Multiobjective engineering design optimization problems: a sensitivity analysis approach

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.

Error Upper Bounds for a Computational Method for Nonlinear Boundary and Initial-Value Problems

This work develops a computational approach for boundary and initial-value problems by using operational matrices, in order to run an evolutive process in a Hilbert space. Besides, upper bounds for errors in the solutions and in their derivatives can be estimated providing accuracy measures.

Probabilistic crack growth analyses using a boundary element model: Applications in linear elastic fracture and fatigue problems

This paper addresses the numerical solution of random crack propagation problems using the coupling boundary element method (BEM) and reliability algorithms. Crack propagation phenomenon is efficiently modelled using BEM, due to its mesh reduction features. The BEM model is based on the dual BEM formulation, in which singular and hyper-singular integral equations are adopted to construct the system of algebraic equations. Two reliability algorithms are coupled with BEM model. The first is the well known response surface method, in which local, adaptive polynomial approximations of the mechanical response are constructed in search of the design point. Different experiment designs and adaptive schemes are considered. The alternative approach direct coupling, in which the limit state function remains implicit and its gradients are calculated directly from the numerical mechanical response, is also considered. The performance of both coupling methods is compared in application to some crack propagation problems. The investigation shows that direct coupling scheme converged for all problems studied, irrespective of the problem nonlinearity. The computational cost of direct coupling has shown to be a fraction of the cost of response surface solutions, regardless of experiment design or adaptive scheme considered. (C) 2012 Elsevier Ltd. All rights reserved.

HEALTH PROBLEMS AWARENESS DURING TRAVEL AMONG FACULTY MEMBERS OF A LARGE UNIVERSITY IN LATIN AMERICA. PRELIMINARY REPORT

Health safety during trips is based on previous counseling, vaccination and prevention of infections, previous diseases or specific problems related to the destination. Our aim was to assess two aspects, incidence of health problems related to travel and the traveler’s awareness of health safety. To this end we phone-interviewed faculty members of a large public University, randomly selected from humanities, engineering and health schools. Out of 520 attempts, we were able to contact 67 (12.9%) and 46 (68.6%) agreed to participate in the study. There was a large male proportion (37/44, 84.1%), mature adults mostly in their forties and fifties (32/44, 72.7%), all of them with higher education, as you would expect of faculty members. Most described themselves as being sedentary or as taking occasional exercise, with only 15.9% (7/44) taking regular exercise. Preexisting diseases were reported by 15 travelers. Most trips lasted usually one week or less. Duration of the travel was related to the destination, with (12h) or longer trips being taken by 68.2% (30/44) of travelers, and the others taking shorter (3h) domestic trips. Most travelling was made by air (41/44) and only 31.8% (14/44) of the trips were motivated by leisure. Field research trips were not reported. Specific health counseling previous to travel was reported only by two (4.5%). Twenty seven of them (61.4%) reported updated immunization, but 11/30 reported unchecked immunizations. 30% (9/30) reported travel without any health insurance coverage. As a whole group, 6 (13.6%) travelers reported at least one health problem attributed to the trip. All of them were males travelling abroad. Five presented respiratory infections, such as influenza and common cold, one neurological, one orthopedic, one social and one hypertension. There were no gender differences regarding age groups, destination, type of transport, previous health counseling, leisure travel motivation or pre-existing diseases. Interestingly, the two cases of previous health counseling were made by domestic travelers. Our data clearly shows that despite a significant number of travel related health problems, these highly educated faculty members, had a low awareness of those risks, and a significant number of travels are made without prior counseling or health insurance. A counseling program conducted by a tourism and health professional must be implemented for faculty members in order to increase the awareness of travel related health problems.

A new method for decision making in multi-objective optimization problems

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.