Affiliation:
1. Department of Computer Sciences, University of Oran 1, Ahmed Ben Bella, El M’Naouer, Oran, Algeria
Abstract
The new wave of industry 4.0 has made battery-based automated guided vehicles (AGVs) an essential tool for material handling in manufacturing systems. However, many challenges related to battery management and machines and AGVs energy consumption. To handle these challenges an efficient battery management strategy is designed. The proposed approach supports multispeed operating modes for machines and AGVs, which offers a high flexibility to the manufacturing system. The aim of the proposed approach is to keep the minimal residual electric charge above the critical level, while enhancing the global performance of the manufacturing system. As a consequence, it increases the AGVs production hours and guarantees batteries safety. The developed approach can bring economic benefits for industry 4.0, by increasing the productivity and avoiding AGVs batteries damage. Extended literature benchmark instances related to the manufacturing 4.0 are used to evaluate the efficiency of the suggested approach.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Cited by
3 articles.
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