Online multi-criteria portfolio analysis through compromise programming models built on the underlying principles of fuzzy outranking

Model to Optimize the Management of Strategic Projects Using Genetic Algorithms in a Public Organization

Public organizations lack adequate models and methods to efficiently support and manage processes related to information security and IT investments. The objective is to optimize the management of strategic projects planned to improve the information security of a public organization and make efficient use of its available resources. The deductive method and exploratory research were used to review and analyze the available information. A mathematical model resulted that optimizes two objectives: (1) minimizing the costs of the strategic projects to be executed, and (2) maximizing the percentage of improvement in the organization’s information security. According to the result of the simulation, a subset of planned strategic projects was obtained that allows improving the information security of a public organization from 84.64% to 92.20%, considering the budgetary limitations of the organization. It was concluded that the proposed model is efficient, practical and can be a support tool for the IT management of a public organization.

Multi-Objective Optimization of Microalgae Metabolism: An Evolutive Algorithm Based on FBA

Studies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and CO2 uptake. The results from the conducted experimental design show a favorable difference in the number of solutions achieved compared to a classic tool solving FBA.