Hybrid machine learning approach for parallel machine scheduling under uncertainty

Abstract

Today’s manufacturing companies face numerous challenges in a dynamic and highly complex business environment. Effective planning, within the management of the production system, has a key role in achieving business success and achieving a competitive advantage for any company. The main setting of the research is the integration of three phases into one intelligent system. The first phase of the research consists of big data optimization of the planning model in the parallel connection of machines, the second phase of the experiment includes the application of different machine learning models, while the third represents the optimization of the planning model in the parallel connection of machines with stochastic processing times, which represents one of the more difficult NP problems of combinatorial optimization. The integration of machine learning models and job planning models in parallel machine connection under conditions of uncertainty is a big challenge. One of the reasons for the application of machine learning models is the influence of the input optimization parameters on the observed objective function. By choosing optimal optimization parameters, it is possible to solve the problem of parallel machine planning with stochastic processing times. The research in the paper aims to significantly improve the performance and reliability of machine planning in various industrial environments by proposing a robust and adaptive solution that can adapt to dynamic conditions and provide optimal results. The main purpose of the paper is the application and integration of an artificial intelligence model in a planning system in order to increase productivity, thereby increasing the competitiveness of small and medium-sized enterprises on the market. These tools can be relatively easily adapted to the needs of the company and would thus enable a better organization of business activities, as well as lower costs and greater business flexibility. The results of the experiment show the success of the proposed methodology.