Sonia Hajri-Gabouj’s research while affiliated with National Institute of Applied Science and Technology and other places

Numerous operational constraints within both industry and service sectors mandate the concurrent scheduling of tasks. This need is particularly evident in the assembly of products within manufacturing processes. This paper concentrates on minimizing makespan in the two-stage assembly flow shop problem. Our contributions include the introduction of novel dominance rules, a proposal for a heuristic method, and the development of a branch and bound algorithm. Additionally, we conduct an empirical analysis of makespan distribution for small-size instances. Through extensive experimentation, our study demonstrates the efficiency of the introduced dominance rules and the strong performance of the developed branch and bound algorithm.

To improve the competitiveness of industry in Tunisia and Morocco, local authorities have adopted strategies to support industrial companies in modernizing their value-adding infrastructures. A sustained effort has been dedicated to encouraging industrial organizations to embrace the Industry 4.0 paradigm and technologies. Despite these continued efforts, engaging with Industry 4.0 is still difficult in countries like Tunisia and Morocco, particularly because industrial organizations struggle to find fresh graduates on the job market who are both skilled and qualified in Industry 4.0 operations. The contribution of this article is to initiate a process to better understand and assess the gap between industrial needs and academic offer with respect to Industry 4.0 skills and qualifications, considering the specificities of Tunisia and Morocco. We particularly focus on analyzing to what extent existing curricula in engineering education institutions satisfy or miss industrial needs and requirements in three core industrial business processes: maintenance, production, and quality (MPQ4.0). Therefore, a survey was conducted, from which a set of MPQ4.0 targeted skills and competencies were extracted and synthesized. Based on these skills and competencies, sample engineering education curricula are analyzed, gaps are identified, and recommendations for improvement are offered.

To improve the competitivity of industry in Tunisia and Morocco, local authorities have adopted strategies to support industrial companies to modernize their value adding infrastructures. A sustained effort has been dedicated to encouraging industrial organizations to embrace the Industry 4.0 paradigm and technologies. Despite these continued efforts, engaging with Industry 4.0 is still difficult in countries like Tunisia and Morocco, particularly because industrial organizations struggle to find fresh graduates on the job market, who are both skilled and qualified in Industry 4.0 operations. The contribution of this article is to initiate a process to better understand and assess the gap between industrial needs and academic offer with respect to Industry 4.0 skills and qualifications, considering the specificities of Tunisia and Morocco. We particularly focus on analyzing to which extent existing curricula in engineering education institutions satisfy or miss industrial needs and requirements in three core industrial business processes, which are Maintenance, Production, and Quality (MPQ4.0). Therefore, a survey is conducted, from which a set of MPQ4.0 targeted skills and competencies are extracted and synthesized. Based on these skills and competencies, a sample of engineering education curricula are analyzed, gaps are identified, and improvement recommendations are drawn.

Manufacturing systems are subject to predictable and unpredictable occurrences of disturbances, which may alter pre-set organization, degrade performance and generate significant risks (direct and indirect consequences) that need to be addressed. In the literature, ontologies were used to capture past occurrences of disturbance and risk scenarios and capitalize reaction decisions in order to enable reuse of this experience in case of future occurrences of similar disturbances and risks. Unfortunately, existing works do not suggest similarity measures that take advantage of semantic and syntactic similarities between new and stored scenarios. This article fills in this gap by suggesting such similarity measures. A retrieval algorithm is developed to compare new disturbance and risk scenarios with stored ones, and to evaluate their similarity in terms of nature and severity of disturbances and risks. A case study shows competitive and promising results.

At first, Prognostics and Health Management (PHM) was interpreted as a new trend of Condition Based Maintenance (CBM) to schedule, in a more effective way, preventive maintenance actions. Lately, PHM is being used in operational decisions like choosing the appropriate mission to execute, planning logistics actions or defining the control parameters. In most of the previous studies, a single Remaining Useful Life (RUL) value was estimated by the prognostic module and integrated in the decision module regardless of the effect of already made decisions on the system health and consequently on its future RUL values. Consequently, this paper presents a jointly maintenance and operational decision-making approach for multi-component systems on a rolling decision horizon by iteratively integrating the new available RUL values to update the tasks schedule including maintenance interventions. The aim of this paper is to emphasize the need of integrating the feedback loop of the effects of already made decisions on the monitored system in the process of prognostics process. The obtained results are then compared to other methods of jointly scheduling operational and maintenance decisions like Moore algorithm combined with CBM.

Although layout problems have been widely investigated in literature, little attention has been given to expression and collection of needs, elicitation of specifications and requirements engineering, which are important activities that condition problem formalization, and selection of suitable resolution algorithms. We suggest a knowledge based approach to specify requirements and recommend design choices to arrange material handling systems and machines on a shop floor. We adopt biological immunity as a methodological guideline to design and develop an ontology to help decision makers express their needs, and to assist system designers with the specification of requirements and further determination of design choices. We show that the developed ontology is a user-friendly decision support tool that allows specifying requirements and recommending suitable material handling systems and resolution algorithms in a semi-formal language that relieves decision makers from the complexities of mathematical formalizations.