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Zopounidis, Constantin; Despotis, Dimitris K.; Kamaratou, I.

doi:10.1023/a:1008660309379

Cited by 40 publications

(2 citation statements)

References 18 publications

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“…The proposed approach can be also adopted in other decision settings within, but also beyond, the field of energy and environment. A similar approach, for example, lies on the basis of ADELAIS, an interactive computer program developed to support the search for a satisfactory solution in multi-objective linear programming problems, which has been used as a tool for the selection of stock portfolios [18]. In contrast, however, to both the initial conception in [15] and the ADELAIS program, the decision framework developed herein concerns a mixed-integer nonlinear mathematical programming problem, which aims at minimizing rather than maximizing the considered multiple objectives.…”

Section: Discussionmentioning

confidence: 99%

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Improving Energy Efficiency in Buildings Using an Interactive Mathematical Programming Approach

Diakaki

Grigoroudis

2021

Sustainability

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Improving energy efficiency in buildings is a major priority and challenge worldwide. The employed measures vary in nature, and the decision analyst, who is typically the architect, the engineer, or the building expert that has undertaken the task to suggest energy efficient solutions, faces a complex decision problem comprising numerous decision variables and multiple, usually competitive objectives. The solution of such multi-objective problems typically involves some sort of objectives aggregation, which reflects the preferences of the involved final decision maker that is the building’s user, occupant, and/or owner. The preferences elicitation, however, is a difficult task, and this paper aims to provide an interactive framework that will allow their consideration in a relatively easy manner. More specifically, a mathematical programming approach is proposed herein, which allows the elicitation and incorporation of the decision maker’s preferences in the decision model via the assessment of his/her utility function with the assistance of the multicriteria decision aid method UTASTAR. To study the feasibility and efficiency of the proposed approach, the case of a simple building is examined as an application example. The study results suggest that the proposed approach is capable of helping the decision analyst to suggest energy measures that satisfy, as much as possible, the decision maker’s preferences, without having to precisely prescribe them beforehand.

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Section: Discussionmentioning

confidence: 99%

“…In this last step of phase two, the utility Function (18) or ( 19) is maximized over the decreased feasible solution space X 1 , defined in (17). More specifically, the following problem:…”

Section: Phase Two-iteration 1-stepmentioning

confidence: 99%

Improving Energy Efficiency in Buildings Using an Interactive Mathematical Programming Approach

Diakaki

Grigoroudis

2021

Sustainability

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Chinese Corporate Social Responsibility by Multiple Objective Portfolio Selection and Genetic Algorithms

Peng

et al. 2013

Multi Criteria Decision Anal

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In this paper, we study corporate social responsibility (CSR) in China through the prism of investments. We work with large stocks and assess their CSR performance from agency CSR data. We formulate Chinese CSR by a multiple objective extension of a traditional portfolio selection model and analytically solve the extension. We also solve the extension by a genetic algorithm and directly evaluate the algorithm's performance against the analytical solution. The multiple objective formulation is tested by randomly choosing nondominated portfolios with out-of-sample data to identify nondominated portfolios that outperform the 1/n portfolio (equally weighted portfolio).

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On convex multiobjective programs with application to portfolio optimization

Jayasekara

Adelgren

Wiecek

2019

Multi Criteria Decision Anal

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Focusing on (strictly) convex multiobjective programs (MOPs), we review some well-established scalarizations in multiobjective programming from the perspective of parametric optimization and propose a modified hybrid scalarization suitable for a class of specially structured convex MOPs. Since multiobjective quadratic programs are a prominent class of convex MOPs due to their broad applicability, we review the state-ofthe-art algorithms for computing their efficient solutions. These two lines of investigation are merged to solve multiobjective portfolio optimization problems with three or more quadratic objective functions, a class of problems that has not been solved before. Computational examples are provided.

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Cited by 40 publications

References 18 publications

Improving Energy Efficiency in Buildings Using an Interactive Mathematical Programming Approach

Improving Energy Efficiency in Buildings Using an Interactive Mathematical Programming Approach

Chinese Corporate Social Responsibility by Multiple Objective Portfolio Selection and Genetic Algorithms

On convex multiobjective programs with application to portfolio optimization

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