Automated learning multi-criteria classifiers for FLIR ship imagery classification

Abstract- This paper proposes an Automated Learning Method (ALM) based on Real-Coded Genetic Algorithm (RCGA) to infer the Multi-Criteria Classifiers (MCC) parameters. The Multi-Criteria Classifiers (or Multi-Criteria Classification Methods) considered are based on concordance and discordance concepts. A military database of 2545 Forward Looking Infra-Red (FLIR) images representing eight different classes of ships is therefore used to test the performance of these classifiers. The empirical results of MCC are compared with those obtained by other classifiers (e.g. Bayes and Dempster–Shafer classifiers). In this paper, we show the benefits of cross-fertilization of multi-criteria classifiers and information fusion algorithms

The impact of operations anticipations on the quality of stochastic location-allocation models: .

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The design of robust value-creating supply chain networks: A critical review

This paper discusses Supply Chain Network (SCN) design problem under uncertainty, and presents a critical review of the optimization models proposed in the literature. Some drawbacks and missing aspects in the literature are pointed out, thus motivating the development of a comprehensive SCN design methodology. Through an analysis of supply chains uncertainty sources and risk exposures, the paper reviews key random environmental factors and discusses the nature of major disruptive events threatening SCN. It also discusses relevant strategic SCN design evaluation criteria, and it reviews their use in existing models. We argue for the assessment of SCN robustness as a necessary condition to ensure sustainable value creation. Several definitions of robustness, responsiveness and resilience are reviewed, and the importance of these concepts for SCN design is discussed. This paper contributes to framing the foundations for a robust SCN design methodology.Supply chain network design Value creation Uncertainty Network disruptions Robustness Scenario planning Location models Capacity models Resilience strategies

Examination of the adoption intention of new energy vehicles from the perspective of functional attributes and media richness

Drawing on the theory of media richness, this paper aims to explore the impact of media richness on consumers' adoption intention through their perception of new energy vehicle (NEV) function attributes, and assess the moderation roles of brand familiarity and locus of control. A structural equation model is applied to analyze the data collected from 427 respondents. Empirical results demonstrate that consumers' perception of an electric attribute (i.e., charging efficiency) and two intelligent attributes (i.e., car networking and self-driving) are determinants of their adoption intention of NEVs. The other electric attribute (range) is trivial in consumers' perception. We also find that low, medium, and high-richness media significantly affect consumers' perception of NEVs' functional attributes. Compared to the high-richness, medium-richness correlates significantly with two types of NEV functional attributes. Regarding moderating effects, consumer familiarity with NEV's brand negatively impacts the relationship between media richness and adoption intention. Furthermore, low and medium-richness media effectively stimulate individuals with external control to adopt NEV, while high-richness media adversely influence individuals with internal control

Dynamic Resource Allocation in Computing Clouds using Distributed Multiple Criteria Decision Analysis.

Abstract-In computing clouds, it is desirable to avoid wasting resources as a result of under-utilization and to avoid lengthy response times as a result of over-utilization. In this paper, we propose a new approach for dynamic autonomous resource management in computing clouds. The main contribution of this work is two-fold. First, we adopt a distributed architecture where resource management is decomposed into independent tasks, each of which is performed by Autonomous Node Agents that are tightly coupled with the physical machines in a data center. Second, the Autonomous Node Agents carry out configurations in parallel through Multiple Criteria Decision Analysis using the PROMETHEE method. Simulation results show that the proposed approach is promising in terms of scalability, feasibility and flexibility

A Framework to Choose a Discrete Multicriterion Aggregation Procedure

A review of the multicriterion literature reveals the large number of multiple criteria aggregation procedures (MCAPs). These MCAPs are based on different hypotheses, premises and epistemological perspectives, and deals with different decision-making situations. It is obvious that none of these MCAPs is perfect nor applicable for all decision-making situations. The challenge is then to determine which one is more appropriate to a a specific decision-making situation. This paper assesses a framework for choice engineering of an appropriate MCAP to a decision-making situation. This framework is based on the representation of the decision-making process proposed by Guitouni (1998). Then, we define a set of inputs and a set of outputs in order to characterize different decisionmaking situations (more than 1400). The inputs are used to characterize the information admissible by a MCAP. The outputs are used to characterize the information produced by a MCAP. Systematically, it is then possible to associate different MCAPs with each particular couple of(Input, Output). The obtained matrix constitutes the endeavour of the framework proposed. This matrix is still incomplete and imperfect, but gives a good basis for the selection of a MCAP to a specific decision-making situation