Abstract in English:When considering hub-and-spoke networks with single allocation, the absence of alternative routes makes this kind of systems specially vulnerable to congestion effects. In order to improve the design of such networks, congestion costs must be addressed. This article deploys two different techniques for addressing congestion on single allocation hub-and-spoke networks: the Generalized Benders Decomposition and the Outer Approximation method. Both methods are able to solve large scale instances. Computational experiments show how the adoption of advanced solution strategies, such as Pareto-optimal cut generation on the Master Problem branch-and-bound tree, may be decisive. They also demonstrate that the solution effort is not only associated with the size of the instances, but also with their combination of the installation and congestion costs.
Abstract in English:The clustering problem consists in finding patterns in a data set in order to divide it into clusters with high within-cluster similarity. This paper presents the study of a problem, here called MMD problem, which aims at finding a clustering with a predefined number of clusters that minimizes the largest within-cluster distance (diameter) among all clusters. There are two main objectives in this paper: to propose heuristics for the MMD and to evaluate the suitability of the best proposed heuristic results according to the real classification of some data sets. Regarding the first objective, the results obtained in the experiments indicate a good performance of the best proposed heuristic that outperformed the Complete Linkage algorithm (the most used method from the literature for this problem). Nevertheless, regarding the suitability of the results according to the real classification of the data sets, the proposed heuristic achieved better quality results than C-Means algorithm, but worse than Complete Linkage.
Abstract in English:This paper presents an application of three multiple criteria methods to determining rank of residential real estate options. Methods SAW and TODIM are based on eliciting the decision maker's preferences (weights and values) directly in a quantitative form while using linear (SAW) and non-linear (TODIM) aggregation functions for alternatives' evaluation. ZAPROS seeks and uses preferences in an ordinal form as an indirect comparison of trade-offs between criteria. Advantages and disadvantages of different approaches are discussed.
Abstract in English:Optimization methods combined with computer-based simulation have been utilized in a wide range of manufacturing applications. However, in terms of current technology, these methods exhibit low performance levels which are only able to manipulate a single decision variable at a time. Thus, the objective of this article is to evaluate a proposed optimization method for discrete-event simulation models based on genetic algorithms which exhibits more efficiency in relation to computational time when compared to software packages on the market. It should be emphasized that the variable's response quality will not be altered; that is, the proposed method will maintain the solutions' effectiveness. Thus, the study draws a comparison between the proposed method and that of a simulation instrument already available on the market and has been examined in academic literature. Conclusions are presented, confirming the proposed optimization method's efficiency.
Abstract in English:DEA models assume the homogeneity of the units under evaluation (DMUs). However,in some cases, the DMUs use different production technologies. In such cases, they should be evaluated separately. In this paper we evaluate the efficiency of family farmers from the Brazilian Eastern Amazon, who use different agricultural production systems. We propose an alternative algorithm to assess the global efficiency, taking into account the non-homogeneity. The results show that the farmers that use the classical technology are more efficient than those considered "environmental friendly", as we took into account only the economic point of view.
Abstract in English:This paper proposes two new approaches for the sensitivity analysis of multiobjective design optimization problems whose performance functions are highly susceptible to small variations in the design variables and/or design environment parameters. In both methods, the less sensitive design alternatives are preferred over others during the multiobjective optimization process. While taking the first approach, the designer chooses the design variable and/or parameter that causes uncertainties. The designer then associates a robustness index with each design alternative and adds each index as an objective function in the optimization problem. For the second approach, the designer must know, a priori, the interval of variation in the design variables or in the design environment parameters, because the designer will be accepting the interval of variation in the objective functions. The second method does not require any law of probability distribution of uncontrollable variations. Finally, the authors give two illustrative examples to highlight the contributions of the paper.
Abstract in English:Despite being widely applied in real problems that tackle evaluating efficiency, Data Envelopment Analysis (DEA) models are frequently criticized on account of the weights of evaluation criteria often being defined loosely. Thus, approaches to incorporating value judgments in DEA models have been used in order to obtain more consistent results with managerial reality. It is against this background that this paper proposes a DEA model for evaluating efficiency, where the value judgments of those responsible for evaluation, regarding the criteria, are defined based on the philosophy of the SMARTS method and incorporated into the model by the Assurance Region (AR) method. The model proposed is applied using information about the investments made in the area of Information Technology and Information Systems by Brazilian banks The aim is to exemplify the application of the model and raise points for discussion with regard to its merits.
Abstract in English:This paper analyzes changes in the assessment of an incremental R&D project by an industrial firm with the progressive consideration of the endogenous treatment of its main sources of uncertainty: technical performance and development time. We found that the project, which was unfeasible under a deterministic assessment by Net Present Value (NPV) without flexibility, became feasible after the treatment of the technical uncertainty by a real options model (NPV with flexibility). Moreover, the project gained approximately 51 percent more value in flexibility when a treatment of the development time uncertainty was added to the model. In terms of additional flexibility per unit cost of the project, the gain is approximately 44 percent. This result demonstrates the importance of addressing the combination of these sources of uncertainty in R&D projects, especially those that are incremental, which is a difficult category to analyze in terms of quantitative benefits.
Abstract in English:A structured evaluation of the construction industry's suppliers, considering aspects which make their quality and credibility evident, can be a strategic tool to manage this specific supply chain. This study proposes a multi-criteria decision model for suppliers' selection from the construction industry, as well as an efficient evaluation procedure for the selected suppliers. The model is based on SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranking) method and its main contribution is a new approach to structure the process of suppliers' selection, establishing explicit strategic policies on which the company management system relied to make the suppliers selection. This model was applied to a Civil Construction Company in Brazil and the main results demonstrate the efficiency of the proposed model. This study allowed the development of an approach to Construction Industry which was able to provide a better relationship among its managers, suppliers and partners.
Abstract in English:This paper presents the application of a stochastic Benders decomposition algorithm for the problem of supply chain investment planning under uncertainty applied to the petroleum byproducts supply chain. The uncertainty considered is related with the unknown demand levels for oil products. For this purpose, a model was developed based on two-stage stochastic programming. It is proposed two different solution methodologies, one based on the classical cutting plane approach presented by Van Slyke & Wets (1969), and other, based on a multi cut extension of it, firstly introduced by Birge & Louveaux (1988). The methods were evaluated on a real sized case study. Preliminary numerical results obtained from computational experiments are encouraging.