A FBWM-PROMETHEE approach for industrial robot selection

Industrial engineering; Multidisciplinary design optimization; Manufacturing engineering; Technology management; Operations management; Industry management; Business management; Industrialization; Industrial robots; Fuzzy best-worst method; PROMETHEE; MCDM; Robot selection; Criteria.publishersversionpublishe

Multicriteria Group Decision Making by Using Trapezoidal Valued Hesitant Fuzzy Sets

The concept of trapezoidal valued hesitant fuzzy set is introduced. Notion for distance between any two trapezoidal valued hesitant fuzzy elements is given. Using this proposed distance measure, we extend the technique for order preference by similarity to ideal solution for trapezoidal valued hesitant fuzzy sets. An example is constructed to show usefulness of this extension for multicriteria group decision making, where the opinions about the criteria values are expressed as trapezoidal valued hesitant fuzzy set

Um Modelo de Programação Matemática para Identificação e Redução da Causa de Atrasos no Projeto Usando o Método Multimoora Cinza

A distinctive problem related to projects is the delay in the process of executing them. In general, delays are due to different factors which are originated from the individuals who are concerned with the projects. This problem is more important in megaprojects because of their complexity, uncertainty and long execution time. If the causes of delays are identified at the beginning of their execution, they can be prevented or reduced through appropriate activities. Changes in any factors of the project such as human forces, constructing material and other resources may cause delays in timing the projects. This is an applied research in which theoretical data were collected using the library method. Interviews and questionnaires distributed among the experts of this field were the tools of collecting data. Through a short review on delays in projects, 10 common effective reasons were found which were related to the employer, contractor and the consultant. This study was conducted and weighed on the projects of local electricity power companies in Fars province. Using the three aspects of Gray MULTIMOORA, a model was designed to prioritize the factors that cause the delay. This study suggests some solutions to control and reduce the delays.Un problema distintivo relacionado con los proyectos es el retraso en el proceso de ejecución. En general, los retrasos se deben a diferentes factores que se originan en las personas que están preocupadas con los proyectos. Este problema es más importante en los megaproyectos debido a su complejidad, incertidumbre y largo tiempo de ejecución. Si las causas de los retrasos se identifican al comienzo de su ejecución, se pueden prevenir o reducir mediante actividades apropiadas. Los cambios en cualquier factor del proyecto, como las fuerzas humanas, la construcción de materiales y otros recursos pueden causar retrasos en el cronograma de los proyectos. Esta es una investigación aplicada en la que los datos teóricos se recopilaron utilizando el método de la biblioteca. Las entrevistas y los cuestionarios distribuidos entre los expertos de este campo fueron las herramientas de recolección de datos. A través de una breve revisión de los retrasos en los proyectos, se encontraron 10 razones efectivas comunes relacionadas con el empleador, el contratista y el consultor. Este estudio se realizó y pesó sobre los proyectos de las compañías eléctricas locales en la provincia de Fars. Utilizando los tres aspectos de Gray MULTIMOORA, se diseñó un modelo para priorizar los factores que causan el retraso. Este estudio sugiere algunas soluciones para controlar y reducir los retrasos.Um problema distinto relacionado aos projetos é o atraso no processo de execução. Em geral, os atrasos são devidos a diferentes fatores que são originados dos indivíduos que estão preocupados com os projetos. Este problema é mais importante em megaprojetos devido à sua complexidade, incerteza e longo tempo de execução. Se as causas de atrasos forem identificadas no início de sua execução, elas podem ser evitadas ou reduzidas por meio de atividades apropriadas. Mudanças em quaisquer fatores do projeto, tais como forças humanas, construção de material e outros recursos podem causar atrasos no cronograma dos projetos. Esta é uma pesquisa aplicada em que dados teóricos foram coletados usando o método de biblioteca. Entrevistas e questionários distribuídos entre os especialistas deste campo foram as ferramentas de coleta de dados. Através de uma breve revisão sobre os atrasos nos projetos, foram encontrados 10 motivos efetivos comuns relacionados ao empregador, ao contratado e ao consultor. Este estudo foi conduzido e pesado sobre os projetos de empresas locais de energia elétrica na província de Fars. Usando os três aspectos do MULTIMOORA Cinza, um modelo foi projetado para priorizar os fatores que causam o atraso. Este estudo sugere algumas soluções para controlar e reduzir os atrasos

An early-stage decision-support framework for the implementation of intelligent automation

The constant pressure on manufacturing companies to improve productivity, reduce the lead time and progress in quality requires new technological developments and adoption.The rapid development of smart technology and robotics and autonomous systems (RAS) technology has a profound impact on manufacturing automation and might determine winners and losers of the next generation’s manufacturing competition. Simultaneously, recent smart technology developments in the areas enable an automation response to new production paradigms such as mass customisation and product-lifecycle considerations in the context of Industry 4.0. New paradigms, like mass customisation, increased both the complexity of the tasks and the risk due to smart technology integration. From a manufacturing automation perspective, intelligent automation has been identified as a possible response to arising demands. The presented research aims to support the industrial uptake of intelligent automation into manufacturing businesses by quantifying risks at the early design stage and business case development. An early-stage decision-support framework for the implementation of intelligent automation in manufacturing businesses is presented in this thesis.The framework is informed by an extensive literature review, updated and verified with surveys and workshops to add to the knowledge base due to the rapid development of the associated technologies. A paradigm shift from cost to a risk-modelling perspective is proposed to provide a more flexible and generic approach applicable throughout the current technology landscape. The proposed probabilistic decision-support framework consists of three parts:• A clustering algorithm to identify the manufacturing functions in manual processes from task analysis to mitigate early-stage design uncertainties• A Bayesian Belief Network (BBN) informed by an expert elicitation via the DELPHI method, where the identified functions become the unit of analysis.• A Markov-Chain Monte-Carlo method modelling the effects of uncertainties on the critical success factors to address issues of factor interdependencies after expert elicitation.Based on the overall decision framework a toolbox was developed in Microsoft Excel. Five different case studies are used to test and validate the framework. Evaluation of the results derived from the toolbox from the industrial feedback suggests a positive validation for commercial use. The main contributions to knowledge in the presented thesis arise from the following four points:• Early-stage decision-support framework for business case evaluation of intelligent automation.• Translating manual tasks to automation function via a novel clustering approach• Application of a Markov-Chain Monte-Carlo Method to simulate correlation between decision criteria• Causal relationship among Critical Success Factors has been established from business and technical perspectives.The implications on practise might be promising. The feedback arising from the created tool was promising from the industry, and a practical realisation of the decision-support tool seems to be desired from an industrial point of view.With respect to further work, the decision-support tool might have established a ground to analyse a human task automatically for automation purposes. The established clustering mechanisms and the related attributes could be connected to sensorial data and analyse a manufacturing task autonomously without the subjective input of task analysis experts. To enable such an autonomous process, however, the psychophysiological understanding must be increased in the future.</div

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises