The need for education and research in administrative data processing

This paper contains some points of view concerning the need felt by many computer-people for more intensive education and research activities in administrative data processing. After a short out-line of the main problems in administrative data processing we will try to indicate some topics where research is most needed. Then follows a survey of the need of knowledge specified for different categories of persons working in this field. Finally, we will draw some conclusions regarding the responsibility of different institutions to certify these needs.     

“You have to get wet to learn how to swim” applied to bridging the gap between research into personnel scheduling and its implementation in practice

Personnel scheduling problems have attracted research interests for several decades. They have been considerably changed over time, accommodating a variety of constraints related to legal and organisation requirements, part-time staff, flexible hours of staff, staff preferences, etc. This led to a myriad of approaches developed for solving personnel scheduling problems including optimisation, meta-heuristics, artificial intelligence, decision-support, and also hybrids of these approaches. However, this still does not imply that this research has a large impact on practice and that state-of-the art models and algorithms are widely in use in organisations. One can find a reasonably large number of software packages that aim to assist in personnel scheduling. A classification of this software based on its purpose will be proposed, accompanied with a discussion about the level of support that this software offers to schedulers. A general conclusion is that the available software, with some exceptions, does not benefit from the wealth of developed models and methods. The remaining of the paper will provide insights into some characteristics of real-world scheduling problems that, in the author’s opinion, have not been given a due attention in the personnel scheduling research community yet and which could contribute to the enhancement of the implementation of research results in practice. Concluding remarks are that in order to bridge the gap that still exists between research into personnel scheduling and practice, we need to engage more with schedulers in practice and also with software developers; one may say we need to get wet if we want to learn how to swim.

     

CPI指数变换对产品销售影响的可拓数据挖掘

目前对数据挖掘的研究主要集中在对静态数据的挖掘,而在实际工作中,经常要处理的矛盾问题,需要通过可拓变换和可拓变换的运算来解决,这就需要用到变换的知识,需要运用动态数据挖掘或可拓数据挖掘来解决问题.运用可拓逻辑和可拓数据挖掘的理论知识,根据国家消费者物价指数的变换对产品销售数据的影响来研究可拓数据挖掘中传导知识的挖掘,为企业的决策者在目前的市场环境下提出更加合理的销售策略提供依据.     

ODE versus SQP methods for constrained optimization

In this paper, we review some methods which are designed to solve equality constrained minimization problems by following the trajectory defined by a system of ordinary differential equations. The numerical performance of a number of these methods is compared with that of some popular sequential quadratic programming algorithms. On a set of eighteen difficult test problems, we observe that several of the ODE methods are more successful than any of the SQP techniques. We suggest that these experimental results indicate the need for research both to analyze and develop new ODE techniques and also to strengthen the currently available SQP algorithms.This work was supported by a SERC Research Studentship for the first author. Both authors are indebted to Dr. J. J. McKeown and Dr. K. D. Patel of SCICON Ltd., the collaborating establishment, for their advice and encouragement.     

Saddle Points Theory of Two Classes of Augmented Lagrangians and Its Applications to Generalized Semi-infinite Programming

In this paper, we develop the sufficient conditions for the existence of local and global saddle points of two classes of augmented Lagrangian functions for nonconvex optimization problem with both equality and inequality constraints, which improve the corresponding results in available papers. The main feature of our sufficient condition for the existence of global saddle points is that we do not need the uniqueness of the optimal solution. Furthermore, we show that the existence of global saddle points is a necessary and sufficient condition for the exact penalty representation in the framework of augmented Lagrangians. Based on these, we convert a class of generalized semi-infinite programming problems into standard semi-infinite programming problems via augmented Lagrangians. Some new first-order optimality conditions are also discussed. This research was supported by the National Natural Science Foundation of P.R. China (Grant No. 10571106 and No. 10701047).     

A common framework and taxonomy for multicriteria scheduling problems with interfering and competing jobs: Multi-agent scheduling problems

Most classical scheduling research assumes that the objectives sought are common to all jobs to be scheduled. However, many real-life applications can be modeled by considering different sets of jobs, each one with its own objective(s), and an increasing number of papers addressing these problems has appeared over the last few years. Since so far the area lacks a unified view, the studied problems have received different names (such as interfering jobs, multi-agent scheduling, and mixed-criteria), some authors do not seem to be aware of important contributions in related problems, and solution procedures are often developed without taking into account existing ones. Therefore, the topic is in need of a common framework that allows for a systematic recollection of existing contributions, as well as a clear definition of the main research avenues. In this paper we review multicriteria scheduling problems involving two or more sets of jobs and propose an unified framework providing a common definition, name and notation for these problems. Moreover, we systematically review and classify the existing contributions in terms of the complexity of the problems and the proposed solution procedures, discuss the main advances, and point out future research lines in the topic.     

Application of fuzzy soft set in decision making problems based on grey theory

There are many uncertain problems in practical production and life which need decisions made with soft sets and fuzzy soft sets. However, the basis of evaluation of the decision method is single and simple, the same decision problem can obtain different results from using a different evaluation basis. In this paper, in order to obtain the right result, we discuss fuzzy soft set decision problems. A new algorithm based on grey relational analysis is presented. The evaluation bases of the new algorithm are multiple. There is more information in a decision result based on multiple evaluation bases, which is more easily accepted and logical to one’s thinking. For the two cases examined, the results show that the new algorithm is efficient for solving decision problems.     

Seeking research-grounded solutions to problems of practice: classroom-based interventions in mathematics education

Research on classroom-based interventions in mathematics education has two core aims: (a) to improve classroom practice by engineering ways to act upon problems of practice; and (b) to deepen theoretical understanding of classroom phenomena that relate to these problems. Although there are notable examples of classroom-based intervention studies in mathematics education research since at least the 1930s, the number of such studies is small and acutely disproportionate to the number of studies that have documented problems of classroom practice for which solutions are sorely needed. In this paper we first make a case for the importance of research on classroom-based interventions and identify three important features of this research, which we then use to review the papers in this special issue. We also consider the issue of ‘scaling up’ promising classroom-based interventions in mathematics education, and we discuss a major obstacle that most such interventions find on the way to scaling up. This obstacle relates to their long duration, which means that possible adoption of these interventions would require practitioners to do major reorganizations of the mathematics curricula they follow in order to accommodate the time demands of the interventions. We argue that it is important, and conjecture that it is possible, to design interventions of short duration in mathematics education to alleviate major problems of classroom practice. Such interventions would be more amenable to scaling up, for they would allow more control over confounding variables and would make more practicable their incorporation into existing curriculum structures.     

The need for knowledge and reliability in numeric computation: Case study of multivariate Padé approximation

In this paper we review and link the numeric research projects carried out at the Department of Mathematics and Computer Science of the University of Antwerp since 1978. Results have and are being obtained in various areas. A lot of effort has been put in the theoretical investigation of the multivariate Padé approximation problem using different definitions (see Sections 3 and 7). The numerical implementation raises two delicate issues. First, there is the need to see the wood for the trees again: switching from one to many variables greatly increases the number of choices to be made on the way (see Sections 1 and 5). Second, there is the typical problem of breakdown when computing ratios of determinants: the added value of interval arithmetic combined with defect correction turns out to be significant (see Sections 2 and 4). In Section 6 these two problems are thoroughly illustrated and the interested reader is taken by the hand and guided through a typical computation session. On the way some open problems are indicated which motivate us to continue our research mainly in the area of gathering and offering more knowledge about the problem domain on one hand, and improving the arithmetic tools and numerical routines for a reliable computation of the approximants on the other hand.     

Memory‐efficient Arnoldi algorithms for linearizations of matrix polynomials in Chebyshev basis

Novel memory‐efficient Arnoldi algorithms for solving matrix polynomial eigenvalue problems are presented. More specifically, we consider the case of matrix polynomials expressed in the Chebyshev basis, which is often numerically more appropriate than the standard monomial basis for a larger degree d. The standard way of solving polynomial eigenvalue problems proceeds by linearization, which increases the problem size by a factor d. Consequently, the memory requirements of Krylov subspace methods applied to the linearization grow by this factor. In this paper, we develop two variants of the Arnoldi method that build the Krylov subspace basis implicitly, in a way that only vectors of length equal to the size of the original problem need to be stored. The proposed variants are generalizations of the so‐called quadratic Arnoldi method and two‐level orthogonal Arnoldi procedure methods, which have been developed for the monomial case. We also show how the typical ingredients of a full implementation of the Arnoldi method, including shift‐and‐invert and restarting, can be incorporated. Numerical experiments are presented for matrix polynomials up to degree 30 arising from the interpolation of nonlinear eigenvalue problems, which stem from boundary element discretizations of PDE eigenvalue problems. Copyright © 2013 John Wiley & Sons, Ltd.     

Education for Operational Research. A Practitioner's Viewpoint

University education for operational research in Britain is making great and welcome strides forward, but there has been little open discussion of its aims in relation to the advance of the profession. Progress in operational research, and in its application to the really worth-while problems of industry in particular, depends primarily on strengthening the industrial operational research group in its “front line” role. We need a research study of the teaching and practice of industrial operational research, in relation to effectiveness. We need “collaborative” programmes of operational research education in which the university and the industrial operational research group each contribute, in a man's training over a period of years, in the ways in which each is most competent. We need emphasis on “breadth” as well as “depth” in operational research education, and greater attention to the mutual contributions of operational research, teaching and practice, and the teaching and development of management. An impressionist sketch of a programme which might meet these needs is put forward for discussion of principles and feasibility.     

Fuzzy stochastic linear programming: Survey and future research directions

In many real-life problems one has to base decision on information which is both fuzzily imprecise and probabilistically uncertain. Although consistency indexes providing a union nexus between possibilistic and probabilistic representation of uncertainty exist, there are no reliable transformations between them. This calls for new paradigms for incorporating the two kinds of uncertainty into mathematical models. Fuzzy stochastic linear programming is an attempt to fulfill this need. It deals with modelling and problem solving issues related to situations where randomness and fuzziness co-occur in a linear programming framework. In this paper we provide a survey of the essential elements, methods and algorithms for this class of linear programming problems along with promising research directions. Being a survey, the paper includes many references to both give due credit to results in the field and to help readers obtain more detailed information on issues of interest.     

An LP-based successive overrelaxation method for linear complementarity problems

A sparsity preserving LP-based SOR method for solving classes of linear complementarity problems including the case where the given matrix is positive-semidefinite is proposed. The LP subproblems need be solved only approximately by a SOR method. Heuristic enhancement is discussed. Numerical results for a special class of problems are presented, which show that the heuristic enhancement is very effective and the resulting program can solve problems of more than 100 variables in a few seconds even on a personal computer.This research was sponsored by the Air Force Office of Scientific Research, Grant No. AFOSR-86-0124. Part of this material is based on work supported by the National Science Foundation under Grant No. MCS-82-00632.The author is grateful to Dr. R. De Leone for his helpful and constructive comments on this paper.     

An approach to the modeling and analysis of multiobjective generalized networks

Generalized networks can often provide substantial advantages in both the modeling and solution of integer programming problems. In this paper we present a straightforward approach which combines generalized networks with goal programming so as to achieve a modeling and solution methodology for multiobjective generalized networks. Such an approach also encompasses the solution to weighted integer foal programming as well as lexicographic integer goal programming problems. In ongoing research, the resulting hybrid algorithms have indicated superior performance, for a number of problems, over that obtained by more conventional approaches. A particularly attractive feature of the methodology is its relative simplicity and robustness.     

Some software aspects of DAE simulation & optimization software

Considerable numerical software has been written for simulation and optimization of dynamical systems. From the beginning of their development, differential algebraic equations (DAEs) have often been proposed as a way to make modeling easier. The modeler need only write down equations relating the variables in the model. However, much DAE software requires at least as much user numerical and mathematical expertise as explicit methods. An important aspect of our research has been working toward helping the idea of DAEs achieve its promise in modeling and simulation by both pushing the software to handle more general problems and to also allow for less user expertise. Some recent examples are presented where this research impacts on software and their underlying algorithms. Space necessitates we assume the reader has a rough idea of what a DAE is. The examples are implicit Scicos, and optimization of DAE models. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Developing a Systems Approach to the Problems of Industry — Lessons Based on Experience at IIASA

This paper describes progress on a number of research projects initiated in the Management and Technology Area of the International Institute of Applied Systems Analysis in the period 1977/80. The projects were undertaken by an international, multi-disciplinary, team concerned with the study of management problems arising from technological change. The experience obtained is used to explore the need for, and feasibility of, undertaking such problem-oriented programme research at the national level. It is concluded that the need to conduct methodological research of this nature is great, though not always self-evident; that the financial and organisational problems would be severe, though not insuperable; and that it is time for practising operational researchers and applied systems analysts to flex their muscles to this end.     

Efficient Global Optimization of Expensive Black-Box Functions

In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.     

Stable exponential-penalty algorithm with superlinear convergence

A renewed interest in penalty algorithms for solving mathematical programming problems has been motivated by some recent techniques which eliminate the ill-conditioning caused by the convergence to zero of the penalty parameter. These techniques are based on a good identification of the active set of constrainst at the optimum. In this sense, interior penalty methods to be more efficient than exterior ones, but their drawback lies in the need of an interior starting point. We propose in this paper an exponential penalty function which does not need interior starting points, but whose ultimate behavior is just like an interior penalty method. A superlinearly convergent algorithm based on the exponential penalty function is proposed.This research was partially supported by FONDECYT Grant 90-0945, DTI Grant E.3101-9012, and NSERC Grant OGP0005491.     

From the Schrödinger problem to the Monge–Kantorovich problem

The aim of this article is to show that the Monge–Kantorovich problem is the limit, when a fluctuation parameter tends down to zero, of a sequence of entropy minimization problems, the so-called Schrödinger problems. We prove the convergence of the entropic optimal values to the optimal transport cost as the fluctuations decrease to zero, and we also show that the cluster points of the entropic minimizers are optimal transport plans. We investigate the dynamic versions of these problems by considering random paths and describe the connections between the dynamic and static problems. The proofs are essentially based on convex and functional analysis. We also need specific properties of Γ-convergence which we didn?t find in the literature; these Γ-convergence results which are interesting in their own right are also proved.