A smart knowledge deployment method for the conceptual design of low-carbon products

As the consciousness of the global environment and sustainability has increased, low-carbon products have played a vital role in the transformation to a circular economy. Advanced smart design technology has enabled product designers to fulfill customer requirements by offering tailor-made functions and low-carbon solutions. However, although the existing approaches used in the conceptual design process can help in functional reasoning, knowledge modelling, and scheme evaluation, the smart reuse of knowledge, such as in design model improvement and concept scheme iteration for lower carbon emission, the corresponding process evaluation concerning carbon footprint has not been given sufficient attention. To resolve this, in this work, a smart knowledge deployment method is proposed for reasoning, configuring, and optimizing the conceptual scheme (CS) based on carbon emission evaluation and interaction. First, to match discretized knowledge, sub-function requirements after function decomposition are mapped with granular clustered knowledge into a matrix based on a requirement function knowledge deployment (RFKD) model. Second, the derived candidate concept schemes (CCSs) are selected in three steps: conflict-based primaries, configuration, and carbon footprint ranking. Finally, the initial conceptual scheme (ICS) with the lowest carbon emission is used as input for the interactive genetic algorithm (IGA) to better capture a comprehensive set of user feedback on potential candidate schemes through interactions. Accordingly, improvements are completed as intended. The prototype design and an experimental study of a brand-new friction-wear testing machine are conducted. The results suggest that the proposed approach could effectively reduce the carbon emissions of products obtained through CS and improve the convergence of the schemes produced via genetic operation

An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Technology Directions for the 21st Century

The Office of Space Communications (OSC) is tasked by NASA to conduct a planning process to meet NASA's science mission and other communications and data processing requirements. A set of technology trend studies was undertaken by Science Applications International Corporation (SAIC) for OSC to identify quantitative data that can be used to predict performance of electronic equipment in the future to assist in the planning process. Only commercially available, off-the-shelf technology was included. For each technology area considered, the current state of the technology is discussed, future applications that could benefit from use of the technology are identified, and likely future developments of the technology are described. The impact of each technology area on NASA operations is presented together with a discussion of the feasibility and risk associated with its development. An approximate timeline is given for the next 15 to 25 years to indicate the anticipated evolution of capabilities within each of the technology areas considered. This volume contains four chapters: one each on technology trends for database systems, computer software, neural and fuzzy systems, and artificial intelligence. The principal study results are summarized at the beginning of each chapter

Identifiability analysis of a tractor and single axle towed implement model

The growing trend of model-based design in off-road vehicle engineering requires models that are sufficiently accurate for their intended application if they are to be used with confidence. Uncertain model parameters are often identified from measured data collected in experiments by using an optimization procedure, but it is important to understand the limitations of such a procedure and to have methods available for assessing the uniqueness and confidence of the results. The concept of model identifiability is used to determine whether system measurements contain enough information to estimate the model parameters. A numerical approach based on the profile likelihood of parameters was utilized to evaluate the local structural and practical identifiability of a tractor and single axle towed implement model with six uncertain tire force model parameters from tractor yaw rate and implement yaw rate data. The analysis first considered datasets generated from simulation of the model with known parameter values to examine the effect of measurement error, sampling rate, and input signal type on the identifiability. The results showed that the accuracy and confidence of identification tended to decrease as the quality, quantity, and richness of the data decreased, to the point that some of the parameters were considered practically unidentifiable from the information available. The profile likelihood plots also indicated potential opportunities for model reduction. Second, the analysis considered the identifiability of the model from two datasets collected during field experiments, and the results again indicated parameters that were practically unidentifiable from the information available. Overall, the study showed how different experimental factors can affect the amount of information available in a dataset for identification and that error in the measured data can propagate to error in model parameter estimates

Incorporating declared capacity uncertainty in optimizing airport slot allocation

Slot allocation is the mechanism used to allocate capacity at congested airports. A number of models have been introduced in the literature aiming to produce airport schedules that optimize the allocation of slot requests to the available airport capacity. A critical parameter affecting the outcome of the slot allocation process is the airport’s declared capacity. Existing airport slot allocation models treat declared capacity as an exogenously defined deterministic parameter. In this presentation we propose a new robust optimization formulation based on the concept of stability radius. The proposed formulation considers endogenously the airport’s declared capacity and expresses it as a function of its throughput. We present results from the application of the proposed approach to a congested airport and we discuss the trade-off between the declared capacity of the airport and the efficiency of the slot allocation process

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Sustainability-oriented housing innovation: Using the Solar Decathlon as a knowledge source

The Solar Decathlon competition started in 2002. Since then, Solar Decathlon has acted as a showcase and source of innovation in the field of sustainability for housing and for the construction industry at large. This thesis has utilised data from Solar Decathlon competitions to understand the nature of innovations involved with progressively building and refining the technology required for sustainable housing. As such, the focus and drive of this thesis is to present an image of the Solar Decathlon competition as openly creating and synthesising new knowledge about sustainability-oriented innovation. It can be stated that understanding the precise factors that make innovation happen can be convoluted in nature. The Solar Decathlon portrays these characteristics, is internationally recognised as the premier competition for prototyping sustainability-oriented innovation, and demonstrates the human features involved with progress in the field of sustainable housing. The thesis is original in that it is the first that addresses innovation and its management through human-centred design, and describes its processes, that can be henceforth taken up by the building industry. Utilising the experience of the Innovations Coordinator of Team UOW (University of Wollongong), this thesis describes and analyses the nature of innovation involved with the Desert Rose house (UOW Solar Decathlon entry), including knowledge of how innovation happened in real time during its construction. This thesis asks the question: What is the nature of innovation involved with sustainable housing? The answer to this question is not resolved simply through experiencing the construction of the Desert Rose, or through an objective analysis of the available Solar Decathlon data sets. Rather, this thesis proposes that the answer can be obtained through comprehensive multi-disciplinary research, including: (i) analysis of available innovation related Solar Decathlon data sets from leading houses, (ii) the development of an innovations management framework for sustainability-oriented technology, (iii) a case study of the Desert Rose Solar Decathlon entry in 2018 in the broader context of design, construction, innovation and sustainability, and (iv) tracing innovation through development of a specific sustainability-oriented technology from Desert Rose