Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement-Intuitionistic Fuzzy Sets and Genetic Algorithm Approach

The aim of this research is to propose a hybrid decision-making model for evaluation and selection of quality methods whose application leads to improved reliability of manufacturing in the process industry. Evaluation of failures and determination of their priorities are based on failure mode and effect analysis (FMEA), which is a widely used framework in practice combining with triangular intuitionistic fuzzy numbers (TIFNs). The all-existing uncertainties in the relative importance of the risk factors (RFs), their values, applicability of the quality methods, as well as implementation costs are described by pre-defined linguistic terms which are modeled by the TIFNs. The selection of quality methods is stated as the rubber knapsack problem which is decomposed into subproblems with a certain number of solution elements. The solution of this problem is found by using genetic algorithm (GA). The model is verified through the case study with the real-life data originating from a significant number of organizations from one region. It is shown that the proposed model is highly suitable as a decision-making tool for improving the manufacturing process reliability in small and medium enterprises (SMEs) of process industry

Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement-Intuitionistic Fuzzy Sets and Genetic Algorithm Approach

The aim of this research is to propose a hybrid decision-making model for evaluation and selection of quality methods whose application leads to improved reliability of manufacturing in the process industry. Evaluation of failures and determination of their priorities are based on failure mode and effect analysis (FMEA), which is a widely used framework in practice combining with triangular intuitionistic fuzzy numbers (TIFNs). The all-existing uncertainties in the relative importance of the risk factors (RFs), their values, applicability of the quality methods, as well as implementation costs are described by pre-defined linguistic terms which are modeled by the TIFNs. The selection of quality methods is stated as the rubber knapsack problem which is decomposed into subproblems with a certain number of solution elements. The solution of this problem is found by using genetic algorithm (GA). The model is verified through the case study with the real-life data originating from a significant number of organizations from one region. It is shown that the proposed model is highly suitable as a decision-making tool for improving the manufacturing process reliability in small and medium enterprises (SMEs) of process industry

Fuzzy Sets in Business Management, Finance, and Economics

This book collects fifteen papers published in s Special Issue of Mathematics titled “Fuzzy Sets in Business Management, Finance, and Economics”, which was published in 2021. These paper cover a wide range of different tools from Fuzzy Set Theory and applications in many areas of Business Management and other connected fields. Specifically, this book contains applications of such instruments as, among others, Fuzzy Set Qualitative Comparative Analysis, Neuro-Fuzzy Methods, the Forgotten Effects Algorithm, Expertons Theory, Fuzzy Markov Chains, Fuzzy Arithmetic, Decision Making with OWA Operators and Pythagorean Aggregation Operators, Fuzzy Pattern Recognition, and Intuitionistic Fuzzy Sets. The papers in this book tackle a wide variety of problems in areas such as strategic management, sustainable decisions by firms and public organisms, tourism management, accounting and auditing, macroeconomic modelling, the evaluation of public organizations and universities, and actuarial modelling. We hope that this book will be useful not only for business managers, public decision-makers, and researchers in the specific fields of business management, finance, and economics but also in the broader areas of soft mathematics in social sciences. Practitioners will find methods and ideas that could be fruitful in current management issues. Scholars will find novel developments that may inspire further applications in the social sciences

A methodology for the design of quality assurance functional model and information system

In spite of all advances in computer, technologies, information processing, automation technologies, manufacturing processes, and the push for integration across all functional areas toward a totally integrated and automated manufacturing system, the suggestion is that quality assurance which covers all quality-based functions in the product-life cycle is often overlooked. In spite of the important role of quality information systems in achieving high quality processes little published research in this area is found in the literature. Study of the available relevant literature and the collection of data from manufacturing industries confirm that different manufacturing situations require different quality assurance systems, and this is evident from the proliferation of differing QA systems found in industry. There are however some common features both universal/or within different classes of industries. Accordingly an 'ISO-9000 based generic structural model incorporating these common quality based functions and their associated information requirements has been developed. This research further investigates and verifies those factors which may affect the design of a QAIS as a guide for designing Quality Assurance Information Systems for manufacturing business organisations. Realising that knowledge-based systems can provide a support environment for designing QAIS, this research also considers and, develops a KB Decision System for Designing Quality Information Systems (DSDQAIS). The DSDQAIS recommends the structure of a QAIS, in the form of an IDEFO model, appropriate to specific company profiles input by the user. Since the available software' applications and development tools which support the sub-systems run on a personal computer, the prototype of this system has also been developed and tested on PC. Recommendations for the further development of the system are given

Improving the efficiency of production processes in the manufacturing industry based on methods of multicriteria analysis and metacheuristics

Problem procjene i rangiranja grešaka koje mogu dovesti do Lean gubitaka imaju presudan uticaj na efektivnost i pouzdanost proizvodnog kao i ostalih poslovnih procesa preduzeća. U ovoj doktorskoj disertaciji su razvijena dva nova fazi višekriterijumska modela optimizacije zasnovan na Analizi mogućih grešaka i efekata grešaka (prema eng. Failure Mode and Effect Analysis - FMEA), odnosno FMEA okviru za rangiranje grešaka na nivou svakog Lean gubitka. Na samom početku izvršena je identifikacija i grafički prikaz grešaka korišćenjem Išikava dijagrama. Ocjena identifikovanih grešaka vrši se u odnosu na tri faktora rizika (RF) definisana u FMEA metodi. Nedostaci FMEA metode, koje su sugerisani od strane drugih autora, su prevaziđeni kombinacijom ove metode sa pravilima fazi logike i metodama višekriterijumskog odlučivanja (prema eng. Multi Criteria Decision Making - MCDM). U prvom modelu relativna važnost RF i njihove vrijednosti opisani su unaprijed definisanim lingvističkim iskazima koji su modelirani sa trapezoidnim intuitivnim fazi brojevima (prema eng. Trapezoidal intuitionistic fuzzy numbers - TrIFN). Za određivanje vektora težine RF koristi se Analitički hijerarhijski proces proširen sa TrIFN (prema eng. Fuzzy Analytic Hierarchy Process with TrIFN - IFAHP). Rang identifikovanih grešaka daje se upotrebom predložene metode Višekriterijumskog kompromisnog rangiranja proširene sa TrIFN (prema eng. VIKOR with TrIFN - IF-VIKOR). Na kraju, urađena je analiza osjetljivosti koja pokazuje stabilnost predloženog pristupa. U drugom modelu, procjena i rangiranje grešaka koji dovode do Lean gubitaka daju se korišćenjem fazi MCDM metoda proširenih sa intervalnim intuitivnim fazi brojevima (prema eng. Interval valued intuitionistic fuzzy numbers - IVIFN). Relativna važnost RF i njihove vrijednosti opisani su unaprijed definisanim lingvističkim iskazima koji su modelirani sa IVIFN. Modifikovana fazi logika sa pravilima za IVIFN koristi se za određivanje nivoa rizika proizvodnog procesa. U drugom dijelu disertacije, predložen je hibridni model odlučivanja za ocjenu i izbor metoda/tehnika kvaliteta čija primjena dovodi do unaprjeđenja efektivnosti i pouzdanosti proizvodnih procesa u malim i srednjim preduzećima (MSP) prerađivačke industrije. Ovaj model kombinuje FMEA sa trougaoni intuitivni fazi brojevima (prema eng. Triangular intuitionistic fuzzy numbers – TIFN). Sve postojeće neizvjesnosti, relativna važnost RF, njihove vrijednosti, primjenljivost metoda kvaliteta, kao i troškovi primjene opisani su unaprijed definisanim jezičkim iskazima koji su modelirani TIFN. Izbor metoda kvaliteta naveden je kao KP problem, odnosno problem rastegljivog ranca koji se razlaže na potprobleme sa određenim brojem elemenata rješenja. Rješenje ovog problema pronalazi se korišćenjem genetskog algoritma (prema eng. Genetic algorithm - GA) (Gojković et al., 2021). Model je verifikovan kroz studiju slučaja sa podacima iz stvarnog života koji potiču od značajnog broja organizacija iz jednog regiona, čime je pokazan potencijal i primjenljivost razvijenih modela. Pokazano je da su predloženi modeli izuzetno pogodan kao alati za donošenje odluka za poboljšanje efektivnosti i pouzdanosti proizvodnog procesa u MSP prerađivačke industrije.The problem of evaluation and ranking failures that can lead to Lean waste has a critical effect on the safety and reliability of the manufacturing process, and other business processes of enterprises. In this doctoral dissertation, two new fuzzy multicriteria optimization models based on Failure Mode and Effect Analysis - FMEA have been developed to rank failures at the level of each Lean waste. At the beginning, failures were identified using the Ishikawa diagram. The evaluation of the identified failures is performed in relation to the three risk factors (RF) defined in the FMEA method. The disadvantages of the FMEA method, which have been suggested by other authors, have been overcome by combining this method with the fuzzy logic rols and the Multi Criteria Decision Making (MCDM). In the first model, the relative importance of RF and their values are described by predefined linguistic statements modeled with trapezoidal intuitionistic fuzzy numbers (TrIFN). The Fuzzy Analytic Hierarchy Process with TrIFN (IF-AHP) was used to determine the RF weight vector. The rank of identified failures is given using the proposed VIKOR with TrIFN (IF-VIKOR). Finally, a sensitivity analysis was performed showing the stability of the proposed approach. In the second model, estimation and ranking of failures leading to Lean waste are given using the fuzzy MCDM with interval valued intuitionistic fuzzy numbers (IVIFN). The relative importance of RF and their values are described by predefined linguistic statements modeled with IVIFN. A modified fuzzy logic ruls with IVIFN rules is used to determine the level of risk of the production process. In the second part of the dissertation, a hybrid decision-making model for evaluation and selection of quality methods/techniques is proposed, the application of which leads to the improvement of efficiency and reliability of production processes in small and medium enterprises (SMEs) of the manufacturing industry. This model combines FMEA with the triangular intuitionistic fuzzy numbers (TIFN). All existing uncertainties, the relative importance of RF, their values, the applicability of quality methods/techniques, as well as the costs of application are described by pre-defined linguistic statements modeled by TIFN. The choice of quality methods/techniques is stated as a KP problem. It is a Rubber Knapsack problem that decomposes into subproblems with a certain number of solution elements. The solution to this problem is found using a genetic algorithm (GA) (Gojković et al., 2021). The model was verified through a case study with real life data originating from a significant number of organizations from one region, showing the potential and applicability of the developed models. He showed that the proposed models are extremely suitable as decision-making tools for improving the efficiency and reliability of the production process in the SME manufacturing industry

Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement—Intuitionistic Fuzzy Sets and Genetic Algorithm Approach

The aim of this research is to propose a hybrid decision-making model for evaluation and selection of quality methods whose application leads to improved reliability of manufacturing in the process industry. Evaluation of failures and determination of their priorities are based on failure mode and effect analysis (FMEA), which is a widely used framework in practice combining with triangular intuitionistic fuzzy numbers (TIFNs). The all-existing uncertainties in the relative importance of the risk factors (RFs), their values, applicability of the quality methods, as well as implementation costs are described by pre-defined linguistic terms which are modeled by the TIFNs. The selection of quality methods is stated as the rubber knapsack problem which is decomposed into subproblems with a certain number of solution elements. The solution of this problem is found by using genetic algorithm (GA). The model is verified through the case study with the real-life data originating from a significant number of organizations from one region. It is shown that the proposed model is highly suitable as a decision-making tool for improving the manufacturing process reliability in small and medium enterprises (SMEs) of process industry

Evaluation and selection of the quality methods for manufacturing process reliability improvement— intuitionistic fuzzy sets and genetic algorithm approach

The aim of this research is to propose a hybrid decision-making model for evaluation and selection of quality methods whose application leads to improved reliability of manufacturing in the process industry. Evaluation of failures and determination of their priorities are based on failure mode and effect analysis (FMEA), which is a widely used framework in practice combining with triangular intuitionistic fuzzy numbers (TIFNs). The all-existing uncertainties in the relative importance of the risk factors (RFs), their values, applicability of the quality methods, as well as implementation costs are described by pre-defined linguistic terms which are modeled by the TIFNs. The selection of quality methods is stated as the rubber knapsack problem which is decomposed into subproblems with a certain number of solution elements. The solution of this problem is found by using genetic algorithm (GA). The model is verified through the case study with the real-life data originating from a significant number of organizations from one region. It is shown that the proposed model is highly suitable as a decision-making tool for improving the manufacturing process reliability in small and medium enterprises (SMEs) of process industry