If a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of an object is not computably enumerable. Third, we are able to characterize innovative problem solving as looking for a series of interactions that produce the desired effects (i.e., the goal). Fourth, we are able to construct a precise problem-solving grammar that is both human and machine friendly. Fifth, we are able to devise a visual and verbal problem-solving representation that both humans and computers can contribute to as they help counteract each other's problem-solving weaknesses. We show how computers can counter some of the known cognitive obstacles to innovation that humans have. We also briefly discuss ways in which humans can return the favor. We conclude that a promising process for innovative problem solving is a human–computer collaboration in which each partner assists the other in unearthing the obscure features of a problem.

The aim of this paper is to investigate the challenges associated with the industrial implementation of generative design systems. Though many studies have been aimed at validating either the technical feasibility or the usefulness of generative design systems, there is, however, a lack of research on the practical implementation and adaptation in industry. To that end, this paper presents two case studies conducted while developing design systems for industrial uses. The first case study focuses on an engineering design application and the other on an industrial design application. In both cases, the focus is on detail-oriented performance-driven generative design systems based on currently available computer-assisted design tools. The development time and communications with the companies were analyzed to identify challenges in the two projects. Overall, the results show that the challenges are not related to whether the design tools are intended for artistic or technical problems, but rather in how to make the design process systematic. The challenges include aspects such as how to fully utilize the potential of generative design tools in a traditional product development process, how to enable designers not familiar with programming to provide design generation logic, and what should be automated and what is better left as a manual task. The paper suggests several strategies for dealing with the identified challenges.

This paper reports on a study characterizing design processes and the potential of design spaces through measuring the information entropy of empirical data derived from protocol studies. The sequential segments in a protocol analysis can be related to each other by examining their semantic content producing a design session's linkograph, which defines the design space for a design session. From a linkograph, it is possible to compute the probabilities of the connectivity of each segment for its forelinks and its backlinks, together with the probabilities of distance among links. A linkograph's entropy is a measure of the information in the design session. It is claimed that the entropy of the linkograph measures the potential of the design space being generated as the design proceeds chronologically. We present an approach to the automated construction of linkographs by connecting segments using the lexical database WordNet and measure its entropy. A case study of two design sessions with different characteristics was conducted, one considered more productive and creative, the other more pragmatic. Those segments with high entropy and those associated with high rates of change of entropy are analyzed. The creative session has a higher linkograph entropy. This result indicates the potential of using entropy to characterize a design process.

The hub location problems involve locating facilities and designing hub networks to minimize the total cost of transportation (as a function of distance) between hubs, establishing facilities and demand management. In this paper, we consider the capacitated cluster hub location problem because of its wide range of applications in real-world cases, especially in transportation and telecommunication networks. In this regard, a mathematical model is presented to address this problem under capacity constraints imposed on hubs and transportation lines. Then, a new hybrid algorithm based on simulated annealing and ant colony optimization is proposed to solve the presented problem. Finally, the computational experiments demonstrate that the proposed heuristic algorithm is both effective and efficient.

Product variant design, as one of the key enabling technologies of mass customization, is the transfer of variant information among mating parts from the perspective of informatics. A dimension constraint network (DCN) among mating parts carries on the task of transferring variant information. What are the information transfer characteristics of dimensions in a constraint network is a fundamental issue to plan the product variant design process reasonably. We begin by showing the natural dynamics of the DCN from two aspects: structure and uncertainty. The information efficiency of the DCN was proposed based on its simple path to specify the information transfer capability of the network. Based on this, the information centrality of the dimension was developed by measuring the efficiency loss of the DCN after the removal of a dimension from the network, which describes the information transfer capability of this dimension. Further, the information centrality of a part was derived. Using a spherical valve subassembly, we calculated the information centrality of the dimensions in a constraint network. We determined that the information centrality of dimension is highly correlated to its out-degree. An approach to plan the sequence of the part variant design according to its information centrality was proposed. We calculated the uncertainties of the DCN and its cumulative uncertainties under different sequences of the part variant design. Results indicate that part variant design under the descending information centrality of the parts minimizes the uncertainty of the DCN. This suggests a new method of planning the sequence of part variant design.

System architecture is important for the design of complex mechatronic systems because it acts as an intermediator between conceptual design and detail design. An explicit and exact system modeling language is imperative for successful architecture design. However, some deficiencies remain, such as the lack of geometry elements, hybrid behavior description, and specific association semantics for existing architecture modeling languages. In this study, a Systems Modeling Language extension for mechatronic system architecture modeling called ArchME is proposed. The requirements for the mechatronic System Modeling Language are analyzed, and the metamodels are defined. Then, the modeling elements are determined. Finally, the profiles based on the systems modeling language are defined to support the modeling of function, behavior, structure, and their association. This enables system designers to model the system architecture and facilitates communication between different stakeholders. A case study is provided to demonstrate the modeling capability of ArchME.

Biologically inspired design can be used to aid in conceptual design. Sketching is an important ideation process in conceptual design for recording and evaluating flashing moments of inspiration. The present study aims to provide a framework for exploring the effects of biological examples on the sketching contours of products, as well as the perceptual matching degree between design ideas generated via sketching and the desired functions. Elliptic Fourier descriptors with principal component analysis and perceptual matching were used to evaluate and compare the effects of biological examples, no examples, and human-engineered examples from different product categories and within one product category on the sketches in an experiment that involved 28 participants. The application of elliptic Fourier descriptors with principal component analysis shows that there are significant differences in the third and seventh principal components. It is also found that exposure to biological examples can produce more sketches with high perceptual matching degree than the other three conditions, but there are no significant effects of the example exposure on the Pearson correlation coefficients of semantic differential evaluation value vectors between design problems and sketches. These results demonstrate that exposure to biological examples will correlate with Elliptic Fourier descriptors of sketches and will not significantly increase the perceptual matching degree between sketches and the desired function.

Little research has considered fuzzy scheduling and sequencing problem in operating rooms. Multiple-period fuzzy scheduling and sequencing of patients in operating rooms optimization models are proposed in this research taking into consideration patient‘s preference. The objective of the scheduling optimization model is obtaining minimal undertime and overtime and maximum patients' satisfaction about the assigned date. The objective of sequencing the optimization model is both to minimize overtime and to maximize patients' satisfaction about the assigned time. A real-life case study from a hospital that offers comprehensive surgical procedures for all surgical specialties is considered for illustration. Research results showed that the proposed models efficiently scheduled and sequenced patients while considering their preferences and hospitals operating costs. In conclusion, the proposed optimization models may result in improving patient satisfaction, utilizing hospital's resources efficiently, and providing assistance to decision makers and planners in solving effectively fuzzy scheduling and sequencing problems of operating rooms.

Erection planning in shipbuilding is a highly complex process. When a process change happens for some reason, it is often difficult to identify how many factors are affected and estimate how sensitive these factors can be. To optimize the planning and replanning of the shipbuilding plan for the best production performance, a data-driven approach for shipbuilding erection planning is proposed, which is composed of an erection plan model, identification of major factors related to the erection plan, and a data-driven algorithm to apply shipbuilding operation data for creating plans and forecasting, for plan adjustment, future availabilities of shipyard resources including machines, equipment, and man power. Through data clustering, the relevant factors are identified as a result of plan change, and critical equipment health management is carried out through data-driven anomaly detection. A case study is implemented, and the result shows that the proposed data-driven method is able to reschedule the shipbuilding plans smoothly.