CORRELATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES WITH SOFT COMPUTING IN VARIOUS AREAS

Artificial Intelligence (AI) is a part of computer science concerned with designing intelligent computer systems that exhibit the characteristics used to associate with intelligence in human behavior. Basically, it define as a field that study and design of intelligent agents. Traditional AI approach deals with cognitive and biological models that imitate and describe human information processing skills. This processing skills help to perceive and interact with their environment. But in modern era developers can build system that assemble superior information processing needs of government and industry by choosing from large areas of mature technologies. Soft Computing (SC) is an added area of AI. It focused on the design of intelligent systems that process uncertain, imprecise and incomplete information. It applied in real world problems frequently to offer more robust, tractable and less costly solutions than those obtained by more conventional mathematical techniques. This paper reviews correlation of artificial intelligence techniques with soft computing in various areas

A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme

Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts’ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts’ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts’ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version

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