now be taken direct to the manager by the O.R. department.     

Orca: A Visualization Toolkit for High-Dimensional Data

Abstract

This article describes constructing interactive and dynamic linked data views using the Java programming language. The data views are designed for data that have a multivariate component. The approach to displaying data comes from earlier research on building statistical graphics based on data pipelines, in which different aspects of data processing and graphical rendering are organized conceptually into segments of a pipeline. The software design takes advantage of the object-oriented nature of the Java language to open up the data pipeline, allowing developers to have greater control over their visualization applications. Importantly, new types of data views coded to adhere to a few simple design requirements can easily be integrated with existing pipe sections. This allows access to sophisticated linking and dynamic interaction across all (new and existing) view types. Pipe segments can be accessed from data analysis packages such as Omegahat or R, providing a tight coupling of visual and numerical methods.     

Experiences with Using Expert Systems in O.R.

Increased awareness of expert systems technology, and the availability of relevant software, has tempted many O.R. groups to investigate the expert systems approach. This paper considers the strategies open to O.R. groups interested in employing expert systems, reviews some of the relevant software, and discusses what those O.R. groups who have already investigated the use of expert systems methods are actually doing.Some of the authors' experience of developing expert systems is discussed. The development of an expert system that helps bankers analyse company accounts is presented. The use of expert system methods as vehicles for decision analysis, and the possibilities for producing systems that act as O.R. consultants are also discussed.     

A modest proposal: an approach to making the internal R system extensible

The R computing environment has become an important part of the statistical community and fostered the development of over a thousand add-on packages, many representing state-of-the-art research in statistical methodology. Although it is relatively easy to develop functionality on top of the system, it is very difficult for developers to directly extend the core system itself—the language, the interpreter and the internal data structures. Yet the ability to easily introduce new core, first-class data structures into the system that are customized and efficient is becoming essential in this era of large, complex data sets and innovative algorithms and data structures. While the community that might use such a facility to introduce new data types may be small, it is potentially very talented and important, and may lead to significant innovations that allow us to continue to leverage R for the next 5 years or more in rich new ways. I describe some of the difficulties that people encounter in extending the system and suggest that an object-oriented architecture for the internal implementation of R (or any system) would make such low-level internals extensible by package developers and not just the core development team. This would promote potentially rich experimentation that would allow us and others to approach new styles of computation in R, while simultaneously maintaining the existing important community which provides so much value-added to the R environment. Specifically, transforming the R implementation from a representation-specific architecture to a C++ abstract/virtual interface-based architecture may be the least disruptive approach to the continued evolution of R, and would bring many advantages and some technical challenges. Such an approach involves many technical details and potential degradations in performance. Due to the length of the this paper, I do not explore these issues in great detail but introduce the basic concepts. I do, however, refer to some technical aspects that are best understood with some knowledge of the implementation of R at the level of using the .Call () interface in R.     

A framework for building spreadsheet based decision models

Spreadsheet based decision modelling is widely used in business today. The functionality provided within the current integrated packages allows users to develop very complex business models. Unfortunately, most spreadsheet users do not follow any particular methodology when building spreadsheet based models. This in turn leads to many wasted hours of debugging and re-programming and often produces models that look more complex than they need to be and are also tedious to extend or modify. This paper presents an example of the type of framework that can be used when developing spreadsheet based business models. The framework encourages the efficient development of more effective models that are easy to use and easy to modify.     

微分方程及其中心流形方法的自动化简

计算机代数是应用数学的一个边缘学科 ,应用 Mathematica系统的强大的符号运算功能以及该系统提供的控制语句 ,自动实现在平衡点附近降低微分方程阶数和实行中心流形方法 ,并将该处理过程做成相关的软件包 ,简便、实用     

Code analysis and parallelizing vector operations in R

This paper presents some current work and preliminary thoughts on two seemingly unrelated areas. The first is the development of code analysis tools to help identify possible errors in R code. Current versions of these tools have been useful in finding bugs in R’s code as well as code in packages submitted to CRAN. The second area, where work is just beginning, is the development of mechanisms to allow R’s internal vectorized operations, as well as vectorized operations defined in packages, to take advantage of multiple processors. These two areas are related through their connections to ongoing efforts to develop a byte code compiler for R.     

Toward a Common Framework for Statistical Analysis and Development

We develop a general ontology of statistical methods and use it to propose a common framework for statistical analysis and software development built on and within the R language, including R's numerous existing packages. This framework offers a simple unified structure and syntax that can encompass a large fraction of existing statistical procedures. We conjecture that it can be used to encompass and present simply a vast majority of existing statistical methods, without requiring changes in existing approaches, and regardless of the theory of inference on which they are based, notation with which they were developed, and programming syntax with which they have been implemented. This development enabled us, and should enable others, to design statistical software with a single, simple, and unified user interface that helps overcome the conflicting notation, syntax, jargon, and statistical methods existing across the methods subfields of numerous academic disciplines. The approach also enables one to build a graphical user interface that automatically includes any method encompassed within the framework. We hope that the result of this line of research will greatly reduce the time from the creation of a new statistical innovation to its widespread use by applied researchers whether or not they use or program in R.     

Subjective Information Modelling

Operational Research is viewed as an aid to decision making and it is believed that at present an increasing part of the decision maker's task is being shut off from the operational researcher because of the latter's inability to make a useful contribution. Particular problems are those in which the decision maker has to use his subjective judgement, intuition and foresight to enable him to make choices between alternatives. There has been a great deal of work over the last ten years in the field of technological forecasting in which attempts are made to qualify, quantify and aggregate such subjective judgements, for example, Delphi and Cross Impact Analysis.The object of this paper is to present a framework within which these new procedures and the traditional O.R. approaches may be brought together. We feel that this may open the way for an improvement in decision making and an expanded role for O.R.     

The Art of Course Planning: Soft O.R. in Action

This article is a case study described from two viewpoints: that of an analyst and that of a ‘decision-maker’. It describes the use of two ‘soft O.R.’ methods in helping the members of a university O.R. group to plan revisions to a postgraduate Diploma course, resulting in the implementation of an improved course. One author conducted the exercise while the other was a member of the client group. Given that case studies are usually written by the analyst alone, it is hoped that a client's eye-view will be useful as feedback for practitioners and can also introduce some of the concepts and terminology of soft O.R. to those engaged in more mathematical O.R. activities.     

O.R. and Public Expenditure Planning and Control

The problems of public expenditure planning and control to which O.R. can make a contribution are identified. Suggestions are made for research by those in O.R. both within government and in universities. Recent work in the Treasury on financial models and information systems are discussed in terms of how such work might be applied elsewhere and extended to include measures of achievement. The topics covered include planning for uncertainty and theory development by O.R. in models of inflation and social justice.