Communication and teamwork are among the most recurrent skills associated with knowledge of engineering sciences. However, their application is not simple, due to the lack of a pedagogical approach that contributes to the development of knowledge based on experience. The problem in factories is the lack of daily self learning to avoid the essential presence of the experts in to resolve problems. In this work, we defined what is a learning organization, what is a tutorial and why a personalized tutorial in a trade, its different forms and steps for the development of a tutorial. After we gave a presentation of the company that is Textile Batna. This article discusses how to design a personalized tutorial, oriented and aimed at learning and knowledge transfer in the industry. By developing this system we aim to build an experimental database serving to preserve the knowledge of the production industry expertise of the Batna textile factory. We have designed a tutorial for the company in the form of a website. For this, the UML language was used. The tutorial features were presented. It helped employees to aquire certain skills without assistance of experts.

Communication and teamwork are among the most recurrent skills associated with knowledge of engineering sciences. However, their application is not simple, due to the lack of a pedagogical approach that contributes to the development of knowledge based on experience. The problem in factories is the lack of daily self learning to avoid the essential presence of the experts in to resolve problems. In this work, we defined what is a learning organization, what is a tutorial and why a personalized tutorial in a trade, its different forms and steps for the development of a tutorial. After we gave a presentation of the company that is Textile Batna. This article discusses how to design a personalized tutorial, oriented and aimed at learning and knowledge transfer in the industry. By developing this system we aim to build an experimental database serving to preserve the knowledge of the production industry expertise of the Batna textile factory. We have designed a tutorial for the company in the form of a website. For this, the UML language was used. The tutorial features were presented. It helped employees to aquire certain skills without assistance of experts.

Communication and teamwork are among the most recurrent skills associated with knowledge of engineering sciences. However, their application is not simple, due to the lack of a pedagogical approach that contributes to the development of knowledge based on experience. The problem in factories is the lack of daily self learning to avoid the essential presence of the experts in to resolve problems. In this work, we defined what is a learning organization, what is a tutorial and why a personalized tutorial in a trade, its different forms and steps for the development of a tutorial. After we gave a presentation of the company that is Textile Batna. This article discusses how to design a personalized tutorial, oriented and aimed at learning and knowledge transfer in the industry. By developing this system we aim to build an experimental database serving to preserve the knowledge of the production industry expertise of the Batna textile factory. We have designed a tutorial for the company in the form of a website. For this, the UML language was used. The tutorial features were presented. It helped employees to aquire certain skills without assistance of experts.

Purpose The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes. Design/methodology/approach The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side. Findings The results carried out from this study help the managers’ staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production. Research limitations/implications The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated. Originality/value The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.

Purpose The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes. Design/methodology/approach The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side. Findings The results carried out from this study help the managers’ staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production. Research limitations/implications The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated. Originality/value The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.

Purpose The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes. Design/methodology/approach The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side. Findings The results carried out from this study help the managers’ staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production. Research limitations/implications The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated. Originality/value The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.

Manufacturing automation is a double-edged sword, on one hand, it increases productivity of production system, cost reduction, reliability, etc. However, on the other hand it increases the complexity of the system. This has led to the need of efficient solutions such as artificial techniques. Data and experiences are extracted from experts that usually rely on common sense when they solve problems. They also use vague and ambiguous terms. However, knowledge engineer would have difficulties providing a computer with the same level of understanding. To resolve this situation, this article proposed fuzzy logic to know how the authors can represent expert knowledge that uses fuzzy terms in supervising complex industrial processes as a first step. As a second step, adopting one of the powerful techniques of machine learning, which is Support Vector Machine (SVM), the authors want to classify data to determine state of the supervision system and learn how to supervise the process preserving habitual linguistic used by operators.

Manufacturing automation is a double-edged sword, on one hand, it increases productivity of production system, cost reduction, reliability, etc. However, on the other hand it increases the complexity of the system. This has led to the need of efficient solutions such as artificial techniques. Data and experiences are extracted from experts that usually rely on common sense when they solve problems. They also use vague and ambiguous terms. However, knowledge engineer would have difficulties providing a computer with the same level of understanding. To resolve this situation, this article proposed fuzzy logic to know how the authors can represent expert knowledge that uses fuzzy terms in supervising complex industrial processes as a first step. As a second step, adopting one of the powerful techniques of machine learning, which is Support Vector Machine (SVM), the authors want to classify data to determine state of the supervision system and learn how to supervise the process preserving habitual linguistic used by operators.

In this work, we introduce the objective of minimizing energy consumption in a job shop scheduling problem with unidirectional transport constraint. In this problem, it is planned to process a set of N jobs (parts) on four machines. The Movement of jobs between these machines is in a single direction that is mean all the parts follow the same direction of movement. Indeed, the energy consumption in this type of problem depends; on the one hand on the speed of the machines processing the jobs and on the other hand on the speed of the means of transport. To solve this optimization problem, we have proposed a metaheuristic method that allows us to find a better sequencing of jobs in order to minimize the cost generated by energy consumption. Several simulations have been studied and the results obtained demonstrate the effectiveness of the proposed approach.

In this work, we introduce the objective of minimizing energy consumption in a job shop scheduling problem with unidirectional transport constraint. In this problem, it is planned to process a set of N jobs (parts) on four machines. The Movement of jobs between these machines is in a single direction that is mean all the parts follow the same direction of movement. Indeed, the energy consumption in this type of problem depends; on the one hand on the speed of the machines processing the jobs and on the other hand on the speed of the means of transport. To solve this optimization problem, we have proposed a metaheuristic method that allows us to find a better sequencing of jobs in order to minimize the cost generated by energy consumption. Several simulations have been studied and the results obtained demonstrate the effectiveness of the proposed approach.

In this work, we introduce the objective of minimizing energy consumption in a job shop scheduling problem with unidirectional transport constraint. In this problem, it is planned to process a set of N jobs (parts) on four machines. The Movement of jobs between these machines is in a single direction that is mean all the parts follow the same direction of movement. Indeed, the energy consumption in this type of problem depends; on the one hand on the speed of the machines processing the jobs and on the other hand on the speed of the means of transport. To solve this optimization problem, we have proposed a metaheuristic method that allows us to find a better sequencing of jobs in order to minimize the cost generated by energy consumption. Several simulations have been studied and the results obtained demonstrate the effectiveness of the proposed approach.

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.