Supply Chain Management Using Evolutionary Algorithms
Skitsko V. I., Voinikov M. Y.

Skitsko, Volodymyr I., and Voinikov, Mykola Yu. (2024) “Supply Chain Management Using Evolutionary Algorithms.” The Problems of Economy 3:240–248.
https://doi.org/10.32983/2222-0712-2024-3-240-248

Section: Economic theory

UDC 004.8:519.85:658.5

Abstract:
The era of digital transformation has made it possible to accumulate large amounts of data that can be used in the decision-making process, in particular in supply chain management. With the complication of the problems to be solved, classical optimization methods lose their effectiveness and do not allow to obtain a solution in an acceptable time, which creates the need to study another suitable tools, among which there are evolutionary algorithms that use the principles of biological evolution, allowing to obtain solutions close to optimal (or even exactly optimal) in an acceptable time. Evolutionary algorithms are part of a broader field in artificial intelligence that is evolutionary computing. The article allocates the characteristics of evolutionary algorithms that distinguish them from other algorithms of evolutionary computing, and analyzes the most popular evolutionary algorithms: genetic algorithm, genetic programming, evolutionary programming, evolutionary strategies and differential evolution, in particular, their features and areas of application in supply chain management. A comparative analysis is carried out and recommendations are provided for the selection of the appropriate algorithm, taking into account the characteristics of the problem, in particular, the structure of the solution (coding), the discreteness or continuity of variables, and the speed of getting into the local optimum. The available literature is analyzed and a list of the use of various evolutionary algorithms for the tasks of supply chain management is provided, in particular, in warehouse planning, transportation organization, work planning, etc. Since the effectiveness of the application of evolutionary algorithms depends not only on the choice of a specific algorithm, but also on the choice of parameters, their flexible configuration, etc., in future studies it is advisable to consider modifications of evolutionary algorithms, both hybrid and adaptive approaches.

Keywords: evolutionary algorithms, supply chain management, genetic algorithms, genetic programming, evolutionary programming, evolutionary strategies, differential evolution.

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Skitsko Volodymyr I. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Mathematical Modeling and Statistics, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: skitsko@kneu.edu.ua
Voinikov Mykola Yu. – Postgraduate Student, Department of Mathematical Modeling and Statistics, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: mykola.voinikov@gmail.com

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