Showing papers in "European Journal of Operational Research in 1999"

TL;DR: A classification scheme is provided, i.e. a description of the resource environment, the activity characteristics, and the objective function, respectively, which is compatible with machine scheduling and which allows to classify the most important models dealt with so far, and a unifying notation is proposed.

TL;DR: A subclass of the deterministic job-shop scheduling problem in which the objective is minimising makespan is sought, by providing an overview of the history, the techniques used and the researchers involved.

TL;DR: The principle aim of the paper is to compare the results obtained from the three alternative methods of estimating multi-output distance functions, and the construction of a parametric frontier using linear programming; data envelopment analysis (DEA) and corrected ordinary least squares (COLS).

TL;DR: It is shown in this paper that even with the introduction of learning to job processing times two important types of single-machine problems remain polynomially solvable.

TL;DR: In this article, the authors present a general framework for understanding the role of artificial neural networks (ANNs) in bankruptcy prediction and demonstrate the link between neural networks and traditional Bayesian classification theory.

TL;DR: It is shown that the standard semideviation (square root of the semivariance) as the risk measure makes the mean-risk model consistent with the second degree stochastic dominance, provided that the trade-off coefficient is bounded by a certain constant.

TL;DR: Simpson and Vakharia as discussed by the authors proposed a method to identify the most important parts of the human brain in order to determine whether a person's brain is composed of neurons or not.

TL;DR: A consistent bootstrap estimation procedure for obtaining confidence intervals for Malmquist indices of productivity and their decompositions and in terms of input-oriented indices is developed.

TL;DR: The rough set approach is used to provide a set of rules able to discriminate between healthy and failing firms in order to predict business failure and the results are very encouraging, compared with those of discriminant and logit analyses.

TL;DR: An original methodology for using rough sets to preference modeling in multi-criteria decision problems is presented, including pairs of actions described by graded preference relations on particular criteria and by a comprehensive preference relation.

TL;DR: The basic theory of the triangular fuzzy number is proved and the formulation of comparing the triangular fuzziness number's size is improved, and a practical example on petroleum prospecting is introduced.

TL;DR: This paper proposes an integrated approach to determine attribute weights in the multiple attribute decision making (MADM) problems that makes use of the subjective information provided by a decision maker and the objective information to form a two-objective programming model.

TL;DR: A new GEM inspired by the Russell Graph Measure of Technical Efficiency is proposed which avoids the computational and interpretative difficulties with this latter measure and satisfies some other desirable properties.

TL;DR: Comparisons are made with some of the best TSP heuristic algorithms and general optimization techniques which demonstrate the advantages of GLS over alternative heuristic approaches suggested for the problem.

TL;DR: A new method for evaluating weapon systems by analytical hierarchy process (AHP) based on linguistic variable weight, which possesses intuition, in accord with human rethinking-model, and is close to humanized uncertainty of language expression.

TL;DR: This two-phase architecture makes it possible to search the solution space efficiently, thus producing good solutions without excessive computation, and shows that the TS algorithm achieves significant improvement over a recent effective LRP heuristic.

TL;DR: The aim of this paper is to introduce constraint statisfaction to the operational researchers.

TL;DR: A Multiple Criteria Data Envelopment Analysis model is presented which can be used to improve discriminating power of DEA methods and also effectively yield more reasonable input and output weights without a priori information about the weights.

TL;DR: This paper deals with multiple criteria decision making problem with incomplete information when multiple decision makers (Multiple Criteria Group Decision Making: MCGDM) are involved and suggests a method, utilizing individual decision results to form group consensus.

TL;DR: A real application of Multi-Criteria Decision Analysis (MCDA) in which several Decision Support Systems were harmoniously integrated in the interactive learning process of tackling the complex strategic problematic situation faced by the Santa Catarina textile industry, in the south of Brazil is described.

TL;DR: In this paper, a method to modify a given comparison matrix, by which the consistency ratio (CR) value of the modified matrix is less than that of the original one, and give an algorithm to derive a positive reciprocal matrix with acceptable consistency (i.e., CR < 0.1), then the convergence theorem for the given algorithm is established and its practicality is shown by some examples.

TL;DR: In this paper, the authors consider DEA models without inputs (or without outputs) and DEA models with a single constant input and show that a CCR model without inputs is meaningless.

TL;DR: The concepts of fuzzy set theory and hierarchical structure analysis are used to develop a weighted suitability decision matrix and the relative approximation values of various alternatives versus positive ideal solution are ranked to determine the best alternative.

TL;DR: An interactive procedure for solving a multiple attribute group decision making (MAGDM) problem with incomplete information, and theoretic models for establishing group's pairwise dominance relations with group's utility range by using a separable linear programming technique.

TL;DR: This paper shows that in this case the problem of finding a feasible solution to the portfolio problem with minimum transaction lots is NP-complete, independently of the risk function.

TL;DR: It is found that the downside risk approach tends to produce – on average – slightly higher bond allocations than the mean–variance approach, and on the basis of simulation analyses, there are marked differences in the degree of estimation accuracy, which calls for further research.

TL;DR: A model for designing the pricing and return-credit strategy for a monopolistic manufacturer of single-period commodities finds that unless an external force supports the retailer, otherwise the manufacturer can usually design a (C,V)-scheme that gives himself the lion's share of the profit.

TL;DR: A comparative study on the performance of dispatching rules in the following sets of dynamic manufacturing systems: flowshop and jobshops, and flowshops with missing operations and job shops reveals some interesting observations on the relative performance.

TL;DR: This study analyzes the beneficial aspects of using both neurofuzzy systems as well as neural networks for credit-risk evaluation decisions.

TL;DR: An improved BL-algorithm for genetic algorithm of the orthogonal packing of rectangles with improvements on the fitness function and solutions of two numerical examples show the effectiveness of these improvements.