XXV International conference “Mathematical Optimization Theory and Operations Research” (MOTOR 2026)

Invited Speakers

 

Invited Speakers

	Prof. Saeid Alikhani Department of Mathematics, Yazd University, Yazd, Iran Majority Distinguishing Colorings of Graphs We study graph colorings that simultaneously satisfy two constraints: the majority condition and the distinguishing property. A majority coloring requires that, at each vertex, no color appears on more than half of the adjacent vertices or incident edges, while a distinguishing coloring breaks all nontrivial automorphisms of the graph. Motivated by recent work on majority distinguishing edge colorings, we introduce and formalize the concept of majority distinguishing vertex colorings. Accordingly, we define and study two new graph parameters, called the majority distinguishing number and the majority distinguishing index. We determine exact values of these parameters for several fundamental graph families, including paths, cycles, friendship graphs, book graphs, and certain classes of trees. In addition, we investigate how these parameters behave under the corona product of graphs and establish general bounds and relationships with classical distinguishing parameters. Our results extend and unify previous studies on symmetry breaking and majority colorings, and they suggest several open problems and directions for further research.
	Prof. Mikhail Y. Kovalyov United Institute of Informatics Problems, National Academy of Sciences of Belarus, Minsk, Belarus Routing and Charging Scheduling for Regular Electric Buses This work is inspired by the results of the project ‘‘Planning Process and Tool for Step-by-Step Conversion of the Conventional or Mixed Bus Fleet to a 100% Electric Bus Fleet’’ of the Electric Mobility Europe initiative. The public transport operators involved in this project faced the challenge of effectively routing and charging electric buses operating on a fixed timetable. They have all the data to make appropriate decisions. Currently, decisions are made primarily based on previous routing schemes and the "first come, first charged" principle.  The number of chargers at a charging station is determined by the maximum number of electric buses that can simultaneously reside at the station between trips. We provide an overview of research in the field of routing and charging scheduling for urban electric buses. We discuss the relationship between these problems and classic vehicle routing and machine scheduling problems.
	Prof. Huỳnh Thị Thanh Bình School of Information and Communication Technology (SoICT), Hanoi University of Science and Technology (HUST), Hanoi, Vietnam Evolutionary Multitasking: Recent Advances and Applications Evolutionary multitasking optimization is a cutting-edge topic in the field of computational intelligence that merges evolutionary computation and multitasking methodologies to address multiple optimization problems concurrently. By leveraging the interactions and dependencies between problems, evolutionary multitasking allows for the sharing and transfer of valuable information between tasks, thereby enhancing the overall search performance. This talk will provide an overview of the fundamental concepts, principles, and recent advancements of evolutionary multitasking optimization. Additionally, the talk will highlight the practical significance of evolutionary multitasking algorithms and demonstrate how these techniques can effectively tackle complex real-world optimization problems. Through these problems, the talk will delve into algorithm design issues such as solution encoding and the knowledge transfer mechanism in the multitasking environment. Performance evaluations on benchmark datasets will also be presented to demonstrate the effectiveness of evolutionary multitasking algorithms.
	Prof. Mikhail A. Marchenko Artificial Intelligence and Information Technology Laboratory, Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia Parallelization of statistical modeling methods The talk will focus on methods of parallelization of statistical modeling algorithms. The main emphasis is placed on the problems of statistical modeling of kinetic processes, what is due to their practical significance. An important area touched upon in the talk is the optimization of parallel statistical modeling algorithms in order to reduce their computational complexity. The level of performance of modern supercomputers makes the use of probabilistic models of kinetic processes extremely relevant. First reason is that such models adequately describe physical phenomena considering the influence of rare events, which is practically impossible for other approaches. Next reason is that they can be effectively implemented as parallel programs. In numerical statistical modeling applications, it is necessary to use parallel pseudorandom number generators, effectively implemented on multiprocessor computers. Then it is necessary to apply a distributed computing methodology that allows for parametric analysis of probabilistic models. The last problem is related to the study of the characteristics of probabilistic models, such as estimation error and computational labor intensity, depending on the parameters of the problem and the algorithm. To solve these problems, it is also necessary to have standard parallelized software tools.

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