Ayeley P. Tchangani

A satisficing game theory approach for group evaluation of production units

The problem under consideration in this paper is that of analysing the performance of a production unit in two directions: resource utilization versus output perfomance on the one hand and inter-unit comparison (within-group evaluation) on the other hand, all this subjected to possible subjective intervention of a decision maker or group of decision makers (DMs). A well known method that deals mainly with the second point (without intervention of DMs) of this problem which is widely covered in the literature is the so called data envelopment analysis (DEA). The point of view that will be expressed in this paper can be thought of as complementary to the DEA approach giving a more complete analysis in terms of the weak points of units identification and DMs recommendations. The performance of each decision unit is evaluated through the so called satisfiability functions in the framework of satisficing game theory.

Sustainable management of end-of-life systems

In a sustainable development context, the stakes of the last stage of system life cycle, the end-of-life stage, have increased over recent years. End-of-life systems have to be de-manufactured in order to be valued so as to respond to environmental concerns. The aim of a disassembly strategy consists in issuing a solution to the whole decision problem raised during the end-of-life stage of systems. Indeed, decision makers have to select valuable components according to technical, economical and environmental criteria and then design and optimise a disassembly support system that will generate these products. The solution obtained is what we refer to in this article as a disassembly trajectory. The work presented in this article is about planning these trajectories on different horizons integrating several arrivals of end-of-life systems. The proposed approach, with Bayesian networks and influence diagrams as the underlying mathematical tools, enables dynamically defined uncertainties to be taken into account.

Evaluation Model For Multiattributes–Multiagents Decision Making: Satisficing Game Approach

This paper considers the evaluation step in a decision-making process that follows decision-making goals setting, feasible alternatives and attributes or criteria that characterize them determination steps. Evaluation step must establish a model or algorithm to evaluate alternatives taking into account their performances with regard to criteria as well as decision makers or stakeholders preferences. Though this problem is rather a classic one, researches related to evaluation model construction continue to be active to find models that cope with more realities or that fit well how human beings behave in group and proceed when facing the problem of choosing, ranking or sorting alternatives or options. The purpose of this paper is to construct an evaluation model that integrate the performances of alternatives with regard to attributes or criteria and decision makers or agents opinions with regard to the importance to assign to each criterion in order to obtain a value function. As any decision problem is almost always a matter of tradeoff, among attributes characterizing alternatives there will be those acting toward the achievement of decision makers goal (benefit) and those that decision makers would like to reduce as much as possible (cost); we will designate the first ones as positive attributes and the later ones as negative attributes. The process of dividing attributes into positive attributes and negative attributes is beyond the scope of this paper and this partition will be considered as a part of the problem specification. The model is constructed in two steps: firstly, satisfiability (selectability and rejectability) measures or functions are obtained for each alternative using attributes values (positive attributes will contribute to selectability measure whereas negative ones are used in the derivation of rejectability measure) and agents opinions in the framework of satisficing game theory and secondly a value function is built on that measures. Agents opinions with regard to attributes will be expressed locally by weighting them by category (positive/negative).

OMTDR using BER estimation for ambiguities cancellation in ramified networks diagnosis

Nowadays, increasing demands for on-line wire diagnosis using reflectometry have imposed serious challenges on signals processing, bandwidth control and interference mitigation. On-line diagnosis aims at detecting and locating faults accurately while the target system is running. In this work, a new reflectometry method, named “Orthogonal Multi-Tone Time Domain Reflectometry” (OMTDR), is proposed. OMTDR, based on Orthogonal Frequency Division Multiplexing (OFDM), is a suitable candidate for on-line diagnosis as it permits interference avoidance, bandwidth control and data rate increase thanks to the use of orthogonal tones and guard intervals. Over the diagnosis function, OMTDR adds communication between sensors to more accurately determine faults position in a multi-branch network using a distributed strategy. OMTDR was tested on a branched network consisting of three cables with different lengths, with sensors at each cable end. Here, the sensors signals are carefully constructed using a resource allocation scheme to use frequencies below and above the prohibited bandwidth, used by the target system, for communication and diagnosis. Simulation results show that the proposed method performs well in a branched wiring network as it permits to detect and locate faults accurately even when the target system is operating.

A bipolar consensus approach for group decision making problems

Adaptive consensus seeking in group decision making.Mutual influence modeling.Taking into account human attitude.Multi-criteria problem using bipolarity concept.Consensus processes with or not individual changes. This paper addresses the collaborative group decision making problems considering a consensus processes to achieve a common legitimate solution. The proposed resolution model is based on individual bipolar assessment. Each decision maker evaluates alternatives through selectability and rejectability measures which respectively represent the positive and negative aspects of alternatives considering objectives achievement. The impact of human behavior (influence, individualism, fear, caution, etc.) on decisional capacity has been taken into account. The influence degrees exerted mutually by decision makers are modeled through concordance and discordance measures. The individualistic nature of decision makers has been taken into account from the individualism degree. In order to achieve a common solution(s), models of consensus building are proposed based on the satisficing game theory formalism for collective decision problems. An application example is given to illustrate the proposed concepts.

Diagnosis sensor fusion for wire fault location in CAN bus systems

This paper proposes a new method for distributed wire diagnosis using reflectometry. It not only uses the reflected part of the test signal to extract information about the fault position, but it also investigates the transmitted part to enable sensors communication. The major novelty is to inject a signal carrying additional information about the fault position as a test signal using Orthogonal Multi-Tone Time Domain Reflectometry (OMTDR) method. While the reflected signal permits to determine the fault position at time t, the transmitted one sends the fault position at time (i-1) to the master sensor. Finally, the latter takes the location decision based on the information gathered from its slaves. This removing location ambiguities in branched networks. Time Division Multiple Access (TDMA) is used to avoid noise interference.

Optimising end-of-life system dismantling strategy

The management of end-of-life systems is becoming a major concern for systems manufacturers, as the negative impact of these systems on the environment is a matter of increasing public awareness, and their appropriate treatment offers economic opportunities. In this context, the disassembly of these systems in order to recycle their components is a possible and sound option that can make it possible to sustain economical progress while respecting environment requirements. The work undertaken in this paper considers modelling and optimising issues of such disassembly activities. An integrated approach is proposed to model and optimise the selection of valuable components of end-of-life systems, their recycling options and the way to obtain them. Because the framework of such problems is highly uncertain, we propose the use of Bayesian networks and their extension in terms of influence diagrams as mathematical tools for structuring and managing uncertainties. With this approach, one can take into account uncertainties rising from different sources on one hand and as a support for optimisation on the other hand.

BOCR Framework for Decision Analysis

This paper considers establishing a framework for modellig decision analysis problems where the analyst must cope with uncertainty, multiple objectives, multiple attributes and multiple actors. These probems rise when considering large scale and complex decision problems encountered in real world applications in domains such as risk assessment and managment, infrastrucutres planning, complex process monitoring, supply chain planning, etc. To tackle this modelling chalenges, we propose to use BOCR (benefit, opportunity, cost, and risk) paradigm to identify attributes that must characterize an alternative with regard to a given objective. Then Bayesian network and/or AHP (analytic hierrarchy process) analysis can be used to assess the values of these later attributes. Finally an aggregation method based on satisficing game is developed that permit to evaluate each alternative by two measures: selectability degree constructed using “positive” attributes (benefit and opportunity) and the rejectability degree built on “negative” attributes (cost and risk).

Distributed sensor fusion for wire fault location using sensor clustering strategy

From reflectometry methods, this work aims at locating accurately electrical faults in complex wiring networks. Increasing demand for online diagnosis has imposed serious challenges on interference mitigation. In particular, diagnosis has to be carried out while the target system is operating. The interference becomes more even critical in the case of complex networks where distributed sensors inject their signals simultaneously. The objective of this paper is to develop a new embedded diagnosis strategy in complex wired networks that would resolve interference problems and eliminate ambiguities related to fault location. To do so, OMTDR (Orthogonal Multi-tone Time Domain Reflectometry) method is used. For better coverage of the network, communication between sensors is integrated using the transmitted part of the OMTDR signal. It enables data control and transmission for fusion to facilitate fault location. In order to overcome degradation of diagnosis reliability and communication quality, we propose a new sensor clustering strategy based on network topology in terms of distance and number of junctions. Based on CAN bus network, we prove that data fusion using sensor clustering strategy permits to improve the diagnosis performance.

A Distributed Diagnosis Strategy using Bayesian Network for Complex Wiring Networks

Abstract In this paper, a distributed diagnosis strategy using reflectometry is proposed. It consists in making reflectometry measurements at different spots of a highly complex wiring network simultaneously in order to reduce ambiguities caused by multipath signals propagation. Although the problem of sensors number optimization is greatly studied in the literature, it is not well investigated in complex wiring networks diagnosis. The proposed approach is based on two principles which are sensors number and location optimisation using Bayesian Networks and measure uncertainty estimation. It consists in four steps: (1) sensors implementation in a deterministic case, (2) influential parameters on diagnosis measure identification, (3) diagnosis measure modelling using Bayesian Networks, (4) sensor number and location optimization. Here, the main objective is to minimize both sensors number and diagnosis measure uncertainty.