Recently, new types of layouts have been proposed in the literature in order to handle a large number of products. Among these are the fractal layout, aiming at minimization of routing distances. There are already researchers focusing on the design; however, we have noticed that the current approach usually executes several times the allocations of fractal cells on the shop floor up to find the best allocations, which may present a significant disadvantage when applied to a large number of fractal cells owing to combinatorial features. This paper aims to propose a criterion, based on similarity among fractal cells, developed and implemented in a Tabu search heuristics, in order to allocate it on the shop floor in a feasible computational time. Once our proposed procedure is modeled, operations of each workpiece are separated in n subsets and submitted to simulation. The results (traveling distance and makespan) are compared to distributed layout and to functional layout. The results show, in general, a trade-off behavior, that is, when the total routing distance decreases, the makespan increases. Based on our proposed method, depending on the value of segregated fractal cell similarity, it is possible to reduce both performance parameters. Finally, we conclude the proposed procedure shows to be quite promising because allocations of fractal cells demand reduced central processing unit time.

Multiagent systems have been considered as a potential solution for developing adaptive systems. In this research, a cellular self-organizing (CSO) approach is proposed for developing such multiagent adaptive systems. The design of CSO systems however is difficult because the global effect emerges from local actions and interactions that are often hard to specify and control. In order to achieve high-level flexible and robustness of CSO systems and retain the capability of specifying desired global effects, we propose a field-based regulative control mechanism, called field-based behavior regulation (FBR). FBR is a real-time, dynamical, distributed mechanism that regulates the emergence process for CSO systems to self-organize and self-reconfigure in complex operation environments. FBR characterizes the task environment in terms of “fields” and extends the system flexibility and robustness without imposing global control over local cells or agents. This paper describes the model of CSO systems and FBR, and demonstrates their effectiveness through simulation-based case studies.

Biomimetic design applies biological analogies to solve design problems and has been known to produce innovative solutions. However, when designers are asked to perform biomimetic design, they often have difficulty recognizing analogies between design problems and biological phenomena. Therefore, this research aims to investigate designer behaviors that either hinder or promote the use of analogies in biomimetic design. A verbal protocol study was conducted on 30 engineering students working in small teams while participating in biomimetic design sessions. A coding scheme was developed to analyze cognitive processes involved in biomimetic design. We observed that teams were less likely to apply overall biological analogies if they tended to recall existing solutions that could be easily associated with specific superficial or functional characteristics of biological phenomena. We also found that the tendency to evaluate ideas, which reflects critical thinking, correlates with the likelihood of identifying overall biological analogies. Insights from this paper may contribute toward developing generalized methods to facilitate biomimetic design.

The goal of the work reported in this paper is to use automated, combinatorial synthesis to generate alternative solutions to be used as stimuli by designers for ideation. FuncSION, a computational synthesis tool that can automatically synthesize solution concepts for mechanical devices by combining building blocks from a library, is used for this purpose. The objectives of FuncSION are to help generate a variety of functional requirements for a given problem and a variety of concepts to fulfill these functions. A distinctive feature of FuncSION is its focus on automated generation of spatial configurations, an aspect rarely addressed by other computational synthesis programs. This paper provides an overview of FuncSION in terms of representation of design problems, representation of building blocks, and rules with which building blocks are combined to generate concepts at three levels of abstraction: topological, spatial, and physical. The paper then provides a detailed account of evaluating FuncSION for its effectiveness in providing stimuli for enhanced ideation.

This paper presents a robust reliability analysis method for systems of multimodular redundant (MMR) controllers using the method of partitioning and parallel processing of a Markov chain (PPMC). A Markov chain is formulated to represent the N distinct states of the MMR controllers. Such a Markov chain has N2 directed edges, and each edge corresponds to a transition probability between a pair of start and end states. Because N can be easily increased substantially, the system reliability analysis may require large computational resources, such as the central processing unit usage and memory occupation. By the PPMC, a Markov chain's transition probability matrix can be partitioned and reordered, such that the system reliability can be evaluated through only the diagonal submatrices of the transition probability matrix. In addition, calculations regarding the submatrices are independent of each other and thus can be conducted in parallel to assure the efficiency. The simulation results show that, compared with the sequential method applied to an intact Markov chain, the proposed PPMC can improve the performance and produce allowable accuracy for the reliability analysis on large-scale systems of MMR controllers.

The systems engineering V (SE-V) is an established process model to guide the development of complex engineering projects (INCOSE, 2011). The SE-V process involves decomposition and integration of system elements through a sequence of tasks that produce both a system design and its testing specifications, followed by successive levels of build, integration, and test activities. This paper presents a method to improve SE-V implementation by mapping multilevel data into design structure matrix (DSM) models. DSM is a representation methodology for identifying interactions between either components or tasks associated with a complex engineering project (Eppinger & Browning, 2012). Multilevel refers to SE-V data on complex interactions that are germane either at multiple levels of analysis (e.g., component versus subsystem) conducted either within a single phase or across multiple time phases (e.g., early or late in the SE-V process). This method extends conventional DSM representation schema by incorporating multilevel test coverage data as vectors into the off-diagonal cells. These vectors provide a richer description of potential interactions between product architecture and SE-V integration test tasks than conventional domain mapping matrices. We illustrate this method with data from a complex engineering project in the offshore oil industry. Data analysis identifies potential for unanticipated outcomes based on incomplete coverage of SE-V interactions during integration tests. In addition, assessment of multilevel features using maximum and minimum function queries isolates all the interfaces that are associated with either early or late revelations of integration risks based on the planned suite of SE-V integration tests.

The term design fixation is often used interchangeably to refer to situations where designers limit their creative output because of an overreliance on features of preexisting designs, or more generally, an overreliance on a specific body of knowledge directly associated with a problem. In this paper, we argue that interdisciplinary interest in design fixation has led to increasingly broad definitions of the phenomenon that may be undermining empirical research efforts, educational efforts to minimize fixation, and the acquisition and dissemination of transdisciplinary knowledge about fixation effects. To address these issues, we recommend that researchers consider categorizing fixation phenomena into one of three classifications: unconscious adherence to the influence of prior designs, conscious blocks to change, and intentional resistance to new ideas. Next, we distinguish between concept-based design fixation, fixation to a specific class of known design concepts, and knowledge-based design fixation, fixation to a problem-specific knowledge base. With these distinctions in place, we propose a system of orders of design fixation, recommend methods for reducing fixation in inventive design, and recommend areas that are in need of further research within the field of design science.

This paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences.

The use of generative design grammars for computational design synthesis has been shown to be successful in many application areas. The development of advanced search and optimization strategies to guide the computational synthesis process is an active research area with great improvements in the last decades. The development of the grammar rules, however, often resembles an art rather than a science. Poor grammars drive the need for problem specific and sophisticated search and optimization algorithms that guide the synthesis process toward valid and optimized designs in a reasonable amount of time. Instead of tuning search algorithms for inferior grammars, this research focuses on designing better grammars to not unnecessarily burden the search process. It presents a grammar rule analysis method to provide a more systematic development process for grammar rules. The goal of the grammar rule analysis method is to improve the quality of the rules and in turn have a major impact on the quality of the designs generated. Four different grammars for automated gearbox synthesis are used as a case study to validate the developed method and show its potential.

This paper presents a new technique for shape and topology optimization of fluid channels using generative design synthesis methods. The proposed method uses the generative abilities of graph grammars with simulation and analysis power of conventional computational fluid dynamics methods. The graph grammar interpreter GraphSynth is used to carry out graph transformations, which define different topologies for a given multiple-inlet multiple-outlet problem. After evaluating and optimizing the generated graphs, they are first transformed into meaningful three-dimensional shapes. These solutions are then analyzed by a computational fluid dynamics solver for final evaluation of the possible solutions. The effectiveness of the proposed method is checked by solving a variety of available test problems and comparing them with those found in the literature. Furthermore, by solving very complex large-scale problems, the robustness and effectiveness of the method is tested. To extend the work, future research directions are presented.

As a benefit of modularization of complex systems, original equipment manufacturers (OEMs) can choose suppliers in a less constricted way when faced with new or evolving requirements. However, new suppliers usually add uncertainties to the system development. Because suppliers are tightly integrated into the design process in modular design and therefore greatly influence the outcome of the OEM's products, the uncertainty along with requirements satisfaction of the suppliers and their modules should be controlled starting from potential supplier identification. In addition, to better satisfy new requirements, the potential supplier identification should be combined with architecture generation to enable the new technology integration. In this paper, we propose the Architecture & Supplier Identification Tool, which generates all possible architectures and corresponding suppliers based on new requirements through matrix mapping and propagation. Using the Architecture & Supplier Identification Tool, the overall uncertainty and requirements satisfaction of generated architectures can be estimated and controlled. The proposed method aims at providing decision support for early design of complex systems, thereby helping OEMs have an integrated view of suppliers and system architectures in requirements satisfaction and overall uncertainty.

This paper explores the role of a designer's sense of engagement in early stage design. In the field of virtual reality, presence and immersion are standard measures of an individual's sense of engagement and involvement in an activity. High levels of presence might indicate that the designer is highly focused on the work. The central research question is the following: do designers who are more engaged in design activity, as measured by presence and immersive tendency questionnaires, produce better designs? An experiment was conducted to assess presence and immersive tendencies within the context of a hands-on, open-ended design-and-build activity. The results indicated that the designers' sense of immersion and presence ranged widely as well as their sense of frustration and calmness while performing the design activity. It was found that higher levels of presence correlated with either high design performance or low design performance. Lower levels of presence correlated with average design performance. No correlations were found between immersive tendency and design performance. This study suggests that some level of presence can be linked with better design, and it implies that level of presence might serve as an indicator of performance and learning in similar design-and-build activities.

The goal of machining scheme selection (MSS) is to select the most appropriate machining scheme for a previously designed part, for which the decision maker must take several aspects into consideration. Because many of these aspects may be conflicting, such as time, cost, quality, profit, resource utilization, and so on, the problem is rendered as a multiobjective one. Consequently, we consider a multiobjective optimization problem of MSS in this study, where production profit and machining quality are to be maximized while production cost and production time must be minimized, simultaneously. This paper presents a new discrete method for particle swarm optimization, which can be widely applied in MSS to find out the set of Pareto-optimal solutions for multiobjective optimization. To deal with multiple objectives and enable the decision maker to make decisions according to different demands on each evaluation index, an analytic hierarchy process is implemented to determine the weight value of evaluation indices. Case study is included to demonstrate the feasibility and robustness of the hybrid algorithm. It is shown from the case study that the multiobjective optimization model can simply, effectively, and objectively select the optimal machining scheme according to the different demands on evaluation indices.

This article focuses on a shape grammar, the rabo-de-bacalhau housing style, that was developed to enable the adaptation of existing houses to new requirements and most particular on the process of inference of the grammar. In the article we describe the process undertaken to develop the grammar and what the achievements of the transformation grammar are regarding the possibilities of a mass customization of a dwelling's rehabilitation work. The goal of this article is to describe and discuss how the designer's knowledge was encoded into shape rules. The process used to extract the architect's knowledge and to incorporate it into the transformation grammar enable us to abstract the designer's actions and to define a sequence of actions that can define a possible strategy of design. The proposed design methodology generates dwelling layouts that are legal because they follow the grammar language and adequate because they meet the a priori user and design requirements.

A computational approach for the design of self-organizing systems is proposed that employs a genetic algorithm to efficiently explore the vast space of possible configurations of a given system description. To generate the description of the system, a two-field based model is proposed in which agents are assigned parameterized responses to two “fields,” a task field encompassing environmental features and task objects, and a social field arising from agent interactions. The aggregate effect of these two fields, sensed by agents individually, governs the behavior of each agent, while the system-level behavior emerges from the actions of and interactions among the agents. Task requirements together with performance preferences are used to compose system fitness functions for evolving functional and efficient self-organizing mechanisms. Case studies on the evolutionary synthesis of self-organizing systems are presented and discussed. These case studies focus on achieving system-level behavior with minimal explicit coordination among agents. Agents were able to collectively display flocking, exploration, and foraging through self-organization. The proposed two-field model was able to capture important features of self-organizing systems, and the genetic algorithm was able to generate self-organizing mechanisms by which agents could form task-based structures to fulfill functional requirements.

Adaptability can have many different definitions: reliability, robustness, survivability, and changeability (adaptability to requirements change). In this research, we focused entirely on the last type. We discuss two alternative approaches to requirements change adaptability. One is the valuation approach that is based on utility and cost of design changes in response to modified requirements. The valuation approach is theoretically sound because it is based on utility and decision theory, but it may be difficult to use in the real world. The second approach is based on examining product architecture characteristics that facilitate changes that include modularity, hierarchy, interfaces, performance sensitivity, and design margins. This approach is heuristic in nature but more practical to use. If calibrated, it could serve as a surrogate for real adaptability. These measures were incorporated in a software tool for exploring alternative configurations of fractionated space satellite systems.

A common way of dynamically scheduling jobs in a manufacturing system is by implementing dispatching rules. The issues with this method are that the performance of these rules depends on the state the system is in at each moment and also that no “ideal” single rule exists for all the possible states that the system may be in. Therefore, it would be interesting to use the most appropriate dispatching rule for each instance. To achieve this goal, a scheduling approach that uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a literature review of the main machine learning based scheduling approaches from the last decade is presented.