A model for the management of a tram fleet

The maintenance of transport vehicles represents an important aspect of the operating costs of a local public transport company (TPL). This work analyses the programmed and repair maintenance procedures of a recently operating tram fleet.By careful technical and economic analysis of data from the machine shops, two models have been developed with the aim of forecasting time and operating costs of maintaining rolling stock, using the parameters of Reliability, Availability, and Maintainability (RAM). It is a probabilistic model for the management of breakdowns on the tramline and for the analysis of shop maintenance operations, and a model for the calculation of Life Cycle Cost (LCC). The procedure has been applied to a TPL company obtaining satisfactory results in line with financial budgets and consistent with the purchasing specifications.     

Tackling an MCDM-problem with the help of some results from fuzzy set theory

The problem to be addressed and tackled in this paper arose as a byproduct from some efforts at solving problems involving multiple goals by linking linear and goal programming models. The critical issue was that some forms for interdependence among the goals could not be handled in the programming models. Here we will deal with a set of goals — with realistic counterparts in a Finnish plywood industry — in which a subset of the goals are (i) conflicting, another subset (ii) unilaterally supporting and a third subset (iii) mutually supporting. It is furthermore observed that the elements of a studied set of goals may be partly independent and partly interdependent, which makes the context a fullfledged MCDM-problem. It is tackled with a technique which is based on the theory of fuzzy sets, the conceptual framework for fuzzy decisions and the algorithms developed for fuzzy mathematical programming. The resulting fuzzy multiobjective programming model is simplified and tested with the help of a fairly complex numerical example.     

Socially Responsible Investment: A multicriteria approach to portfolio selection combining ethical and financial objectives

In a context of Socially Responsible Investment (SRI), this paper deals with portfolio selection for investors interested in ethical policies. In the opportunity set there are ethical assets and other assets which are not characterized as ethical. Two goals are considered, the traditional financial goal in the classical utility theory under uncertainty and an ethical goal in the same utility framework. A new financial-ethical bi-criteria model is proposed with absolute risk aversion coefficients and targets depending on the investor’s ethical profile. This approach is relevant as an increasing number of mutual funds are becoming interested in SRI strategies. From the proposed model, an actual case on green investment is developed. Concerning this case (without generalizing to other contexts), an analysis of the numerical results shows that efficient portfolios obtained by the traditional E-V model outperform the strong green portfolios in terms of expected return and risk, but this does not significantly occur with weak green investment.     

Negotiation support systems: A multi-criteria and multi-agent approach

This research concerns the development of an Negotiation Support Systems (NSS) based on a multi-criteria conceptual framework of the negotiation and developed according to a multi-agent architecture from Distributed Artificial Intelligence (DAI). A first prototype of such a system, NegocIAD, has already been developed [8], but the weakness of its assistance to the negotiation process have led us to revise the conceptual framework in order to define a more relevant assistance to the negotiation process. This paper presents this new conceptual framework defined in order to develop a new prototype. First, we point out the originality of our multi-criteria and multi-agent approach, the general architecture and the limitations of NegocIAD. Then we present the new multi-criteria conceptual framework mainly based on the definition and the use of projection plans (group Gaia plans) emerging from principal component analysis (PCA) already proposed in a single decision maker context in extension of the Promethee method. In the next part, we develop the possible levels of use of these plans during the negotiation process and the type of assistance provided to the mediator. This assistance is mainly based on the elaboration and the interpretation of group Gaia plans for which we propose a set of interpretation rules and the outline of a method to make use of these rules for a relevant support to the mediator in the management of the negotiation process. Finally, we conclude on the perspectives of our future researches and developments for the new generation of our prototype in a multi-agent architecture context.     

Hierarchical game joint optimization for product family-driven modular design

Product family design takes advantage of modularity to enable product variety while maintaining mass production efficiency. Focusing on a set of similar product variants, product family modularity (PFM) is achieved by reusing common components and minimizing fulfillment costs throughout the product realization process. On the other hand, traditional modular design emphasizes technical system modularity (TSM) that focuses on a single product and is geared towards product decomposition in light of technical feasibility. While it is appealing to incorporate product family considerations into the prevailing modularization theories and methods, the key challenge lies in that TSM and PFM are essentially associated with different goals and decision criteria. This leads to a dilemma that TSM and PFM are competing in decision making for identification of modules by grouping similar components. Realizing the importance of game-theoretic decision making underlying product family-driven modular design, this paper proposes to leverage TSM and PFM within a coherent framework of joint optimization. A hierarchical game joint optimization model is developed in line with bilevel programming. A two-dimension evaluation criteria taxonomy is presented for TSM and PFM criteria measure. A bilevel nested genetic algorithm is put forward for efficient solution of the non-linear hierarchical joint optimization model. A case study of robotic vacuum cleaner modular design is reported to gain insight into joint optimization of TSM and PFM. Results and analyses demonstrate that the proposed hierarchical joint optimization model is robust and can empower modular design in cohesion with product family concerns.     

Benchmarking in a multiple criteria performance context: An application and a conceptual framework

This article presents a conceptual benchmarking framework which applies a multiple criteria approach to assess performance. In the process, a multiple criteria procedure is used to assess the performance of three hundred and ninety two (392) Portuguese firms. Based on the results of this procedure, a conceptual framework is devised to facilitate addressing relevant benchmarking implications. The framework is designed to provide a conceptual linkage between the performance measurement methodology and the organizational benchmarking system.     

Applying stochastic goal programming: A case study on water use planning

A decision support model to help public water agencies allocate surface water among farmers and authorize the use of groundwater for irrigation (especially in Mediterranean dry regions) is developed. This is a stochastic goal programming approach with two goals, the first concerning farm management while the other concerns environmental impact. Targets for both goals are established by the agency. This model yields three reduction factors to decide the different reductions in available surface water, standard groundwater and complementary groundwater that the agency should grant/authorize for irrigation, this depending on if it is a dry or wet year. In drought periods, the model recommends using more groundwater (in percentage) than in wet periods. A case study using year-to-year statistical information on available water over the period 1941–2005 is developed through numerical tables. A step-by-step computational process is presented in detail.     

System dynamic modelling and simulation of hydrodynamic machines

A graph-theoretic framework for the dynamic simulation of hydrodynamic (both axial and radial flow) machines is presented in this article. The physics based analytical models are developed by considering the dynamics of the hydraulic fluid flow and its interaction with the mechanical components. A linear graph is used to capture the topology of the system and the interconnection of the constituent components. Using the graph-theoretic framework, a dynamic model of an automotive hydrodynamic torque converter is developed to simulate its behaviour under different flow conditions. The ability of the model to capture different features of the torque converter will also be demonstrated by simulation. The simulation results are compared with and validated by experimental results in the literature.     

Modeling complex multi-component reactive-transport systems: towards a simulation environment based on the concept of a Knowledge Base

A modelling framework within which transport processes in the hydrosphere can be described and interfaced with relevant biogeochemical reactions is presented. Three key elements of this simulation environment are discussed: (1) a numerical engine for solving sets of coupled non-linear process equations; (2) an automated procedure for model code generation (`Automatic Code Generator'); (3) a Web-distributed Knowledge Base (KB) of processes. The Automatic Code Generator translates the information selected in the KB into computer algorithms using the principles defined in the numerical engine. The code CONTRASTE is a first attempt at developing such a modelling framework. It allows one to easily select, adapt and combine a specific set of biogeochemical processes relevant to a user-defined application. The workings of CONTRASTE are described by means of examples which demonstrate how the various components of the simulation environment are coupled and automated. Prospects for future developments towards a fully automated model generation procedure are discussed.     

Cutting Down Trim-loss for Competitive Advantage: a Singaporean Company's Experience

This paper concerns a study of the cutting-stock problem faced by a small company in the building components industry in Singapore. It involves the development of a simple heuristic procedure which is able to recommend cutting patterns tailored to the specific needs of the company. To help determine the appropriate stock lengths to order, a simulation-cum-sampling approach is proposed. The pattern-generating heuristic has been tested using real-life project data. The results obtained are very encouraging in that the goals set by the management of the firm are met well within the numerous constraints faced.     

Building a local conceptual framework for epistemic actions in a modelling environment with experiments

A local conceptual framework for the construction of mathematical knowledge in learning environments with experiments is developed. For this purpose, the mathematical modelling framework and the epistemic action model for abstraction in context are used simultaneously. In a case study, experiments of pre-service teachers with the motion of a ball on a circular billiard table are analysed within the local conceptual framework. The role of the experiments for epistemic actions of mathematical abstractions is described. In the case study, two different types of students’ approaches to the role of experiments can be distinguished.     

Conceptual modelling for simulation Part I: definition and requirements

Conceptual modelling is probably the most important aspect of a simulation study. It is also the most difficult and least understood. Over 40 years of simulation research and practice have provided only limited information on how to go about designing a simulation conceptual model. This paper, the first of two, discusses the meaning of conceptual modelling and the requirements of a conceptual model. Founded on existing literature, a definition of a conceptual model is provided. Four requirements of a conceptual model are described: validity, credibility, utility and feasibility. The need to develop the simplest model possible is also discussed. Owing to a paucity of advice on how to design a conceptual model, the need for a conceptual modelling framework is proposed. Built on the foundations laid in this paper, a conceptual modelling framework is described in the paper that follows.     

A planning and scheduling model for onsertion in printed circuit board assembly

A planning and scheduling model is developed for the onsertion-phase in printed circuit board (PCB) assembly. PCBs are manufactured by machines that mount components, such as chips, on a PCB. On an onsertor machine, a certain number of component types can be stored. These components are subsequently used for the production of a certain PCB batch type. This research develops a production planning and scheduling formulation to determine the component–machine allocations, as well as a PCB sequence. Two strategies are proposed; one focusing on minimal number of changeovers (C-strategy), and the other on minimal process time (P-strategy). Both strategies were validated in a pilot study with real company data, with encouraging results for potential implementation.     

Shortcomings of Mathematics Education Reform in The Netherlands: A Paradigm Case?

This article offers a reflection on the findings of three PhD studies, in the domains of, respectively, subtraction under 100, fractions, and algebra, which independently of each other showed that Dutch students' proficiency fell short of what might be expected of reform in mathematics education aiming at conceptual understanding. In all three cases, the disappointing results appeared to be caused by a deviation from the original intentions of the reform, resulting from the textbooks' focus on individual tasks. It is suggested that this “task propensity”, together with a lack of attention for more advanced conceptual mathematical goals, constitutes a general barrier for mathematics education reform. This observation transcends the realm of textbooks, since more advanced conceptual mathematical understandings are underexposed as curriculum goals. It is argued that to foster successful reform, a conscious effort is needed to counteract task propensity and promote more advanced conceptual mathematical understandings as curriculum goals.     

Reduced-Rank Regularized Multivariate Model for High-Dimensional Data

This article proposes a modeling framework for high-dimensional experimental data, such as brain images or microarrays, that discovers statistically significant structures most relevant to the experimental covariates. To deal with the curse of dimensionality, three regularization schemes are used: a reduced-rank model, penalization of the covariance matrix, and regularization of the basis-expanded predictor set. The latter allows us to flexibly model associations while controlling for overfitting. The modeling framework is derived from a reduced-rank multiresponse linear model, which offers a familiar interface for researchers. The novel regularizations of both sides of the model make it applicable in high-dimensional settings, without a need for prior dimension reduction, and can model nonlinear relationships. An efficient, dual-space algorithm is proposed to estimate its components in low-dimensional space. It permits the use of the bootstrap, to provide pointwise standard error bands on association graphs, and other resampling techniques to optimize hyperparameters. We evaluate the model on a small neuroimaging dataset, and in a simulation study using simple images corrupted by additive Gaussian iid and random field noise components with signal-to-noise ratios below 0.1. Our model compares well with a general linear model (GLM) even when the nonlinear associations are specified explicitly in GLM.     

A Penalized Likelihood Approach to Rotation of Principal Components

A new paradigm for enhancing the interpretability of principal components through rotation is presented within the framework of penalized likelihood. The rotated components are computed as the maximizers of a Gaussian-based profile log-likelihood function plus a penalty term defined by a standard rotation criterion. This method enjoys a number of advantages over other methods for principal component rotation, notably (1) the rotation specifically targets ill-defined principal components, which may benefit the most from rotation, and (2) the connection with likelihood allows assessment of the fidelity of the rotated components to the data, thereby guiding the choice of penalty parameter. The method is illustrated with an application to a small functional dataset. Efficient computation of the penalized likelihood solution is possible using recently developed algorithms for optimization under orthogonality constraints.     

A multiple attribute group decision making framework for the evaluation of lean practices at logistics distribution centers

In recent years, to adapt rapidly to changing market environments and outdo the competition more companies and organizations have adopted lean management practices. One problem that has arisen in these companies and organizations is the need to develop methods to accurately evaluate the lean practices performance. This study proposes a multiple attribute group decision making (MAGDM) framework to facilitate such evaluations. It deals with the consensus process and selection process for MAGDM problems based on the 2-tuple linguistic computation model. The similarity degree and consensus for the linguistic decision matrix are defined using an Euclidian distance function. An algorithm describing the consensus reaching process is presented and its properties analyzed. The entropy method is generalized to a linguistic setting to derive the importance weights for the attributes. One of the main ideas behind the entropy method is that attributes with quite different values are considered more important and therefore have higher weights. Finally, the developed MAGDM framework is applied to a lean practices evaluation problem for a commercial tobacco company’s logistics distribution centers in China.     

ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms

This paper presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search metaheuristics: ParadisEO-MO. A substantial number of single solution-based local search metaheuristics has been proposed so far, and an attempt of unifying existing approaches is here presented. Based on a fine-grained decomposition, a conceptual model is proposed and is validated by regarding a number of state-of-the-art methodologies as simple variants of the same structure. This model is then incorporated into the ParadisEO-MO software framework. This framework has proven its efficiency and high flexibility by enabling the resolution of many academic and real-world optimization problems from science and industry.     

Fuzzy criteria for feature selection

The presence of less relevant or highly correlated features often decrease classification accuracy. Feature selection in which most informative variables are selected for model generation is an important step in data-driven modeling. In feature selection, one often tries to satisfy multiple criteria such as feature discriminating power, model performance or subset cardinality. Therefore, a multi-objective formulation of the feature selection problem is more appropriate. In this paper, we propose to use fuzzy criteria in feature selection by using a fuzzy decision making framework. This formulation allows for a more flexible definition of the goals in feature selection, and avoids the problem of weighting different goals is classical multi-objective optimization. The optimization problem is solved using an ant colony optimization algorithm proposed in our previous work. We illustrate the added value of the approach by applying our proposed fuzzy feature selection algorithm to eight benchmark problems.     

Risk analysis with contractual default. Does covenant breach matter?

Mergers and acquisitions (M&A), private equity and leveraged buyouts, securitization and project finance are characterized by the presence of contractual clauses (covenants). These covenants trigger the technical default of the borrower even in the absence of insolvency. Therefore, borrowers may default on loans even when they have sufficient available cash to repay outstanding debt. This condition is not captured by the net present value (NPV) distribution obtained through a standard Monte Carlo simulation. In this paper, we present a methodology for including the consequences of covenant breach in a Monte Carlo simulation, extending traditional risk analysis in investment planning. We introduce a conceptual framework for modeling technical and material breaches from the standpoint of both lenders and shareholders. We apply this framework to a real case study concerning the project financing of a 64-million euro biomass power plant. The simulation is carried out on the actual model developed by the financial advisor of the project and made available to the authors. Results show that both technical and material breaches have a statistically significant impact on the net present value distribution, and this impact is more relevant when leverage and cost of debt increase.