fr / eng

Sustainable systems

The research conducted by ISAESupméca's Sustainable Systems team focuses on several scientific bottlenecks.

The first goal is to address the challenges related to the management of production systems used to create goods and services. The related work focuses on performance evaluation, system management, and the detection of performance limits that require a partial or complete system redesign.  We also study the challenges that are posed by these redesign or upgrade processes, namely “problem posing” and “problem solving”.

In the background of all the aforementioned research, the foundational scientific bottleneck is related to the study of change propagation using dependency or correlation networks.

The goal of these research efforts is to develop decision-support methodologies, methods, tools and techniques (prototypes of software programs, formal techniques).



Doctoral candidates

The topics can be found on the list of on-going thesis projects


Head of the team

Management of Production Systems

Performance-driven management is the major axis of the conducted research. Consequently, this requires the ability to evaluate current performance levels, to appraise potential and target performance levels and to determine the plan the system must follow to reach these levels. If the targeted performance levels cannot be reached the studied system must be redesigned. In this last scenario our goal is to develop a new configuration for the system to ensure that the targeted performance levels can be reached.


Production Systems in the Creation of Goods

  • Our intent with Besbes‘ thesis project is to propose production-facility reconfiguration techniques. Optimisation is not the primary goal here. Instead we are searching for techniques that would enable to take into account most of the real-life constraints found in these facilities. We use metaheuristics as part of our tools.
  • The aim of Kechaou‘s thesis project, which is funded in the frame of the EUGENE FUI 25 Unique Inter-ministry Fund project, is to identify the deep-rooted reasons behind the low levels of performance observed in various production lines. By following a diagnostic approach tailored to OEE (Overall Equipment Effectiveness), the issue is to determine what causal relationships there are between this performance index and the levers for action from each area of influence (maintenance, planning). The employed techniques stem from probabilistic graphical models, including Bayesian networks.
  • Taking tomorrow’s industrial context into account, in the frame of the industrial world of the future, poses the issue of the considerable amount of data generated by the means of production and also generated by the very products and related logistical support tools or systems that are used in a given company. Exploiting this data to extract knowledge will become a key solution to performance-driven management. In the context of Soufi‘s thesis project the aim is to explore and determine what the necessary data is by cross-checking data acquired from machines with product flow data, in order to process it as effectively as possible to define the level of performance of factories in the future. The targeted techniques stem from statistical methods, including principal component analysis.

Production Systems in the Creation of Services

Operating very large systems such as vehicle-sharing systems poses issues that are directly related to their size (aggregation of models or controllability).

  • By analysing Paris’ Vélib bicycle-sharing system, the aim of Samet‘s research is to provide a formal modelling framework (queuing theory) to estimate what the attainable performance levels are. The results have enabled to show the relevance of certain management techniques in the short or medium term without any network modification (fleet size, station capacity).
  • Both the stations and bicycles are capable of providing data that is not utilised much to manage the network. Processing the very large amount of available data is the goal of Feng‘s thesis project. As such it was possible to analyse the dynamic behaviours of the network (from an overall point of view) but also that of each station. The utilisation of unsupervised machine-learning techniques, including cluster analysis (hybrid or non-hybrid methods) has enabled to discern different behaviour patterns that are necessary to elaborate management recommendations.


The most important challenge is related to engineering changes that can be integrated into initial designs. We have carried out research in order to determine, model, and as a consequence develop the ability to predict system behaviours and performance levels after the integration of changes.

Among the industrial subjects there are on-going studies for we decided to focus on obsolescence. The studies conducted thus far have been covering:

Obsolescence Management

  • Long-term Storage. The purpose of the research carried out for Kevin Boissie‘s thesis project is to identify the most economical techniques to preserve the quality of the components and modules involved in the design and manufacturing of Valeo’s commercial subsystems. The objective here is to establish what the most important characteristics of these subsystems are in order to identify the optimal storage-facilities for these all over the world. The number of purchased components and modules is in the nine-digit range. Also, the overall cost for the company is taken into account in the implemented techniques.
  • With a view towards predictive obsolescence-management, the goal of Treblisi‘s thesis project is to determine the data that is necessary to predict when a feature, technology or component may become obsolete. This thesis project aims at exploring specialised databases or any other kind of data that is deemed believable or trustworthy. AI techniques such as neural networks are currently being developed to be able to attribute a confidence score to the probability of occurrence and impact-severity of obsolescence.

Obsolescence-Resilient design

  • Resilient Design. The aim of Soltan‘s thesis project is to extract the dependency models of a given system by exploiting its very models. Thales’ ARCADIA system-modelling methodology is utilised as well as Capella, the related support tool. Models of varying levels of detail must be developed (or used when they exist) in order to determine the related dependency models. These models are then transformed into one or several Bayesian networks using a set of developmental rules. These networks enable to conduct numerical experiments in order to propose redesign recommendations that minimise the propagation of obsolescence.

Team members

    Caroline Bourcier

+33 1 49 45 29 07



    Roberta Costa Affonso

+33 1 49 45 25 46

Associate professor


    Florent Couffin

+33 1 49 45 29 41

Associate professor


    Patrice Leclaire

+33 1 49 45 25 44

Associate professor


    Marc Zolghadri

+33 1 49 45 29 44

University professor



Doctoral candidates

  • Amel Soltan (2018-2021) Thesis conducted under joint supervision from ISAESupméca and the National Engineering School of Sfax, Tunisia (ENIS)
  • Fatma Kechaou (2017-2020) Thesis project conducted in the frame of the EUGENE project
  • Imen Trabelsi (2018-2021) Thesis conducted under joint supervision from ISAE‑Supméca and the National Engineering School of Sfax, Tunisia (ENIS)
  • Amel Souifi (2018-2021) Thesis conducted under joint supervision from ISAE‑Supméca and the National Engineering School of Sfax, Tunisia (ENIS)
  • Yunlong Feng (2016-2019) Winner of the IPGP thesis-project funding (Paris institute dedicated to the study of Earth and planetary sciences)
  • Mariem Besbes (2016-2019) Thesis conducted under joint supervision from ISAE‑Supméca and the National Engineering School of Sfax, Tunisia (ENIS)
  • Kévin Boissie (2016-2019) Thesis project funded by Valéo
  • Bacem Samet (2014-2019) Thesis conducted under joint supervision from ISAE‑Supméca and the National Engineering School of Sfax, Tunisia (ENIS)

Recent publications

An Obsolescence Management Model for the Aerospace Industry of Developing Nations using the SORA tool (lire)
TRA 2024 2024
Energy consumption prediction of industrial HVAC systems using Bayesian Networks (lire)
Energy and Buildings Elsevier 2024 309 pp. 114039
A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant (lire)
Journal of Intelligent Manufacturing Springer Verlag (Germany) 2024
A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant (lire)
Journal of Intelligent Manufacturing Springer Verlag (Germany) 2024
Prévision de l’obsolescence (lire)
Les Techniques de l'Ingenieur Editions T.I. 2023
Inventory sizing of components at risk of obsolescence or shortage using genetic algorithm (lire)
Modelling Industry 4.0 transformation: a comparative approach between academic literature and French companies’ transformation cases (lire)
56th CIRP International Conference on Manufacturing Systems 2023 Elsevier 2023 120 pp. 786-791
Obsolescence Management in the Aerospace Industry of Developing Nations (lire)
Inventory sizing of components at risk of obsolescence or shortage using genetic algorithm (lire)
CIRP CMS 2023 120 pp. 1630-1635
Obsolescence et perte des connaissances dans les organisations (lire)
Les Techniques de l'Ingenieur Editions T.I. 2023 pp. Réf : G7022 v1
DEPS: a model- and property-based language for system synthesis problems (lire)
Software and Systems Modeling Springer Verlag 2023
Déspécialisation: une démarche systématique de la (ré)ingénierie des systèmes pour une meilleure résilience face à l'obsolescence et à la pénurie (lire)
1er congrès annuel SAGIP 2023
Despecialization: a systematic approach to (re-)engineer systems ensuring a better resilience to obsolescence and shortages (lire)
Procedia CIRP ELSEVIER 2023 120 pp. 828-833
Designing the architecture of electrochemical energy storage systems. A model-based system synthesis approach (lire)
Journal of Energy Storage Elsevier 2022 54 pp. 105351
Facility Layout Design through Integration of Lean Manufacturing in Industry 4.0 context (lire)
IFAC-PapersOnLine Elsevier 2022 55 pp. 798-803
Uncertainty of key performance indicators for Industry 4.0: A methodology based on the theory of belief functions (lire)
Computers in Industry Elsevier 2022 140 pp. 103666
A proposed integrated manufacturing system of a workshop producing brass accessories in the context of industry 4.0 (lire)
International Journal of Advanced Manufacturing Technology Springer Verlag 2022
Toward a correct by construction design of complex systems : The MBSS approach (lire)
Procedia CIRP ELSEVIER 2022 109
Obsolescence management practices overview in Automotive Industry (lire)
11th IFAC Symposium on Intelligent Autonomous Vehicles, IAV 2022 2022 55 pp. 52-58
A methodology for health assessment (SD-22,2021) using Genetic algorithm (lire)
IIOM 2022
Identification and Assessment of Obsolescence in the Early Stages of System Design (lire)
Journal of Integrated Design and Process Science IOS Press 2022 24 pp. 15-33
A comparative study of overall equipment effectiveness measurement systems (lire)
Production Planning and Control Taylor & Francis 2022 35 pp. 1-20
Digital twin-driven dynamic scheduling of a hybrid flow shop (lire)
Journal of Intelligent Manufacturing Springer Verlag (Germany) 2022
Obsolescence des systèmes informatiques et du logiciel (lire)
Les Techniques de l'Ingenieur Editions T.I. 2022 pp. Réf : H7002 v2
Obsolescence des systèmes informatiques et du logiciel (lire)
Technologies logicielles Architectures des systèmes 2022
Voir toutes les publications (HAL)

Ongoing thesis projects

Analysis of a Complex System and Performance Improvement Through the Implementation of Operations Management Decisions and System Redesign - Bacem SAMET

French supervisory team: Marc Zolghadri (Professor), Florent Couffin (Associate professor)

Tunisian supervisory team: Mohamed Haddar (Professor), Maher Barkallah ( Assosiate professor)

Keywords: Complex system; decision support; performance-level; management; re-design; queuing


Research summary

Long service-life complex-systems are large systems that generally function according to a stochastic behaviour. This thesis project focuses on the study of one such system in particular: a bicycle-sharing system. With this type of transportation-service bicycles are made available in several stations across the city. Users come and pickup bicycles for their trips before returning them to a station.
Since the operational life of these systems is long, new needs (e.g. station attractiveness) or performance degradation can arise. Therefore, a decision-support tool is necessary to analyse and improve system-performance by implementing certain operations management decisions (e.g. changing fleet size) or a redesign (e.g. changing station capacity).
The chosen approach is that of stochastic modelling, using queuing networks with limited queue-capacity and a blocking mechanism. The solution method for the proposed model is defined in Kouvatsos’ research work (1994).
Our case study is a twenty-station sub-network of the Vélib’ bicycle-sharing system in Paris. The performance analysis that was conducted after the exogenous changes and implementation of the improvement operations (operations management and redesign related) enabled us to derive a series of recommendations that may improve system-performance. 

The solution method for this model is highly complex. Therefore, we propose to use a station aggregation method in order to reduce the size of the problem by having controllable errors. This method has been implemented and evaluated for a specific system with exclusively homogenous parameters. Finally, we propose to investigate this method in non-homogenous systems and other prospects in order to expand the research.


Approved scientific outputs:

  1. Samet, Bacem, et al. « Model reduction for studying a Bike Sharing System as a closed queuing network. » 8th Swedish Production Symposium, SPS 2018. 2018.
  2. Samet, Bacem, et al. « Optimal Work-In-Process control for a closed multistage production system with machine preference. » International Conference Design and Modeling of Mechanical Systems. Springer, Cham, 2017.

Link to source code:


Methods and Tools for Risk-Management in Obsolescence Engineering in an Uncertain Environment: Application to a Motor-Vehicle Equipment Supplier – Kevin BOISSIE

Valeo Comfort & Driving Assistance

French supervisory team: Marc Zolghadri & Sid-Ali Addouche

Valeo supervisor: Daniel Richard

Keywords: Obsolescence, DMSMS, automobile sector, OES, decision, strategy


Research summary

The focus of this research is obsolescence analysis and “Diminishing Manufacturing Sources and Material Shortages” (DMSMS) more broadly speaking, in the context of the automotive industry exclusively and in the case of equipment manufacturers (OEM) and suppliers (OES) specifically. This is a very peculiar market in terms of contractual conditions (guaranteed service-life of at least 15 years in case of direct relationship with customers vs. 6 years or more with suppliers) and production volume (about three trillion components made available on the market every year).

Equipment manufacturers and suppliers are also negatively impacted by shorter component life-cycles which have gone from 25 years in 1960 to 4 years in 2016, which is completely incompatible with ever more technology-oriented customer requirements, as induced by the growing complexity of developed solutions and products (connected vehicles, telematics, autonomous driving, etc.).

The conducted research is aimed at developing methods and tools that enable to manage, contain and anticipate technology life cycles in order to interact with the organisation right from the design stage. By applying scientific reasoning to real-life data the goal is to develop obsolescence management into obsolescence engineering.

These concepts will also enable to develop current design standards strategically in order to limit the impact of end-of-life components, but also to define new standardisation rules that include strategic sourcing, as well as warehouse mapping, in the context of long-term storage.


Approved scientific outputs:

  • Obsolescence prediction: a Bayesian model.
    Marc Zolghadri, Sid-Ali Addouche, Kevin Boissie, Daniel Richard.
    Procedia CIRP, ELSEVIER, 2018, 70, pp.392 – 397.〈010.1016/j.procir.2018.02.037〉. 〈hal-01925143〉
  • Obsolescence Mitigation in Automotive Industry using Long Term Storage Feasibility Model.
    Kevin Boissie, Sid-Ali Addouche, Marc Zolghadri, Daniel Richard.
    Procedia Manufacturing, 2018, 16, pp.39 – 46.〈10.1016/j.promfg.2018.10.156〉. 〈hal-01925129

Methods and Tools for Product/System Design Taking Obsolescence and Rarefaction into Account – Amel SOLTAN

French supervisory team: Marc Zolghadri & Sid-Ali Addouche

Tunisian supervisory team: Maher BARKALLAH & Mohamed HADDAR (ENIS National Engineering School)

Keywords: System design, obsolescence, DMSMS, resilient architecture


Research summary

Customers and clients are more and more demanding and competition is increasingly fierce in the industrial world. That is why businesses are speeding up change. They are modifying the functions, components and even the features of given products. This speeding up of change has created other dangerous phenomena: obsolescence and rarefaction. New products replace older ones indeed. This reduces product life cycles. When a product is impacted by obsolescence the related revenue rapidly decreases. This causes serious losses for businesses. Therefore it is necessary to apply obsolescence mitigation strategies. Our study proposes a mitigation strategy that is to be implemented right from the design stage. It is indeed at this stage that the costs and time necessary to implement a solution are the lowest.

If a component becomes obsolete, the components that are directly or indirectly related to it are at-risk of being impacted. It is the propagation of that risk to a product’s entire architecture that is discussed here. Therefore it is very important to mitigate this propagation and slow down the pace at which it is impactful. Besides, several obsolescence-mitigation solutions have been cited. However, none of those cover obsolescence propagation at the design stage or the creation of a robust architecture.

We offer a four-step obsolescence mitigation methodology for the resilient design of products (figure 1).

Figure 1: obsolescence mitigation methodology


Approved scientific outputs:

“Obsolescence paths through the value chain”,Amel Soltan, Sid-Ali Addouche , Marc Zolghadri, Maher Barkallah, Mohamed Haddar, 7th International Conference on Through-life Engineering Services , Cranfield University, UK

A Proposed Decision-Support Tool for the (Re)Design of Modular Production Workshops – Mariem BESBES

French supervisory team: Marc Zolghadri & Roberta Costa Affonso

Valeo supervisory team: Faouzi Masmoudi & Mohamed Haddar

Keywords: Infrastructure layout; genetic algorithm; A* search algorithm; manufacturing systems; barriers; Monte Carlo methods


Research summary

Meeting customer or client demands in a fast and effective manner requires, among other things, to possess adequately-designed production systems. This issue that is known as “facility layout design” (or redesign) consists in finding the most efficient way to organise a production system by including N number of equipment items in a defined space and in compliance with various constraints, while optimising a series of performance objectives. The authors in (Tompkins et al. 2010) say that equipment layout has a significant impact on the level of productivity and effectiveness of a company (20 to 50% of the total operating expenses in manufacturing environments are attributed to the costs of merchandise transportation in the workshops).

Facility-layout design issues are addressed according to: (a) the type of layout (single-row, multi-row, multi-floor, cellular), (b) the types of problems (machine allocation, departments, product flow), or (c) the number of objectives (one or more). These studies also take the static or dynamic nature of the problem into account. However, in spite of the significant amount of research data, facility layout design is considered as one of the most complex issues since the range of plausible solutions can be very broad due to the high number of variables. In light of these limitations the purpose of our work is to offer a new modelling and solution method to support workshop (re)design while taking several constraints into account. The goal is to minimise the costs of transportation between workstations. These costs depend on the distance there is between the equipment items. The first improvement opportunity that we pursued involved the A* search algorithm to evaluate the distances between workstations more realistically. This algorithm determines the shortest possible route between workstations by avoiding obstacles, taking the different product manufacturing processes into account, and complying with the available means of transportation. The second idea consists in developing a method that combines the genetic algorithm with the A* search algorithm in order to explore broad ranges of potential solutions while making sure that a “proper” configuration is obtained. A sensitivity analysis of crossover and mutation probability as well as the values of the chosen parameters was conducted using Monte Carlo methods.


Approved scientific outputs:

  • Mariem BESBES, Roberta COSTA AFFONSO, Marc ZOLGHADRI, Faouzi MASMOUDI, Mohamed HADDAR “Multi-criteria decision making approaches for Facility Layout (FL) evaluation and selection: A survey”, The International Congress Design and Modelling of Mechanical Systems (CMSM’2017) 27th – 29th March, 2017, Hammamet, Tunisia.
  • Mariem BESBES, Roberta COSTA AFFONSO, Marc ZOLGHADRI, Faouzi MASMOUDI, Mohamed HADDAR “Multi-criteria decision making for the selection of a performant manual workshop layout: a case study”, The 20th World Congress of the International Federation of Automatic Control, (IFAC2017) 9th – 14th July, 2017, Toulouse, France.
  • Mariem BESBES, Faouzi MASMOUDI, Marc ZOLGHADRI , Roberta COSTA AFFONSO , Mohamed HADDAR “Machines assignment in a constrained workshop”, ICAV 2018, 19th – 21th March, 2017, Hammamet, Tunisia.
  • Mariem BESBES, Roberta COSTA AFFONSO, Marc ZOLGHADRI, Faouzi MASMOUDI, Mohamed HADDAR “A survey of different design rules-based techniques for facility layout problems ”, Tools and methods of competitive engineering (TMCE2018) 7th – 11th May, 2018, Las palmas Gran Canaria, Spain.


The developed MATLAB code:


Recent thesis projects

Knowledge-Based Operational-Performance Management of Production Systems – Fatma KECHAOU

Supervisory team: Marc ZOLGHADRI, Sid-Ali ADDOUCHE

Keywords: Failure diagnosis and prognosis; building upon and leveraging knowledge; supervision; proactive maintenance; availability; Bayesian networks

Research summary

In order to remain competitive, manufacturers in the cosmetics sector must be able to optimise the cost of producing luxury goods. Failure diagnosis and prognosis is an area of interest when improving manufacturing efficiency. The goal of the thesis project is to implement a responsive solution for supervision purposes while ensuring there is a better compromise between equipment availability, operational costs, product quality and competitiveness.

The research explores human expertise as well as a causal-independence reasoning centred on a Bayesian probabilistic formalism, in order to develop a methodology that enables to build a model that estimates production-equipment condition. What stems from the results is a decision-support, overall visualisation tool for manufacturers. This thesis project is conducted in the frame of the French EUGENE project in collaboration with PUIG, PKB and DPS.

Utilising Big Data for Multi-Objective, Performance-Driven Management in the Context of Industry 4.0 – Amel SOUIFI

French supervisory team: Marc Zolghadri, Zohra Cherfi-Boulanger (Professor, University of Technology of Compiègne (UTC))

Tunisian supervisory team: Mohamed Haddar (Professor), Maher Barkallah (Associate professor)



Generally speaking, production systems must be managed in such a way as to reach their performance objectives. Performance evaluation can include the traditional Time-Cost-Quality triangle but it can also be more complex and involve ecology or human-related aspects.

The advent of Industry 4.0 is putting emphasis on the use of digital twins by businesses for all types of processes. It is very much based on the utilisation of robots. A distinctive aspect of these radical changes occurring in production systems is related to the availability of considerable amounts of data. Another distinctive aspect is related to how these changes will require significant investments before they can generate real benefits. Therefore, industrial management in the digital era requires taking those distinctive aspects into account.

The purpose of this thesis project is to take the concepts related to performance measurement and evaluation and include them into a durable management loop (taking the availability of massive quantities of data into account) for the “hyper-digital” manufacturing industry. The goal is to identify appropriate techniques that enable to:

  • evaluate the performance level of actual processes and/or
  • estimate performance levels (by anticipation) using digital twins.

Afterwards these measurements/estimates must be used to provide a summary of the best real-time management strategies in the short and medium terms.


Imen Trabelsi

Title: Proposed Techniques and Methods Based on Artificial Intelligence for the Improved Detection and Forecasting of Obsolescence

Thesis supervisor: Marc Zolghadri

Co-supervisors: Besma Zeddini (Associate professor, EISTI international school of information processing sciences, Quartz laboratory), Yan Liu (Changchun University, China)


Obsolescence, as a phenomenon that impacts clients and suppliers as well as end-consumers has always existed, but its perception by the general public was limited or non-existent up until a dozen years ago. The knowledge of how obsolescence impacts workflow management and treasury is not consistent across all industrial sectors. Obsolescence has been studied extensively by manufacturers in the aeronautics and space industries. On the other hand, the “recent” uptake of new technologies (software programs and products) in the automotive and home automation sectors for instance is creating genuine marketing, technical and scientific challenges. Similarly to those new sectors that are impacted by obsolescence, changes occurring to automobiles — including electric, self-driving, hybrid as well as standard models — show how crucial it is to solve the issues posed by obsolescence in this sector, and many others.

Obsolescence can then be studied from two complementary angles: obsolescence management on the one hand, and obsolescence-resilient product-design on the other. The focus of this thesis is the first aspect and the forecasting of obsolescence in particular. The limited amount of research available on forecasting techniques focuses on the number of sold products, modelled as a random process. An obsolescence stage is then defined by taking the standard deviation and average sales into account. This technique has several shortcomings because it is exclusively based on the sales. Functional characteristics or technological advances that are not related to the product are not taken into consideration.

Therefore, it is necessary to conduct a thorough analysis of the product using a systems approach in the frame of a methodology that will be developed in this thesis project. Afterwards, the other objective is to develop/tailor/offer modern, artificial-intelligence techniques in order to predict the occurrence of obsolescence as precisely as possible.

This thesis project will focus on the automotive sector although other areas may be considered (e.g. smartphones) depending on the practical possibilities offered by the research.

Data mining is the most suitable of the considered tools. It is a set of techniques that enables to collect and build upon a large amount of raw data, in order to extract relevant and previously-untapped knowledge or information in an automatic or semi-automatic, supervised or unsupervised manner. Forecasting systems then utilise this collected knowledge or information to predict product obsolescence risk.

Postdoctoral research

Design of a Modular Product-Variety Generator – Mahmoud MASMOUDI

Supervisor: Marc Zolghadri

Collaborator: Mariem Besbes

Keywords: Product design and development; product families; modularity; product variety

Research summary                  

This research is mainly based on the area of product design and development, specifically on approaches that are based on (a) product platforms and (b) common modules that are shared within product families. These two types of approaches can help reduce costs and introduce multiple product-variations more rapidly. Good practices have enabled us to analyse the current situation of modular design methods or platforms and identify the existing shortcomings and challenges. Based on these aspects, we are currently proposing a new variety-generation approach and developing a decision-support tool. This variety generator can help designers find several variations of an existing product easily. This research contains two stages: (i) the mathematical modelling of the modules that form the product and the interactions (constraints) between them and (ii) the use of the genetic algorithm as a solution method. Afterwards, various criteria can be applied in order to choose the variation that is most consistent with the designer’s expectations. We chose to apply this method to planes built with LEGO bricks.


Scientific outputs

Journal article

  • Mahmoud Masmoudi, Patrice Leclaire, Zolghadri Marc, Mohamed Haddar. Change Propagation Prediction: a formal model for 2D geometrical models of products. Concurrent Engineering: Research and Applications, SAGE Publications, 2017. 〈hal-01478397〉

Thesis report

  • Mahmoud Masmoudi. Contribution au développement d’une méthode analytique pour l’identification des dépendances et la propagation des changements d’ingénierie en re-conception de produits (contribution to the development of an analytical method to be used for dependency identification and in Engineering Change Propagation in the context of product redesigns). Computation and language [cs.CL]. Centrale Supélec, 2017. French. 〈NNT: 2017SACLC021〉. 〈tel-01709930〉

Conference papers:

  • Mahmoud Masmoudi, Patrice Leclaire, Zolghadri Marc, Mohamed Haddar. Engineering Change Management: A novel approach for dependency identification and change propagation for product redesign. 20th IFAC World Congress, Jul 2017, Toulouse, France. 〈〉. 〈hal-01478353〉
  • Mahmoud Masmoudi, Patrice Leclaire, Marc Zolghadri, Mohamed Haddar. DEPENDENCY IDENTIFICATION FOR ENGINEERING CHANGE MANAGEMENT (ECM): AN EXAMPLE OF COMPUTER-AIDED DESIGN (CAD)-BASED APPROACH. the 20th International Conference on Engineering Design (ICED 15), Jul 2015, Milan, Italy. Proceedings of the 20th International Conference on Engineering Design (ICED 15), 3, Organisation and Management. 〈hal-01340960〉
  • Mahmoud Masmoudi, Patrice Leclaire, Marc Zolghadri, Mohamed Haddar. A Design Structure Matrix (DSM) -based method for dependency characterisation in Engineering change management (ECM) domain. ATAVI, International Conference on Acoustics and Vibration, Mar 2016, Hammamet, Tunisia. 〈hal-01341202〉
  • Mahmoud Masmoudi, Patrice Leclaire, Zolghadri Marc, Haddar Mohamed. New Methods to Assess the Effects of Engineering Change Propagation. The Second International Conference on Acoustics and Vibration, Mar 2018, Hammamet, Tunisia. 2018. 〈hal-01713203〉

Book chapters:

  • Mahmoud Masmoudi, Patrice Leclaire, Marc Zolghadri, Mohamed Haddar. Modelling Dependencies in Engineering Change Management (ECM). Design and Modeling of Mechanical Systems – II, pp 161-168, 2015, 978-3-319-17527-0. 〈1007/978-3-319-17527-0_16〉. 〈hal-01340961〉
  • Mahmoud Masmoudi, Patrice Leclaire, Zolghadri Marc, Mohamed Haddar. Engineering Change Management (ECM) methods: classification according to their dependency models. Design and Modeling of Mechanical Systems – III, 2017, 978-3-319-66697-6. 〈hal-01478416〉

Mahmoud Masmoudi, Patrice Leclaire, Vincent Cheutet, Enrique Casalino. Modelling and Simulation of the Doctors’ Availability in Emergency Department with SIMIO Software. Case of Study: Bichat-Claude Bernard Hospital, Mechatronic Systems: Theory and Applications, Springer Verlag (Germany), pp.119-129, 2014, 〈1007/978-3-319-07170-1_12〉