The era of Industry 4.0 has already begun, however, several improvements should be achieved concerning this revolution. Data mining is one of the modest and efficient tools. Based on a specific query entered in Scopus, related to Industry 4.0, data mining (DM) and logistics, selected documents were studied and analyzed. A brief background of Industry 4.0 and DM are presented. A generic analysis showed that the attentiveness for the cited subject area by countries, universities, authors and especially companies and manufacturers increased through the years. Content analysis reveals that the improvement in quality of the technologies used in manufacturing was noticed, concluding that DM would give Industry 4.0 a leap forward, yet research is dealing with several challenges.

Despite technological advancements, chemical and process industries are still prone to accidents due to their complexity and hazardous installations. These accidents lead to significant losses that represent economic losses and most importantly human losses. Risk management is one of the appropriate tools to guarantee the safe operations of these plants. Risk analysis is an important part of risk management, it consists of different methods such as Fault tree, Bow-tie, and Bayesian network. The latter has been widely applied for risk analysis purposes due to its flexible and dynamic structure. Bayesian networks approaches have shown a significant increase in their application as shown by in the publication in this field. This paper summarizes the result of a literature review performed on Bayesian network approaches adopted to conduct risk assessments, safety and risk analyses. Different application domains are analysed (i.e. accident modelling, maintenance area, fault diagnosis) in chemical and process industries from the year 2006 to 2018. Furthermore, the advantages of different types of Bayesian networks are presented.

Despite technological advancements, chemical and process industries are still prone to accidents due to their complexity and hazardous installations. These accidents lead to significant losses that represent economic losses and most importantly human losses. Risk management is one of the appropriate tools to guarantee the safe operations of these plants. Risk analysis is an important part of risk management, it consists of different methods such as Fault tree, Bow-tie, and Bayesian network. The latter has been widely applied for risk analysis purposes due to its flexible and dynamic structure. Bayesian networks approaches have shown a significant increase in their application as shown by in the publication in this field. This paper summarizes the result of a literature review performed on Bayesian network approaches adopted to conduct risk assessments, safety and risk analyses. Different application domains are analysed (i.e. accident modelling, maintenance area, fault diagnosis) in chemical and process industries from the year 2006 to 2018. Furthermore, the advantages of different types of Bayesian networks are presented.

Despite technological advancements, chemical and process industries are still prone to accidents due to their complexity and hazardous installations. These accidents lead to significant losses that represent economic losses and most importantly human losses. Risk management is one of the appropriate tools to guarantee the safe operations of these plants. Risk analysis is an important part of risk management, it consists of different methods such as Fault tree, Bow-tie, and Bayesian network. The latter has been widely applied for risk analysis purposes due to its flexible and dynamic structure. Bayesian networks approaches have shown a significant increase in their application as shown by in the publication in this field. This paper summarizes the result of a literature review performed on Bayesian network approaches adopted to conduct risk assessments, safety and risk analyses. Different application domains are analysed (i.e. accident modelling, maintenance area, fault diagnosis) in chemical and process industries from the year 2006 to 2018. Furthermore, the advantages of different types of Bayesian networks are presented.

Despite technological advancements, chemical and process industries are still prone to accidents due to their complexity and hazardous installations. These accidents lead to significant losses that represent economic losses and most importantly human losses. Risk management is one of the appropriate tools to guarantee the safe operations of these plants. Risk analysis is an important part of risk management, it consists of different methods such as Fault tree, Bow-tie, and Bayesian network. The latter has been widely applied for risk analysis purposes due to its flexible and dynamic structure. Bayesian networks approaches have shown a significant increase in their application as shown by in the publication in this field. This paper summarizes the result of a literature review performed on Bayesian network approaches adopted to conduct risk assessments, safety and risk analyses. Different application domains are analysed (i.e. accident modelling, maintenance area, fault diagnosis) in chemical and process industries from the year 2006 to 2018. Furthermore, the advantages of different types of Bayesian networks are presented.

Nowadays, successful and innovative product development is highly correlated with the company’s success and reason for existence. A development process is a major factor influencing cost, timing and quality of product development. It requires additional attention to decisions made about programme, budget, technical and market risks. In this paper a product development process model is proposed in an innovation context and strategy framework of design process and project management. The process modelling is complex network theory based, to improve characterization analysis for product development process modelling. Required concepts for complex process are established to build product development mathematical model, and provide an overview of key definitions and complex networks advanced tools. Finally, a case study for an Algerian electric generator company is carried out to prove the practicality of the proposed model.

Nowadays, successful and innovative product development is highly correlated with the company’s success and reason for existence. A development process is a major factor influencing cost, timing and quality of product development. It requires additional attention to decisions made about programme, budget, technical and market risks. In this paper a product development process model is proposed in an innovation context and strategy framework of design process and project management. The process modelling is complex network theory based, to improve characterization analysis for product development process modelling. Required concepts for complex process are established to build product development mathematical model, and provide an overview of key definitions and complex networks advanced tools. Finally, a case study for an Algerian electric generator company is carried out to prove the practicality of the proposed model.

Prevention and protection of explosions are two notions often used subjectively, and to transform them into operative terms of decision support, it is indispensable to develop quantitative or semiquantitative approaches to determine the hazardous zones. The “classical and point-source” approaches that determine ATEX (explosive atmospheres) zones are semiquantitative methods that can meet the requirements of the ATEX directives (Directives 99/92/EC and 94/9/EC). The methodology’s principle in determining ATEX zones consists in making a comparison with typical examples “classical approach” and to identify the source points, determine the degree of discharge, identify the type of the zone, determine the radius of the zone and ultimately the extent and shape of this zone “source point approach.” The aim of this work is, on the one hand, to propose and present a classification methodology of the ATEX zones and, on the other hand, to apply the proposed methodology in a hydrocarbon separator.

Prevention and protection of explosions are two notions often used subjectively, and to transform them into operative terms of decision support, it is indispensable to develop quantitative or semiquantitative approaches to determine the hazardous zones. The “classical and point-source” approaches that determine ATEX (explosive atmospheres) zones are semiquantitative methods that can meet the requirements of the ATEX directives (Directives 99/92/EC and 94/9/EC). The methodology’s principle in determining ATEX zones consists in making a comparison with typical examples “classical approach” and to identify the source points, determine the degree of discharge, identify the type of the zone, determine the radius of the zone and ultimately the extent and shape of this zone “source point approach.” The aim of this work is, on the one hand, to propose and present a classification methodology of the ATEX zones and, on the other hand, to apply the proposed methodology in a hydrocarbon separator.

Prevention and protection of explosions are two notions often used subjectively, and to transform them into operative terms of decision support, it is indispensable to develop quantitative or semiquantitative approaches to determine the hazardous zones. The “classical and point-source” approaches that determine ATEX (explosive atmospheres) zones are semiquantitative methods that can meet the requirements of the ATEX directives (Directives 99/92/EC and 94/9/EC). The methodology’s principle in determining ATEX zones consists in making a comparison with typical examples “classical approach” and to identify the source points, determine the degree of discharge, identify the type of the zone, determine the radius of the zone and ultimately the extent and shape of this zone “source point approach.” The aim of this work is, on the one hand, to propose and present a classification methodology of the ATEX zones and, on the other hand, to apply the proposed methodology in a hydrocarbon separator.

Prevention and protection of explosions are two notions often used subjectively, and to transform them into operative terms of decision support, it is indispensable to develop quantitative or semiquantitative approaches to determine the hazardous zones. The “classical and point-source” approaches that determine ATEX (explosive atmospheres) zones are semiquantitative methods that can meet the requirements of the ATEX directives (Directives 99/92/EC and 94/9/EC). The methodology’s principle in determining ATEX zones consists in making a comparison with typical examples “classical approach” and to identify the source points, determine the degree of discharge, identify the type of the zone, determine the radius of the zone and ultimately the extent and shape of this zone “source point approach.” The aim of this work is, on the one hand, to propose and present a classification methodology of the ATEX zones and, on the other hand, to apply the proposed methodology in a hydrocarbon separator.

The coronavirus (COVID-19) pandemic poses an unprecedented global challenge, impacting profoundly on health and wellbeing, daily life, and the economy around the world. The COVID-19 pandemic has also changed education forever. The COVID-19 has resulted in schools shut all across the world. Globally, all children at schools or students at universities are out of the classroom. As a result, education has changed dramatically, with the notable rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. Batna 2 University -situated in East of Algeria- is one of the universities suggested after the spread of COVID-19 in March, that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay. All institutes and departments, including the Industrial Engineering department, are started using the e-learning Moodle platform to publish courses for all degrees of study and establish online sessions, especially for Ph.D. students.

The coronavirus (COVID-19) pandemic poses an unprecedented global challenge, impacting profoundly on health and wellbeing, daily life, and the economy around the world. The COVID-19 pandemic has also changed education forever. The COVID-19 has resulted in schools shut all across the world. Globally, all children at schools or students at universities are out of the classroom. As a result, education has changed dramatically, with the notable rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. Batna 2 University -situated in East of Algeria- is one of the universities suggested after the spread of COVID-19 in March, that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay. All institutes and departments, including the Industrial Engineering department, are started using the e-learning Moodle platform to publish courses for all degrees of study and establish online sessions, especially for Ph.D. students.

In this paper, a new length changeable extreme learning machine is proposed. The aim of the proposed method is to improve the learning performances of a Single hidden layer feedforward neural network (SLFN) under rich dynamic imbalanced data. Particle Swarm Optimization (PSO) is involved for hyper-parameters tuning and updating during incremental learning. The algorithm is evaluated using a subset from C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset of gas turbine engine and compared to its derivatives. The results prove that the new algorithm has a better learning attitude. The toolbox that contains the developed algorithms of this comparative study is publicly available.

In this paper, a new length changeable extreme learning machine is proposed. The aim of the proposed method is to improve the learning performances of a Single hidden layer feedforward neural network (SLFN) under rich dynamic imbalanced data. Particle Swarm Optimization (PSO) is involved for hyper-parameters tuning and updating during incremental learning. The algorithm is evaluated using a subset from C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset of gas turbine engine and compared to its derivatives. The results prove that the new algorithm has a better learning attitude. The toolbox that contains the developed algorithms of this comparative study is publicly available.

In this paper, a new length changeable extreme learning machine is proposed. The aim of the proposed method is to improve the learning performances of a Single hidden layer feedforward neural network (SLFN) under rich dynamic imbalanced data. Particle Swarm Optimization (PSO) is involved for hyper-parameters tuning and updating during incremental learning. The algorithm is evaluated using a subset from C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset of gas turbine engine and compared to its derivatives. The results prove that the new algorithm has a better learning attitude. The toolbox that contains the developed algorithms of this comparative study is publicly available.

In this paper, we consider a parallel machine scheduling problem with non-renewable resources. Each job consumes several components and must be processed in one stage composed of identical parallel machines. Resources availability operations, jobs assignment and sequencing are considered and optimized simultaneously. In order to find an optimal solution, an exact method is applied to optimize the total completion time. Due to the problem complexity and prohibitive computational time to obtain an exact solution, a metaheuristic approach based genetic algorithm is proposed and several heuristics are adapted to solve it. Moreover, the impact of non-renewable resources procurement methods on production scheduling is analyzed. The system performances are evaluated in terms of measures such as the solution quality and the execution time. The simulation results show that the proposed genetic algorithm gives the same results as the exact method for small instances and performs the best compared to heuristics for medium and large instances.

In this paper, we consider a parallel machine scheduling problem with non-renewable resources. Each job consumes several components and must be processed in one stage composed of identical parallel machines. Resources availability operations, jobs assignment and sequencing are considered and optimized simultaneously. In order to find an optimal solution, an exact method is applied to optimize the total completion time. Due to the problem complexity and prohibitive computational time to obtain an exact solution, a metaheuristic approach based genetic algorithm is proposed and several heuristics are adapted to solve it. Moreover, the impact of non-renewable resources procurement methods on production scheduling is analyzed. The system performances are evaluated in terms of measures such as the solution quality and the execution time. The simulation results show that the proposed genetic algorithm gives the same results as the exact method for small instances and performs the best compared to heuristics for medium and large instances.

In this paper, we consider a parallel machine scheduling problem with non-renewable resources. Each job consumes several components and must be processed in one stage composed of identical parallel machines. Resources availability operations, jobs assignment and sequencing are considered and optimized simultaneously. In order to find an optimal solution, an exact method is applied to optimize the total completion time. Due to the problem complexity and prohibitive computational time to obtain an exact solution, a metaheuristic approach based genetic algorithm is proposed and several heuristics are adapted to solve it. Moreover, the impact of non-renewable resources procurement methods on production scheduling is analyzed. The system performances are evaluated in terms of measures such as the solution quality and the execution time. The simulation results show that the proposed genetic algorithm gives the same results as the exact method for small instances and performs the best compared to heuristics for medium and large instances.