Studies in Informatics and Control – ICI Bucharest
Marius-Adrian PĂUN2,4*, Henri-George COANDĂ3, Eugenia MINCĂ3, Sergiu Stelian ILIESCU1,2,
Octavian Gabriel DUCA4, Grigore STAMATESCU2
1 Technical Sciences Academy of Romania – MO-ASTR, 26 Dacia Avenue, 030167 Bucharest, Romania
iliescu.shiva@gmail.com
2 Faculty of Automatic Control and Computer Science, National University of Science and Technology Politehnica Bucharest,
313 Splaiul Independenţei, 060042, Bucharest, Romania
paun_marius_2009@yahoo.com (*Corresponding author), grigore.stamatescu@upb.ro
3 Faculty of Electrical Engineering, Electronics, and Information Technology, Valahia University of Targoviste,
13 Aleea Sinaia St., 130004, Targoviste, Romania
coanda_henri@yahoo.com, eugenia.minca@gmail.com
4 The Scientific and Technological Multidisciplinary Research Institute, Valahia University of Targoviste,
13 Aleea Sinaia St., 130004, Targoviste, Romania
octavian_duca@yahoo.com
Abstract: The efficient management of energy consumption is an essential concern in the manufacturing industry, with far-reaching implications for both financial management and environmental sustainability. This paper proposes a new approach regarding the conceptualization and implementation of techniques for optimizing energy consumption in a production flow. In the first stage, an energy consumption monitoring system was developed, which is capable of collecting the energy consumption data for different production scenarios. The collected data represented the basis for evaluating and testing an optimization algorithm called the improved genetic algorithm (IGA), which is conceptually subordinated to the structure of the standard genetic algorithm (GA). The improved multi-objective genetic algorithm featured a high performance in terms of execution time and optimization of energy consumption. Thus, in the framework of the IGA algorithm, a layered approach to the optimization process was proposed by successively employing two genetic algorithms in the MATLAB programming environment. The first genetic algorithm identified the value of the minimum energy consumption, and the second GA adjusted the parameters of the first GA iteratively, in order to obtain the minimum consumption. Comparing the results obtained by employing the IGA algorithm with those obtained for the Non-dominated Sorting Genetic Algorithm (NSGA-II) in terms of real-time execution time, it can be noticed that a significant improvement was achieved from 25.7 s for the standard NSGA-II to 0.0527 for the IGA without any change in the performance of IGA with regard to the minimization of power consumption. By harnessing the inherent capabilities of the GA, the aim of this paper is to increase the energy efficiency for the analysed production system, thereby contributing both to cost savings and to reducing the environmental impact of manufacturing processes.
Keywords: Power consumption optimization, Power monitoring, Genetic algorithm (GA), Industrial production line.
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Marius-Adrian PĂUN, Henri-George COANDĂ, Eugenia MINCĂ, Sergiu Stelian ILIESCU, Octavian Gabriel DUCA, Grigore STAMATESCU, Improved Multi-objective Genetic Algorithm Used to Optimizing Power Consumption of an Integrated System for Flexible Manufacturing, Studies in Informatics and Control, ISSN 1220-1766, vol. 33(1), pp. 27-36, 2024. https://doi.org/10.24846/v33i1y202403