AN ALGORITHM FOR COORDINATING THE START TIME, WORK VOLUMES, AND EXECUTION RATES IN TECHNOLOGICAL SYSTEM PROJECTS

Abstract

The article addresses an information and management problem aimed at improving the efficiency of managing projects of technological systems in arable farming, taking into account the probabilistic influence of agrometeorological conditions and the combined impact of the project environment. Emphasis is placed on the fact that the probabilistic nature of natural and climatic conditions significantly complicates the coordination of schedules, work volumes, and execution rates, which directly affects crop yields and the economic performance of agricultural enterprises. The expediency of applying modern information technologies, modeling methods, and information-analytical systems to support managerial decision-making during the planning and implementation of technological system projects is substantiated. The theoretical prerequisites related to the systemic nature and partial controllability of the processes forming the efficiency indicators of such projects are outlined. A methodological approach based on the integration of open information resources, long-term agrometeorological data, and production observation results with IT services for simulation modeling of seasonal work execution in virtual projects of technological systems is presented. An algorithm for coordinating the components of arable farming technological system projects has been developed, along with a software tool that enables statistical simulation modeling of the relevant project works and, consequently, the evaluation of performance indicators, timeliness, statistical characteristics, risks, and patterns of change. The structure and main stages of functioning of a statistical simulation model of a virtual project are described, allowing the reproduction of the probabilistic dynamics of the project environment, the assessment of the naturally determined time fund for work execution and the productivity of technical equipment, as well as repeated analysis of alternative project implementation scenarios. The methodology was practically tested using sugar beet harvesting projects based on long-term data from an agrometeorological station. The results of computer experiments made it possible to determine the statistical characteristics of the optimal start time of operations and the actually harvested areas, as well as to substantiate the corresponding distribution laws. The study confirms the possibility of improving the validity of managerial decisions, reducing risks, and ensuring maximum crop harvesting by coordinating the start time, volumes, and execution rates of works while accounting for the probabilistic behavior of the project environment.