The expediency of developing and using information systems to support decision-making in harvesting projects is described. The main elements of this information system and the processes that should be taken into account to obtain reliable modeling results, and therefore the development of recommendations for the management of technological systems projects, are revealed. The peculiarities of the impact of the project environment on the start dates and duration of works performed in these projects are described. The importance of taking into account the influence of the subject component in the information system is determined, which will make it possible to objectively assess the conditions of work in projects (the rate of cultivation and the impact of agro-meteorological conditions) and take into account time restrictions on the use of technical equipment of projects. The results of the use of the information system, in particular the block of simulation performance indicators of works in projects are given. Emphasis is placed on the expediency of simulation modeling of works in projects taking into account the probabilistic conditions of the functioning of technical equipment. Simulation of works in projects with given initial data on technical equipment (Mekosan CLAAS Mega 360 harvester), time of the start of works, limits of its production area and crop type was carried out. The results of determining the influence of the main components of crop harvesting projects on the performance indicators of their implementation are presented. The simulation was performed for the specified limits of the production area of winter rape - 10500 hectares with a step-by-step increase of 10 hectares. The regularities of changes in the main indicators of work efficiency in projects, taking into account the stochastic influence of the project environment.
simulation timeliness of work, efficiency.
modeling, information system, project management, technical equipment,
An important component of the state food security of modern countries is the branch of
agricultural production. In Ukraine, this industry is also of priority importance [
2023 Copyright for this paper by its authors.
Harvesting of agricultural crops is one of the final technological operations of their mechanized
cultivation, which directly affects the volume of the obtained harvest and the final product of crop
cultivation projects. It is also known that the amount of losses of the final product largely depends on
the correctness and timeliness of work in these projects. In particular, for winter rape, they can
amount to 25-60% [
Project management processes in the production sphere are focused on a significant list of
components and patterns that form their final product. It is known that the research of such projects is
most expedient to be carried out using the methods of statistical simulation modeling [
The aim of the paper is to increase the efficiency of the management of crop harvesting projects based on the application of an information system that takes into account the consistency of the parameters of the technical equipment of the projects, the time of the start of work and their volumes according to the criterion of the maximum product obtained (the volume of the harvested crop).
To achieve the goal we solved the following tasks: - perform computer experiments with a simulation model, process their results, and establish statistical regularities of project performance indicators;
- to develop recommendations on the coordination of parameters of technical equipment of projects, the time of commencement of works their volumes the criterion of the maximum of the obtained product.
The development and use of information systems implementing decision-making support in
production projects must be carried out in the context of displaying and forecasting time constraints
for the performance of works that create the final product [
Other features of the subject area of winter rapeseed harvesting projects, which shape the terms
and pace of work, include the fact that the biological process of reaching the crop (pods and seeds of
the plant) is uneven and long-lasting in time. Irregular ripening leads to the cracking of pods and the
self-shedding of seeds, which can reach 90-100% of losses [
At the same time, according to the general theory of systems, each of the problems solved at one
or another stage of project management requires justification of the method of solution, as well as
coordination of the obtained results with the results of solving other problems, etc. In other words, all
defining problems must be solved by taking into account the principles of systems engineering [
One of the general methods of solving the problem of harmonizing the components of the
technological system is the method of finding correspondence between technical means
(technological modes of operation) of harvesting projects, production conditions, and organizational
forms and harvesting methods of their implementation [
JV
Analyzing the recommendations of practitioners, we understand that it is advisable to harvest
winter rape from the moment when 70% of dry (ε0.7) pods appear in the field. However, under such
conditions, a certain amount of crop yield is lost, which can be obtained due to the increase in seed
weight between the start and completion of work in the projects [
Guided by these requirements, we have created a statistical simulation model of work in projects,
which takes into account the described interaction of the project environment and production
components. Its application implements information and analytical support in relevant information
systems to support decision-making in production projects. In addition, the information system built
in this way makes it possible to perform computer experiments on modeling work in virtual projects
for various initial conditions and options for the interaction of component projects. Processing the
results of simulation modeling in the information system makes it possible to obtain functional
indicators and assess the probability of timely completion of works in projects with different terms of
their start and different technical support [
The use of mathematical statistics methods to process the modeling results made it possible to establish estimates of the mathematical expectation of the following indicators: 1) volumes of harvested crops; 2) volume of lost harvest due to untimely spraying with stem binders; 3) volume of lost harvest due to untimely harvesting.
Simulation modeling of work (application of pod gluers and harvester harvesting) in crop harvesting projects was carried out for various options of technical equipment operating in the agrometeorological conditions of the Yavoriv district of the Lviv region during the harvesting of winter rape of the Antaria variety. In particular, the simulation results are shown for a single complex of Mekosan Tecnoma Laser 4240-30 and CLAAS Mega 360 machines. Statistical simulation modeling was performed for the given limits of the production area (Sr) of the culture – 10-600 ha with a step-by-step increase of 10 ha. This made it possible to establish patterns of changes in the main functional indicators of project implementation with appropriate technical equipment.
Extraction of the quantitative values of the volumes (Ωn) of untimely sprayed areas from the simulation results made it possible to establish that they depend on the cumulative effect of the time of the start of the works and the volumes of their execution. It is obvious that these indicators also depend on the technical support parameters. The probable nature of Ωn for the corresponding area Sr is due to the influence of uncontrollable components of the project environment, in particular, the terms and rates of crop harvest and the influence of agrometeorological conditions during the period of the relevant works in the projects (Fig. 2).
0.25 0.20 і Р ,y0.15 c n e u q rF0.10 e
The processing of modeling results using the methods of mathematical statistics made it possible to establish that the construction of variation series of quantitative values of Ωn on the example of three variants of areas – 150, 250, and 350 ha enables the establishment of clear regularities. In particular, histograms of empirical distributions, as well as estimates of statistical characteristics, were obtained. According to these results, the hypothesis was put forward that the probability value Ωn is consistent with the theoretical law of the Weibull-Hnidenko distribution. The application of Pearson's Х2 criterion to check the closeness of the empirical and theoretical distributions confirmed the correctness of the proposed hypothesis (Fig. 2). The differential functions of these distributions are presented in table 1.
The application of the developed methods and models for displaying the influence of subjectbiological and agrometeorological components in production projects on the time limitations of the functioning of their technical equipment makes it possible to combine the results of production observations with computer experiments. On this basis, trends of changes in functional efficiency indicators are established under different production conditions of project implementation (parameters of technical support, start time and terms of performance of works, area of culture). Quantitative assessment of functional performance indicators in harvesting projects and establishment of their regularities is a strong basis for substantiating the effectiveness of the appropriate technical equipment and its coordination with the start time, as well as the scope of work.
On the other hand, the time of the start of winter rape harvest ( sw ) also needs justification. Due to
the uneven ripening of the stem of the culture, sw is chosen based on the proportion of ripe pods of
0.7...1.0 (70-100%). Therefore, it is necessary to establish one sw in which field work will be
coordinated with the pace of harvest, and therefore it will be ensured its maximum collection with
minimal losses. According to the obtained results of computer modeling, the influence of terms sw
on the volumes of collected areas was revealed (fig. 3). It has been established that the use of a
СLAAS Mega 360 combine harvester on an area of up to 100 ha, inclusive, makes it possible to
collect the entire crop (without technological losses) regardless of the time of the start of these works
– sw0.7 , or sw1.0 . An increase in the loading of combine harvesters (from 100 to 400 hectares) leads to
the fact that during the late sw harvest of winter rapeseed, it can last until the beginning of the next
crop [
Therefore, the use of an information system that includes the possibility of simulating relevant works in projects makes it possible to substantiate management decisions based on functional performance indicators. Accordingly, in order to obtain reliable research results, this simulation model should be based on methods and models that take into account the influence of the project environment. For harvesting projects, the impact of the project environment is largely reflected in the objective conditions – the patterns of crop harvest and the impact of agrometeorological conditions on the course of field work. By modeling these natural processes and determining the volume of work that occurs in the corresponding calendar period with the use of specialized technical support, technological losses are quantified (the volume of the lost harvest due to cracking of winter rapeseed pods and self-shedding of seeds). The construction of patterns of changes in this indicator is the basis of the justification of decisions regarding the parameters of the technical equipment of harvesting projects, the time of the start of work and the amount of areas that should be planned in the projects.
120 га 100
It is obvious that the development of recommendations to support decision-making in harvesting projects, which are formed according to the criterion of specific volumes of the harvested crop Uc, form their practical value. In particular, according to the established regularities of changes in estimates of mathematical expectation M [Uc] (fig. 4) for different values sw0.7 … sw1.0 its maximum value will be different, and therefore there will be different values of the optimal production area of the culture ( Sropt ) for the given technical equipment (CLAAS Mega 360) projects: - for the start sw0.7 of work in the projects, the Sropt value is 180 hectares with an estimate of the mathematical expectation of the specific volumes of the harvested crop – M [U c0.7 ]=26,224 c/hа; - for sw0.75 and Sropt = 150 hа will be provided M [U c0.75 ]=26,44 c/hа; - for sw0.8 and Sropt = 130 hа, in accordance, M [U c0.8 ]=26,556 c/hа; - for sw0.85 and Sropt = 120 hа, in accordance, M [U c0.85 ]=26,61 c/hа; - for sw0.9 and Sropt = 110 hа, in accordance, M [U c0.9 ]=26,736 c/hа; - for sw0.95 and Sropt = 80 hа, in accordance, M [Uc0.95 ]=26,821 c/hа; - for sw1.0 and Sropt = 50 hа, in accordance, M [Uc1.0 ]=26,87 c/hа.
The use of the criterion M [U c ] for matching sw , Sr and technical equipment parameters of harvesting projects makes it possible to achieve a rational use of a unit of sown area in the AIP, as well as a relatively greater loading of these technical means.
In turn, matching sw and Sr of winter rapeseed with the specified parameters of the technical equipment of the projects make it possible to find their values that will ensure the extremum of the objective function, and therefore the condition for the maximum of the estimates of the mathematical expectation of the specific volume of the harvested crop ( M [U cmax ]). projects (at the sw0.7 ) with the use of appropriate technical equipment (Tecnoma Laser 4240-30 and four variants of combine harvesters of different power) made it possible to coordinate the production area of the crop with the parameters of this technical equipment (fig. 5).
The obtained results indicate that the use of high-clearance sprayers of a different capacity in combination with the considered combines does not lead to a significant change in project efficiency indicators (table 2).
The justification of decisions regarding the choice of the capacity of technical equipment for harvesting projects makes it possible to increase their efficiency due to the increase in specific volumes of the harvested product (crop) and relatively higher loading of technical equipment. The implementation of such solutions in production makes it possible to create conditions for achieving the planned economic effect from matching sw , Sr with the parameters of technical equipment. 6. Conclusions
1. The development of information systems that apply simulation modeling methods and take into account the impact of the project environment on the pace of work in crop harvesting projects enables a comprehensive assessment of management decisions regarding the formation of the technological system of agriculture and the management of its development projects.
2. Simulation modeling of work in harvesting projects is aimed at multiple implementation of virtual projects with different initial conditions of their implementation. This approach makes it possible to take into account the stochastic influence of the project environment at different times of the start of work, the production area of the culture and the parameters of the technical equipment (fig. 3-5). It was established that at different times of commencement of works in the projects of estimating the mathematical expectation of the specific volumes of the harvested crop, there will be different regularities of changes and different maximums of their values in the range from 26,2 to 26,8 t/ha.
3. The application of the information system for decision-making in harvesting projects made it possible to establish patterns of changes in estimates of the mathematical expectation of specific volumes ( M [U c ]) of the harvested crop, and therefore to coordinate the parameters of the technical equipment of these projects with the time of the start of work ( sowpt )and the volume of areas ( Sropt ) cultures. The justification of decisions regarding technical equipment in these projects makes it possible to form a package of recommendations to ensure the maximum volume of the final product (harvested harvest), and therefore effectively use the available production resources of the production enterprise.
4. Coordination of the components of harvesting projects makes it possible to increase the efficiency of their implementation due to the implementation of management decisions regarding the use of such actions and resources in projects that ensure the maximum volume of the project product. In particular, it was established that the use of technical equipment consisting of a Tecnoma Laser 4240-30 sprayer and a CLAAS Mega360 harvester on an area of winter rapeseed – 180 hectares, provides the maximum estimates of the mathematical expectation of the volume of the harvested crop from a unit of area – 26,41 t/ha.
[12] S. Chernov, S. Titov, L. Chernova, et al., Efficient algorithms of linear optimization problems solution. CEUR Workshop Proceedings, 2851 (2021) 116-131. [13] A. Dzyuba, A. Torskyy, Algorithm of the successive approximation method for optimal control problems with phase restrictions for mechanics tasks. MMC. Vol. 9, N.3: (2022) 734-749. doi: 10.23939/mmc2022.03.734. [14] S. Bushuyev, D. Bushuiev, V. Bushuieva, Interaction Multilayer model of Emotional Infection with the Earn Value Method in the Project Management Process, in: 15th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2020 Proceedings, 2 (2020) 146-150. [15] O. Zachko, V. Demchyna, I. Zachko, Intellectual Models of Projects for the Development of Transport Infrastructure of Urban Territorial Systems. CEUR Workshop Proceedings, 3295 (2022) 159-169. [16] N. Heorhiadi, Information management systems: essence, types, functions, principles of construction, Visnyk Natsional'noho universytetu «L'vivs'ka politekhnika», vol. 567 (2006) 2834. [17] A. Bondar, S. Bushuyev, V. Bushuieva, S. Onyshchenko, Complementary strategic model for managing entropy of the organization, in: CEUR Workshop Proceedings, 2851 (2021) 293-302. [18] R. Yatsenko, Information systems in logistics: a study guide / Yatsenko R.M., Nikolaev I.V. –
Kharkiv: KHNEU Publishing House, 2012. 232. [19] A. Alpyssov, N. Uzakkyzy, A. Talgatbek, A. Tolstoy, Assessment of plant disease detection through deep learning. Eastern-European Journal of Enterprise Technologies, 1/2 (121) (2023) 41-48. doi: 10.15587/1729-4061.2023.274483. [20] N. Yurchuk, Information systems in the management of enterprise activities, Ahrosvit, vol. 19 (2015) 53-58. [21] M. Yevlanov, N. Vasyltsova, O. Neumyvakina & I. Panfyorova, Development of a method for solving the problem of IT product configuration analysis. Eastern-European Journal of Enterprise Technologies, 6.2(120) (2022) 6-19. doi: 10.15587/1729-4061.2022.269133. [22] A. Mathew, P. Amudha, S. Sivakumari, Deep Learning Techniques: An Overview. Advanced Machine Learning Technologies and Applications, (2020) 599-608. doi: 10.1007/978-981-153383-9_54. [23] P. Prystavka, K. Dukhnovska, O. Kovtun, et al., Recognition of aerial photography objects based on datasets with different class aggregation. Eastern-European Journal of Enterprise Technologies, 1.2(121) (2023) 6-13. doi: 10.15587/1729-4061.2023.272951. [24] O. Kopnova, A. Shaporeva, K. Iklassova, et al., Construction of an information and analytical system within the framework of a corporate information system for combining and structuring the organization's data (on the example of a university). Eastern-European Journal of Enterprise Technologies, 6/2 (120), 20-29. doi: 10.15587/1729-4061.2022.267893. [25] O. Holovin, V. Piterska, A. Shakhov, O. Sherstiuk, Project-based Management of the Production Equipment Maintenance and Repair Information System. CEUR Workshop Proceedings, 3295, (2022) 76-85. [26] V. Orobey, O. Nemchuk, O. Lymarenko, V. Piterska, Diagnostics of the strength and stiffness of the loader carrier system structural elements in terms of thinning of walls by numerical methods.
Diagnostyka, 22(3) (2021) 73-81. [27] K. Kolesnikova, T. Olekh, D. Lukianov, D.J. Obenewaa, Markov Principles of Project Effectiveness. SIST 2021 - 2021 IEEE International Conference on Smart Information Systems and Technologies, 2021, 9465881. [28] K. Kolesnikova, D. Mochalova, V. Lavrynovych, Machine Learning Model for the Atherosclerosis Prediction Based on Clinical Data. CEUR Workshop Proceedings, 3179 (2021) 134-143. [29] K. Kolesnikova, O. Mezentseva, O. Kolesnikov, D. Obenewaa, Using Agile Frameworks in Big Data projects. International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2 (2021) 415-418. [30] K. Kolesnikova, O. Mezentseva, O. Savielieva, Neural Network Imitation Model of Realization of the Business Analysis Process. Lecture Notes in Networks and Systems, 204 (2021) 1-12.