•Journal• ISSN: 0101-7438
Pesquisa Operacional
Sociedade Brasileira de Pesquisa Operacional
About: Pesquisa Operacional is an academic journal published by Sociedade Brasileira de Pesquisa Operacional. The journal publishes majorly in the area(s): Computer science & Data envelopment analysis. It has an ISSN identifier of 0101-7438. It is also open access. Over the lifetime, 633 publications have been published receiving 5975 citations.
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Papers
TL;DR: The focus of this survey is on algorithms and their convergence properties and present reformulations of the generalized Nash equilibrium problem, results on error bounds and properties of the solution set of the equilibrium problems.
Abstract: Generalized Nash equilibrium problems have become very important as a modeling tool during the last decades. The aim of this survey paper is twofold. It summarizes recent advances in the research on computational methods for generalized Nash equilibrium problems and points out current challenges. The focus of this survey is on algorithms and their convergence properties. Therefore, we also present reformulations of the generalized Nash equilibrium problem, results on error bounds and properties of the solution set of the equilibrium problems.
210 citations
TL;DR: This study reviews the pertinent literature on rank reversal, based on 130 related articles published from 1980 to 2015 in international journals, which were gathered and analyzed according to the following perspectives: multicriteria technique, year and journal in which the papers were published, co-authorship network, rank reversal types, and research goal.
Abstract: Despite the importance of multicriteria decision-making (MCDM) techniques for constructing effective decision models, there are many criticisms due to the occurrence of a problem called rank reversal. Nevertheless, there is a lack of a systematic literature review on this important subject which involves different methods. This study reviews the pertinent literature on rank reversal, based on 130 related articles published from 1980 to 2015 in international journals, which were gathered and analyzed according to the following perspectives: multicriteria technique, year and journal in which the papers were published, co-authorship network, rank reversal types, and research goal. Thus our survey provides recommendations for future research, besides useful information and knowledge regarding rank reversal in the MCDM field.
109 citations
TL;DR: Four variable selection methods, focused on DMUs sorting, are applied to a real situation of assessing the efficiency of third-party logistics in the activity of newspaper home delivery, in Rio de Janeiro.
Abstract: One of the main issues in DEA modeling is the variables choice. This may have conflictive objectives, like increasing the mean efficiency or maximizing the model ranking capability - a DEA classic fragility. In this paper, we compare four variable selection methods, focused on DMUs sorting. These methods are applied to a real situation of assessing the efficiency of third-party logistics in the activity of newspaper home delivery, in Rio de Janeiro.
83 citations
TL;DR: In this paper, a solution to the problem of finding an index number I for ranking a set of n objects, according to criteria defined by the set of m variables, is presented.
Abstract: This paper presents a solution to the problem of finding an index number I for ranking a set of n objects, according to criteria defined by a set of m variables. In general, it is necessary to choose a suitable set of variables, and weights for each variable. Cluster Analysis is used for variable selection, and Principal Component Analysis is used to attain weights. Two applications are presented.
81 citations
TL;DR: A rigorous and comprehensive survey on extensions to the multicriteria setting of three well-known scalar optimization algorithms, including multiobjective versions of the steepest descent, the projected gradient and the Newton methods is presented.
Abstract: We present a rigorous and comprehensive survey on extensions to the multicriteria setting of three well-known scalar optimization algorithms. Multiobjective versions of the steepest descent, the projected gradient and the Newton methods are analyzed in detail. At each iteration, the search directions of these methods are computed by solving real-valued optimization problems and, in order to guarantee an adequate objective value decrease, Armijo-like rules are implemented by means of a backtracking procedure. Under standard assumptions, convergence to Pareto (weak Pareto) optima is established. For the Newton method, superlinear convergence is proved and, assuming Lipschitz continuity of the objectives second derivatives, it is shown that the rate is quadratic
81 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 29 |
| 2024 | 41 |
| 2023 | 47 |
| 2022 | 38 |
| 2021 | 19 |
| 2020 | 16 |