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Abstract

The new industrial era, industry 4.0, leans on Cyber Physical Systems CPS. It is an emergent approach of Production System design that consists of the intimate integration between physical processes and information computation and communication systems. The CPSs redefine the decision-making process in shop floor level to reach an intelligent shop floor control. The scheduling is one of the most important shop floor control functions. In this paper, we propose a cooperative scheduling based on multi-agents modelling for Cyber Physical Production Systems. To validate this approach, we describe a use case in which we implement a scheduling module within a flexible machining cell control tool.
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Authors and Affiliations

Hassan Khadiri
1
Souhail Sekkat
2
Brahim Herrou
3

  1. Sidi Mohamed Ben Abdellah University, Laboratory of Industrial Technologies, Morocco
  2. Moulay Ismail University, ENSAM-Meknes, Morocco
  3. Sidi Mohamed Ben Abdellah University, Superior School of Technology, Morocco
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Abstract

The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario.
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Authors and Affiliations

Habbadi SAHAR
1
Brahim HERROU
Souhail SEKKAT
2

  1. Sidi Mohamed Ben Abdellah University, Faculté des Sciences Techniques de Fès, Industrial Engineering Department, Morocco
  2. Ecole Nationale Supérieure d’Arts et Métiers ENSAM MEKNES, Industrial Engineering Department, Morocco

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