Approach For Energy Management In Industrial Systems : Use Case : Scheduling Of Automated Guided Vehicles In Job-shop Manufacturing System With Energy Constraints

Résumé: Industry 4.0 concepts are moving towards flexible and energy-efficient factories. For the same purposes, battery-based Automated Guided Vehicles (AGV) are used in flexible production lines as part of their material handling systems to allow greater control over transportation tasks. Therefore, an optimal battery management for these vehicles can significantly shorten lead times. In this thesis, a Job-shop scheduling problem in AGV based manufacturing facility is viewed as being mainly related to both the assignment of resource functions and the AGV batteries management. The studied Job-shop production cell has two types of resources: machines that manufacture products, and transportation vehicles that ensure the transfer of parts between these machines. To achieve this purpose, we use an approach that supports the expected results from the Industry 4.0, relying mainly on increasing productivity by makespan minimization, and improving energy use through management of transport vehicle batteries replenishment. This approach is based on two modified versions of the Variable Neighborhood Search algorithm (VNS), and a new mathematical formulation that improves the computation of the lower bound for the manufacturing process makespan. Finally, experimental tests will be conducted on enhanced instances of a widely used literature benchmark to evaluate the efficiency of the implemented models.

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