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Published in : IFAC-PapersOnLine 55(10):1764-1768. doi : 10.1016/j.ifacol.2022.09.653; International audience; This paper deals with the simulation and optimisation of disassembly-reconditioningassembly system (DRAS) taken into account random machines failures and repairs. The proposed system is composed of one disassembly machine, two parallel structures, one assembly machine and stocks to store the used products, components and finished product. In this study a complete disassembly and assembly process in a supply chain is considered, and which allows to acquire used products, disassemble the used products, storing reconditioning components, assemble the components and selling the finished product to reply customers demand. The objective is to determine the optimal acquisition quantity of used products that maximises the total profit. A mathematical model is formulated on the basis of discrete flow model that allows to express the behaviour of the system. The proposed model is then simulated and the optimal acquisition quantity is determined. Numerical results are presented to discuss the optimal acquisition quantity and the lost profit risk.

DC (Difference of Convex functions) programming and DCA (DC Algorithm) play a key role in nonconvex programming framework. These tools have a rich and successful history of thirty five years of development, and the research in recent years is being increasingly explored to new trends in the development of DCA: design novel DCA variants to improve standard DCA, to deal with the scalability and with broader classes than DC programs. Following these trends, we address in this paper the two wide classes of nonconvex problems, called partial DC programs and generalized partial DC programs, and investigate an alternating approach based on DCA for them. A partial DC program in two variables $$(x,y)\in \mathbb {R}^{n}\times {\mathbb {R}}^{m}$$ takes the form of a standard DC program in each variable while fixing other variable. A so-named alternating DCA and its inexact/generalized versions are developed. The convergence properties of these algorithms are established: both exact and inexact alternating DCA converge to a weak critical point of the considered problem, in particular, when the Kurdyka–Łojasiewicz inequality property is satisfied, the algorithms furnish a Frechet/Clarke critical point. The proposed algorithms are implemented on the problem of finding an intersection point of two nonconvex sets. Numerical experiments are performed on an important application that is robust principal component analysis. Numerical results show the efficiency and the superiority of the alternating DCA comparing with the standard DCA as well as a well known alternating projection algorithm.

We investigate DC (Difference of Convex functions) programming and DCA (DC Algorithm) for a class of online learning techniques, namely prediction with expert advice, where the learner’s prediction is made based on the weighted average of experts’ predictions. The problem of predicting the experts’ weights is formulated as a DC program for which an online version of DCA is investigated. The two so-called approximate/complete variants of online DCA based schemes are designed, and their regrets are proved to be logarithmic/sublinear. The four proposed algorithms for online prediction with expert advice are furthermore applied to online binary classification. Experimental results tested on various benchmark datasets showed their performance and their superiority over three standard online prediction with expert advice algorithms—the well-known weighted majority algorithm and two online convex optimization algorithms.

New jobs arrival, jobs cancelation or machine breakdowns, are unexpected events that may occur during production run in real environments, thus disrupting the established schedule. Hence, rescheduling process is necessary for efficiently revising the initial schedule, preferably with little movements. In the literature, many scheduling problems consider unlimited buffer spaces between successive machines in flow shop environments. However, in real life systems, the capacity between machines may be limited or no-store existing. To fill the void, this paper considers a flow shop rescheduling problem with mixed blocking constraints, under disruptions caused by new jobs arrival. A new performance measure is investigated, considering simultaneously the schedule efficiency measured by the total weighted waiting time, and the schedule stability evaluated by the total weighted completion time deviation. This combination could be very helpful for real life industrial systems. A mixed integer linear programming model (MILP) is designed, as well as an iterative methodology based on the predictive reactive rescheduling strategy. At last, two heuristic methods are implemented and discussed, in terms of solution quality and computing time.

International audience; During successive industrial revolutions, the objective has not only been for industry to improve and satisfy its direct needs, it has also been to improve society’s standard of living and make life easier for the consumer. Therefore, economic growth should always go hand in hand with each industrial revolution. The medical industry, energy conservation and, in particular, production technologies will be transformed through new value chain models. Globalization, urbanization, demographic change and energy transformation are the transforming forces estimating the technological impetus for a better identification of solutions for a world on the move. In recent years, successive revolutions have made remarkable contributions to a person’s quality of life, safety, industrial economy, comfort and health. This work aims at improving the energy consumption of buildings. To achieve this goal, we have created a Digital Twin that accurately reflects the behavior and characteristics of a future or existing building. To reproduce a copy of the future or existing building, we chose to use the Autodesk REVIT solution to meet several constraints. These have a significant influence on the energy behavior of the building.

PurposeTotal productive maintenance (TPM) has been widely recognized as a strategic weapon for improving manufacturing performance. Evaluate efficiency of TPM implementation is considered as a key element in order to motivate staff and to give decision-makers more confidence.Design/methodology/approachThis study consists in developing a new method of evaluating TPM implementation, relying on analytical models and considering two preventive maintenance strategies: periodic and age-dependent.FindingsThe preventive maintenance period and TPM period defined as decision variables are obtained simultaneously by maximizing the expected profit under TPM implementation. A numerical example is presented and a sensitivity study is developed to validate the proposed models.Originality/valueThe aim of this research is to quantify, through analytic development, the impact of TPM implementation in a company by calculating and comparing the profit made with and without TPM.

The goal of industrial revolutions is not only to improve and respond directly to the needs of industry in a productive side fact, but also to improve the standard of living of society and make life easier for customers. Therefore, economic growth should always go hand in hand with each industrial revolution. In this context, the increasing demand for electricity, the non-renewable nature of conventional sources and the rising price of fuel, are considered the constraints of the 21st century and need to be definitively managed in order to meet the constantly increasing demand for electricity from the population in the best possible conditions. Information and Communication Technologies (ICTs) are the foundation on which tomorrow’s innovative solutions are created. However, smart embedded systems and the internet are two major players driving ICT technologies forward. These technologies have impacted several sectors, where we find a crossroads of several industrial sectors: medical, manufacturing, automation, energy and others. The application these technologies in a smart grid framework with the integration of renewable energy sources and used for an optimization and efficiency mission let Internet of Things (IoT) to take the sustainable and industrial character and named Green Industrial Internet of Things (GIIoT). This last, seems to be a promoting axis for the development of future applications. In this paper, we present an overview of the impact of the Internet of Things on the evolution of applications in relation to the different revolutions, an inventory of the different industrial revolutions, an overview of the GIIoT as well as some applications and perspectives.

Since 1924, Lebanon planned to use renewable energy and in particular hydraulic energy to produce the national need of electricity. Until the beginning of the 70, many steps have been achieved by the government where in 1974 around 41.5% of the Lebanese electricity was produced from hydroelectric stations. Unfortunately since then and due to the instability in the political regimen, electric plants and networks suffered from destruction, aging, increased demand, hacking and many other deterioration factors. Therefore and based on all the last mentioned troubles the electric sector in Lebanon transformed to be the major economic and environmental problem. This paper aims at presenting and analyzing the actual state of the electric sector in Lebanon emphasizing on its impact on air pollution and on the development of factories and small enterprises. Therefore, it will be shown that the problem with the electrical sector is essentially due to the old networks easily hacked, strong reliance on fuel-based electricity, which leads to increased air pollution without meeting the energy consumption demand. This underproduction of electrical energy leads to the development of a parallel electrical private sector also based on fuel burning what worsened air pollution problems despite all the measures taken by the governments to switch to greener electricity production. A case study on electricity consumption in different locations in Beirut is also presented and used to prove that shifting from fuel to solar production will decrease the economic impact of electricity. Hydroelectric energy was not considered due to a decrease in the precipitation level that made it difficult to produce enough energy from rivers and due to the high cost of rehabilitation and upgrading.

The reliability-based design optimization (RBDO) is performed for mechanical design to compromise effectively between economy and safety requirements. In real mechanical applications, such RBDO problems are a highly complex task by involving computational difficulties and its resolution requires the use of appropriate optimization techniques. In this paper, a new RBDO solution approach is introduced for mechanical engineering problems. It is a combination of the reliable design space (RDS) technique with an efficient hybrid algorithm (AMDE-NM) based on the adaptive mixed differential evolution (AMDE) and Nelder–Mead local search (NM). First, the RDS strategy is used to turn the RBDO problem into a simple deterministic optimization (SDO) one, through converting the probabilistic constraints to approximate deterministic constraints, while the resolution is then carried out with the AMDE-NM algorithm. The new proposed integrated approach (RDS–AMDE-NM) is able to handle the mixed design variables with continuous, discrete, and integer types. Six mechanical problems with different features are studied to analyze the applicability and the efficiency of RDS–AMDE-NM. The obtained simulation results show the performance of the proposed approach, while new optimal solutions for two RBDO problems are presented. Furthermore, an industry case on a cylindrical spur gear is studied to investigate the reliability of the proposed method in solving real challenging mechanical RBDO problems. The obtained results reveal really that RDS–AMDE-NM is a promising RBDO approach with extensive applicability.

We study the problem of enumerating the $k$-arc-connected orientations of a graph $G$, i.e., generating each exactly once. A first algorithm using submodular flow optimization is easy to state, but intricate to implement. In a second approach we present a simple algorithm with $O(knm^2)$ time delay and amortized time $O(m^2)$, which improves over the analysis of the submodular flow algorithm. As ingredients, we obtain enumeration algorithms for the $\alpha$-orientations of a graph $G$ in $O(m^2)$ time delay and for the outdegree sequences attained by $k$-arc-connected orientations of $G$ in $O(knm^2)$ time delay., Comment: 13 pages, 1 Figure, corrected typos

PurposeThe main contribution of this manuscript is to suggest new approaches in order to deal with dynamic lot-sizing and maintenance problem under aspect energetic and risk analysis. The authors introduce a new maintenance strategy based on the centroid approach to determine a common preventive maintenance plan for all machines to minimize the total maintenance cost. Thereafter, the authors suggest a risk analysis study further to unforeseen disruption of availability machines with the aim of helping the production stakeholders to achieve the obtained forecasting lot-size plan.Design/methodology/approachThe authors tackle the dynamic lot-sizing problem using an efficient hybrid approach based on random exploration and branch and bound method to generate possible solutions. Indeed, the feasible solutions of random exploration method are used as input for branch and bound to determine the near-optimal solution of lot-size plan. In addition, our contribution to the maintenance part is to determine the optimal common maintenance plan for M machines based on a new algorithm called preventive maintenance (PM) periods means.FindingsFirst, the authors have funded the optimal lot-size plan that should satisfy the random demand under service level requirement and energy constraint while minimizing the costs of production and inventory. Indeed, establishing a best lot-size plan is to determine the appropriate number of available machines and manufactured units per period. Second, for risk analysis study, the solution of subcontracting is proposed by specifying a maximum cost of subcontractor in the context of a calling of tenders.Originality/valueFor maintenance problem, the originality consists in regrouping the maintenance plans of M machines into only one plan. This approach lets us to minimize the total maintenance cost and reduces the frequent breaks of production. As a second part, this paper contributed to the development of a new risk analysis study further to unforeseen disruption of availability machines. This risk analysis developed a decision-making system, for production stakeholders, in order to achieve the forecasting lot-size plan and keeps its profitability, by specifying the unit cost threshold of subcontractor in the context of a calling of tender.

In a highly competitive environment, characterized by shorter product life cycles, mass customization and fluctuating markets, the supply chain network design is considered as one of the most important steps for reducing their costs, increasing their performance, competitiveness and profits. In addition, due to the growing social, governmental and industrial concerns about environmental protection and greenhouse gas reduction, policy makers are now forced to incorporate the environmental issue and sustainability constraints into their logistics network design process. The purpose of the present paper is to address the issue of sustainable supply-chain-network design by integrating simultaneously the strategic decisions related to the location of facilities with limited capacity and transport network design with capacity constraints on the links to be built, and constraints related to environmental protection. A multi-objective model is proposed to minimize the cost and environmental impact associated with transport activities by considering different environmental impacts of the links to be built. An adaptation of the Non-dominated Sorting Genetic Algorithm II, using mixed coding is developed to effectively solve the proposed model. In order to evaluate the performance of the proposed approach, a number of numerical experiments on instances of different sizes have been performed. Finally, a sensitivity analysis is presented to analyse the behaviour of the proposed model in response to changes in the key parameters of the problem.

The search region in multiobjective optimization problems is a part of the objective space where nondominated points could lie. It plays an important role in the generation of the nondominated set of multiobjective combinatorial optimization (MOCO) problems. In this paper, we establish the representation of the search region by half-open polyblocks (a variant concept of “polyblock” in monotonic optimization) and propose a new procedure for updating the search region. We also study the impact of stack policies to the new procedure and the existing methods that update the search region. Stack policies are then analyzed, pointing out their performance effectiveness by means of the results of rich computational experiments on finding the whole set of nondominated points of MOCO problems.

International audience; In the area of software engineering and system architecture, the design and implementation process has taken a general methodological approach, encompassing all the activities necessary to design, evolve and verify a system providing an economical and efficient solution to needs of a client while satisfying all the stakeholders. For a long time, system engineers have used modeling techniques, the best known of which are AADL (Analysis and Architecture Design Language) and SysML (System Modeling Language). AADL was born as a language specific to the field focused on avionics and later was revised to represent and support a more general category of embedded systems in real time. SysML is an extension of the Unified Modeling Language (UML) to support engineering and modeling of the system. Based on these two languages and these two tools, several approaches have been created and developed to meet the needs and requirements of the system and also to overcome the problems and limitations of these two languages. In this paper, we will study and examine the two AADL and SysML modeling languages in a thorough way in order to extend the SysML language by the AADL language specifications for the modeling and design of sensor networks without (WSN).

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International audience

International audience; We propose a general framework that allows for the study of enumeration of vertex set properties in graphs. We prove that when such a property is locally definable with respect to some order on the set of vertices, then it can be enumerated with linear delay. Our method consists in reducing the considered enumeration problem to the enumeration of paths in directed acyclic graphs. We then apply this general method to enumerate minimal connected dominating sets and maximal irredundant sets in interval graphs and in permutation graphs, as well as maximal irredundant sets in circular-arc graphs and in circular permutation graphs, with linear delay.

International audience; Due to environmental preoccupations and potential economic benefits, production firms are devoted to developing and improving the decisions within sustainable supply chains (SSC). Consequently, currently, many manufacturing managers are working hard to find optimal decisions and coordination in SSC, such as manufacturing and remanufacturing planning, acquisition quantities of worn products, adaptation of new maintenance strategies and technics, optimal manufacturing design, reconfiguration, etc. However, these decisions may account for economic or ecologic risks, e.g., profit losses or excessive carbon emissions, which have prompted many researchers to develop different models that estimate and analyze risk in supply chains. Thus, to bridge this gap in the literature, this proposed thematic issue aims to contribute to the existing literature by investigating new decisions in SSC and risk analysis under undesirable events.

International audience

Smartphones are nowadays one of the most used electronic devices in everyday life. They are known to have an average useful lifespan between 18 and 24 months. Indeed, users are led up to dispose of them at the end of their lifespan, and buy last generation smartphones, increasing thus the electronic wastes. To reduce the environmental issues, we propose in this paper to extend the useful lifespan of smartphones based on recovery and remanufacturing options. The developed remanufacturing policies aim to determine the best decisions to ensure the required level of smartphone performances, with the customer profiles and the recovery and remanufacturing center capacity. We propose a new heuristic which constructs a feasible plan for the recovery and the remanufacturing of used smartphones and determines the grade to achieve for each customer, minimising the economic and the environment generated costs.

International audience

International audience; Thanks to smart technological tools, customers can at any moment create or modify their commands. This reality forced many production firms to become sensitive in rescheduling processes. In the literature, most of rescheduling problems consider classical efficiency measures. However, some existing works also consider stability as a measure for limiting the deviation from initial schedule. In this work, we aim to bridge the gap in existing works on rescheduling by investigating a new approach to measure simultaneously efficiency by the total weighted waiting times and stability by the total weighted completion time deviation. This combination of criteria is very significant in industrial and hospital environments. In this paper, a single machine rescheduling problem with jobs arriving over time is considered. A mixed integer linear programming (MILP) model is designed for this problem and an iterative predictive-reactive strategy for dealing with the online part. Numerical results show that, at each time the jobs are rescheduled, the low weight ones move forward. Consequently, a new concept consisting in increasing the jobs weight as function of time is established. The effect of this new conception is evaluated by the variation of the average flowtime. Eventually, the computing time of the MILP resolution is studied to explore its limitations.

International audience

International audience; The present paper deals with a variant of hub location problems (HLP): the uncapacitated single allocation p-Hub median problem (USApHMP). This problem consists to jointly locate hub facilities and to allocate demand nodes to these selected facilities. The objective function is to minimize the routing of demands between any origin and destination pair of nodes. This problem is known to be NP-hard. Based on the artificial immune systems (AIS) framework, this paper develops a new approach to efficiently solve the USApHMP. The proposed approach is in the form of a clonal selection algorithm (CSA) that uses appropriate encoding schemes of solutions and maintains their feasibility. Comprehensive experiments and comparison of the proposed approach with other existing heuristics are conducted on benchmark from civil aeronautics board, Australian post, PlanetLab and Urand data sets. The results obtained allow to demonstrate the validity and the effectiveness of our approach. In terms of solution quality, the results obtained outperform the best-known solutions in the literature.

We investigate a nonconvex, nonsmooth optimization approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for the so-called DC fitting problem, which aims to fit a given set of data points by a DC function. The problem is tackled as minimizing the squared Euclidean norm fitting error. It is formulated as a DC program for which a standard DCA scheme is developed. Furthermore, a modified DCA scheme with successive DC decomposition is proposed. These standard/modified versions of DCA are applied for solving the continuous piecewise-linear fitting problem. Numerical experiments on many synthetic and real datasets with small-to-large sizes show the efficiency of our DCA-based approach in comparison with the existing approaches for constructing continuous piecewise-linear models.

This paper proposes a new inspection model for a multi-component system, subject to a deterioration process which impacts its efficiency. To control the deterioration process, several inspections are carried out after which various decisions are made on how to evaluate the functioning of the system. The proposed inspection model is applied on the case of Pitot sensors system for aircraft. This study is derived from an aeronautic problem defined following the crash of the Airbus 330 in the Rio-Paris flight. Since the Pitot sensors system is non-self-announcing, the paper proposes a technical solution which consists in setting up an additional self-control device in order to inspect and calibrate the already installed Pitot sensors measurements.The objective of this paper is to determine an optimal inspection policy which minimizes the total measurement quality degradation per unit of time in a finite time horizon. Numerical examples and a comparative study are provided to illustrate the proposed analytic model and to highlight the contribution of the paper.

It is undoubtedly that mathematical modelling and optimisation play a key role in the supply chain and the production management (SCPM). In this paper, we provide a survey on DC (Difference of Conv...

In this paper, we study characterizations of differentiability for real-valued functions based on generalized differentiation. These characterizations provide the mathematical foundation for Nester...

Remanufacturing an equipment before the end of its life may generate substantial profits for both the user and the remanufacturer. However, the equipment operating conditions as well as the quality of maintenance actions undergone throughout the life of equipment largely affect the total life cycle costs from the user’s perspective, and the quality of the recovered equipment from the remanufacturer’s perspective. This research aims at investigating the remanufacturing opportunities of production equipment from the user’s perspective, used to produce a single product in order to meet deterministic and dynamic demands over a finite horizon. Preventive maintenance actions on equipment are assumed to be imperfect. Indeed, when performed, these actions can improve the equipment to reach a state between the “As-good-as-new” level and the “As-bad-as-old” level. It is assumed that the stakeholders (the equipment’s user and the original equipment manufacturer-remanufacturer) are interested by all remanufacturing opportunities. The objective of the present work is to develop an integrated approach to jointly optimize the production plan, the remanufacturing plan and the quality of spare parts used in each remanufacturing action. In order to minimize the total cost during the production horizon, a mixed-integer nonlinear program is developed. The optimization problem is solved using a hybrid genetic algorithm based on a fix-and-relax heuristic. A numerical experiment and a sensitivity analysis are presented to illustrate the applicability of the proposed approach.

Industrial sensors efficiency is commonly determined by the ability to provide accurate indications during a mission. In addition, several techniques and methods are incorporated to increase sensors measurement accuracy. These examinations are classified into experimental and theoretical classes. Experimental examinations focus on determining the flow coefficient variation, which results from the installation effect and flow characteristics. Theoretical proposals deal principally with formulation of the instrument reliability function. These formulations integrate different parameters which affect the sensor measurement accuracy during a given mission. This paper discusses the failure risk and effect analysis for the Pitot sensors system measurement accuracy during a given mission. In addition, a set of factors is used to determine the causal diagram and to model the corresponding hazard function. These factors are subject to statistical analysis in the second part of this paper. The current study is also driven by a practical need to well manage aeronautical problems linked to Pitot sensors system malfunctioning during a flight.

Background Transvenous embolisation is a promising technique but the benefits remain uncertain. We hypothesised that transvenous embolisation leads to a higher rate of arteriovenous malformation angiographic occlusion than transarterial embolisation. Methods The Transvenous Approach for the Treatment of cerebral Arteriovenous Malformations (TATAM) is an investigator initiated, multicentre, prospective, phase 2, randomised controlled clinical trial. To test the hypothesis that transvenous embolisation is superior to transarterial embolisation for arteriovenous malformation obliteration, 76 patients with arteriovenous malformations considered curable by up to two sessions of endovascular therapy will be randomly allocated 1:1 to treatment with either transvenous embolisation (with or without transarterial embolisation) (experimental arm) or transarterial embolisation alone (control arm). The primary endpoint of the trial is complete arteriovenous malformation occlusion, assessed by catheter cerebral angiography. Complete occlusions will be confirmed at 3 months, while incompletely occluded arteriovenous malformations, considered treatment failures, will then be eligible for complementary treatments by surgery, radiation therapy, or even transvenous embolisation. Standard procedural safety outcomes will also be assessed. Patient selection will be validated by a case selection committee, and participating centres with limited experience in transvenous embolisation will be proctored. Discussion The TATAM trial is a transparent research framework designed to offer a promising but still unvalidated treatment to selected arteriovenous malformation patients. Clinical Trial Registration-URL: http://www.clinicaltrials.gov . Unique identifier: NCT03691870.

Data and object connectivity are the key and the critical component of industry 4.0, Connectivity of software, equipment, data ... are all massive data to be processed. Combined with cybersecurity, they become fundamental elements for creating intelligence in an industrial system that can increase the flexibility of production in addition to allocating resources more efficiently. In order to successfully transition to ecosystem 4.0, the industry must examine the new skills that are required and the need for qualified personnel. The fourth industrial revolution corresponds to the widespread use of sensors and intelligent agents to connect machines, components, processes and operators. This is generally called the digital factory or industry 4.0. It suggests a global system of effective communication between the modules of decision-making. World 4.0 will allow interaction between products, machines and even between machines (M2M). The product will also be able to communicate with machines in the production phase. This is called intelligent production, which will allow the modification and customization of products in their production phase. In this article, we present a communication model based on Modbus and multiband communication using NB-IOT, this communication model allows data to be exchanged between different industrial equipment (PLCs, SCADA systems, etc.) via MODBUS RTU and ASCII, via RS485, or via analog and digital inputs for sensors and actuators.

Domination is one of the classical subjects in structural graph theory and in graph algorithms. The Minimum Dominating Set problem and many of its variants are NP-complete and have been studied from various algorithmic perspectives. One of those variants called irredundance is highly related to domination. For example, every minimal dominating set of a graph G is also a maximal irredundant set of G. In this paper we study the enumeration of the maximal irredundant sets of a claw-free graph. We show that an n-vertex claw-free graph has O ( 1.9341 n ) maximal irredundant sets and these sets can be enumerated in the same time. We complement the aforementioned upper bound with a lower bound by providing a family of graphs having 1.5848 n maximal irredundant sets.

International audience; This article investigates integrated maintenance, production, and product quality control policy for a supply chain consisting of a single machine producing only one type of product, a main storage warehouse, and multi-purchases warehouses. The variation of the production rate and its use over time impact the manufacturing system’s degradation degree. Hence, the machine is subject to a random failure that directly affects the quality of the products. The goal of this study is to establish an optimal production and delivery planning with inventory management considering the production, holding, and delivery costs, and then an appropriate maintenance strategy, considering the influence of the production rate on the system degradation. Also, we provide a quality control policy to reduce the proportion of non-compliant products by using the statistical process control chart to forecast production. Forecasting the production aims to satisfy the varying demands during a finite horizon under service and quality levels. Numerical examples are presented to justify the effectiveness of the suggested strategy.

Due to the fourth revolution experiencing, referred to as Industry 4.0, many production firms are devoted to integrating new technological tools to their manufacturing process. One of them, is rescheduling the tasks on the machines responding to disruptions. While, for static scheduling, the efficiency criteria measure the performance of scheduling systems, in dynamic environments, the stability criteria are also used to assess the impact of jobs deviation. In this paper, a new performance measure is investigated for a flowshop rescheduling problem. This one considers simultaneously the total weighted waiting time as the efficiency criterion, and the total weighted completion time deviation as the stability criterion. This fusion could be a very helpful and significant measure for real life industrial systems. Two disruption types are considered: jobs arrival and jobs cancellation. Thus, a Mixed Integer Linear Programming (MILP) model is developed, as well as an iterative predictive-reactive strategy for dealing with the online part. At last, two heuristic methods are proposed and discussed, in terms of solution quality and computing time.

International audience

International audience; The selection of the suitable production parameters for dry machining remains a challenging task which impacts the productivity, the system degradation and the quality of the output product. This study proposes an integrated production-maintenance policy allowing the simultaneous consideration of the production parameters, mainly the cutting speed, the production time and cost, the preventive maintenance period and the quality levels and the selling prices of the output product. A special feature of this strategy, defined on a finite time span, is to allow a change of the cutting speed at a certain instant to be determined in an optimal way concurrently with the preventive maintenance period, such as to maximise the total expected profit per time unit. A mathematical model is developed to express the average profit per time unit as a function of the decision variables. A solving procedure is also proposed. A numerical example and a sensitivity analysis are presented in order to show the impact of some major input parameters on the optimal policy.

International audience; In this paper, N types of vehicles having different environmental impacts and different failure rates are considered to perform a set of missions during a predefined period. The sizing problem of the fleet of vehicles is typically based on the literature for the environmental impact of each type of vehicle. This work intends to develop a model that allows considering not only the extent of recourse to non-polluting vehicles but also the preventive maintenance (PM) policy to be adopted for each of the N types of vehicles. More specifically, the objective of this work consists in determining simultaneously the quasi-optimal number of vehicles of each type to be used, the duration of their use, and their average usage rate as well as the period according to which each type of vehicle should be submitted to preventive maintenance. A mathematical model is developed to express and optimize the expected total cost, which includes the costs related to acquisition, operating, maintenance, and environmental impact in addition to considering the resale value. Then, the situation of using the acquired vehicle fleet in a context of a health crisis with containment measures is considered. The latter make it impossible to perform preventive maintenance actions during the containment period. For such situations, given the accumulated degradation in absence of preventive maintenance, the cost model is modified to generate a new preventive maintenance plan to be applied for each vehicle after the containment exit. Numerical results related to fuel and electric vehicles of two brands (Renault and Nissan) are presented and discussed.

Urban public transport systems influence the infrastructure of urban areas and the lives of their inhabitants while directly stimulating the economy. Intelligent urban public transport systems help to improve the quality of life and the environment in cities. The rapid development of urban transport solutions has led to a large number of operators entering the market, thus preventing a global optimum. These discrete optimisations, without any articulation between transport operators, avoid the identification of a global optimum. As a result, the inefficient operation of urban public transport systems does not necessarily reduce the environmental cost. To address these challenges, this thesis proposes a methodology associated with mathematical models developing optimisation approaches for multimodal public transport networks, for achieving the best service policy while minimising operation costs in order to satisfy the principle of sustainability, frequently expressed in urban development goals., Les systèmes de transport public urbain influencent l'infrastructure des agglomérations et la vie de leurs habitants tout en stimulant directement l'économie. Les systèmes de transport public urbain intelligents contribuent à améliorer la qualité de vie et l'environnement dans les villes. Le développement rapide des solutions de transport urbain a conduit de très nombreux opérateurs à se porter sur ce marché empêchant ainsi une logique globale de l’offre. Ces optimisations discrètes, privées de toutes concertations entre opérateurs de transport intervenant sur un même périmètre, interdit l’identification d’un optimum global. En conséquence, le fonctionnement inefficace des systèmes de transport public urbain ne réduit pas nécessairement la charge environnementale, et les opérateurs de transport urbain peuvent ne pas être en mesure de la gérer de manière durable. Pour répondre à ces défis, cette thèse propose une méthodologie associée à des modèles mathématiques qui sont développés à travers des approches d’optimisation pour une gestion systémique des réseaux de transport public multimodal, et ce afin d’assurer le meilleur taux de service aux usagers tout en minimisant les coûts et les externalités sociétales afin de satisfaire au principe de durabilité, fréquemment exprimé dans les plans de développement urbains.

International audience; This work targets the challenging issue to produce high throughput and low-cost configurable architecture of Discrete wavelet transforms (DWT). More specifically, it proposes a new hardware architecture of the first and second generation of DWT using a modified multi-resolution tree. This approach is based on serializations and interleaving of data between different stages. The designed architecture is massively parallelized and sharing hardware between low-pass and high-pass filters in the wavelet transformation algorithm. Consequently, to process data in high speed and decrease hardware usage. The different steps of the post/pre-synthesis configurable algorithm are detailed in this paper. A modulization in VHDL at RTL level and implementation of the designed architecture on FPGA technology in a NexysVideo board (Artix 7 FPGA) are done in this work, where the performance, the configurability and the generic of our architecture are highly enhanced. The implementation results indicate that our proposed architectures provide a very high-speed data processing with low needed resources. As an example, with the parameters depth order equal 2, filter order equal 2, order quantization equal 5 and a parallel degree P = 16, we reach a bit rate around 3160 Mega samples per second with low used of logic elements (≈400) and logic registers (≈700).

The identification of heart related diseases is challenging due to several contributory factors associated with patients, medical staff or medical materials used for diagnosis. Electrocardiogram (ECG) signal represents the electrical activity of the heart. It is the most common method used to diagnose patients with cardiovascular abnormalities. The evaluation commonly practiced by trained physicians can be sometimes subjective, time consuming and related to the observer status. This subjectivity can be more critical due to the double signification of the recorded ECG signals, mainly frequency and duration. In our paper, we present a comparative study of different Artificial Intelligence (AI) approaches as a very relevant tool to assist and improve the accuracy of cardiovascular diseases diagnosis. These models are trained on an online available MIT-BIH arrhythmia, normal rhythm sinus and BIDMC congestive heart failure databases and tested on our own collected data consisting of more than 72000 samples recorded in accordance with patients suffering from the same pathologies. The abnormal ECG signals are judged abnormal by comparison with normal heart beats. The work consists of testing and evaluating the performance of trained support vector machine (SVM), convolutional neural networks (CNN), quadratic discriminant, k-nearest neighbors and naive Bayes as classifiers to correctly and efficiently classify newly unlabeled data. Further, methodology comprises continuous wavelet transform (CWT), discrete wavelet transforms (DWT), maximum overlap discrete transform (MODWT) and autoregressive modelling (AM) as feature extraction techniques. We tested the prelisted methods with principal component analysis (PCA) to evaluate the dimensionality reduction influence on the overall accuracy and runtime measures. The consistency of performance is evaluated using overall accuracy with confidence interval (CI), misclassification cost and runtime. The study resulted on an overall accuracy of 99.92% with a CI of 99.07–100% and 98.63% with a CI of 95.1%-100% using quadratic discriminant and KNN respectively, both with a certainty level of 99%. The developed approach is robust and accurate and can be used for automated diagnosis of cardiovascular diseases.

L'accès au document peut-être possible sur demande au contributeur.; Following the logistics outsourcing strategies implemented for several decades, Logistics Services Providers (PSL) have become key players in any Supply Chain (SC). To integrate these actors into its SC, the shipper must make a rigorous selection of the most efficient, those having to meet its requirements in terms of reliability, responsiveness, and with whom it can consider establishing partnership relations. The PSL selection process is the object of our research developed in this HDR thesis, which we have studied in terms of selection criteria and methods. The main criteria identified in this process such as ICT, quality, relationship, and cost, as well as the evolution of their relative importance in recent years, have been detailed. In terms of methods, we have categorized them into five groups, which are: MADM, statistical approaches, mathematical programming, artificial intelligence, and hybrid models. As part of our research work, we have implemented the ELECTRE method and the hybrid ELECTRE -AHP model.; Suite aux stratégies d’externalisation logistique mises en œuvre depuis plusieurs décennies, les prestataires de services logistiques (PSL) sont devenus des acteurs incontournables de toute chaîne logistique ou Supply Chain (SC). Pour intégrer ces acteurs dans sa SC, le chargeur doit procéder à une sélection rigoureuse des plus performants, ceux devant satisfaire ses exigences en termes de fiabilité, de réactivité, et avec lesquels il pourra envisager de nouer des relations partenariales. Le processus de sélection des PSL est l’objet de notre recherche développé dans ce mémoire d’HDR, que nous avons étudié en termes de critères et de méthodes de sélection. Les principaux critères identifiés dans ce processus tels que :TIC, qualité, relations, et coût, ainsi que l’évolution de leur importance relative au cours des dernières années ont été détaillés. En termes de méthodes, nous les avons catégorisés en cinq groupes, qui sont : MADM, approches statistiques, programmation mathématique, intelligence artificielle, et modèles hybrides. Dans le cadre de nos travaux, nous avons implémenté la méthode ELECTRE et le modèle hybride ELECTRE-AHP.

International audience

International audience

International audience

This paper proposes two novel families of non-stationary subdivision schemes with a shape parameter of hierarchically and efficiently generating mixed hyperbolic/trigonometric curves of order 3 and 4. An analysis of convergence and smoothness of the proposed schemes is established by using the asymptotic equivalence method. This paper also discusses the bivariate tensor-product subdivision scheme for the surface modeling on the regular meshes. The numerical results they produce are very encouraging. In this context, the performance of our algorithms has been exposed by considering examples, which illustrate how the shape parameter and the control points are assigned to reproduce such analytic curves and surfaces using tensor-product notion.

Overcrowded hospitals in its different process’s levels is a very common issue all over the globe. Research through different works have studied this problem from different angles. Most of those focused on a very specific part of the internal flows. This study presents a mathematical model aiming to reflect patient flow and resources usage. This model presents a new perspective on how it is possible to organize a hospital flow for inpatients of different pathologies. It is based on Non-Homogenous Discrete Time Markovian chains. It takes into consideration the patient’s pathology, survival function and the current beds distribution and discharges. To model the different variations of a patient’s flow, our work uses an actual case study to validate that our mathematical model based on Markov Chains can represent a real scenario of patients flow in a hospital and can predict the resources occupancy. We used as entry to this model the arrival distribution of patients as represented in the NHS dataset. Served patients and overflowing patients relate proportionally to admission rate based on how much time every diagnosis and preliminary treatment will take, then using the previous beds availability ration will permit, with knowledge of all present patients in the system, calculate the beds occupancy in the actual time. Only based on inputs from the NHS database, bed occupancy can be calculated using our model. Then compare this calculus with published data of the bed’s occupancy in the NHS. in the period between March 2020 and February 2021. The results of the simulation model were then compared to the dataset using the chi-square goodness of fit test. Non-homogenous discrete Markovian model, survival function, patient’s pathology, time-dependent, pathology, primary allocations, secondary allocations.

International audience; L'économie circulaire qui découle de la logistique inverse est actuellement un concept populaire promu par l'union européenne, par plusieurs gouvernements nationaux et par de nombreuses entreprises dans le monde entier. Dans ce papier préliminaire nous proposons une étude bibliographique non exhaustive sur la planification des opérations de désassemblage et d'assemblage, en mettant l'accent sur l'incertitude des délais. Ce papier est classé par catégories et discuté en fonction des principales opérations, à savoir le désassemblage et l'assemblage. Pour chacune des options de susmentionnées, ce papier élucide les définitions correspondantes, avec un aperçu structuré des méthodes de résolution. Une analyse transversale de la documentation examinée a mis en évidence un certain nombre de lacunes en matière de recherche et a révélé de multiples possibilités de recherche pour soutenir le développement de l'économie circulaire. Les principales conclusions montrent un intérêt croissant pour la durabilité des processus industriels linéaires traditionnels dans le contexte de l'économie circulaire.

International audience