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Second order of stochastic dominance efficiency vs mean variance efficiency

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期290卷 1192-1206页

作者： Malavasi, Matteo Lozza, Sergio Ortobelli Truck, Stefan Macquarie Univ Macquarie Business Sch Dept Actuarial Studies & Business Analyt Eastern Rd N Ryde NSW 2109 Australia Univ Bergamo Dept Management Econ & Quantitat Methods Via Caniana 2 I-24127 Bergamo Italy VSB TU Ostrava Dept Finance Ostrava Czech Republic

In this paper, we compare two of the main paradigms of portfolio theory: mean variance analysis and expected utility. In particular, we show empirically that mean variance efficient portfolios are typically sub-optimal for non satiable and risk averse investors. We illustrate that the second order stochastic dominance (SSD) efficient set is the solution of a multi-objective optimization problem. We further show that the market portfolio is not necessarily a solution to this optimization problem. We also conduct an empirical analysis, examining the ex ante and ex post performance of SSD and mean variance efficient portfolios, using a bootstrap approach. In an ex ante analysis, we compare empirical moments, the level of diversification and set distances of mean variance and SSD efficient sets. We also show that the global minimum variance (GMV) portfolio and the part of the mean variance efficient frontier (MVEF) composed of highly diversified portfolios is second order stochastically dominated. This result also provides a possible alternative explanation for the diversification puzzle. Conducting an ex post analysis, we construct second order stochastic dominating strategies that outperform the GMV portfolio in terms of wealth and various other performance measures, producing a positive ex post opportunity cost. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Investment analysis Efficient set Stochastic dominance Mean variance multiple objective programming

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Integrating blockchain and carbon capture, utilization, and storage for carbon-neutral multi-objective production routing optimization

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JOURNAL OF CLEANER PRODUCTION 2024年 469卷

作者： Liu, Hong-yu Ji, Shou-feng Ji, Yuan-yuan Ji, Ting-ting Northeastern Univ Sch Business Adm Shenyang Peoples R China

Transitioning to a carbon-neutral supply chain poses challenges in visualizing and reducing carbon emissions throughout logistics networks. This study presents a comprehensive framework that integrates blockchain technology (BCT) and carbon capture, utilization, and storage (CCUS) into the two-echelon production routing problem (PRP). A multi-objective mixed-integer linear programming model is developed for the problem, considering economic, environmental, and social objectives. To approximate a Pareto optimal solution for the complex model, a Lagrangian matheuristic algorithm (AugMathLagr) is employed. Computational results demonstrate that the integration of BCT and CCUS may not always be the optimal approach for reducing carbon emissions and creating social value, as it can also lead to higher costs. Sensitivity analyses indicate that the adoption of BCT and CCUS is influenced by parameters such as carbon caps, carbon accounting errors, and carbon capture rates. These findings offer valuable insights for policymakers aiming to achieve a carbon-neutral supply chain through the integration of BCT and CCUS.

关键词： Blockchain Carbon neutrality CCUS Production routing multiple objective programming

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multiple criteria decision making combined with finance: A categorized bibliographic study

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2003年第3期150卷 496-515页

作者： Steuer, RE Na, P Univ Georgia Terry Coll Business Athens GA 30602 USA Bayer Landesbank Risk Methodol & Syst New York NY 10022 USA

This paper provides a categorized bibliography on the application of the techniques of multiple criteria decision making (MCDM) to problems and issues in finance. A total of 265 references have been compiled and classified according to the methodological approaches of goal programming, multiple objective programming, the analytic hierarchy process, etc., and to the application areas of capital budgeting, working capital management, portfolio analysis, etc. The bibliography provides an overview of the literature on "MCDM combined with finance," shows how contributions to the area have come from all over the world, facilitates access to the entirety of this heretofore fragmented literature, and underscores the often multiple criterion nature of many problems in finance. (C) 2002 Elsevier B.V. All rights reserved.

关键词： bibliographic study multiple criteria decision making finance portfolio analysis goal programming multiple objective programming multi-criteria decision analysis discrete alternative methods analytic hierarchy process

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Surrogate upper bound sets for bi-objective bi-dimensional binary knapsack problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第2期244卷 417-433页

作者： Cerqueus, Audrey Przybylski, Anthony Gandibleux, Xavier Univ Nantes UFR Sci LINA UMR CNRS 6241 F-44322 Nantes 03 France Univ Nantes UFR Sci IRCCyN UMR CNRS 6597 F-44322 Nantes 03 France

The paper deals with the definition and the computation of surrogate upper bound sets for the bi-objective bi-dimensional binary knapsack problem. It introduces the Optimal Convex Surrogate Upper Bound set, which is the tightest possible definition based on the convex relaxation of the surrogate relaxation. Two exact algorithms are proposed: an enumerative algorithm and its improved version. This second algorithm results from an accurate analysis of the surrogate multipliers and the dominance relations between bound sets. Based on the improved exact algorithm, an approximated version is derived. The proposed algorithms are benchmarked using a dataset composed of three groups of numerical instances. The performances are assessed thanks to a comparative analysis where exact algorithms are compared between them, the approximated algorithm is confronted to an algorithm introduced in a recent research work. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization multiple objective programming Bi-dimensional binary knapsack problem Surrogate relaxation Bound sets

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Constraint qualifications in linear vector semi-infinite optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2013年第1期227卷 12-21页

作者： Goberna, M. A. Guerra-Vazquez, F. Todorov, M. I. Univ Alicante Dept Stat & Operat Res Alicante 03071 Spain Univ Americas Puebla Dept Math & Phys Cholula 72820 Mexico

Linear vector semi-infinite optimization deals with the simultaneous minimization of finitely many linear scalar functions subject to infinitely many linear constraints. This paper provides characterizations of the weakly efficient, efficient, properly efficient and strongly efficient points in terms of cones involving the data and Karush-Kuhn-Tucker conditions. The latter characterizations rely on different local and global constraint qualifications. The global constraint qualifications are illustrated on a collection of selected applications. (C) 2012 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Linear vector semi-infinite optimization Constraint qualifications Cone conditions KKT conditions

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Multiobjective aerial surveillance over disjoint rectangles

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 148卷 106732-106732页

作者： Karasakal, Orhan Karasakal, Esra Maras, Guliz Cankaya Univ Ind Engn Dept Eskisehir Yolu 29 Km TR-06790 Ankara Turkey Middle East Tech Univ Ind Engn Dept TR-06800 Ankara Turkey Aselsan AS Macun Mah 296 Cad 16 TR-06370 Ankara Turkey

In Aerial Surveillance Problem (ASP), an air platform with surveillance sensors searches a number of rectangular areas by covering the rectangles in strips and turns back to base where it starts. In this paper, we present a multiobjective extension to ASP, for which the aim is to help aerial mission planner to reach his/her most preferred solution among the set of efficient alternatives. We consider two conflicting objectives that are minimizing distance travelled and maximizing minimum probability of target detection. Each objective can be used to solve single objective ASPs. However, from mission planner's perspective, there is a need for simultaneously optimizing both objectives. To enable mission planner reaching his/her most desirable solution under conflicting objectives, we propose exact and heuristic methods for multiobjective ASP (MASP). We also develop an interactive procedure to help mission planner choose the most satisfying solution among all Pareto optimal solutions. Computational results show that the proposed methods enable mission planner to capture the tradeoffs between the conflicting objectives for large number of alternative solutions and to eliminate the undesirable solutions in small number of iterations.

关键词： Travelling salesman Aerial surveillance multiple objective programming Unmanned aerial vehicles

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A stochastic multiple gradient descent algorithm

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第3期271卷 808-817页

作者： Mercier, Quentin Poirion, Fabrice Desideri, Jean-Antoine Univ Paris Saclay ONERA DMAS Onera French Aerosp Lab 29 Ave Div Leclerc F-92320 Chatillon France INRIA 2004 Route Lucioles F-06902 Valbonne France

In this article, we propose a new method for multiobjective optimization problems in which the objective functions are expressed as expectations of random functions. The present method is based on an extension of the classical stochastic gradient algorithm and a deterministic multiobjective algorithm, the multiple Gradient Descent Algorithm (MGDA). In MGDA a descent direction common to all specified objective functions is identified through a result of convex geometry. The use of this common descent vector and the Pareto stationarity definition into the stochastic gradient algorithm makes the algorithm able to solve multiobjective problems. The mean square and almost sure convergence of this new algorithm are proven considering the classical stochastic gradient algorithm hypothesis. The algorithm efficiency is illustrated on a set of benchmarks with diverse complexity and assessed in comparison with two classical algorithms (NSGA-II, DMS) coupled with a Monte Carlo expectation estimator. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Multiobjective stochastic optimization Stochastic gradient algorithm multiple gradient descent algorithm Common descent vector

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Fuzzy Piecewise Logistic Growth Model for Innovation Diffusion: A Case Study of the TV Industry

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2016年第3期18卷 511-522页

作者： Yu, Jing Rung Tseng, Fang-Mei Natl Chi Nan Univ Dept Informat Management 470 Univ Rd Puli Nantou 545 Taiwan Yuan Ze Univ Innovat Ctr Big Data & Digital Convergence Coll Management 135 Yuan Dung Rd Taoyuan 320 Taiwan

The logistic model is adopted in order to fit growth trends of innovative products for a single growth process. In the current competitive environment, we are incapable of predicting a product's life cycle such that it can be described as a smooth S curve. Given this, we propose the use of a fuzzy piecewise regression model as a revision of the traditional logistic model. While no proper probability distribution for market share data currently exists, the proposed method is not only able to detect change-points, but can also identify predicted intervals when the growth trend of an analyzed generation is affected by other product generations. The market shares of four television technologies are used in order to demonstrate the performance of the proposed model. The results show that the proposed model outperforms the logistic model, providing both the best and worst possible market shares for the corresponding generation, and highlighting the time of impact of external influences by identifying change-points.

关键词： Fuzzy piecewise regression Logistic model multiple objective programming

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A review of interactive methods for multiobjective integer and mixed-integer programming

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第1期180卷 99-115页

作者： Alves, Maria Joao Climaco, Joao Univ Coimbra Fac Econ INESCC P-3004512 Coimbra Portugal

This paper makes a review of interactive methods devoted to multiobjective integer and mixed-integer programming (MOIP/MOMIP) problems. The basic concepts concerning the characterization of the non-dominated solution set are first introduced, followed by a remark about non-interactive methods vs. interactive methods. Then, we focus on interactive MOIP/MOMIP methods, including their characterization according to the type of preference information required from the decision maker, the computing process used to determine non-dominated solutions and the interactive protocol used to communicate with the decision maker. We try to draw out some contrasts and similarities of the different types of methods. (c) 2006 Elsevier B.V. All rights reserved.

关键词： integer programming multiple objective programming interactive methods

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A new exact method for linear bilevel problems with multiple objective functions at the lower level

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期303卷 312-327页

作者： Alves, Maria Joao Antunes, Carlos Henggeler Univ Coimbra Fac Econ CeBER Av Dias Silva 165 P-3004512 Coimbra Portugal Univ Coimbra Dept Elect & Comp Engn Coimbra Portugal INESC Coimbra Coimbra Portugal

In this paper we consider linear bilevel programming problems with multiple objective functions at the lower level. We propose a general-purpose exact method to compute the optimistic optimal solution, which is based on the search of efficient extreme solutions of an associated multiobjective linear problem with many objective functions. We also explore a heuristic procedure relying on the same principles. Although this procedure cannot ensure the global optimal solution but just a local optimum, it has shown to be quite effective in problems where the global optimum is difficult to obtain within a reasonable timeframe. A computational study is presented to evaluate the performance of the exact method and the heuristic procedure, comparing them with an exact and an approximate method proposed by other authors, using randomly generated instances. Our approach reveals interesting results in problems with few upper-level variables.(c) 2022 Elsevier B.V. All rights reserved.

关键词： multiple objective programming Linear bilevel optimization Semivectorial bilevel problem Multiobjective simplex method

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