Acquiring symbolic design optimization problem reformulation knowledge: On computable relationships between design syntax and semantics

This thesis presents a computational method for the inductive inference of explicit and implicit semantic design knowledge from the symbolic-mathematical syntax of design formulations using an unsupervised pattern recognition and extraction approach. Existing research shows that AI / machine learning based design computation approaches either require high levels of knowledge engineering or large training databases to acquire problem reformulation knowledge. The method presented in this thesis addresses these methodological limitations. The thesis develops, tests, and evaluates ways in which the method may be employed for design problem reformulation. The method is based on the linear algebra based factorization method Singular Value Decomposition (SVD), dimensionality reduction and similarity measurement through unsupervised clustering. The method calculates linear approximations of the associative patterns of symbol cooccurrences in a design problem representation to infer induced coupling strengths between variables, constraints and system components. Unsupervised clustering of these approximations is used to identify useful reformulations. These two components of the method automate a range of reformulation tasks that have traditionally required different solution algorithms. Example reformulation tasks that it performs include selection of linked design variables, parameters and constraints, design decomposition, modularity and integrative systems analysis, heuristically aiding design “case” identification, topology modeling and layout planning. The relationship between the syntax of design representation and the encoded semantic meaning is an open design theory research question. Based on the results of the method, the thesis presents a set of theoretical postulates on computable relationships between design syntax and semantics. The postulates relate the performance of the method with empirical findings and theoretical insights provided by cognitive neuroscience and cognitive science on how the human mind engages in symbol processing and the resulting capacities inherent in symbolic representational systems to encode “meaning”. The performance of the method suggests that semantic “meaning” is a higher order, global phenomenon that lies distributed in the design representation in explicit and implicit ways. A one-to-one local mapping between a design symbol and its meaning, a largely prevalent approach adopted by many AI and learning algorithms, may not be sufficient to capture and represent this meaning. By changing the theoretical standpoint on how a “symbol” is defined in design representations, it was possible to use a simple set of mathematical ideas to perform unsupervised inductive inference of knowledge in a knowledge-lean and training-lean manner, for a knowledge domain that traditionally relies on “giving” the system complex design domain and task knowledge for performing the same set of tasks

Collaborative problem solving within supply chains: general framework, process and methodology

The Problem Solving Process is a central element of the firms' continuous improvement strategies. In this framework, a number of approaches have succeeded to demonstrate their effectiveness to tackle industrial problems. The list includes, but is not limited to PDCA, DMAICS, 7Steps and 8D/9S. However, the emergence and increasing emphasis in the supply chains have impacted the effectiveness of those methods to solve problems that go beyond the boundaries of a single firm and, in consequence, their ability to provide solutions when the contexts on which firms operate are distributed. This can be explained because not only the problems, but also the products, partners, skills, resources and pieces of evidence required to solve those problems are distributed, fragmented and decentralized across the network. This PhD thesis deals with the solving of industrial problems in supply chains based in collaboration. It develops a general framework for studying this paradigm, as well as both a generic process and a collaborative methodology able to deal with the process in practice. The proposal considers all the technical aspects (e.g. products modeling and network structure) and the collaborative aspects (e.g. the trust decisions and/or the power gaps between partners) that simultaneously impact the supply chain operation and the jointly solving of problems. Finally, this research work positions the experiential knowledge as a central lever of the problem solving process to contribute to the continuous improvement strategies at a more global level

Résolution collaborative de problèmes au sein des chaînes logistiques : cadre conceptuel, processus et méthodologie

La Résolution de Problèmes est l'un des piliers des stratégies d'amélioration continue des entreprises. Dans ce cadre, un certain nombre des méthodes ont réussi à démontrer son efficacité pour adresser des problèmes particulièrement complexes. Parmi ces méthodes, on peut distinguer le PDCA, le DMAICS, le 7Steps et le 8D/9S. Pourtant, l'apparition des réseaux distribuées de partenaires, ainsi que le positionnement du concept d'entreprise étendue, ont obligé les entreprises à aller au-delà de ses frontières pour travailler en synergie avec tous les partenaires en amont et en aval de sa chaîne. Dans ce contexte, l'efficacité de ces méthodes de résolution des problèmes a été fortement impactée. Ceci car non seulement les problèmes, mais aussi les produits, les partenaires, les ressources et l'information nécessaires pour sa résolution sont extrêmement fragmentés et décentralisés. Cette thèse s'intéresse donc à la résolution collaborative de problèmes au sein des chaînes distribuées de partenaires et son objectif est de proposer un processus et une méthodologie adaptés à ces contextes. Les propositions faites prennent en compte les aspects techniques (e.g. la modélisation des flux et la configuration de la chaîne) ainsi que les aspects collaboratifs (e.g. le niveau de confiance et/ou le rapport de pouvoir entre les partenaires) que conditionnent l'opération et l'efficacité du réseau. Finalement, cette thèse s'intéresse à l'articulation d'un système de retour d'expérience dans la résolution de problèmes distribués afin d'améliorer son efficacité. ABSTRACT : The Problem Solving Process is a central element of the firms' continuous improvement strategies. In this framework, a number of approaches have succeeded to demonstrate their effectiveness to tackle industrial problems. The list includes, but is not limited to PDCA, DMAICS, 7Steps and 8D/9S. However, the emergence and increasing emphasis in the supply chains have impacted the effectiveness of those methods to solve problems that go beyond the boundaries of a single firm and, in consequence, their ability to provide solutions when the contexts on which firms operate are distributed. This can be explained because not only the problems, but also the products, partners, skills, resources and pieces of evidence required to solve those problems are distributed, fragmented and decentralized across the network. This PhD thesis deals with the solving of industrial problems in supply chains based in collaboration. It develops a general framework for studying this paradigm, as well as both a generic process and a collaborative methodology able to deal with the process in practice. The proposal considers all the technical aspects (e.g. products modeling and network structure) and the collaborative aspects (e.g. the trust decisions and/or the power gaps between partners) that simultaneously impact the supply chain operation and the jointly solving of problems. Finally, this research work positions the experiential knowledge as a central lever of the problem solving process to contribute to the continuous improvement strategies at a more global level

Eco‐Holonic 4.0 Circular Business Model to Conceptualize Sustainable Value Chain Towards Digital Transition

The purpose of this paper is to conceptualize a circular business model based on an Eco-Holonic Architecture, through the integration of circular economy and holonic principles. A conceptual model is developed to manage the complexity of integrating circular economy principles, digital transformation, and tools and frameworks for sustainability into business models. The proposed architecture is multilevel and multiscale in order to achieve the instantiation of the sustainable value chain in any territory. The architecture promotes the incorporation of circular economy and holonic principles into new circular business models. This integrated perspective of business model can support the design and upgrade of the manufacturing companies in their respective industrial sectors. The conceptual model proposed is based on activity theory that considers the interactions between technical and social systems and allows the mitigation of the metabolic rift that exists between natural and social metabolism. This study contributes to the existing literature on circular economy, circular business models and activity theory by considering holonic paradigm concerns, which have not been explored yet. This research also offers a unique holonic architecture of circular business model by considering different levels, relationships, dynamism and contextualization (territory) aspects

Architecture value mapping: using fuzzy cognitive maps as a reasoning mechanism for multi-criteria conceptual design evaluation

The conceptual design phase is the most critical phase in the systems engineering life cycle. The design concept chosen during this phase determines the structure and behavior of the system, and consequently, its ability to fulfill its intended function. A good conceptual design is the first step in the development of a successful artifact. However, decision-making during conceptual design is inherently challenging and often unreliable. The conceptual design phase is marked by an ambiguous and imprecise set of requirements, and ill-defined system boundaries. A lack of usable data for design evaluation makes the problem worse. In order to assess a system accurately, it is necessary to capture the relationships between its physical attributes and the stakeholders\u27 value objectives. This research presents a novel conceptual architecture evaluation approach that utilizes attribute-value networks, designated as \u27Architecture Value Maps\u27, to replicate the decision makers\u27 cogitative processes. Ambiguity in the system\u27s overall objectives is reduced hierarchically to reveal a network of criteria that range from the abstract value measures to the design-specific performance measures. A symbolic representation scheme, the 2-Tuple Linguistic Representation is used to integrate different types of information into a common computational format, and Fuzzy Cognitive Maps are utilized as the reasoning engine to quantitatively evaluate potential design concepts. A Linguistic Ordered Weighted Average aggregation operator is used to rank the final alternatives based on the decision makers\u27 risk preferences. The proposed methodology provides systems architects with the capability to exploit the interrelationships between a system\u27s design attributes and the value that stakeholders associate with these attributes, in order to design robust, flexible, and affordable systems --Abstract, page iii

Chemical enterprise model and decision-making framework for sustainable chemical product design

The chemical product substitution process is undertaken by chemical industries for complying with regulations, like REACH in Europe. Initially devoted to chemists, chemicals substitution is nowadays a complex process involving corporate, business and engineering stakeholders across the chemical enterprise for orienting the search toward a sustainable solution. We formalize a decision making process framework dedicated to the sustainable chemical product design activity in an industrial context. The framework aims at improving the sharing of information and knowledge and at enabling a collaborative work across the chemical enterprise stakeholders at the strategic, tactical and operational levels. It is supported by information and communication technologies (ICT) and integrates a computer aided molecular design tool. During the initial intelligence phase, a systemic analysis of the needs and usages enables to define the product requirements. In the design phase, they are compiled with the help of a facilitator to generate the input file of a computer aided product design tool. This multiobjective tool is designed to find mixtures with molecular fragments issued from renewable raw materials, and is able to handle environment-health and safety related properties along with process physicochemical properties. The final choice phase discusses the solution relevancy and provides feedback, before launching the product manufacturing. The framework is illustrated by the search of a bio-sourced water–solvent mixture formulation for lithographic blanket wash used in printing industry. The sustainability of the solution is assessed by using the sustainability shades metho

Assessing organizations collaboration readiness: a behavioral approach

Dissertation presented at the Faculty of Sciences and Technology of the New University of Lisbon to obtain the degree of Doctor in Electrical Engineering, specialty of Robotics and Integrated ManufacturingThis thesis presents an approach for assessing organizations‘ readiness to collaborate. This assessment is based in three fundamental aspects, namely (1) on collaboration preparedness, which aims at assessing whether a partner has adequate collaboration-related character traits; (2) on competencies fitness which is predominantly aimed at assessing how well an organization is able to use its competencies in a collaboration context; and (3) on willingness to collaborate, which is a concept applied to assess whether an organization is, or is not, really interested to engage in concrete collaboration opportunities. The proposed approach contributes to the formation of improved collaborative networks, increasing their likelihood of success. The principal characteristic of the model lies in the fact that it follows a behavioral perspective. As such, collaboration preparedness is based on the idea of the organizations‘ character, traits and behavioral patterns. Competencies fitness is in turn based on the so-called soft competencies, exploring the performance influences/effects of the soft competencies on the hard ones in a collaboration context. Finally, willingness to collaborate is based on the organization‘s planned behavior, attitudes and intentions that are perceived in/from a partner. The work involved in the conceptualization of readiness to collaborate includes the utilization of text data mining to discover the behavioral aspects, namely the collaboration-related organization‘s traits which are relevant for assessing collaboration readiness. Bayesian belief networks are proposed as a way to deal with the underlying uncertainty in assessing collaboration readiness. A soft versus hard competencies dichotomy is used to develop the concept of competencies fitness, proposing the adjusted competencies profile and the fitness level, as the way to assess whether a partner‘s competencies fit in a collaboration opportunity. The Theory of the Planned Behavior is adapted from social sciences and used in organizations in collaboration contexts. Various modeling experiments were performed to assist in the development of this readiness concept. The validation through some cases of partnerships is proposed to evaluate the underlying collaboration readiness assessment model

Una extensión a los esquemas preconceptuales para el refinamiento en la representación de eventos y la notación matemática

An event is an occurrence within a particular software system or domain. Software and scientific models are representations of computing and natural systems. Such models have software and scientific components—domain knowledge elements. Scientists and business analysts use such models and their components for recognizing a domain, e.g., pre-conceptual schemas (PCS) used in software engineering. Scientific software domains (SSD) comprise fields in engineering and science, which are focused on developing and simulating scientific software systems for event or phenomenon research. Event-based software development has increased in scientific domains. Approaches to event-driven modeling are used from software/scientific modeling. Some advances have emerged in such approaches for integrating software and scientific components in science and engineering projects. However, scientists and business analysts lack a computational model for SSD in order to integrate both components in the same model. PCS notation includes software components based on structural and dynamic features, which allow for representing events and mathematical operations. Nonetheless, PCS lack scientific components for representing events in SSD. In this Ph.D. Thesis, we propose an extension to pre-conceptual schemas for refining event representation and mathematical notation. Such an extension comprises scientific components as graphical, linguistic, and mathematical structures for the sake of such refinement. We validate our proposal by using both an experimental process and a software application. Extension to PCS is included as a new work product for representing events in SSD. Therefore, the extended PCS are intended to be computing models for scientists and business analysts in scientific software development and simulation processes.Un evento es una ocurrencia en un sistema de software o dominio particular. Los modelos científicos y de software son representaciones de sistemas informáticos o naturales. Esos modelos tienen componentes científicos y de software (elementos del conocimiento del dominio). Científicos y analistas de negocio usan estos modelos y sus componentes para reconocer un dominio. Un ejemplo de esos modelos son los esquemas preconceptuales (EP), que se usan en ingeniería de software. Los dominios de software científico comprenden áreas en ingeniería y ciencia que se enfocan en el desarrollo y simulación de sistemas de software científico para la investigación de eventos o fenómenos. El desarrollo de software dirigido por eventos se viene incrementando en dominios científicos. Enfoques de modelado basado en eventos se usan desde el modelado científico y el modelado de software. En estos enfoques surgen algunos avances para integrar componentes científicos y componentes de software en proyectos de ingeniería y ciencia. Sin embargo, científicos y analistas de negocio carecen de un modelo computacional para dominios de software científico que integre ambos componentes en el mismo modelo. La notación de los EP incluye componentes de software que se basan en características estructurales y dinámicas, los cuales permiten representar eventos y operaciones matemáticas. No obstante, los EP carecen de componentes científicos para representar eventos en dominios de software científico. En esta Tesis Doctoral se propone una extensión a los esquemas preconceptuales para el refinamiento en la representación de eventos y la notación matemática. Esta extensión integra componentes científicos (estructuras gráficas, lingüísticas y matemáticas) para lograr este refinamiento. También, se valida la propuesta mediante un proceso experimental y una aplicación de software. La extensión a los EP se incluye como un nuevo producto de trabajo para representar eventos en dominios de software científico. Por lo tanto, se pretende que los EP extendidos sean modelos de computación, para científicos y analistas de negocio en procesos de desarrollo y simulación de software científico.MincienciasDoctorad