Machinery & Energetics (Aug 2023)
Optimisation of transport and technological system parameters of an agricultural enterprise in conditions of partial uncertainty
Abstract
At the stage of production of a wide range of agricultural products, to ensure the smooth operation of agricultural enterprises, it is necessary to solve the problems of fast and efficient delivery of relevant equipment, spare parts, and consumables with the rational use of available production resources. The research aims to improve the transport and technological system for the supply of orders in the form of consignments to meet the needs of the production activities of an agricultural enterprise. For this purpose, an agent-based simulation model was developed in the AnyLogic 8.7 environment using the Java compiler, since this toolkit allows simultaneously combining discrete-event and agent-based approaches. The model was implemented on the example of an enterprise of a separate subdivision of the National University of Life and Environmental Sciences of Ukraine “Agronomic Research Station”. As a result, a comprehensive, optimisation mathematical model of the supply of goods on an extensive network of road routes using the agricultural enterprise’s fleet of vehicles under conditions of partial uncertainty was obtained. In the course of experiments and calculations based on the real process of an agricultural enterprise, a range of values of the size of the truck fleet that meets the optimisation conditions has been found. It has been determined that the range of values close to the optimal size of the unloading truck fleet varies from 9 to 14 units. It has been established that the values of the optimisation criterion describing the average delivery time from the beginning of the need for an order to the moment of its delivery vary from 9.96 to 12.78 hours. The limit level of load of the transport and technological system is determined, at which the limit level of technological fault tolerance is ensured. It is 135 or more orders per year for each supplier with an estimated fleet of 12 trucks. The results of the study, such as the use of analytical tools and algorithms to optimise routes and allocate resources, can be used to improve transport efficiency, and help companies choose the most profitable and environmentally friendly routes for transportation
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