La croissance de processus automatisé complexe dans les installations industrielles nécessite, une surveillance continue en vue d'assurer leur fiabilité, leur disponibilité, leur sûreté de fonctionnement et surtout leur tolérance aux fautes. Pour cela, le développement d'outils d'analyse du comportement du système dynamique, permettant de le maintenir en fonctionnement normal en présence de fautes est nécessaire. Ces outils doivent permettre de maintenir un niveau minimal de sécurité et garantir la stabilité et la robustesse du processus. ces travaux concernent l'étude de diagnostic des actionneurs tout en établissant une commande tolérante aux fautes s'appuyant sur les techniques de commande avancées pour garantir la robustesse et la stabilité du système. Pour contourner les problèmes de stabilité et de robustesse en présence de défauts, deux commandes adaptative floue directe et indirecte ont été établies. Dans les deux cas la robustification est obtenue par mode glissant. Afin de rendre la commande insensible aux bruits de mesure et aux perturbations et de prendre en compte les incertitudes non exploitées par les systèmes flous type-1, les systèmes flous type-2 sont introduits.

In this chapter, we consider the single machine scheduling problem including uncertain parameters and position based learning effect with the aim to minimize the weighted sum of jobs completion times. Due to the ill-known quantities within the model, the determination procedures of optimal solutions in the conventional way is not an affordable task and more elaborated frameworks should be developed. In this context, we introduce two solution approaches for the proposed fuzzy scheduling problem in order to obtain an exact or a satisfactory near optimal solution. The first approach is based on the extension of the well-known Smith’s rule resulting in a polynomial algorithm with a complexity O(n l o g(n)). However, a severe constraint on jobs (fuzzy agreeability concept) should be satisfied in this case. The second approach based on optimization methods is built upon the assumption of unequal fuzzy release dates in addition to the absence of fuzzy agreeability constraint. Three trajectory based metaheuristics (Simulated annealing, taboo search and kangaroo search) are implemented and applied to solve the resulting problem. For the proposed methods throughout the chapter, several numerical experimentations jointly with statistical deductions are provided.

In recent years, companies have emerged in an advanced competitive environment. To meet the requirements of cost reduction, customer demand, minimising delays, quality and variety improvement, companies must improve their performance to remain competitive, survive and expand. To achieve this goal, several models are used such as total quality management, Kaizen, just in time, enterprise resource planning, business process reengineering and Six Sigma, etc. In this work, we look for an effective model (drawn from Six Sigma approach) used mainly to warrant the competitiveness of a company denoted as the weighted defects per million opportunities model. The aim of this paper is to apply this model to measure process levels (weights) and assess the company competitiveness. The results of this model are applied in a real manufacturing system which produces gas bottles.

The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis.

This paper considers a supply chain management problem which integrates production, inventory, and distribution decisions. The supply chain is composed of one supplier production facility and several retailers located in a given geographic region. The supplier is responsible for the production and the replenishment of the inventory of retailers, in a vendor managed inventory (VMI) context. The distance between retailers is negligible compared to the distance between the supplier and the retailers’ region. Thus, for each vehicle, there is a major fixed cost for traveling to the cluster of retailers and a minor fixed cost for visiting each individual retailer. The problem consists of determining quantities to be produced, quantities to be delivered to retailers, vehicles to be used, and retailers to be serviced by each vehicle. This problem is an extension of the one warehouse multi-retailer problem with the consideration of production planning and storage and vehicle capacity limitations in addition to fixed vehicle utilization costs and retailer servicing costs. The objective is to minimize a total cost composed of production, transportation, and inventory holding costs at the supplier and at the retailers. Two mixed integer linear programming formulations are proposed and six families of valid inequalities are added to strengthen these formulations. Two of these families are new and the others are adapted from the literature. The numerical results show that the valid inequalities considerably improve the quality of the formulations. Moreover, the parameters that influence the most computational times are analyzed

Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop scheduling problem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a new chromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series of benchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficient and competitive than some other existing algorithms.

The single machine scheduling problem has been often regarded as a simplified representation that contains many polynomial solvable cases. However, in real-world applications, the imprecision of data at the level of each job can be critical for the implementation of scheduling strategies. Therefore, the single machine scheduling problem with the weighted discounted sum of completion times is treated in this paper, where we assume that the processing times, weighting coefficients and discount factor are all described using trapezoidal fuzzy numbers. Our aim in this study is to elaborate adequate measures in the context of possibility theory for the assessment of the optimality of a fixed schedule. Two optimization approaches namely genetic algorithm and pattern search are proposed as computational tools for the validation of the obtained properties and results. The proposed approaches are experimented on the benchmark problem instances and a sensitivity analysis with respect to some configuration parameters is conducted. Modeling and resolution frameworks considered in this research offer promise to deal with optimality in the wide class of fuzzy scheduling problems, which is recognized to be a difficult task by both researchers and practitioners.

The industrial sector accounts for almost one fourth of the final energy consumption in Algeria. An energy survey was carried out in three Algerian dairies, and the energy use and cost analysis is presented in this article. Electricity and natural gas are the main resources that are used in the dairy industry. The specific energy consumption related to electricity and natural gas was found to be in the ranges of 0.03–0.08 kWh/kg and 0.10-0.22 kWh/kg respectively. Also, a relationship of energy consumption, energy costs and production is presented and the findings could be used to establish an energy management system aimed at using energy more efficiently. Finally, this paper calls for more investigations of other industries to obtain reliable indicators.

The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis

The purpose of this paper is to propose a hybrid method SKACICM of development of knowledge management systems. Based on weaknesses of the method of performance dashboards SKANDIA, we proposed a pragmatisation and adaptation of Skandia to give (ASKANDIA), by enrichment of its performance book. We ameliorated CICM model against the requirements of GERAM to give ACICM model by mappings between their proposed metamodels. We tried to hybridise ACICM, ASKANDIA and business intelligence to propose a new method SKACICM of development of knowledge management systems. We applied SKACICM on a cement company to develop software containing three main modules, module knowledge management, module business intelligence and performance dashboard system. The developed system ameliorated the performance of the enterprise by 26% and could be generalised to other manufacturing or service systems.

In this study, a metaheuristic based on the Non-dominated Sorting Genetic Algorithm type II (NSGA-II) is proposed to solve the Multi-Criterions Job Shop Scheduling Problem (MCJSSP) under resources availability constraints. Availability periods and starting time of maintenance activities are supposed to be flexible. The MCJSSP requires, simultaneous minimization several antagonistic criteria, such as the maximum completion time of all jobs (Makespan), production cost and maintenance cost. To validate the proposed approach we tested it on forty-four instances references. The results show that our approach is experimentally promising to solve practical problems.

The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis.

This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors. Nomenclature PV Photovoltaic SVM Support Vector Machines SVR Support Vector Regression k-NNR k-Nearest Neighbor Regression X SVR input vector Y SVR output vector f Linear function Ф Nonlinear mapping function w Weight vector e Squared loss function x Problem variable x * New problem variable α Lagrange multipliers N Number of classes m Number of index of minimum distances I / V Current / Voltage IPH Photocurrent