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The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation.

Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models.

Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs.

The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and C considering multiple criteria and relationships among those criteria. The framework is flexible enough to add additional criteria and to modify fuzzy-rule-base at any point of time by the decision makers. This model can be easily integrated to any customized Enterprise Resource Planning applications.

The value of this paper is in applying Fuzzy-rule-based approach for Multi-criteria Inventory Classification of spare parts. This rule-based approach considering multiple criteria is not very common in classification of spare parts inventories. Total cost comparison is made to compare the performance of proposed model with the traditional classifications and the result shows that proposed fuzzy-rule-based classification approach performs better than the traditional ABC and gives almost the same cost as aggregate scoring model. Hence, this method is valid and adds a new value to spare parts classification for better management decisions.

The impact of ‘context of use’ to the whole life costs (WLC) of building elements has not yet been studied in previous researches. Lack of hard and detailed historical data constrained the use of traditional methods for this purpose. A fuzzy rule‐based system (FRBS) for any type of carpet cleaning cost estimate is one of a series of fuzzy models developed to estimate the WLC of building elements with the consideration of context of use to the elements. The fuzzy reasoning method, as the representation of human reasoning, is applied to WLC for the first time for carpet cleaning cost. The data used are the linguistic judgments from some experienced experts based on interview surveys. The implementation of the model is demonstrated in a case study. The result is assessed by the experts as an acceptable estimate.The paper concludes that Fuzzy Rule Based System is an appropriate method to model running costs of building elements. The model allows user to predict the cost variation of cleaning cost of carpet flooring according to its designed context of use.

Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

The traditional models of real estate market have several sources of imprecision, such as transitions between submarkets, generating difficulties in property valuation. The purpose of this paper is to examine an alternative to improve mass appraisal models, using fuzzy rules.

Fuzzy rule‐based systems (FRBS) are able to generate flexible systems and may be useful in considering vagueness or imprecision presents in real estate market. An application to the housing market of Porto Alegre (Brazil), with more than 30,000 apartments, transacted in 1998‐2001, illustrates the fuzzy system, comparing with traditional hedonic regression model.

The results have indicated the potential of fuzzy rules to use in mass appraisal.

This paper presents a procedure to develop mass appraisal models using FRBS.

The purpose of this study is to present a system called NEBULOSUS, which is a fuzzy rule-based expert system for assessing the maturity level of an agency regarding technical interoperability.

The study introduces the use of artificial intelligence and fuzzy logic to deal with the imprecision and uncertainty present in the assessment process. To validate the system proposed and demonstrate its operation, the study takes into account the Brazilian technical interoperability maturity model, based on the Brazilian Government Interoperability Framework (GIF).

With the system proposed and its methodology, it could be possible to increase the assessment process to management level and to provide decision-making support without worrying about technical details that make it complex and time-consuming. Moreover, NEBULOSUS is a standalone system that offers an easy-to-use, open and flexible structuring database that can be adapted by governments throughout the world. It will serve as a tool and contribute to governments’ expectations for continuous improvement of their technologies.

This study contributes toward filling a gap in general interoperability architectures, which is a means to provide an objective method to evaluate GIF adherence by governments. The proposed system allows governments to configure their technical models and GIF to assess information and communication technology resources.

The bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.

This study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.

Three real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.

This study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

The developing academic field of machine ethics seeks to make artificial agents safer as they become more pervasive throughout society. In contrast to computer ethics, machine ethics is concerned with the behavior of machines toward human users and other machines. This study aims to use an action-based ethical theory founded on the combinational aspects of deontological and teleological theories of ethics in the construction of an artificial moral agent (AMA).

The decision results derived by the AMA are acquired via fuzzy logic interpretation of the relative values of the steady-state simulations of the corresponding rule-based fuzzy cognitive map (RBFCM).

Through the use of RBFCMs, the following paper illustrates the possibility of incorporating ethical components into machines, where latent semantic analysis (LSA) and RBFCMs can be used to model dynamic and complex situations, and to provide abilities in acquiring causal knowledge.

This approach is especially appropriate for data-poor and uncertain situations common in ethics. Nonetheless, to ensure that a machine with an ethical component can function autonomously in the world, research in artificial intelligence will need to further investigate the representation and determination of ethical principles, the incorporation of these ethical principles into a system’s decision procedure, ethical decision-making with incomplete and uncertain knowledge, the explanation for decisions made using ethical principles and the evaluation of systems that act based upon ethical principles.

To date, the conducted research has contributed to a theoretical foundation for machine ethics through exploration of the rationale and the feasibility of adding an ethical dimension to machines. Further, the constructed AMA illustrates the possibility of utilizing an action-based ethical theory that provides guidance in ethical decision-making according to the precepts of its respective duties. The use of LSA illustrates their powerful capabilities in understanding text and their potential application as information retrieval systems in AMAs. The use of cognitive maps provides an approach and a decision procedure for resolving conflicts between different duties.

This paper suggests that cognitive maps could be used in AMAs as tools for meta-analysis, where comparisons regarding multiple ethical principles and duties can be examined and considered. With cognitive mapping, complex and abstract variables that cannot easily be measured but are important to decision-making can be modeled. This approach is especially appropriate for data-poor and uncertain situations common in ethics.

Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry.

Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran.

Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization.

This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system.

This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.

In this study, we introduce and discuss a concept of fuzzy plug‐ins and investigate their role in system modeling. Fuzzy plug‐ins are rule‐based constructs augmenting a given global model (arising in the form of some regression relationship, neural network, etc.) in the sense that they compensate for the mapping errors produced by the global model. The proposed design method develops around information granules of error defined in the output space and the induced fuzzy relations expressed in the space of input variables. The construction of the linguistic granules is carried out with the aid of context‐based fuzzy clustering – a generalized version of the well‐known FCM algorithm that is well‐suited to the design of fuzzy sets and relations being used as a blueprint of the plug‐ins. An overall modeling architecture combining the global model with its plug‐ins is discussed in detail and a complete design procedure is provided. Finally, some illustrative numerical examples are shown as well.