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Job scheduling in flexible production systems is a complex task even for simple cases. This paper deals with this problem using fuzzy set theory and genetic algorithms. Fuzzy techniques allow us to define global performance measures expressing different and often conflicting objectives of the production. Moreover, according to a fuzzy multi-criteria algorithm, we propose a methodology to combine various heuristics, with different weights, in a single dispatching criterion. A genetic optimization process selects the weights guaranteeing good system performances. A case study and some extensive simulations show the efficiency of the methodology.

Summarization: During the intensive cultivation of Pistacia vera L., considerable amounts of energy and water are consumed and as a result noticeable volumes of greenhouse gases (GHGs) are emitted. Thus, in order to minimize the environmental footprint of pistachio production in small islands such as Aegina, Greece, where availability of natural resources is extremely low and groundwater resources are scarce and of poor quality, a viable economic, eco-friendly and social development strategy is required. In this context, a particularly challenging approach in order to overcome the limitations of other methods that are often vague and contradictory, involves the consideration of the most important economic, environmental and social indicators. Therefore, this study aims to design and explore a two-stage methodology in a holistic environment that integrates life cycle analysis (LCA), environmental risk assessment and on-farm surveys using multi-criteria decision methods such as the Analytical Hierarchy Process (AHP). The proposed sustainability assessment methodology can be easily adapted to other similar crops and areas in the Mediterranean region and thus enable efficient decision making for various stakeholders such as farmers, agricultural associations, policy makers, local and regional authorities. Παρουσιάστηκε στο: Acta Horticulturae

Performance requirements for high-performing haptic devices are usually multi-criteria. Sometimes the requirements are interacting, and several of them are conflicting. Optimization is one of the main approaches to scrutinize the design space and to search for a design that satisfies all requirements. Many researchers have used and published optimization approaches to search for an optimal haptic device design. However, predicting the performance of a high-performing haptic device usually involves computationally intensive simulations and analyses with complex and heterogeneous models. In order to study what are the common design and performance requirements of haptic devices and what optimization approaches have been used to improve optimization effectiveness and efficiency, a literature review on the present state-of-the-art in these areas has been performed. The most commonly used performance requirements presented in the literature are the number of degrees-of-freedom, dynamic inertia, kinematic isotropy, stiffness, peak and continuous force, position/force resolution, and bandwidth. Furthermore, parallel and hybrid kinematic structures are more commonly used than serial structures. Multi-objective optimization (MOO) is a commonly used approach to simultaneously optimize all performance criteria. The most common optimization targets, as presented in published literature, are to maximize workspace, kinematic isotropy, as well as the peak force/torque provided by the device, and to minimize the dynamic inertia. Commonly used indices to constrain the design space are a minimum workspace, avoidance of singularities and motion limits of active and passive joints. The number of design variables varies from 2 to 9, and the most commonly used design variables are a set of mechanical parameters, such as the lengths and diameters of the mechanical components. To increase the efficiency of complex and multi-criteria optimization tasks, the Pareto-front approach combined with multidisciplinary design optimization (MDO) and metamodel techniques are recommended. QC 20171108

Background Determining the most suitable anesthesia method for circumcision surgery plays a fundamental role in pediatric surgery. This study is aimed to present pediatric surgeons’ perspective on the relative importance of the criteria for selecting anesthesia method for circumcision surgery by utilizing the multi-criteria decision making methods. Methods Fuzzy set theory offers a useful tool for transforming linguistic terms into numerical assessments. Since the evaluation of anesthesia methods requires linguistic terms, we utilize the fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both mathematical decision-making methods are originated from individual judgements for qualitative factors utilizing the pair-wise comparison matrix. Our model uses four main criteria, eight sub-criteria as well as three alternatives. To assess the relative priorities, an online questionnaire was completed by three experts, pediatric surgeons, who had experience with circumcision surgery. Results Discussion of the results with the experts indicates that time-related factors are the most important criteria, followed by psychology, convenience and duration. Moreover, general anesthesia with penile block for circumcision surgery is the preferred choice of anesthesia compared to general anesthesia without penile block, which has a greater priority compared to local anesthesia under the discussed main-criteria and sub-criteria. Conclusions The results presented in this study highlight the need to integrate surgeons’ criteria into the decision making process for selecting anesthesia methods. This is the first study in which multi-criteria decision making tools, specifically fuzzy AHP and fuzzy TOPSIS, are used to evaluate anesthesia methods for a pediatric surgical procedure. Electronic supplementary material The online version of this article (doi:10.1186/s12911-017-0409-5) contains supplementary material, which is available to authorized users.

Summarization: This study aims to present and test a methodology for clarifying and prioritizing, the most suitable locations for siting solar power installations. By employing Geographical Information Systems and the Analytical Hierarchy Process (AHP), the recommended sustainable siting areas for solar power generation farms siting are identified, allowing the application of a spatial multi-criteria decision-making approach. The methodology identifies the available areas for solar farms siting, based on exclusion criteria defined from the current legislation. Furthermore, the available siting areas are evaluated and ranked through a multi-criteria analysis, considering also the relative importance of the selected criteria, through the AHP implementation performed by involved energy-related groups. The developed methodology employs different types of criteria, such as techno-economic and socio-environmental, it takes into account the distinct opinions of different stakeholders and it enables the evaluation of different scenarios. Finally, the methodology was applied for the case study of the Regional Unit of Rethymno, where the results, in terms of the potential maximum power that can be produced from the highest priority areas are 530 MWp for photovoltaics and 30 MW for Concentrated Solar Power systems. Παρουσιάστηκε στο: Renewable Energy

International audience; Decision-Aid Methods (DAMs) such as the Cost- Benefit Analysis (CBA) and the Analytical Hierarchy Process (AHP) help decision-makers to rank alternatives or to choose the best one among several potential ones. The new Belief Function based Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) methods have been recently developed for Multi-Criteria Decision-Making problems. In this paper, we compare CBA, AHP and BF-TOPSIS methods through an actual application case to natural risks in mountains. The CBA is the most used approach but is limited. Classical Multi-Criteria Decision-Aid methods (MCDAs) such as the AHP help to go further. The BF-TOPSIS methods notably show a robustness to rank reversal problems in the problem under concern, with a tractable complexity. Moreover, some steps of these new methods can be included in other MCDAs developed under the belief function theory framework.

The model of construction organization alternatives, taking into account total energy consumption for a construction process has been developed in the paper. An energy estimating procedure to identify the imbedded energy content in-process of construction and of in-site end building has been described. The procedure has been applied to a construction process to identify major areas of energy use and opportunities for better energy using. The model includes applying game theory. A case study – selection of construction strategy of blockhouse flats, which are presented.

When evaluating data mining algorithms that are applied to solve real-world problems there are often several, conflicting criteria that need to be considered. We investigate the concept of generic multi-criteria (MC) classifier and algorithm evaluation and perform a comparison of existing methods. This comparison makes explicit some of the important characteristics of MC analysis and focuses on finding out which method is most suitable for further development. Generic MC methods can be described as frameworks for combining evaluation metrics and are generic in the sense that the metrics used are not dictated by the method; the choice of metric is instead dependent on the problem at hand. We discuss some scenarios that benefit from the application of generic MC methods and synthesize what we believe are attractive properties from the reviewed methods into a new method called the candidate evaluation function (CEF). Finally, we present a case study in which we apply CEF to trade-off several criteria when solving a real-world problem. Traditionellt utvärderas inlärningsalgoritmer och klassificerare avseende ett enskilt kriterium. Detta har visats sig för begränsat för många inlärningsproblem. Denna studie undersöker existerande alternativ för generisk multikriteria-utvärdering och presenterar en ny metod tillsammans med en fallstudie.

International audience; Farm modelling has been widely used over the past ten years for the assessment of agricultural systems in face of policy changes, technological innovation, economic and climate changes. Beyond the various modelling methods (optimisation, rule-based, agent-based...), these models are framed by their objectives -defining input data and users- which can be: policy assessment, technological innovation assessment, farming systems resilience studies, optimisation of water management at regional level, agricultural landscape design, a « boundary object » for strategic thinking in a stakeholder arena. Indeed these farm modelling tools are still poorly used for the design of innovative cropping systems which remains mostly based on field level experiments and modelling.Nevertheless driving forces and assessment criteria for the design of sustainable cropping systems are increasingly expressed at larger scales than field or even farm. For exemple most environmental services are provisionned at landscape (e.g. biodiversity) or whatershed levels (e.g. water quality), at regional level (e.g. climate mitigation) and in more global socio-systems (e.g. food security, rural development). On the other hand, a large part of the functions supporting these services are operating at field level where the interactions between farmer’s decisions (crop selection, combination and management) and biophysical processes (soil, plants, pests, weeds and diseases) are occuring. A large part of these biophysical processes also occur at landscape level (biodiversity, water flows...) but they require information at field level to be upscaled in a landscape mosaïc. In-between these two levels (field and region) the farm level plays a key role in agricultural systems imulation: (i) it is the decision level of farm activities (crops, livestock, trees...) further applied to field level ; (ii) it is the first level of expression of socio-economical services (labour, income, food production...) and (iii) farm diversity is a major driver of assessment indicators value and evolution at regional scale. In order to combine these up and downscalling processes between farm, field and regional levels for a multi-criteria design of agricultural systems, we propose a «farm-centered» integrated modelling of agricultural systems framework. In this approach farm models and typologies are the central node of scenario based analysis of technological innovation in a biophysical and socio-economic context. We will illustrate how it has been used in a wide range of assessment studies and how we plan to further develop it for the design of a « plant-diversity based » agro- ecological transition of agricultural systems.

Summarization: In this paper we develop a multicriteria decision aid model, to investigate whether it is possible to replicate the credit ratings of Fitch on Asian banks using publicly available data. The model is developed with the Multi-group Hierarchical DIScrimination (MHDIS) approach, following a tenfold cross validation procedure. Five financial variables are selected from a list of nineteen ones through factor analysis. An additional set of five non-financial variables covering ownership, corporate governance, auditing, strength of bank’s franchise and its banking environment is also being used. The results show that equity/customer and short term funding, net interest margin and return on average equity are the most important financial variables. The number of shareholders, the number of subsidiaries and the banking environment of the country in which the banks operate are the most important non-financial ones. In terms of classification accuracies, the results show that the MHDIS model can replicate the credit ratings of Fitch with a satisfactory accuracy and is more efficient than discriminant analysis and ordered logistic regression that are used for comparison purposes. Presented on: Annals of Finance

It is a challenging task to develop and optimize a high-performing haptic device for, e.g., medical training,because of the multi-objective performance requirements, and the complex relations between the design variablesand the performance objectives. Consequently, the solution space is scrutinized with efficient optimization techniques,and the cause-effect relations are preferably represented in a way that enables further reasoning in amulti-discipline setting, e.g., as Pareto-front curves and surfaces. Furthermore, if we have found a Pareto-optimalsolution, how can we benefit from the knowledge and optimization results obtained in that process if we are facinga trailing challenge (a new design situation) to re-design the initial solution to another application and/or otherrequirements, i.e. how can we address this new situation by efficiently re-using as much as possible of the initialsolutions? In this paper, we first present multi-objective optimizations of a 6-degree-of-freedom TAU haptic device.To investigate how we efficiently can address new design situations, by making use of the initial solutions, we havedefined, solved and analyzed six re-design cases. For the different cases, it is shown what can be reused and inwhat way that can be done. The six studied cases are then analyzed and a generic process for situated designoptimization, based on available knowledge and Pareto-optimal solutions, is proposed. QC 20171108

Περίληψη: Αυτή η διπλωματική εισάγει, για πρώτη φορά στη βιβλιογραφία, την χρήση υπεργράφων για την ταχεία δημιουργία αποτελεσματικών συνασπισμών αυτόνομων πρακτόρων. Προτείνουμε ορισμένες μεθόδους σχηματισμού, που βασίζονται σε υπάρχοντες αλγορίθμους υπεργράφων, όπως οι pruning, transversal, clustering και hybrid, και εκμεταλλευόμαστε την δομή του υπεργράφου για να εντοπίσουμε πράκτορες με επιθυμητά χαρακτηριστικά. Η προσέγγισή μας επιτρέπει τον σχεδόν στιγμιαίο σχηματισμό συνασπισμών υψηλής ποιότητας, ικανοποιώντας πολλαπλές ποιοτικές απαιτήσεις. Επιπλέον, οι μέθοδοί μας κλιμακώνονται ώστε να δέχονται δεκάδες χιλιάδες πράκτορες ως είσοδο και να εμφανίζουν τα αποτελέσματα μέσα σε κλάσματα του δευτερολέπτου, με μια από αυτές να λειτουργεί με εκατομμύρια πράκτορες μέσα σε δευτερόλεπτα. Εφαρμόζουμε την προσέγγισή μας στο πρόβλημα της δημιουργίας συνασπισμών για την παροχή ρεύματος από ηλεκτρικά οχήματα προς το ηλεκτρικό δίκτυο (το λεγόμενο πρόβλημα Vehicle-to-Grid, ή V2G). Η προσέγγισή μας είναι η πρώτη που είναι σε θέση να ασχοληθεί με μεγάλης κλίμακας, και σε πραγματικό χρόνο σχηματισμό συνασπισμών για το πρόβλημα V2G, λαμβάνοντας υπ'όψιν πολλαπλά κριτήρια για τη δημιουργία των συνασπισμών ηλεκτρικών οχημάτων. Ένα προσχέδιο των ιδεών αυτών εμφανίστηκε αρχικά σε μια σύντομη δημοσίευση στο 22ο European Conference on Artificial Intelligence (ECAI-2016) και έπειτα σε μια πλήρη στο 14o European Conference on Multi-Agent Systems (EUMAS-2016). Summarization: This thesis proposes, for the first time in the literature, the use of hypergraphs for the efficient formation of effective agent coalitions. We put forward several formation methods that build on existing hypergraph pruning, transversal, clustering and hybrid algorithms, and exploit the hypergraph structure to identify agents with desirable characteristics. Our approach allows the near-instantaneous formation of high quality coalitions, adhering to multiple stated quality requirements. Moreover, our methods are shown to scale to dozens of thousands of agents within fractions of a second; with one of them scaling to even millions of agents within seconds. We apply our approach to the problem of forming coalitions to provide (electric) vehicle-to-grid (V2G) services. Ours is the first approach able to deal with large-scale, real-time coalition formation for the V2G problem, while taking multiple criteria into account for creating the electric vehicle coalitions. A sketch of these ideas appeared originally in a short paper in the 22nd European Conference on Artificial Intelligence (ECAI-2016). Afterwards, a full paper describing our work was published in the 14th European Conference on Multi-Agent Systems (EUMAS-2016).

In new conditions an institution for successful work needs optimization of control, adaption of existing mathematical tools and development of new aspects. In the article it is suggested viewing the activities of higher educational institutions as production activities and higher educational institutions as complex organizational and economic systems consisting of a large number of subsystems. The relevance of the research is stipulated on the one hand by insufficient level of implementation of mathematical tools in the management process of both separate elements in the structure of sub-systems and sub-systems themselves, as well as institutions of higher education in general. On the other hand it is stipulated by the necessity to search for new forms and methods of organization and management of the educational process in the context of new educational standards. The conclusion about the interconnection and interdependence of the selected subsystems is made. The directions of further development within the frame of the designated problem are given.