## 2 Related Literature  
The article [3] deals with production planning in agricultural systems with scarce water resources in arid regions. The farmers' profits were increased by reducing machinery transportation costs based on optimal schedules. Farm production planning and machinery scheduling for perennial crops were introduced to maximize the net value. The optimization method allowed for production planning, machinery scheduling and crop rotation. The optimal machinery transportation routes were determined and the irrigation water requirements were analyzed. The model yielded a mixed-integer linear optimization that was assessed on two case studies. The paper [4] presents an optimization method for farm management during a planning horizon. The model features were the incorporation of crop rotations and the consideration of crop impacts on the environment by environmental constraints. The decision tool produces a crop rotation plan which maximizes profits while satisfying the specified constraints and requirements. The proposed method was formulated as a mixed-integer linear programming optimization. The authors of [4] investigated the impact of various environmental constraints, aiming to limit the environmental impacts of farm activities to below given levels. Some constraints were derived by adopting life cycle assessment algorithms, which were illustrated using the agriculture system data available from Luxembourg. The impacts of a variety of environmental constraints, including greenhouse gas emissions, were investigated. Agricultural works can be performed by different sets of machines belonging to different brands characterized by different sizes, prices and interchangeability. Machines with different productivity result in uneven time and costs of performing work. Since mechanized works and their implementation conditions differ, each set of machines will be effective when performing one agricultural operation and less effective or completely unprofitable when performing another. When first scheduling the implementation of a complex of works, sets of machines can be assigned to specific operations in certain agro-technical periods with a given composition of MT units that are available to the enterprise. The initial plan for solving an optimization problem, especially a nonlinear one, largely determines the final result of the model calculations [5]. In this regard, the task is to determine the distribution of mechanized works according to the methods of execution (sets of machines) within a given fleet (possibly with the option of its replenishment), in which all works are performed during the planned period in the best possible way in accordance with the established optimization criteria. A technically and economically feasible way of performing a specific list of works should be determined. In order to increase the efficiency of the use of an MTF in an agricultural business, it is required to determine its rational composition with the help of economic and mathematical modeling [6]. Field operation planning is critical for the efficiency of agricultural activities. This planning problem was addressed in [7], where particular algorithms were developed based on discrete event simulation and computer programming. The developed model captured the equipment and tracks for the evolution of its movement across the field via so-called state transition tables. The validity of the model was tested by comparing its performance with empirical data collected from harvesting equipment. In the paper [8], an asymmetric multi-depot vehicle routing problem for the maintenance of a farm machinery was studied. To provide a door-to-door service for farm machinery maintenance, a node service and arc service were used. Multiple constraints included the customer's time window, maximum repair work duration, fleet size and vehicle capacity. A mathematical programming problem was formulated with the criterion to minimize total costs.