An evolutionary model for nonroutine design is presented, which is called hierarchical coevolution. The requirements for an evolutionary model of nonroutine design are provided, and some of the problems with existing approaches are discussed. Some of the ways in which these problems have been addressed are examined in terms of the design knowledge required by evolutionary processes. Then, a synthesis of these approaches as a hierarchical coevolutionary model of nonroutine design is presented and the manner in which this model addresses the requirements of an evolutionary design model is discussed. An implementation in the domain of space planning provides an example of a hierarchical design problem.

Within the engineering design community there is support for further research into the development of improved approaches to design management. Such research has lead to coordination being identified as an important and pervasive characteristic of many existing approaches (e.g., concurrent engineering and work-flow management). In this article, operational design coordination is proposed as the basis for an improved approach. This article also presents a novel integrated approach that incorporates the key elements of operational design coordination: coherence, communication, task management, resource management, schedule management, and real-time support. Through unifying these key elements, this approach provides an integrated means of managing design in a controlled and harmonious fashion. The approach also provides knowledge of the constituent techniques involved in operational design coordination, the interrelationships and dynamic interactions between them, and the knowledge used and maintained within and between them. The approach has been realized within an agent-oriented system called the Design Coordination System, which provides a systematic means of simultaneously coordinating operational management tasks and technical design tasks. To evaluate the approach, the system has been applied to an industrial case study involving the computational process of turbine blade design. This application has been shown to enable the structured undertaking of interrelated tasks by allocating and using resources of varying performance efficiency in an optimized fashion in accordance with dynamically derived schedules in a coherent, appropriate, and timely manner. This is achieved by managing tasks, their dependencies, and the information required to undertake them. In addition, the approach enables and sustains the continuous optimized use of resources by monitoring, forecasting, and disseminating resource performance efficiency. The approach facilitates dynamic scheduling and the subsequent enactment of the resulting schedules. Decision making for rescheduling is also incorporated within the approach such that it is only performed as and when appropriate. If rescheduling is performed, it is done so in parallel with task enactment such that resources continue to be utilized in an optimized manner.

Genetic algorithm (GA) and singular value decomposition (SVD) are deployed for the optimal design of both Gaussian membership functions of antecedents and the vector of linear coefficients of consequents, respectively, of adaptive neurofuzzy inference systems (ANFIS) networks that are used for modeling of the explosive cutting process of plates by shaped charges. The aim of such modeling is to show how the depth of penetration varies with the variation of important parameters, namely, the apex angle, standoff, liner thickness, and mass of charge. It is demonstrated that SVD can be effectively used to optimally find the vector of linear coefficients of conclusion parts in ANFIS models and their Gaussian membership functions in premise parts are determined by a GA.

Selective disassembly is an important issue in industrial and mechanical engineering for environmentally conscious manufacturing. This paper presents an intelligent selective disassembly approach based on ant colony algorithms, which take inspiration from the behavior of real ant colonies and are used to solve combinatorial optimization problems. For diverse assemblies, the algorithm generates different amounts of ants cooperating to find disassembly sequences for selected components, minimizing the reorientation of assemblies and removal of components. A candidate list that is composed of feasible disassembly operations, which are derived from a disassembly matrix of products, guides sequence construction in the implicit solution space and ensures the geometric feasibility of sequences. Preliminary implementation results show the effectiveness of the proposed method.

We present a methodology of learning fuzzy rules using an iterative genetic algorithm (GA). The approach incorporates a scheme of partitioning the entire solution space into individual subspaces. It then employs a mechanism to progressively relax or tighten the constraint. The relaxation or tightening of constraint guides the GA to the subspace for further iteration. The system referred to as the iterative GA learning module is useful for learning an efficient fuzzy control algorithm based on a predefined linguistic terms set. The overall approach was applied to learn a fuzzy algorithm for a water bath temperature control. The simulation results demonstrate the effectiveness of the approach in automating an industrial process.

The exponential growth of the Internet and increasing communication and computational power have created many opportunities for advancing engineering, manufacturing, and business activities. Among them are electronic catalogs. These have become basic information resources to a number of people, ranging from shoppers looking for personal items to engineers selecting electromechanical parts to build a product. Although rich in content, current catalog systems are limited both in search quality and in realizing the full potential of the retrieved information. The active catalog system brings a conceptually new idea to electronic commerce by providing a new, computationally usable, catalog information environment about components and their use in applications. It utilizes a rich body of domain knowledge to facilitate access and retrieval of component information. The utility of retrieved information is enhanced by using it to rapidly construct simulation programs and test alternatives, supporting a “try before you buy” paradigm in which users evaluate candidate components within simulations of their design. We describe services provided in the active catalog system to support engineers in selecting and evaluating electromechanical components and subsystems. The services include mechanisms for creating queries for parts based on their intended use rather than merely parametric specifications, refining those queries to take account of constraints imposed by domain knowledge, providing multimodal information to help engineers assess and compare candidate parts, and generating simulation models for candidate parts and integrating them to provide simulation models for candidate systems.