Applying the superior identification group linguistic variable to construct kano model oriented quality function deployment

This study attempts to manipulate 2-tuple linguistic variables rather than pure linguistic variables in quality function deployment (QFD) in order to significantly improve the identification of the QFD model. The Kano model, a two-dimensional quality technique, is also integrated to recognize the degree of urgency in terms of enhancing and prioritizing quality-related requirements of customers via a fuzzy linguistic quantifier with a soft majority concept to fit the optimal aggregation weights. This study also retains the goodness on the usage of multi-granularity linguistic approach to facilitate the implementation of a group decision. Simultaneously, two-dimensional analysis is performed to explain the results synthetically between relationship matrix and correlation matrix from a management perspective, capable of providing comprehensive information for the decision process. Owing to the integration of several quality and management methods, results of this study demonstrate the capability of TRIZ

A Linguistic Recommender System For University Digital Libraries To Help Users In Their Research Resources Accesses

The Web is one of the most important information media and it is influencing in the development of other media, as for example, newspapers, journals, books, libraries, etc. Moreover, in recent days people want to communicate and collaborate. So, libraries must develop services for connecting people together in information environments. Then, the library staff needs automatic techniques to facilitate that a great number of users can access to a great number of resources. Recommender systems are tools whose objective is to evaluate and filter the great amount of information available on the Web. We present a model of a fuzzy linguistic recommender system to help University Digital Library users in their research resources accesses. This system recommends researchers specialized and complementary resources in order to discover collaboration possibilities to form multi-disciplinaryy groups. In this way, this system increases social collaboration possibilities in a university framework and contributes to improve the services provided by a University Digital Library

Shipbuilding 4.0 Index Approaching Supply Chain

The shipbuilding industry shows a special interest in adapting to the changes proposed by the industry 4.0. This article bets on the development of an index that indicates the current situation considering that supply chain is a key factor in any type of change, and at the same time it serves as a control tool in the implementation of improvements. The proposed indices provide a first definition of the paradigm or paradigms that best fit the supply chain in order to improve its sustainability and a second definition, regarding the key enabling technologies for Industry 4.0. The values obtained put shipbuilding on the road to industry 4.0 while suggesting categorized planning of technologies

Fuzzy multicriteria analysis and its applications for decision making under uncertainty

Multicriteria decision making refers to selecting or ranking alternatives from available alternatives with respect to multiple, usually conflicting criteria involving either a single decision maker or multiple decision makers. It often takes place in an environment where the information available is uncertain, subjective and imprecise. To adequately solve this decision problem, the application of fuzzy sets theory for adequately modelling the uncertainty and imprecision in multicriteria decision making has proven to be effective. Much research has been done on the development of various fuzzy multicriteria analysis approaches for effectively solving the multicriteria decision making problem, and numerous applications have been reported in the literature. In general, existing approaches can be categorized into (a) multicriteria decision making with a single decision maker and (b) multicriteria group decision making. Existing approaches, however, are not totally satisfactory due to various shortcomings that they suffer from including (a) the inability to adequately model the uncertainty and imprecision of human decision making, (b) the failure to effectively handle the requirements of decision maker(s), (c) the tedious mathematical computation required, and (d) cognitively very demanding on the decision maker(s). This research has developed four novel approaches for effectively solving the multicriteria decision making problem under uncertainty. To effectively reduce the cognitive demand on the decision maker, a pairwise comparison based approach is developed in Chapter 4 for solving the multicriteria problem under uncertainty. To adequately meet the interest of various stakeholders in the multicriteria decision making process, a decision support system (DSS) based approach is introduced in Chapter 5. In Chapter 6, a consensus oriented approach is presented in multicriteria group decision making on which a DSS is proposed for facilitating consensus building in solving the multicriteria group decision making problem. In Chapter 7, a risk-oriented approach is developed for adequately modelling the inherent risk in multicriteria group decision making with the use of the concept of ideal solutions so that the complex and unreliable process of comparing fuzzy utilities usually required in fuzzy multicriteria analysis is avoided. Empirical studies of four real fuzzy multicriteria decision making problems are presented for illustrating the applicability of the approaches developed in solving the multicriteria decision making problem. A hospital location selection problem is discussed in Chapter 8. An international distribution centre location problem is illustrated in Chapter 9. A supplier selection problem is presented in Chapter 10. A hotel location problem is discussed in Chapter 11. These studies have shown the distinct advantages of the approaches developed respectively in this research from different perspectives in solving the multicriteria decision making problem

MCDA for sustainability assessment – insights to Helmholtz Association activities

The "MCDA for sustainability assessment – insights to Helmholtz Association activities" Working Paper aims to compile and reflect previous and on-going work within the Helmholtz Association related to MCDA, in particular, to present use cases and key methodological aspects. It has a focus on but is not limited to energy technologies and systems and is mainly based on the presentations held at the online workshop “Multi criteria decision analysis for sustainability assessment of energy technologies and systems”. The workshop was organized within the activities of the Helmholtz program ESD Topic 1 "Energy System Transformation" and took place on November 22nd, 2021

Interorganizational Relationships Climate And Interorganizational Information Systems Success; A Supply Chain Perspective

During the last two decades, an increasing amount of attention has been paid by practitioners as well as academics to Interorganizational Information Systems (IOSs) design, deployment and use within supply chains. However, our understanding of the main factors that affect IOSs use and success is hardly complete. Through brief review of coordination mechanisms theory and its related theory such as transaction cost theory (TCT) and Resource Based View (RBV), the paper generates theoretical propositions and attempt to conceptualize a theoretical model which map the role of Interorganizational relationships (IORs) climate attributes in linking IOSs technology and supply success. The theoretical model encompasses two major causal relations: (1) a direct relation linking IOS use with Supply chain performance (IOS success) and (2) a moderating relation linking IOS success with IORs attributes. Else more, the paper attributes to IORs success climate a set of constructs drawn from the literature review, namely; interorganizational cooperation/ interorganizational coordination, interorganizational trust, interorganizational commitment, and interorganizational dependence

Lean, agile, resilient and green supply chain management interoperability assessment methodology

Dissertação para obtenção de grau de Mestre em Engenharia e Gestão Industrial (MEGI)Supply Chain Management has become a tactic asset for the current global competition situation. Innovative strategies such as Lean, Agile, Resilient and Green emerged as a response, requiring high levels of cooperation and of great complexity. However, the strategic alignment of operations with partners in supply chains is affected by lack of interoperability. The present work provides a framework to enhance SC competitiveness and performance by assessing interoperable SCM Practices applied in automotive industry. Through a pragmatic interoperability approach, this methodology describes in detail the form of application using analytical hierarchical process (AHP) and Fuzzy sets as support decision making models, ensuring a systematic approach to the analysis of interoperability with appropriate criteria for assessment of situations that require high levels of collaboration between partners. Through a case study in a Portuguese automaker, it was possible to test the methodology and analyse which areas lack interoperability in the implementation of SCM practices

Information fusion for automated question answering

Until recently, research efforts in automated Question Answering (QA) have mainly focused on getting a good understanding of questions to retrieve correct answers. This includes deep parsing, lookups in ontologies, question typing and machine learning of answer patterns appropriate to question forms. In contrast, I have focused on the analysis of the relationships between answer candidates as provided in open domain QA on multiple documents. I argue that such candidates have intrinsic properties, partly regardless of the question, and those properties can be exploited to provide better quality and more user-oriented answers in QA.Information fusion refers to the technique of merging pieces of information from different sources. In QA over free text, it is motivated by the frequency with which different answer candidates are found in different locations, leading to a multiplicity of answers. The reason for such multiplicity is, in part, the massive amount of data used for answering, and also its unstructured and heterogeneous content: Besides am¬ biguities in user questions leading to heterogeneity in extractions, systems have to deal with redundancy, granularity and possible contradictory information. Hence the need for answer candidate comparison. While frequency has proved to be a significant char¬ acteristic of a correct answer, I evaluate the value of other relationships characterizing answer variability and redundancy.Partially inspired by recent developments in multi-document summarization, I re¬ define the concept of "answer" within an engineering approach to QA based on the Model-View-Controller (MVC) pattern of user interface design. An "answer model" is a directed graph in which nodes correspond to entities projected from extractions and edges convey relationships between such nodes. The graph represents the fusion of information contained in the set of extractions. Different views of the answer model can be produced, capturing the fact that the same answer can be expressed and pre¬ sented in various ways: picture, video, sound, written or spoken language, or a formal data structure. Within this framework, an answer is a structured object contained in the model and retrieved by a strategy to build a particular view depending on the end user (or taskj's requirements.I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence, inclusion, aggregation and alternative. Quantitatively, answer candidate modeling im¬ proves answer extraction accuracy. It also proves to be more robust to incorrect answer candidates than traditional techniques. Qualitatively, models provide meta-information encoded by relationships that allow shallow reasoning to help organize and generate the final output

HIERARCHICAL-GRANULARITY HOLONIC MODELLING

This thesis aims to introduce an agent-based system engineering approach, named Hierarchical-Granularity Holonic Modelling, to support intelligent information processing at multiple granularity levels. The focus is especially on complex hierarchical systems. Nowadays, due to ever growing complexity of information systems and processes, there is an increasing need of a simple self-modular computational model able to manage data and perform information granulation at different resolutions (i.e., both spatial and temporal). The current literature lacks to provide such a methodology. To cite a relevant example, the object-oriented paradigm is suitable for describing a system at a given representation level; notwithstanding, further design effort is needed if a more synthetical of more analytical view of the same system is required. In the literature, the agent paradigm represents a viable solution in complex systems modelling; in particular, Multi-Agent Systems have been applied with success in a countless variety of distributed intelligence settings. Current agent-oriented implementations however suffer from an apparent dichotomy between agents as intelligent entities and agents\u2019 structures as superimposed hierarchies of roles within a given organization. The agents\u2019 architectures are often rigid and require intense re-engineering when the underpinning ontology is updated to cast new design criteria. The latest stage in the evolution of modelling frameworks is represented by Holonic Systems, based on the notion of \u2018holon\u2019 and \u2018holarchy\u2019 (i.e., hierarchy of holons). A holon, just like an agent, is an intelligent entity able to interact with the environment and to take decisions to solve a specific problem. Contrarily to agent, holon has the noteworthy property of playing the role of a whole and a part at the same time. This reflects at the organizational level: holarchy functions first as autonomous wholes in supra-ordination to their parts, secondly as dependent parts in sub-ordination to controls on higher levels, and thirdly in coordination with their local environment. These ideas were originally devised by Arthur Koestler in 1967. Since then, Holonic Systems have gained more and more credit in various fields such as Biology, Ecology, Theory of Emergence and Intelligent Manufacturing. Notwithstanding, with respect to these disciplines, fewer works on Holonic Systems can be found in the general framework of Artificial and Computational Intelligence. Moreover, the distance between theoretic models and actual implementation is still wide open. In this thesis, starting from the Koestler\u2019s original idea, we devise a novel agent-inspired model that merges intelligence with the holonic structure at multiple hierarchical-granularity levels. This is made possible thanks to a rule-based knowledge recursive representation, which allows the holonic agent to carry out both operating and learning tasks in a hierarchy of granularity levels. The proposed model can be directly used in terms of hardware/software applications. This endows systems and software engineers with a modular and scalable approach when dealing with complex hierarchical systems. In order to support our claims, exemplar experiments of our proposal are shown and prospective implications are commented