Supplier selection is one of the most important and challenging issue in supply chain management and is usually tackled with multi-criteria decision making (MCDM) methods. Considering sustainability, the complexity of the supplier selection problem has increased in recent years, yet, the various sustainability criteria. However, these criteria are usually considered independent of each other. Therefore, in this paper, to take into account the interdependency among criteria, Fuzzy Cognitive Maps (FCM) have been used. Moreover, supply chains are susceptible to various endogenous and exogenous risks which need to be considered in the decision-making processes in order to meet market needs and sustainability requirements. In order to integrate risks into the decision problem, fuzzy axiomatic design approach with risk factors (RFAD) has been utilized. The proposed method is applied to a case study adapted from literature.

Supply chain decisions should aim for sustainability, in order to meet the global market needs, as well as the Industry 4.0 requirements, therefore they should consider beyond economic and environmental, societal dimensions as well. The complexity in decision making increases, moreover, supply network relationships become important, including inter-relationships and those developed with the suppliers. Agent technology is compatible with Industry 4.0, whereas multi-agent systems (MAS) can provide decision support for supply chain management and model the relatationships and interactions between entities in the supply chain environment. Therefore, in this paper, a MAS-based framework is proposed to address sustainability focused decision making in supplier selection, order allocation and routing. Fuzzy Multi Criteria Decision Making (MCDM) approaches and multi-objective programming are used by the agents in the MAS in order to adress sustainability requirements. Futrhermore, developed agent services for the supply chain business processes are integrated with web services, in order to facilitate business process execution as web services.

Fuzzy logic is based on sets of rules that can be easily understood by the students, since they bear a close resemblance to natural language. The introduction of fuzzy logic, within the framework of Environmental Education, is considered to be necessary in order to provide an insight to the complex environmental interactions. Fuzzy inference is introduced in this paper as an extension of hypothetico-predictive argumentation and it allows the investigation of alternative hypotheses. This is achieved through the development of an interactive computer graphics environment that encompasses a set of fuzzy logic analysis tools and a fuzzy inference model of a lake. The fuzzy model guarantees the scientific integrity of the simulation results, and the graphical interface presents to the students only the comprehensible characteristics of the environmental stressors in the ecosystem of the lake. The proposed graphical interface was developed in successive design stages, with the active participation of the students. The results of the students’ experimentation with the graphical interface indicate that their comprehension of the significant environmental problems of the lake is considerably improved and some misconceptions are resolved. Thus, it is considered valuable as an aid to environmental education.

This paper presents a new parallel algorithm for collision-free path planning of a diamond-shaped robot among arbitrarily shaped obstacles, which are represented as a discrete image, and its implementation in VLSI. The proposed algorithm is based on a retraction of free space onto the Voronoi diagram, which is constructed through the time evolution of cellular automata, after an initial phase during which the boundaries of obstacles are identified and coded with respect to their orientation. The proposed algorithm is both space and time efficient, since it does not require the modeling of objects or distance and intersection calculations. Additionally, the proposed twodimensional multistate cellular automaton architecture achieves high frequency of operation and it is particularly suited for VLSI implementation due to its inherent parallelism, structural locality, regularity, and modularity.

A new, parallel, nearest-neighbor (NN) pattern classifier, based on a 2D Cellular Automaton (CA) architecture, is presented in this paper. The proposed classifier is both time and space efficient, when compared with already existing NN classifiers, since it does not require complex distance calculations and ordering of distances, and storage requirements are kept minimal since each cell stores information only about its nearest neighborhood. The proposed classifier produces piece-wise linear discriminant curves between clusters of points of complex shape (nonlinearly separable) using the computational geometry concept known as the Voronoi diagram, which is established through CA evolution. These curves are established during an "off-line" operation and, thus, the subsequent classification of unknown patterns is achieved very fast. The VLSI design and implementation of a nearest neighborhood processor of the proposed 2D CA architecture is also presented in this paper.>