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Encompassing statistically unquantifiable randomness in goal programming: an application to portfolio selection
[EN] Random events make multiobjective programming solutions vulnerable to changes in input data. In many cases statistically quantifiable information on variability of relevant parameters may not be available for decision making. This situation gives rise to the problem of obtaining solutions based on subjective beliefs and a priori risk aversion to random changes. To solve this problem, we propose to replace the traditional weighted goal programming achievement function with a new function that considers the decision maker's perception of the randomness associated with implementing the solution through the use of a penalty term. This new function also implements the level of a priori risk aversion based around the decision maker's beliefs and perceptions. The proposed new formulation is illustrated by means of a variant of the mean absolute deviation portfolio selection model. As a result, difficulties imposed by the absence of statistical information about random events can be encompassed by a modification of the achievement function to pragmatically consider subjective beliefs.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
s This work is devoted to the memory of Professor Enrique Ballestero for his selfess
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Towards optimal multi-objective models of network security: survey
Information security is an important aspect of a successful business today. However, financial difficulties and budget cuts create a problem of selecting appropriate security measures and keeping networked systems up and running. Economic models proposed in the literature do not address the challenging problem of security countermeasure selection. We have made a classification of security models, which can be used to harden a system in a cost effective manner based on the methodologies used. In addition, we have specified the challenges of the simplified risk assessment approaches used in the economic models and have made recommendations how the challenges can be addressed in order to support decision makers
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An ongoing water supply planning problem in the Regional Municipality of Waterloo, Ontario, Canada, is studied to select the best water supply combination, within a multiple-objective framework, when actions are interdependent. The interdependencies in the problem are described and shown to be essential features. The problem is formulated as a multiple-criteria integer program with interdependent actions. Because of the large number of potential actions and the nonconvexity of the decision space, it is quite difficult to find nondominated subsets of actions. Instead, a modified goal programming technique is suggested to identify promising subsets. The appropriateness of this technique is explained, and the lessons learned in applying it to the Waterloo water supply planning problem are described
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