Artificial intelligence methods for short-term planning in petroleum products realization

In this article, a critical analytical review of the application of artificial intelligence methods in the field of scheduling theory is presented, exemplified by the constraints of the short-term planning problem in the process of petroleum products realization via road transport. The objective of the research was to systematize and evaluate existing approaches to solving planning tasks while considering specific temporal constraints inherent to the petroleum products realization process. During the study, both exact and approximate methods for solving scheduling theory problems were analyzed, including heuristic algorithms and approaches based on artificial neural networks. It was established that existing methods have significant limitations when addressing semi-online planning tasks. The research findings demonstrate the necessity for developing a new method capable of promptly restructuring schedules in response to unpredictable changes that arise during the petroleum products realization process. The results of the study highlight the promising potential for advancing artificial intelligence methods to address short-term planning challenges.