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Different entities in a supply chain network operate in a highly interdependent environment when it comes to improving performance of the network in terms of objectives such as delivery performance, quality assurance and cost minimization, etc. In this research, an attempt has been made to evaluate the supplier performance by adopting evolutionary fuzzy system owing to the linguistic nature of the attributes associated with the suppliers and manufacturing units. The proposed methodology offers consistently good performance when applied to a variety of standard problems related to evaluation of supplier's performance available in the literatures.

In order to ensure the uninterrupted supply of items, the purchasing manager needs to evaluate suppliers' performance periodically. The evaluation process typically consists of identifying the attributes and factors relevant to the decision, and measuring the performance of a supplier by considering the relevant factors. Linguistic assessment of suppliers may be carried out based on several criteria. In this paper, an attempt has been made to evaluate the suppliers' performance by adopting an evolutionary fuzzy system. One of the key considerations in designing the proposed system is the generation of fuzzy rules. A genetic algorithm‐based methodology is developed to evolve the optimal set of fuzzy rule base, and a fuzzy inference system of the MATLAB fuzzy logic toolbox is used to assess the suppliers' performance. The proposed methodology, illustrated with the data collected in a process plant, provides acceptable results in determining the suppliers' performance score.

The purpose of this paper is to propose a new population-based metaheuristic optimization algorithm, assimilation-accommodation mixed continuous ant colony optimization (ACACO), to improve the accuracy of Takagi-Sugeno-Kang-type fuzzy systems design.

The original N solution vectors in ACACO are sorted and categorized into three groups according to their ranks. The Research Learning scheme provides the local search capability for the best-ranked group. The Basic Learning scheme uses the ant colony optimization (ACO) technique for the worst-ranked group to approach the best solution. The operations of assimilation, accommodation, and mutation in Mutual Learning scheme are used for the middle-ranked group to exchange and accommodate the partial information between groups and, globally, search information. Only the N top-best-performance solutions are reserved after each iteration of learning.

The proposed algorithm outperforms some reported ACO algorithms for the fuzzy system design with the same number of rules. The performance comparison with various previously published neural fuzzy systems also shows its superiority even with a smaller number of fuzzy rules to those neural fuzzy systems.

Future work will consider the application of the proposed ACACO to the recurrent fuzzy network.

The originality of this work is to mix the work of the well-known psychologist Jean Piaget and the continuous ACO to propose a new population-based optimization algorithm whose superiority is demonstrated.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems.

Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages.

Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches.

The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

This study aims to short-therm forecasting of power generation output for this purpose, an adaptive neuro-fuzzy inference system (ANFIS) is designed to forecast the output power of power plant based on climate factors considering wind speed and wind direction simultaneously.

Several methods and algorithms have been proposed for systems forecasting in various fields. One of the strongest methods for modeling complex systems is neuro-fuzzy that refers to combinations of artificial neural network and fuzzy logic. When the system becomes more complex, the conventional algorithms may fail for network training. In this paper, an integrated approach, including ANFIS and metaheuristic algorithms, is used for increasing forecast accuracy.

Power generation in power plants is dependent on various factors, especially climate factors. Operating power plant in Iran is very much influenced because of climate variation, including from tropical to subpolar, and severely varying temperature, humidity and air pressure for each region and each season. On the other hands, when wind speed and wind direction are used simultaneously, the training process does not converge, and the forecasting process is unreliable. The real case study is mentioned to show the ability of the proposed approach to remove the limitations.

First, ANFIS is applied for forecasting based on climate factors, including wind speed and wind direction, that have rarely been used simultaneously in previous studies. Second, the well-known and more widely used metaheuristic algorithms are applied to improve the learning process for forecasting output power and compare the results.

This paper aims to propose a novel computational framework called EvoPOL (EVOlving POLicies) to support governmental policy analysis in restricting recruitment of smokers. EvoPOL is a fuzzy inference‐based decision support system that uses an evolutionary algorithm (EA) to optimize the if‐then rules and its parameters. The performance of the proposed method is compared with a fuzzy inference method adapted using neural network learning technique (neuro‐fuzzy).

EA is a population‐based adaptive method, which may be used to solve optimization problems, based on the genetic processes of biological organisms. The Takagi‐Sugeno fuzzy decision support system was developed based on three sub‐systems: fuzzification, fuzzy knowledge base (if‐then rules) and defuzzification. The fine‐tuning of the fuzzy rule base and membership function parameters is achieved by using an EA.

The proposed EvoPOL technique is simple and efficient when compared to the neuro‐fuzzy approach. However, EvoPOL attracts extra computational cost due to the population‐based hierarchical search process. When compared to neuro‐fuzzy model the error values on the test sets have improved considerably. Hence, when policy makers require more accuracy EvoPOL seems to be a good solution.

When policy makers require more accuracy EvoPOL seems to be a good solution. For complicated decision support systems involving more input variables, EvoPOL would be an excellent candidate for framing if‐then rules with precise decision scores that could help the government representatives as to what extent to concentrate on available social regulation measures in restricting the recruitment of smokers.

Lean implementation is a strategic decision. The capacity of organisation to be “Lean” can be identified before lean implementation by assessing leanness of an organisation. This study aims to attempt developing a holistic leanness assessment tool for assessing organisational leanness.

A neuro-fuzzy leanness assessment model for assessing the leanness of a manufacturing system is presented. The model is validated academically and industrially by conducting a case study.

Neuro-fuzzy hybridisation helped assess the leanness accurately. Fuzzy logic helped to perform the leanness assessment more realistically by accounting ambiguity and vagueness in organisational functioning and decision-making processes. Neural network increased the learning capacity of assessment model and increased the accuracy of leanness index.

The industrial case study in the paper shows the results in telecom equipment manufacturing industry. This may not represent entire manufacturing sector. The generic nature of the model developed in this research ensures its wide applicability.

The neuro-fuzzy hybrid model for assessing leanness helps to identify the potential of an organisation to become “Lean”. The organisational leanness index developed by the study helps to monitor the effectiveness and impact of lean implementation programmes.

The leanness assessment models available in literature lack depth and coverage of leanness parameters. The model developed in this research assesses leanness of an organisation by accounting for leanness aspects of inventory management, industrial scheduling, organisational flexibility, ergonomics, product, process, management, workforce, supplier relationship and customer relationship with the help of neuro-fuzzy hybrid modelling.

The purpose of this paper is to propose an improved optimization methodology of information granulation‐based fuzzy radial basis function neural networks (IG‐FRBFNN). In the IG‐FRBFNN, the membership functions of the premise part of fuzzy rules are determined by means of fuzzy c‐means (FCM) clustering. Also, high‐order polynomial is considered as the consequent part of fuzzy rules which represent input‐output relation characteristic of sub‐space and weighted least squares learning is used to estimate the coefficients of polynomial. Since the performance of IG‐RBFNN is affected by some parameters such as a specific subset of input variables, the fuzzification coefficient of FCM, the number of rules and the order of polynomial of consequent part of fuzzy rules, we need the structural as well as parametric optimization of the network. The proposed model is demonstrated with the use of two kinds of examples such as nonlinear function approximation problem and Mackey‐Glass time‐series data.

The type of polynomial of each fuzzy rule is determined by selection algorithm by considering the local error as performance index. In addition, the combined local error is introduced as a performance index considered by two kinds of parameters such as the polynomial type of each rule and the number of polynomial coefficients of each rule. Besides this, other structural and parametric factors of the IG‐FRBFNN are optimized to minimize the global error of model by means of the hierarchical fair competition‐based parallel genetic algorithm.

The performance of the proposed model is illustrated with the aid of two examples. The proposed optimization method leads to an accurate and highly interpretable fuzzy model.

The proposed hybrid optimization methodology is interesting for designing an accurate and highly interpretable fuzzy model. Hybrid optimization algorithm comes in the form of the combination of the combined local error and the global error.

This article aims to provide a brief overview of the field now known as “evolutionary developmental robotics (evo-devo-robo)”, which is based on the concept and principles of evolutionary and development principles such as evolutionary developmental psychology, evolutionary developmental biology (evo-devo) and evolutionary cognitive neuroscience.

Evo-devo-robo is a new field bringing together developmental robotics and evolutionary robotics to form a new research area. Basic concepts and the origins of the field are described, and then some basic principles of evo-devo-robo that have been developed so far are discussed.

Finally, some misunderstand concepts and the most promising future research developments in this area are discussed.

Basic concepts and the origins of the field are described, and then some basic principles of evo-devo-robo that have been developed so far are discussed. Finally, some misunderstood concepts and the most promising future research developments in this area are discussed.