To obtain optimal design efficiently in the initial design stage of a ship, a hybrid system is developed by integrating the optimization algorithm and knowledge-based system. The hybrid system can manipulate numeric and symbolic data simultaneously. To increase search efficiency in a design space, the optimization algorithm (optimizer) is implemented by coupling a genetic algorithm (GA) and search method. The optimizer determines a candidate region around the optimum point by using the GA, then searches the optimum point by the search method concentrating in this region, thus reducing calculation time and increasing search efficiency. To generate input data for the optimizer, a rule-based system is developed. Some domain knowledge for ship optimization in the initial design stage is retrieved from a database of existing ship and design experts. The obtained knowledge is stored in the knowledge base. The optimizer incorporates a knowledge-based system with heuristic and analytic knowledge, thereby narrowing the feasible space of the design variables. Therefore, search speed and the capability of finding an optimum point will be increased in comparison with conventional approach. The developed system is applied principally to particulars of optimization of ships with multicriteria. Through application ship design, it shows that the hybrid system can be a useful tool for optimum design.

The paper presents a knowledge-based system (KBS) for the conceptual design of grippers for handling fabrics. Its main purpose is the integration of the domain knowledge in a single system for the systematic design of this type of grippers. The knowledge presented, in terms of gripper, material and handling process, are classified. The reasoning strategy is based upon a combination of a depth-first search method and a heuristic method. The heuristic search method finds a final solution from a given set of feasible solutions and can synthesize new solutions to accomplish the required specifications. Details of the main features of the system are given, including its ability to take critical design decisions according to four criteria, weighted by the designer. The knowledge-based system was implemented in the Kappa P. C. 2.3.2 environment. Two examples are given to illustrate some critical aspects concerning the KBS development, to explain the operation of the proposed searching heuristic method, and to show its effectiveness in producing design concepts for grippers.

Most engineering design problems involve optimizing a number of often conflicting performance measures in the presence of multiple constraints. Traditional vector optimization techniques approach these problems by generating a set of Pareto-optimal solutions, where any specific objective can be further improved only at the cost of degrading one or more other objectives. The solutions obtained in this manner, however, are only single points within the space of all possible Pareto-optimal solutions and generally do not indicate to designers how small deviations from predicted design parameters settings affect the performance of the product or the process under study.

In this paper we introduce a new approach to robust design based on the concept of inductive learning with regression trees. Given a set of training examples relating to a multiobjective design problem, we demonstrate how a multivariate regression tree can utilize an information-theoretic measure of covariance complexity to capture optimal, tradeoff design surfaces. The novelty of generating design surfaces as opposed to traditional points in the design space is that now designers are able to easily determine how the responses of a product or process vary as design parameters change. This ability is of paramount importance in situations where design parameter settings need to be modified during the lifetime of a product/process due to various economic or operational constraints. As a result, designers will be able to select optimal ranges for design parameters such that the product's performance indices exhibit minimal or tolerable deviations from their target values. To highlight the advantages of our methodology, we present a multiobjective example that deals with optimum design of an electric discharge machining (EDM) process.

This paper describes a generic ontology-based approach that eases the requirement analysis (RA) work. The approach enables the user to derive method-specific RA tools for different applications. The derivation process is based on a unified framework that contains a software methodology ontology and a knowledge acquisition ontology. The former contains a library of software RA methods and a set of modeling support entities, which use the library to construct methodological knowledge. The latter contains a library of knowledge acquisition techniques and a set of acquisition support entities, which work on the library, guided by the generated methodological knowledge, to extract domain knowledge. The generated methodological knowledge, coupled with the domain knowledge, forms the RA tools. We have demonstrated the use of the approach by deriving an RA tool to assist the system analyst to acquire and formalize a requirement specification for a network management system. This approach facilitates the integrating, sharing, and reuse of software methodologies and knowledge acquisition techniques and alleviates the problems associated with the correct generation of requirement specification for different domains.