Proactive and reactive approach to employee competence configuration problem in planning and scheduling processes
Autor: | Jarosław Wikarek, Paweł Sitek |
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Rok vydání: | 2021 |
Předmět: |
021103 operations research
business.industry Computer science 0211 other engineering and technologies Scheduling (production processes) 02 engineering and technology Automation Procurement Risk analysis (engineering) Artificial Intelligence Production schedule Genetic algorithm 0202 electrical engineering electronic engineering information engineering Production (economics) 020201 artificial intelligence & image processing business Set (psychology) Competence (human resources) |
Zdroj: | Applied Intelligence. 52:3445-3464 |
ISSN: | 1573-7497 0924-669X |
Popis: | At the time of commonplace automation, robotization and the rapid development of IT, high qualifications of employees have become the critical element of every industry system. This follows from their limited availability, frequently high costs of procurement and possible employee absenteeism. Moreover, the concept of Industry 4.0 will transform current industry employees into knowledge employees. This is due to the fact that hard and routine tasks will be executed by robots and computers. This constitutes change in the required employee competences. Unfortunately, the aspect of management and configuration of employee competences is often overlooked in industrial practice. In response to the existing problem, the article puts forward the original model of employee competence configuration which is a basis for responses to numerous questions of managers of production processes, both general ones, e.g., Do we have a sufficient set of competences to execute a production schedule? as well as detailed ones, e.g., Which and how many competences are missing? etc. An important novelty of the presented model is the possibility of its application with both proactive and reactive questions. Due to the discrete and combinatorial nature of the problem under consideration, the use of mathematical programming methods was limited only to small data instances. Therefore, a proprietary dedicated genetic algorithm was proposed to solve this problem, which turned out to be extremely effective. The use of this genetic algorithm has enabled finding a solution depending on the instance data up to 70 times faster than by use of the mathematical programming. |
Databáze: | OpenAIRE |
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