An algorithm for estimating Box–Cox transformation parameter in ANOVA

In this study, we construct a feasible region, in which we maximize the likelihood function, by using Shapiro–Wilk and Bartlett's test statistics to obtain Box–Cox power transformation parameter for solving the issues of non-normality and/or heterogeneity of variances in analysis of variance (ANOVA). Simulation studies illustrate that the proposed approach is more successful in attaining normality and variance stabilization, and is at least as good as the usual maximum likelihood estimation (MLE) in estimating the transformation parameter for different conditions. Our proposed method is illustrated on two real-life datasets. Moreover, the proposed algorithm is released under R package AID under the name of “boxcoxfr” for implementation.     

A simulation comparison on spatial Poisson mixed models

The statistical methods for analyzing spatial count data have often been based on random fields so that a latent variable can be used to specify the spatial dependence. In this article, we introduce two frequentist approaches for estimating the parameters of model-based spatial count variables. The comparison has been carried out by a simulation study. The performance is also evaluated using a real dataset and also by the simulation study. The simulation results show that the maximum likelihood estimator appears to be with the better sampling properties.     

Differential evolutionary algorithm in the construction process of optimal experimental designs

This article shows how a differential evolution algorithm can be used to find statistical designs under several optimality criteria as A, D, and T. The general algorithm of differential evolution is described and then applied on constructing optimal designs for several well-known models and compare them with those already available by other algorithms, in terms of relative efficiency. Moreover, the great effect of population size in the running of this algorithm establishes a precedent in the use of differential evolution algorithms over genetic algorithms.     

Optimal designs for minimising covariances among parameter estimators in a linear model

We construct approximate optimal designs for minimising absolute covariances between least‐squares estimators of the parameters (or linear functions of the parameters) of a linear model, thereby rendering relevant parameter estimators approximately uncorrelated with each other. In particular, we consider first the case of the covariance between two linear combinations. We also consider the case of two such covariances. For this we first set up a compound optimisation problem which we transform to one of maximising two functions of the design weights simultaneously. The approaches are formulated for a general regression model and are explored through some examples including one practical problem arising in chemistry.     

Applying classification algorithms in practice

In this paper we present a perspective on the overall process of developing classifiers for real-world classification problems. Specifically, we identify, categorize and discuss the various problem-specific factors that influence the development process. Illustrative examples are provided to demonstrate the iterative nature of the process of applying classification algorithms in practice. In addition, we present a case study of a large scale classification application using the process framework described, providing an end-to-end example of the iterative nature of the application process. The paper concludes that the process of developing classification applications for operational use involves many factors not normally considered in the typical discussion of classification models and algorithms.     

Multi-phase Algorithms for Throughput Maximization for Real-Time Scheduling

We consider the problem of off-line throughput maximization for job scheduling on one or more machines, where each job has a release time, a deadline and a profit. Most of the versions of the problem discussed here were already treated by Bar-Noy et al. (Proc. 31st ACM STOC, 1999, pp. 622–631; http://www.eng.tau.ac.il/amotz/). Our main contribution is to provide algorithms that do not use linear programming, are simple and much faster than the corresponding ones proposed in Bar-Noy et al. (ibid., 1999), while either having the same quality of approximation or improving it. More precisely, compared to the results of in Bar-Noy et al. (ibid., 1999), our pseudo-polynomial algorithm for multiple unrelated machines and all of our strongly-polynomial algorithms have better performance ratios, all of our algorithms run much faster, are combinatorial in nature and avoid linear programming. Finally, we show that algorithms with better performance ratios than 2 are possible if the stretch factors of the jobs are bounded; a straightforward consequence of this result is an improvement of the ratio of an optimal solution of the integer programming formulation of the JISP2 problem (see Spieksma, Journal of Scheduling, vol. 2, pp. 215–227, 1999) to its linear programming relaxation.     

总成本约束下的技术增长率优化模型

为了考察企业生产中技术要素的作用、规律,在一定的假设条件下,对其技术要素下了定义,认为其产出函 数包括技术、劳动及资本要素;通过构建与求解生产总成本,构建最大条件下的优化模型,推导出技术最优增长率 与传统生产要素劳动与资本的优化配比关系,进一步构造了企业在生产总成本约束下的技术增长率优化模型,并 设计了遗传算法程序对模型进行求解。实证研究表明,企业技术投入是有成本、有代价的,其所建模型可用于计算 考察企业技术投入的适度规模,以指导企业生产与技术管理实践。     

Graph Algorithms with Small Communication Costs

We study minimizing communication cost in parallel algorithm design, by minimizing the number of communication phases in coarse-grained parallel computers. There have been several recent papers dealing with parallel algorithms of small communication cost under different models. Most of these results are for computational geometry problems. For these problems it has been possible to decompose tasks into appropriate subproblems in a communication-efficient way. It appears to be somewhat more difficult to design parallel algorithms with small communication phases for graph theory problems. In this paper we focus on the design of deterministic algorithms with a small number of communication phases for the list ranking problem and the shortest path problem.     

Supply Chain Scheduling: Distribution Systems

We study conflict and cooperation issues arising in a supply chain where a manufacturer makes products which are shipped to customers by a distributor. The manufacturer and the distributor each has an ideal schedule, determined by cost and capacity considerations. However, these two schedules are in general not well coordinated, which leads to poor overall performance. In this context, we study two practical problems. In both problems, the manufacturer focuses on minimizing unproductive time. The distributor minimizes customer cost measures in the first problem and minimizes inventory holding cost in the second problem. We first evaluate each party's conflict, which is the relative increase in cost that results from using the other party's optimal schedule. Since this conflict is often significant, we consider several practical scenarios about the level of cooperation between the manufacturer and the distributor. These scenarios define various scheduling problems for the manufacturer, the distributor, and the overall system. For each of these scheduling problems, we provide an algorithm. We demonstrate that the cost saving provided by cooperation between the decision makers is usually significant. Finally, we discuss the implications of our work for how manufacturers and distributors negotiate, coordinate, and implement their supply chain schedules in practice.     

混合遗传算法在矩形板材下料问题上的应用研究

根据基本遗传算法在解决板材下料优化中存在的问题,提出了将局部搜索算法和遗传算法相结合,取各自的优点来更好地解决矩形板材下料优化问题.