Leap-frogging Newton's method

Using Newton's method as an intermediate step, we introduce an iterative method that approximates numerically the solution of f (x) = 0. The method is essentially a leap-frog Newton's method. The order of convergence of the proposed method at a simple root is cubic and the computational efficiency in general is less, but close to that of Newton's method. Like Newton's method, the new method requires only function and first derivative evaluations. The method can easily be implemented on computer algebra systems where high machine precision is available.     

A practical model of Heineken's bottle filling line with dependent failures

This article describes a mass balance production line model based on constant machine rates, fixed finite buffer size and stochastic failure and repair behavior. Failure and repair processes need not be exponential and need not be independent. The model is easy to initialize with field data and we report favorable results from a validation exercise at Heineken Brewery. This article presents a new diagnostic for distance to infinite buffer equilibrium and a new test for independence of failure–repair processes are derived. It is shown that the stochastic fluctuations in a typical line are large, and machine unreliability can perturb the line's design.     

Complexity of games and bounded rationality

An attempt is made to propose a concept of limited rationality for choice junctions based on computability theory in computer science.

Starting with the observation that it is possible to construct a machine simulating strategies of each individual in society, one machine for each individual's preference structure, we identify internal states of this machine with strategies or strategic preferences. Inputs are possible actions of other agents in society thus society is effectively operating as a game generated by machines. The main result states that effective realization of game strategies bound by the “complexity of computing machines'.     

A heuristic approach for multiple item and location ordering problem with quantity discount and capacity constraint

This paper addresses a geographically distributed vending machine inventory control problem where the multiple product choices in every vending machine and the varied demand for each product in every location's vending machine exist. The ordering quantity for each product and the desirable number of each product type in a vending machine must be simultaneously decided to maximize the total expected profit. Considering that the ordering costs are in piece-wise function and only one type of product is allocated in a slot of a vending machine, the proposed problem correlates to the piece-wise constrained nonlinear integer-programming problem. There is difficulty in deriving the exact optimal solutions to this problem since its computational complexity appears to be a nondeterministic polynomial problem. This paper presents a novel heuristic approach in finding (1) the ordering quantity for each product, (2) the product types, and (3) the corresponding quantity allocated in each vending machine. The numerical results show the effectiveness of the proposed methodology in solving the aforementioned problem. In this paper, the solutions found by this study's approach are as well as those found by the brute-force method, and if not equivalent, perform better than those by the Lingo software.     

Batching and routing: Two functions in the operational planning of flexible manufacturing systems

The success of a Flexible Manufacturing System (FMS) is based on many factors such as the appropriate selection of parts, machine tools and material handling systems, effective planning and a full specification of the control software. In particular, planning must be carried out effectively at many different levels. The production operations of FMS's are planned at one of these levels, and this paper presents a new functional structure for this planning task, called operational planning. Four operational planning functions are identified: batching, routing, dispatching and sequencing. The models that are used to represent the batching and routing problems are presented in detail. The models are formulated as mixed integer programming models, which broadly specify the capacity constraints of the FMS, enabling the decision-maker to look for planning alternatives in a variable time horizon. Results are derived using data from an existing FMS.     

An e–E-insensitive support vector regression machine

According to the Statistical Learning Theory, the support vectors represent the most informative data points and compress the information contained in training set. However, a basic problem in the standard support vector machine is that when the data is noisy, there exists no guaranteed scheme in support vector machines’ formulation to dissuade the machine from learning noise. Therefore, the noise which is typically presents in financial time series data may be taken into account as support vectors. In turn, noisy support vectors are modeled into the estimated function. As such, the inclusion of noise in support vectors may lead to an over-fitting and in turn to a poor generalization. The standard support vector regression (SVR) is reformulated in this article in such a way that the large errors which correspond to noise are restricted by a new parameter \(E\) . The simulation and real world experiments indicate that the novel SVR machine meaningfully performs better than the standard SVR in terms of accuracy and precision especially where the data is noisy, but in expense of a longer computation time.     

Guiding Data Analysts with Visual Statistical Strategies

Abstract

The concept of statistical strategy is introduced and used to develop a structured graphical user interface for guiding data analysis. The interface visually represents statistical strategies that are designed by expert data analysts to guide novices. The representation is an abstraction of the expert's concepts of the essence of a data analysis. We argue that an environment that visually guides and structures data analysis will improve data analysis productivity, accuracy, accessibility, and satisfaction in comparison to an environment without such aids, especially for novice data analysts. Our concepts are based on notions from cognitive science, and can be empirically evaluated. The interface consists of two interacting windows—the guidemap and the workmap. Each window contains a graph that has nodes and edges. The guidemap graph represents the statistical strategy for a specific statistical task (such as describing data). Nodes represent potential data analysis actions that can be taken by the system. Edges represent potential actions that can be taken by the analyst. The guidemap graph exists prior to the data analysis session, having been created by an expert. The workmap graph represents the complete history of all steps taken by the data analyst. It is constructed during the data analysis session as a result of the analyst's actions. Workmap nodes represent data sets, data models, or data analysis procedures that have been created or used by the analyst. Workmap edges represent the chronological sequence of the analyst's actions. One workmap node is highlighted to indicate which statistical object is the focus of the strategy. We illustrate our concepts with ViSta, the Visual Statistics system that we have developed.     

Role of design-philosophies in interfacing manufacturing with marketing

Product design involves the mapping of a product's marketing attributes to its engineering and manufacturing features. In practice, it is a non-trivial task for design engineers to determine an appropriate mapping between a product's marketing and manufacturing attributes so as to generate a product design that satisfies customer-needs while being feasible to produce within the technical and financial constraints of the firm's manufacturing domain. In this paper, we hypothesize that in a mature industry, the expertise that guides designers in this mapping process exists in the form of an engineering design-philosophy that governs the design of products in that industry. Clearly, if we are able to discern the design-philosophies that exist in an industry, they can then be used to channrl designers' creativity to be congruent with the commercial objectives of firms in that industry. Using real-world data from the automobile industry, we show the existence of design-philosophies and illustrate methodologies for identifying and interpreting them. We also describe predictive models that allow managers to exploit the knowledge available in these design-philosophies and accurately specify a new product's manufacturing attribute values, given market input regarding the product's desired features. This total approach should save firms valuable product designing time and enable them to generate products that can be successfully produced and sold.     

Wang’s B machines are efficiently universal,as is Hasenjaeger’s small universal electromechanical toy

In the 1960s Gisbert Hasenjaeger built Turing Machines from electromechanical relays and uniselectors. Recently, Glaschick reverse engineered the program of one of these machines and found that it is a universal Turing machine. In fact, its program uses only four states and two symbols, making it a very small universal Turing machine. (The machine has three tapes and a number of other features that are important to keep in mind when comparing it to other small universal machines.) Hasenjaeger’s machine simulates Hao Wang’s B machines, which were proved universal by Wang. Unfortunately, Wang’s original simulation algorithm suffers from an exponential slowdown when simulating Turing machines. Hence, via this simulation, Hasenjaeger’s machine also has an exponential slowdown when simulating Turing machines. In this work, we give a new efficient simulation algorithm for Wang’s B machines by showing that they simulate Turing machines with only a polynomial slowdown. As a second result, we find that Hasenjaeger’s machine also efficiently simulates Turing machines in polynomial time. Thus, Hasenjaeger’s machine is both small and fast. In another application of our result, we show that Hooper’s small universal Turing machine simulates Turing machines in polynomial time, an exponential improvement.     

MOP/GP models for machine learning

Techniques for machine learning have been extensively studied in recent years as effective tools in data mining. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problems with two class sets, its idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. This task is performed by solving a quadratic programming problem in a traditional formulation, and can be reduced to solving a linear programming in another formulation. However, the idea of maximal margin separation is not quite new: in the 1960s the multi-surface method (MSM) was suggested by Mangasarian. In the 1980s, linear classifiers using goal programming were developed extensively.This paper presents an overview on how effectively MOP/GP techniques can be applied to machine learning such as SVM, and discusses their problems.     

Using economic X control chart design methodology to estimate and optimize machine efficiency in the case of multimachine assignments

Estimation and optimization of machine efficiency in the case of multi machine assignments is a difficult issue for some industries such as textile industry, due to the machine interference. Although the problem is not new, the solutions proposed are not full satisfactory. For example, the analytical formulas are not suitable for high number of machines; queuing theory approaches are complex and impractical for mill managers. On the other hand, simulation methodology seems to be a perfect tool to estimate and analyze; however, the simulation package programs are expensive and are not suitable for systematic optimization. In this paper, Duncan's economic control chart design methodology is proposed as an alternative way for one of the oldest industrial problems. This study shows that this new approach is a more efficient solution to this stochastic problem. Copyright © 2009 John Wiley & Sons, Ltd.     

A Mean Variance Approach to the Optimal Machine Maintenance and Replacement Problem

This paper considers a simultaneous maintenance and replacement problem under uncertainty. The effects of maintenance and deterioration are assumed to have a probabilistic effect (of the Markovian type) on a machine's salvage value. This leads to the definition of a non-stationary stochastic process of the machine's salvage value whose mean and variance evolutions are found. These evolutions together with an expected discounted profit functional (as that given by Thompson) allows us to apply the tools of optimal control theory to determine a "certainty-equivalent" maintenance program and the optimum replacement date of the machine. A discussion of the uncertain effects of deterioration and maintenance and managers attitudes towards risk is included as well.     

Support vector machine classifiers with uncertain knowledge sets via robust optimization

In this article we study support vector machine (SVM) classifiers in the face of uncertain knowledge sets and show how data uncertainty in knowledge sets can be treated in SVM classification by employing robust optimization. We present knowledge-based SVM classifiers with uncertain knowledge sets using convex quadratic optimization duality. We show that the knowledge-based SVM, where prior knowledge is in the form of uncertain linear constraints, results in an uncertain convex optimization problem with a set containment constraint. Using a new extension of Farkas' lemma, we reformulate the robust counterpart of the uncertain convex optimization problem in the case of interval uncertainty as a convex quadratic optimization problem. We then reformulate the resulting convex optimization problems as a simple quadratic optimization problem with non-negativity constraints using the Lagrange duality. We obtain the solution of the converted problem by a fixed point iterative algorithm and establish the convergence of the algorithm. We finally present some preliminary results of our computational experiments of the method.     

Single machine scheduling when processing times are correlated normal random variables

A single machine is available to process a collection of jobs whose processing times are jointly multivariate normal. Processing is nonpreemptive. We show that in the equicorrelation case, the permutation policy which schedules the jobs in ascending order of their (marginal) mean processing times is optimal for general order-specific costs and also for job-specific costs under an agreeability condition. Suboptimality bounds on the performance of Smith's rule are obtained for the weighted flow-time criterion. A computational study shows that a dynamic version of Smith's rule comes very close to optimality.     

Parameter estimation under ambiguity and contamination with the spurious model

Recently, we proposed variants as a statistical model for treating ambiguity. If data are extracted from an object with a machine then it might not be able to give a unique safe answer due to ambiguity about the correct interpretation of the object. On the other hand, the machine is often able to produce a finite number of alternative feature sets (of the same object) that contain the desired one. We call these feature sets variants of the object. Data sets that contain variants may be analyzed by means of statistical methods and all chapters of multivariate analysis can be seen in the light of variants. In this communication, we focus on point estimation in the presence of variants and outliers. Besides robust parameter estimation, this task requires also selecting the regular objects and their valid feature sets (regular variants). We determine the mixed MAP-ML estimator for a model with spurious variants and outliers as well as estimators based on the integrated likelihood. We also prove asymptotic results which show that the estimators are nearly consistent.The problem of variant selection turns out to be computationally hard; therefore, we also design algorithms for efficient approximation. We finally demonstrate their efficacy with a simulated data set and a real data set from genetics.     

Wavelet Shrinkage Denoising Using the Non-Negative Garrote

Abstract

In this article, we combine Donoho and Johnstone's wavelet shrinkage denoising technique (known as WaveShrink) with Breiman's non-negative garrote. We show that the non-negative garrote shrinkage estimate enjoys the same asymptotic convergence rate as the hard and the soft shrinkage estimates. Simulations are used to demonstrate that garrote shrinkage offers advantages over both hard shrinkage (generally smaller mean-square-error and less sensitivity to small perturbations in the data) and soft shrinkage (generally smaller bias and overall mean-square-error). The minimax thresholds for the non-negative garrote are derived and the threshold selection procedure based on Stein's unbiased risk estimate (SURE) is studied. We also propose a threshold selection procedure based on combining Coifman and Donoho's cycle-spinning and SURE. The procedure is called SPINSURE. We use examples to show that SPINSURE is more stable than SURE: smaller standard deviation and smaller range.     

Online simulation for a real-time route dispatching problem

In this article we present an online simulation application for a decision problem that operates in real time, where products have to be dispatched from two depots to clients that are geographically distributed throughout the city. The system's behaviour is highly stochastic, due to the random behaviour of the client's demand (in time and space), and the random times of order preparation, travelling times of dispatchers (these are motorcycle drivers) and absence rate of drivers each day. A decision scheme is proposed that combines elements of vehicle routing with time windows, real-time dispatching of drivers and online simulation, through which information on future events is considered in the decision-making process. Two major conclusions are obtained when this scheme is applied to real data. First, we show that the proposed algorithm for order consolidation and route dispatching can be very advantageous from the point of view of logistics costs and quality of service. Second, we show that online simulation and, specifically, the Simulation-based Real-time Decision Making methodology (SRDM) can further improve the quality of the results. New ideas for further work are also proposed.     

Analysing Policy Effectiveness Using Cone Restricted Data Envelopment Analysis

Reducing weight flexibility has been suggested as a method for ensuring that the solution to data envelopment analyses do not give unreasonably low weightings to certain inputs or outputs. In this paper we extend the use of reducing weight flexibility and use it to model the effects of the decision-making unit's objectives on its efficiency relative to other DMUs with possibly different objectives. We show how such an approach can identify situations in which the weights imputed by a data envelopment analysis can be inconsistent with the decision-making weights used by the firm, and how this approach can be used to provide efficiency measures that are consistent with the DMU's own objectives. The method allows the analyst to distinguish between a decision-making unit's technological inefficiency and its inability to implement its own policies.     

长沙市居民娱乐消费与收入关系的数学模型及分析

运用调查统计、回归建模和数学分析等方法,研究了长沙居民收入与娱乐消费之间的定量关系,其中包括1区别于统计局发布的对长沙市居民消费分类,通过社会采访调查的方法分性别、年龄、收入差异等3个维度更准确地测算长沙市城镇居民娱乐消费的消费额.2基于调查数据,分别构建了不同年龄段、不同性别的居民娱乐消费与其收入之间的回归模型.3通过分析回归模型的性质,揭示了长沙市城镇居民娱乐消费的规律性.研究结果对政府部门的决策优化具有重要的启示作用.     

Fractions as Subtraction: An Activity‐Oriented Perspective from Elementary Children

A sample of third‐, fourth‐, and fifth‐grade student responses to the question “What is a fraction?” were examined to gain an understanding of how children in upper elementary grades make sense of fractions. Rather than measure children's understanding of fractions relative to mathematically conventional part–whole constructions of fractions, we attempted to understand children's actions and processes. A small but nontrivial group of children used subtraction (takeaway and removal) as a framework for understanding how fractions were created and written. An analysis of the content of their responses as well as a comparison of the performance of these children with that of children who used other ways of describing fractions suggests that the use of subtraction may be a reasonable (or at least not harmful) way for children to begin to access concepts related to fractions. Also, this study suggests that attention to children's understanding through the lens of children's activity might reveal ways of thinking and insights that are masked when we compare children's thinking in more structured research settings.