This paper proposes two new approaches for the sensitivity analysis of multiobjective design optimization problems whose performance functions are highly susceptible to small variations in the design variables and/or design environment parameters. In both methods, the less sensitive design alternatives are preferred over others during the multiobjective optimization process. While taking the first approach, the designer chooses the design variable and/or parameter that causes uncertainties. The designer then associates a robustness index with each design alternative and adds each index as an objective function in the optimization problem. For the second approach, the designer must know, a priori, the interval of variation in the design variables or in the design environment parameters, because the designer will be accepting the interval of variation in the objective functions. The second method does not require any law of probability distribution of uncontrollable variations. Finally, the authors give two illustrative examples to highlight the contributions of the paper.

Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.

Neste trabalho é apresentada uma metodologia para o planejamento da operação de instalação de âncoras de equipamentos offshore, navios e plataformas, visando a otimização dos recursos necessários, embarcações de apoio e cabos de trabalho, por meio da minimização de uma função objetivo com base em múltiplos critérios. Como ganho adicional, inerente à metodologia proposta, consegue-se a automação do processo de planejamento de instalação, que nos moldes tradicionais é feito na base da tentativa e erro, onde o planejador, utilizando algum aplicativo para o cálculo de ancoragem, decide quanto de recursos deve ser aplicado ao problema tentando fazer com que a âncora atinja o alvo pré-definido no projeto do sistema de ancoragem.

In this work is explored a planning methodology for deep water anchor lines deployment in offshore platforms and floating production systems aiming operational resources optimization, by minimizing a multi criteria objective function. As an additional advantage, inherited from the proposed methodology, the planning automation is achieved. The planning automation overcomes the traditional way to do in a trial error basis, where an engineer, using a anchoring software, decides how much of work wire and anchoring line must be paid out from both the floating system and the supply vessel and additionally which horizontal force must be applied to the line trying settle the anchor on a previously defined target in the ocean floor.