A process knowledge representation approach for decision support in design of complex engineered systems

The automation and intelligence highlighted in Industry 4.0 put forward higher requirements for reasonable trade-offs between humans and machines for decision-making governance. However, in the context of Industry 4.0, the vision of decision support for design engineering is still unclear. Additionally, the corresponding methods and system architectures are lacking to support the realization of value-chain-centric complex engineered systems design lifecycles. Hence, we identify decision support demands for complex engineered systems designs in the Industry 4.0 era, representing the integrated design problems at various stages of the product value chain. As a response, in this paper, the architecture of a Knowledge-Based Design Guidance System (KBDGS) for cloud-based decision support (CBDS) is presented that highlights the integrated management of complexity, uncertainty, and knowledge in designing decision workflows, as well as systematic design guidance to find satisfying solutions with the iterative process “formulation-refinement-exploration-improvement” (FREI). The KBDGS facilitates diverse multi-stakeholder collaborative decisions in end-to-end cloud services. Finally, two design case studies are conducted to illustrate the proposed work and the efficacy of the developed KBDGS. The contribution of this paper is to provide design guidance to facilitate knowledge discovery, capturing, and reuse in the context of decision-centric digital design, thus improving the efficiency and effectiveness of decision-making, as well as the evolution of decision support in the field of design engineering for the age of Industry 4.0 innovation paradigm.

The evolution of knowledge acquisition and representation in manufacturing technologies is vital for translating complex manufacturing data into actionable insights and advancing a comprehensive knowledge framework. This framework is pivotal in driving innovation and efficiency in intelligent manufacturing. This review aggregates recent research on knowledge acquisition and representation within the manufacturing process, addressing existing challenges and mapping potential future developments. It includes an analysis of 123 papers that focus on harnessing advanced intelligent analytics to extract operationally relevant knowledge from the extensive datasets typical in manufacturing environments. The narrative then examines the methodologies for constructing models of knowledge in manufacturing processes and explores their applications in manufacturing principles, design, management, and decision-making. This paper highlights the limitations of current technologies and projects emerging research avenues in the acquisition and representation of process knowledge within intelligent manufacturing systems, with the objective of informing future technological breakthroughs.

The National Repository of Digital Forensics Information (NRDFI) is a knowledge repository for law enforcement digital forensics investigators (LEDFI). Over six years, the NRDFI has undertaken significant design revisions in order to more closely align the architecture of the system with theory addressing motivation to share knowledge and communication within ego-centric groups and communities of practice. These revisions have been met with minimal change in usage patterns by LEDFI community members, calling into question the applicability of relevant theory when the domain for knowledge sharing activities expands beyond the confines of an individual organization to a community of practice. When considered alongside an empirical study that demonstrated a lack of generalizability for existing theory on motivators to share knowledge, a call for deeper investigation is clear. In the current study, researchers apply grounded theory methodology through interviews with members of the LEDFI community to discover aspects of community context that appear to position communities of practice along a continuum between process focus and knowledge focus. Findings suggest that these contextual categories impact a community’s willingness to participate in various classes of knowledge support initiatives, and community positioning along these categories dictates prescription for design of knowledge based decision support systems beyond that which can be found in the current literature.

A strategy for determining the optimal domain for knowledge based decision support systems

Parents' and nonparents' perceptions of cries

In this paper we introduce the heuristic Decision Support Problem (HDSP) as the means for integrating heuristic search and knowledge-based systems to provide decision support in design. Furthermore, we present a unique application of the HDSP to the design of products made by process manufacturing (e.g., gasoline, cosmetics) rather than products made by discrete manufacturing (e.g., machinery). The HDSP provides a format for representing the design problem that is independent of the specific application. The representation of the design problem is not restricted to mathematically exact information. A heuristic search algorithm, called the synthesis engine, is used to provide decision support in the form of solutions. The synthesis engine is able to evaluate feasibility and goodness of competing designs even though design variables and their associated relationships are not mathematically exact. The design of lubricant blends is used as a specific example of continuous-manufacturing products.

Flooding has become one of the most rapidly growing types of natural disaster that has spread around the globe. It is is one of the major natural hazards in many countries and mostly affected in the low-lying or flood prone areas. In order to minimize loss of life and economic losses, a detailed and comprehensive decision making tool is necessary for both flood control planning and emergency service operations. In this paper, we demonstrate our research design for mobile based decision support of Flood Early Warning System (FEWS). We outlined four research objectives. Firstly, critical criteria for flood risk assessment will be identified and the second step will involve develop measurement model for relative flood risk using Geographic Information System (GIS), Multi Attribute Decision Making (MADM) and data mining technique. In the third objectives, the holistic architectural design is develop by incorporating the communication technology and other related ICT requirements for the mobile decision support. The fourth objective is to validate the mathematical model and architectural design. Case study approach is chosen in order to understand the flood event and validate the decision support model. Following well-defined procedures, flood maps were drawn based on the data collected from expert responses to a questionnaire, the field survey, satellite images, and documents from flood management agencies. It is anticipates that by integrating of mathematical model, GIS and mobile application in flood risk assessment could provide useful detailed information for flood risk management, evacuation, communication. The decision support design from this study is perhaps to improve the warning system and contribute to reduction of casualties.

In the last several years, considerable ICME related research has been directed towards the study of multiscale materials design. However, relatively little effort has gone into the study of the integrated design of engineered materials and products. In this paper, we describe a foundational problem that is being used to validate and demonstrate the ICME construct and enable design approaches and a computational platform. The foundational problem involves the integrated design of steel and gears, traversing across the chain of steel making, mill production, component fabrication and performance testing tracking the evolution of the material during these processes and linking this to the mechanical design of the component. The design approaches are focused on addressing the following key challenges in the integrated design of products and materials: uncertainty management, complexity management, and holistic verification and validation. The management of uncertainty and complexity is critical to understanding the relationship between computational costs and the value of the resulting information in the design decision making process.

Knowledge-based decision support system: a structure for performance improvement in a manufacturing process

Recently, very large-scale decision support systems (DSSs) have been developed, which tackle very complex problems, associated with very extensive and polymorphic information, which probably is geographically highly dispersed. The management, updating, modification and upgrading of the data and program core of such an information system is, as a rule, a very difficult task, which encompasses many hazards and risks. The purpose of the present work was (a) to list the more significant of these hazards and risks and (b) to introduce a new general methodology for designing decision support (DS) systems that are robust and circumvent these risks. The core of this new approach was the introduction of a meta-database, called teleological, on the base of which management, updating, modification, reduction, growth and upgrading of the system may be safely and efficiently achieved. The very same teleological meta-database can be used for the construction of a sound decision support system, incorporating elements of a previous one at a future stage.

The first three steps of a new design method for decision support systems in production scheduling tasks are applied to a large bulk terminal. The operational characteristics of the company largely correspond with those of typical semiprocess industries. The design model used consists of five steps: production analysis, task analysis, task redesign, decision support design and decision support implementation. The results of the application of the design model up to the task redesign step are described.

As consumers and governments prioritize cost-effectiveness and ecological sustainability, the limitations of traditional manufacturing paradigms become apparent in the context of constrained resources. The adverse effects of these paradigms on the environment and society hinder the achievement of a sustainable product life cycle. Intelligent manufacturing processes offer a solution by efficiently gathering meaningful data, such as usage and product recycling information, from previous product generations to enhance product design and subsequent sustainable manufacturing processes (SMPs). Modular product family architecture (PFA) design holds promise in promoting product sustainability and addressing diverse consumer needs. PFA design and SMPs are inherently interconnected within intelligent manufacturing frameworks. This paper aims to integrate the decision-making processes underlying PFA with SMPs. We model integrated PFA and SMP decisions as a Stackelberg game, which involves hierarchical joint optimization (HJO) for assessing product modularity and sustainable manufacturing fulfillment. We develop a bilevel 0–1 integer nonlinear programming model to represent the HJO decision-making process and propose a nested genetic algorithm (NGA) to solve the HJO problem. A case study with a laptop is conducted to validate the feasibility and potential of the proposed HJO model for joint optimization problems in PFA design and SMPs.

Healthcare and supply chain management have recently been the two most active areas for RFID applications. The healthcare environment is a natural fit for generating and utilizing instance-level data for decision support. We consider a scenario from the French healthcare environment involving tracking and tracing of surgical equipment within and among hospitals and develop a knowledge-based system for decision support that helps improve the overall performance of the surgical instrument management process while reducing errors. We illustrate the process through the developed healthcare knowledge-based system and evaluate its performance.

The current condition, learning media in Manufacturing Process Design practicum still lacks teaching aids so that it cannot adjust its output according to industry needs. This needs to be improved to support industry-based learning. Before implementing teaching aids, the learning process was theoretical and did not involve students in real simulations, so their understanding of the manufacturing workflow was limited. This study aims to design and implement teaching aids in Manufacturing Process Design and Assembling practicums to support learning with the Learning Factory approach. The design of the teaching aids begins with observing the existing conditions, then determining the improvement plan through focus group discussions. After that, the design is carried out according to the needs of the practicum. The teaching aids made use a Programmable Logic Controller consisting of 4 stations. The use of designed teaching aids provides students with direct experience in the design and assembly process, and facilitates more interactive learning. The results of the study showed a significant increase in students' understanding of the manufacturing process, with more active involvement and an increase in the quality of assignments and practicum reports. These teaching aids have proven effective in creating a learning environment that is closer to real industrial conditions, so that they can improve students' competence in understanding manufacturing and assembly processes comprehensively.

For the decision support in design of spacecrafts’ systems, the specific optimization tools reflecting properties of arising problems are necessary. Such tools including local and adaptive search algorithms, user support for decomposition and unification of multi-scale variables, solving multicriterial optimization problems and user support for choice of appropriate algorithm, that are under development are discussed.