Financial portfolio management through the goal programming model: Current state-of-the-art

Aouni, Belaı̈d; Colapinto, Cinzia; Torre, Davide La

doi:10.1016/j.ejor.2013.09.040

Cited by 118 publications

(64 citation statements)

References 83 publications

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“…An assessment of this orientation from the point of view of decision system design can be seen in Zopounidis and Doumpos (2013). Finally, on these lines, when the MCDM tool used is specifically for goal programming (GP), some interesting operational results have been obtained (Aouni, Colapinto, & La Torre, 2014).…”

Section: Introductionmentioning

confidence: 96%

Corporate social responsibility in portfolio selection: A “goal games” against nature approach

Trenado

Romero

Cuadrado

et al. 2014

Computers & Industrial Engineering

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Nowadays, there is an uprising social pressure on big companies to incorporate into their decisionmaking process elements of the so-called social responsibility. Among the many implications of this fact, one relevant one is the need to include this new element in classic portfolio selection models. This paper meets this challenge by formulating a model that combines goal programming with "goal games" against nature in a scenario where the social responsibility is defined through the introduction of a battery of sustainability indicators amalgamated into a synthetic index. In this way, we have obtained an efficient model that only implies solving a small number of linear programming problems. The proposed approach has been tested and illustrated by using a case study related to the selection of securities in international markets.

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Section: Introductionmentioning

confidence: 96%

Corporate social responsibility in portfolio selection: A “goal games” against nature approach

Trenado

Romero

Cuadrado

et al. 2014

Computers & Industrial Engineering

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“…We have chosen to develop the decision support on utility theory, as we seek to represent preference trade-offs rather than vagueness of decision makers. Multi-attribute utility has been used in a similar manner in the area of portfolio resource allocation [30,14,1] and the simplifying assumption of utility independence is identified as desirable to specify a utility function.…”

Section: Introductionmentioning

confidence: 99%

Allocation of tasks for reliability growth using multi-attribute utility

Wilson

Quigley

2016

European Journal of Operational Research

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In reliability growth models in particular, and project risk management more generally, improving the reliability of a system or product is limited by constraints on cost and time. There are many possible tasks which can be carried out to identify and design out weaknesses in the system under development. This paper considers the allocation problem: which subset of tasks to undertake. While the method is applicable to project risk management generally, the work has been motivated by reliability growth programmes. We utilise a model for reliability growth, based on an efficacy matrix, developed with engineering experts in the aerospace industry. We develop a general multi-attribute utility function based on targets for cost, time on test and system reliability. The optimal subset is identified by maximising the prior expected utility. We derive conditions on the model parameters for risk aversion and loss aversion based on observed properties of preference. We give conditions for multivariate risk aversion under the general form of the utility function. The method is illustrated using an example informed by work with aerospace organisations.

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“…Polynomial GP (PGP) accommodates the DMs preferences by specifying a polynomial expression for the objective function as the respective deviational variables approaches zero. Aouni et al (2014) present a comprehensive mathematical treatment of various types of GP models. GP models can incorporate randomness and fuzzy measures in situations where the DM is not sure of the model parameters and the goals.…”

Section: Introductionmentioning

confidence: 99%

Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review

2015

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Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (Manag Sci 2:138-151, 1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and computational improvements have greatly contributed to the diverse applications and several variants of GP models. In this paper we present a state of the art literature review on GP applications in three selected (prominent and popular) areas, namely engineering, management and social sciences. Forthcoming in Annals of Operations ResearchAbstract Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and computational improvements have greatly contributed to the diverse applications and several variants of GP models. In this paper we present a state of the art literature review on GP applications in three selected (prominent and popular) areas, namely engineering, management and social sciences.

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Financial portfolio management through the goal programming model: Current state-of-the-art

Cited by 118 publications

References 83 publications

Corporate social responsibility in portfolio selection: A “goal games” against nature approach

Corporate social responsibility in portfolio selection: A “goal games” against nature approach

Allocation of tasks for reliability growth using multi-attribute utility

Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review

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