Managing Interacting Criteria: Application to Environmental Evaluation Practices

The need for organizations to evaluate their environmental practices has been recently increasing. This fact has led to the development of many approaches to appraise such practices. In this paper, a novel decision model to evaluate company’s environmental practices is proposed to improve traditional evaluation process in different facets. Firstly, different reviewers’ collectives related to the company’s activity are taken into account in the process to increase company internal efficiency and external legitimacy. Secondly, following the standard ISO 14031, two general categories of environmental performance indicators, management and operational, are considered. Thirdly, since the assumption of independence among environmental indicators is rarely verified in environmental context, an aggregation operator to bear in mind the relationship among such indicators in the evaluation results is proposed. Finally, this new model integrates quantitative and qualitative information with different scales using a multi-granular linguistic model that allows to adapt diverse evaluation scales according to appraisers’ knowledge

COMBINED DEEP AND SHALLOW KNOWLEDGE IN A UNIFIED MODEL FOR DIAGNOSIS BY ABDUCTION

Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant.shallow knowledge, diagnosis, flow systems

Combined Deep and Shallow Knowledge in a Unified Model for Diagnosis by Abduction

Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant.Faulty Diagnosis, abduction, plausibility criteria, relevant criterion

The Symmetric Sugeno Integral

We propose an extension of the Sugeno integral for negative numbers, in the spirit of the symmetric extension of Choquet integral, also called \Sipos\ integral. Our framework is purely ordinal, since the Sugeno integral has its interest when the underlying structure is ordinal. We begin by defining negative numbers on a linearly ordered set, and we endow this new structure with a suitable algebra, very close to the ring of real numbers. In a second step, we introduce the Möbius transform on this new structure. Lastly, we define the symmetric Sugeno integral, and show its similarity with the symmetric Choquet integral.

Improving the quality of the industrial enterprise management based on the network-centric approach

The article examines the network-centric approach to the industrial enterprise management to improve the ef ciency and effectiveness in the implementation of production plans and maximize responsiveness to customers. A network-centric management means the decentralized enterprise group management. A group means a set of enterprise divisions, which should solve by joint efforts a certain case that occurs in the production process. The network-centric management involves more delegation of authority to the lower elements of the enterprise’s organizational structure. The industrial enterprise is considered as a large complex system (production system) functioning and controlled amidst various types of uncertainty: information support uncertainty and goal uncertainty or multicriteria uncertainty. The information support uncertainty occurs because the complex system functioning always takes place in the context of incomplete and fuzzy information. Goal uncertainty or multicriteria uncertainty caused by a great number of goalsestablished for the production system. The network-centric management task de nition by the production system is formulated. The authors offer a mathematical model for optimal planning of consumers’ orders production with the participation of the main enterprise divisions. The methods of formalization of various types of uncertainty in production planning tasks are considered on the basis of the application of the fuzzy sets theory. An enterprise command center is offered as an effective tool for making management decisions by divisions. The article demonstrates that decentralized group management methods can improve the ef ciency and effectiveness of the implementation of production plans through the self-organization mechanisms of enterprise divisions.The work has been prepared with the financial support from the Russian Ministry of Education and Science (Contract No. 02.G25.31.0068 of 23.05.2013 as part of the measure to implement Decision of the Russian Government No. 218)

On fuzzy-qualitative descriptions and entropy

This paper models the assessments of a group of experts when evaluating different magnitudes, features or objects by using linguistic descriptions. A new general representation of linguistic descriptions is provided by unifying ordinal and fuzzy perspectives. Fuzzy qualitative labels are proposed as a generalization of the concept of qualitative labels over a well-ordered set. A lattice structure is established in the set of fuzzy-qualitative labels to enable the introduction of fuzzy-qualitative descriptions as L-fuzzy sets. A theorem is given that characterizes finite fuzzy partitions using fuzzy-qualitative labels, the cores and supports of which are qualitative labels. This theorem leads to a mathematical justification for commonly-used fuzzy partitions of real intervals via trapezoidal fuzzy sets. The information of a fuzzy-qualitative label is defined using a measure of specificity, in order to introduce the entropy of fuzzy-qualitative descriptions. (C) 2016 Elsevier Inc. All rights reserved.Peer ReviewedPostprint (author's final draft