The purpose of the study is to improve the quality of operational planning (program control) of logistics processes in the conditions of modern urban systems with the interaction of a group of robotic systems. The quality of management in this study will be assessed by the number of deliveries completed after established directive deadlines. The goal set during the study is decomposed into the following tasks: system analysis of the current state of research in the field of metropolitan logistics, implementation of a substantive and formal formulation of the problem of operational planning of logistics processes in a metropolis using a group of robotic complexes, development of a model and algorithm for operational planning of logistics processes in a metropolis using a grouping of robotic complexes, development of special model-algorithmic support and its software prototype for solving the problem of operational planning of logistics processes in a metropolis using a grouping of robotic complexes. Proactive (anticipatory) management of a group of robotic systems when solving transport and logistics problems in a metropolis within the framework of the “Smart City” concept allows increasing the economic efficiency of cargo delivery. The article examines the scientific and technical problem of synthesizing technologies (plans) for the timely delivery of small-sized cargo using a group of robotic systems. The scientific significance lies in the application of the concept of integrated (system) modeling and proactive (anticipatory) management, and the practical significance lies in ensuring timely delivery of goods using a group of robotic complexes in a metropolis. The article discusses an example of solving the problem of operational planning of logistics processes using the example of Innopolis using the characteristics of Yandex delivery robots (as robotic complexes). During the study, an analysis of various options for objective functions was carried out: maximizing profit and minimizing delivery time; profit maximization; minimizing time; minimizing the number of robotic systems. The following indicators were chosen to evaluate the results obtained: total profit from deliveries; the number of deliveries not delivered on time and the total number of completed orders. The most suitable objective functions for solving the problem are time minimization or simultaneous time minimization and profit maximization. In addition, the conclusion provides directions for further research
