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The purpose of this paper is to present an application of the simulated annealing algorithm to the redundant system reliability optimization. Its main aim is to analyze and compare this optimization method performance with those of similar application.

The methods that were used to compare results are the genetic algorithm, the Lagrange Multipliers, and the evolution strategy. A hybrid algorithm composed by simulated annealing and genetic algorithm was developed in order to achieve the general applicability of the methods. The hybrid algorithm also tries to exploit the positive aspects of each method.

The results presented by the simulated annealing and the hybrid algorithm are significant, and validate the methods as a robust tool for parameter optimization in mechanical projects development.

The main objective is to propose a method for redundancy optimization in mechanical systems, which are not as large as electric and electronic systems, but involves high costs associated to redundancy and requires a high level of safety standards like: automotive and aerospace systems.

This paper presents an availability optimization problem of an engineering system assembled in a series configuration which has the redundancy of units and teams of maintenance as optimization parameters. The objective is to reach the maximum value of availability, considering installation and maintenance costs, weight, volume and available maintenance teams as constraints. The optimization method uses a genetic algorithm (GA), which is based on biological concepts of species evolution. It is a robust method, because it does not converge to a local optimum. It does not need the use of differential calculus, facilitating the computational implementation. The final results are significantly indicative about the fitting of the GA parameters and the application of the methodology to solve engineering design problems involving systems availability.

The aim of both value analysis (VA) and quality function deployment (QFD) is to reduce waste by avoiding redesign and providing optimal location of costs in general. To satisfy the consumer's most important needs, the VA prioritizes the increase in the cost of the product and not the subsequent price rise. QFD aims at generating clear engineering needs from consumer requirements thus, minimizing the reprojecting cost (“cost” should read “waste”) and changes in the products. The existing common concepts between two design tools, QFD (the project tool) and VA (the product optimization tool) motivated this study. QFD establishes a link among parameters such as the consumer needs, engineering requirements and a comparative analysis of the consumer perception against that of rival companies. The VA prioritizes a rise in the aggregate value (perceived by the consumer) by optimization development and production costs. The proposed methodology is capable of integrating these two tools, integrating costs with product development (“for the consumer”) for a joint analysis. This way it is possible to establish optimum cost values for each engineering requirement. It is also possible to evaluate the cost of each product function. Furthermore, the methodology provides a tool that supports decision making in product development and projects. This work evaluates the integrated use of the QFD and VA tools. Employing a survey that was carried out which intended to reveal the young male consumers’ requirements concerning a sports bicycle.