Supply Chain Management in Food Chains: Improving Performance by Reducing Uncertainty

This paper investigates the impact of Supply Chain Management on logistical performance indicators in food supply chains. From a review of quantitative and more qualitative managerial literature, we believe that Supply Chain Management should be concerned with the reduction or even elimination of uncertainties to improve the performance of the chain. The following clusters of sources of uncertainty are identified: order forecast horizon, input data, administrative and decision processes and inherent uncertainties. For each source of uncertainty, several improvement principles are identified. A case study was conducted in a food chain in which a simulation model helped quantify the effects of alternative configurations and operational management concepts. By comparing this simulation study with a pilot study, the model is validated against real data, and organisational consequences are identified. The results of the case study suggest that reduction of uncertainties can improve service levels significantly, although current supply chain configurations restrict possible benefits. The availability of real-time information systems is found to be a requirement for obtaining efficient and effective Supply Chain Management concepts.     

An invitation to Friendly Testing

1IntroductionSinceitspresentationin[1,2]TestingSemanticshasbeenwidelystudiedandusedasanaturalwaytodefineanobservationalbehaviorwithareasonablepowertodistinguishsemanticallydifferentprocesses.TestingSemanticsisdefinedbyobservingtheoperationalsemanticsofprocessesbymeansoftests.Testsarejustprocesseswhichmayexecuteanewactionwreportingsuccessofthetestapplication.Todefinetheapplicationofatesttoaprocesslweconsiderthedifferentcomputationsoftheexperimentalsystemwhichisobtainedbycomposinginparallelthe…     

基于NURBS曲线拟合的图像亚像素边缘提取方法

通过介绍激光扫描测量原理 ,说明准确地从光刀图像中提取光刀边缘位置是影响测量精度的关键因素 .由于噪声的存在 ,使用常见的边缘检测方法 ,在光刀图像中提取同一位置的光刀边缘 ,以及计算光刀中心位置 ,会带来很大的误差 .为了降低误差 ,本论文提出了基于 NURBS曲线拟合的亚像素光刀边缘提取方法 .通过实验 ,说明该方法重复提取的结果比较稳定 ,而且将误差控制在 0 .4个像素以下 ,从而使重复测量的高度误差在 0 .0 8mm以下     

Meta-modelling and graph grammars for multi-paradigm modelling in AToM<Superscript>3</Superscript>

This paper presents the combined use of meta-modelling and graph grammars for the generation of visual modelling tools for simulation formalisms. In meta-modelling, formalisms are described at a meta-level. This information is used by a meta-model processor to generate modelling tools for the described formalisms. We combine meta-modelling with graph grammars to extend the model manipulation capabilities of the generated modelling tools: edit, simulate, transform into another formalism, optimize and generate code. We store all (meta-)models as graphs, and thus, express model manipulations as graph grammars.We present the design and implementation of these concepts in AToM³ (A_To_ol for M_ulti-formalism, M_eta-M_odelling). AToM³ supports modelling of complex systems using different formalisms, all meta-modelled in their own right. Models in different formalisms may be transformed into a single common formalism for further processing. These transformations are specified by graph grammars. Mosterman and Vangheluwe [18] introduced the term multi-paradigm modelling to denote the combination of multiple formalisms, multiple abstraction levels, and meta-modelling. As an example of multi-paradigm modelling we present a meta-model for the Object-Oriented Continuous Simulation Language OOCSMP, in which we combine ideas from UML class diagrams (to express the OOCSMP model structure), Causal Block Diagrams (CBDs), and Statecharts (to specify the methods of the OOCSMP classes). A graph grammar is able to generate OOCSMP code, and then a compiler for this language (C-OOL) generates Java applets for the simulation execution.     

On-Line Dial-a-Ride Problems Under a Restricted Information Model

In on-line dial-a-ride problems servers are traveling in some metric space to serve requests for rides which are presented over time. Each ride is characterized by two points in the metric space, a source, the starting point of the ride, and a destination, the endpoint of the ride. Usually it is assumed that at the release of a request, complete information about the ride is known. We diverge from this by assuming that at the release of a ride, only information about the source is given. At visiting the source, the information about the destination will be made available to the servers. For many practical problems, our model is closer to reality. However, we feel that the lack of information is often a choice, rather than inherent to the problem: additional information can be obtained, but this requires investments in information systems. In this paper we give mathematical evidence that for the problem under study it pays to invest.     

On combining support vector machines and simulated annealing in stereovision matching

This paper outlines a method for solving the stereovision matching problem using edge segments as the primitives. In stereovision matching, the following constraints are commonly used: epipolar, similarity, smoothness, ordering, and uniqueness. We propose a new strategy in which such constraints are sequentially combined. The goal is to achieve high performance in terms of correct matches by combining several strategies. The contributions of this paper are reflected in the development of a similarity measure through a support vector machines classification approach; the transformation of the smoothness, ordering and epipolar constraints into the form of an energy function, through an optimization simulated annealing approach, whose minimum value corresponds to a good matching solution and by introducing specific conditions to overcome the violation of the smoothness and ordering constraints. The performance of the proposed method is illustrated by comparative analysis against some recent global matching methods.     

Software process representation and analysis for framework instantiation

Object-oriented frameworks are currently regarded as a promising technology for reusing designs and implementations. However, developers find there is still a steep learning curve when extracting the design rationale and understanding the framework documentation during framework instantiation. Thus, instantiation is a costly process in terms of time, people, and other resources. These problems raise a number of questions including: "How can we raise the level of abstraction in which the framework instantiation is expressed, reasoned about and implemented?" "How can the same high-level design abstractions that were used to develop the framework be used during framework instantiation instead of using source code as is done currently?" "How can we define extended design abstractions that can allow framework instantiation to be explicitly represented and validated?" We present an approach to framework instantiation based on software processes that addresses these issues. Our main goal is to represent the framework design models in an explicit and declarative way, and support changes to this design based on explicit instantiation tasks based on software processes while maintaining system integrity, invariants, and general constraints. In this way, the framework instantiation can be performed in a valid and controlled way.     

Synchronized world embedding in virtual environments

We introduce a novel distributed approach that lets users copy a given CVE section and keep it consistent with all its other copies and its original CVE area. Although 3D world modeling tools such as Alias' Maya and Discreet's 3DS Max already use object embedding, our method is the first to introduce it in a functioning CVE system. We also expand the traditional object-embedding concept by introducing a mutual synchronization function between the source and destination worlds. The mutual synchronization scheme propagates events $such as changes in an object's color, shape, or position - to other copies of a world. This synchronized world embedding thus offers a new way to expand worlds. Our method is built on existing CVE functionality, which lets us deploy it without significantly interfering with existing CVE designs. We've also altered the method so that it's usable when consistency needs are more relaxed.     

Outlier Detection and Data Cleaning in Multivariate Non-Normal Samples: The PAELLA Algorithm

A new method of outlier detection and data cleaning for both normal and non-normal multivariate data sets is proposed. It is based on an iterated local fit without a priori metric assumptions. We propose a new approach supported by finite mixture clustering which provides good results with large data sets. A multi-step structure, consisting of three phases, is developed. The importance of outlier detection in industrial modeling for open-loop control prediction is also described. The described algorithm gives good results both in simulations runs with artificial data sets and with experimental data sets recorded in a rubber factory. Finally, some discussion about this methodology is exposed.     

Benchmarking Least Squares Support Vector Machine Classifiers

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.