Planning is the process of identifying the present situation, setting goals and define ways to achieve the objectives. Planning is the primary function of business management because of time preparing space for all other aspects of management, is considered a link between past and present [2]. Use of computer technology in agriculture will have a significant effect, provided achieving integrated information systems at macro (national agriculture, ministry) and micro (farm, farms etc.). Expanding use of computers in agriculture will ensure rational use of resources, their savings, the provision of complete and operative information on the conditions of production, the supply-sales market, the cost on profitability, etc. Thus, this work demonstrates the need and applicability of computer systems at the level of companies

Nitu, Mihaela

Vlad, Mihaela Cristina

Munich Personal RePEc Archive

MPRA
Munich Personal RePEc Archive
Designing a mathematical model to
optimize the size activities in the
production plan for SC RENTEA SRL
Mihaela Cristina Vlad and Mihaela Nitu
Research Institute for Agricultural Economics and Rural
Development, National Institute of Research Development for
Machines and Installations Designed to Agriculture and Food
Industry
20 November 2015
Online at https://mpra.ub.uni-muenchen.de/69380/
MPRA Paper No. 69380, posted 10 February 2016 16:17 UTC
DESIGNING A MATHEMATICAL MODEL TO OPTIMIZE THE SIZE 
ACTIVITIES IN THE PRODUCTION PLAN FOR SC RENTEA SRL 
 
VLAD CRISTINA MIHAELA1, NITU MIHAELA2 
  
Summary: Planning is the process of identifying the present situation, setting goals and define ways to achieve the 
objectives. Planning is the primary function of business management because of time preparing space for all other 
aspects of management, is considered a link between past and present [2]. Use of computer technology in agriculture 
will have a significant effect, provided achieving integrated information systems at macro (national agriculture, 
ministry) and micro (farm, farms etc.). Expanding use of computers in agriculture will ensure rational use of resources, 
their savings, the provision of complete and operative information on the conditions of production, the supply-sales 
market, the cost on profitability, etc. Thus, this work demonstrates the need and applicability of computer systems at the 
level of companies. 
 
Keywords: informatics, planning, goals 
 
INTRODUCTION 
 
To enhance management practices to ensure high quality products and competitive skills, 
farms will need to adopt methods of decision support and proper management. Accordingly, 
holdings want to become and/or remain competitive they need adequate quality resources: financial, 
material, energy, human and not least information. In order to increase competitiveness of 
agricultural products on domestic and foreign markets it is imperative to improve existing 
information flows at the branch level and to provide methods for grounding the business and the use 
of information technology to innovate the field, taking into account the strategies defined at 
European level[1]. 
 
MATERIAL AND METHODS 
 
AGR4 program allows developing production and business plan at farm level (vegetable 
farms) by calculating the alternatives on: 
 plan culture, technical and economic indicators at the level of the holding as a whole cultures 
and component technologies for each crop, the necessary material resources, manpower and 
financial resources annually, months and decades each culture and the whole farm; 
  demand for tractors and agricultural machinery months and decades; 
  calculating the normal plowing hectares (hantri) to mechanized farming operations executed 
AGR4 program can update all the indicators monthly or at any time, information which is 
realistic, "Today" in the act of operative management, increasing the effectiveness of the decisions. 
Production plans and budgets, revenues and expenditures have several sections and include 
objectives that must be completed in a year. These are the main tools in the work of management in 
agricultural companies and farms components. 
The main indicators calculated using the program AGR4 are: list of crops in the plan and the 
main indicators of materials necessary technical-economic indicators at farm, vegetable farms, 
calculate the necessary amount per calendar month assignment requisition means calendar month 
calculating the necessary mechanical means, the necessary operating hours on Monday and decades 
of agricultural machinery. 
In compiling mathematical model was used matrix coefficients characterizing techniques 
technological alternatives, the crops developed using AGR4 program product, and the product-
PROCSMS_SIMPLEX.prg Program, which gives us the opportunity to add interactive 
                                                 
1 Dr.ec. Cristina Mihaela Vlad Research Institute for Agricultural Economics and Rural Development, 
cristina.vlad@iceadr.ro 
2 Drd.ing. Nitu Mihaela, National Institute of Research Development for Machines and Installations Designed to 
Agriculture and Food Industry, rosumihaelan@yahoo.com 
mathematical model of linear programming. Simplex method for solving the model was used 
Prog_lin.exe program in Matlab library (subroutine fd_prog_xls_9). 
 
RESULTS AND DISCUSSIONS 
 
Starting from technologies used in SC RENTEA SRL in Dolj County were developed 
technological options for crops they practice: winter wheat, winter barley, barley, maize, sunflower, 
alfalfa. Technological descriptions were drawn on the experience of farmers and literature data. 
These technologies have been included in a linear programming model that offered us by solving 
solutions for crop structure. 
Calculation of technical and economic elements specific to each culture was performed with 
the Software AGR4, which allows a quick understanding of the economic indicators representative 
for each technology. The program allows calculating technical key indicators - economic 
characterizing production technology based on the data sheet mechanical technology on the work, 
the workmanship, materials and material consumption. These expenses are spread over months, 
depending on when they were scheduled to be executed.    
With AGR4 program was compiled and calculated by calendar month, consumption of 
mechanical force (mechanical ZN), manual labor (manual ZN) expenditure.  
In compiling mathematical model on optimizing gross margin in SC RENTEA SRL, Dolj 
county, to use technical coefficients matrix characterizing technological alternatives, the crops 
developed using AGR4 program product, and the product-PROCSMS_SIMPLEX.prg Program, 
which gives us the possibility of completing interactive mathematical model of linear programming. 
We PROCSMS_SIMPLEX product-schedule finalized linear programming model as 
follows: 
- Including economic function (object) (FE) 
[Max] Gross margin (lei) = 528x1 + 610x2 + 464x3 + 688x4 + 380x5 + 690x6; 
In this way it obtained mathematical model for SC Rent Srl, which is stored in a text file in 
order to deal with the method simplex. The three model variants corresponding to each of the 
economic functions (Table 1).   
Simplex method for solving the model was used Prog_lin.exe program in Matlab library 
(subroutine fd_prog_xls_9). This program allows you to retrieve data from a text file generated by 
AGR4 envelope, address it and display the results in a xls file type. 
The model that includes enrollment in xls spreadsheet type (Table 1) ("Sheet1"), resolution, 
and playback model results, respectively dual solution and the solution first. 
The first model interpreting solution for SC RENTEA SRL, Dolj County, get the size and 
structure of acreage and gross margin value. 
Following the data in Table 2 following notice: 
 In Sc Rentea Srl, wheat occupies the largest share 28.9% (22 ha) followed by corn and 
sunflower 25% (19 ha), barley 13.2% (10 ha) and alfalfa 7.9% (6ha); 
 The variants crop structure optimization is changed as follows: 
 Option 1 proposes a structure with 59 hectares planted, 20 ha maize (33.9%), by 15 ha for 
alfalfa and sunflower (25.4%) and 9 ha barley (15.2%); 
 Version 2 uses 70.7 ha spread over 22.4 hectares barley (31.7%), 19.8 ha maize (28%), 15 
ha sunflower (21.2%) and 13.5 ha alfalfa (19.1%); 
The third variant uses all the available area, 76 ha with three crops - barley 36 ha (47.4%), 
25 ha maize (32.9%) and 15 hectares of sunflower (19.7%). 
 
 
 
 
 
Table 1 model, transcribed for solving the Prog_lin.exe 
6 19  1  0  0  0  0 0  
528 610  464  688  380  690    
 
3002 3115  2805  3510  2950  2310    
 
1,125 0,267  0,267  1  1.1  1    132    ZNMAN 3 < 
1263 0,234  0,234  0,137  1.17  0,137  132   ZNMAN 7 < 
0.059 0.06  0.06  2.81  0,069  2.81  200 ZNMEC 3 < 
0.35 0.4  0.4  042  0,118  0.42  100 ZNMEC 4 < 
0.22 0.04  0.04  0.196  0.212  0.196  100 ZNMEC 5 < 
3.22 3.22  3.22  2.03  0  2.03  200 ZNMEC 9 < 
2.23 2.23  2.23  1.44  0  1.44  200 ZNMEC10 < 
1.20 120  64  250  150  250  15000 CONSAZOT < 
50 50  50  50  150  50  16500 CONSFOSFOR < 
50 50  64  120  150  140  15000 CONSPOTA < 
38 38  38  73  223  46  6000 RTOTMAN < 
36 40  40  41  68  32  15000 RTOTMEC < 
0 0  0  200  0  0  15000 LUCTERTI < 
1 1  1  1  1  1  76 Total area < 
0 0  0  1  0  1  40 Horing area < 
1 1  1  0  0  0  50 Straw area < 
0 0  0  0  0  1  15 Sunflower < 
0 0  0  0  1  0  15 Alfalfa < 
0 0  0  0  1  0  15 Alfalfa > 
Corn Barley Barley Maize Alfalfa Sunflower   
 
Processed: Evidence of technical and operative SC RENTEA SRL, Dolj County 
 
Table 2 The size and structure of cultivated areas in SC RENTEA SRL in optimized variants (V1, V2) V3) 
No. 
No. 
 
SC RENTEA 
SRL V1 V2 V3 
ha % ha % ha % ha % 
1 Corn 22 28.9 0.0 0.0 0.0 0.0 0.0 0.0 
2 Barley 10 13.2 9.0 15.2 22.4 31.7 36.0 47.4 
3 Barley 
 
0.0 0.0 0.0 0.0 0.0 0.0 0.0 
4 Corn consumption 19 25.0 200 33.9 19.8 28.0 25.0 32.9 
5 Alfalfa 6 7.9 15.0 25.4 13.5 19.1 0.0 0.0 
6 Sunflower 19 25.0 15.0 25.4 15.0 21.2 15.0 19.7 
7 Total 76 100.0 59.0 100.0 70.7 100.0 76.0 100.0 
Processed: Evidence of technical and operative SC RENTEA SRL, Dolj Couty 
 
In terms of size and structure Gross margin Sc Rentea Srl it has a value of 607.6 lei / ha, 
with a total of 46,178 lei (table 3). 
 Option 1, the proposed structure brings a total of 35,303 lei, with a gross margin of 598.3 
lei/ha; 
 Option 2 is at a level of gross margin 604.8 lei / ha worth a total of 42,751 lei; 
 An important contribution shown in the third embodiment, a plurality of 49 510 lei and a 
gross margin of 651.4 lei per ha / ha. 
From the analysis it is observed that Alternative 3 is the most advantageous. 
 
Table 3 Gross margin size and structure of SC Rentea Srl, and optimized variants (V1, V2) V3) 
No. 
No. 
Specification 
MB (euro / 
ha) 
SC RENT SRL V1 V2 V3 
ha lei ha lei ha lei ha lei 
1 Corn 528 22 11616 0.0 0 0.0 0 0.0 0 
2 Barley 610 10 6100 9.0 5474 22.4 13655 36.0 21960 
3 Barley 464 
 
0 0.0 0 0.0 0 0.0 0 
4 Corn consumption 688 19 13072 200 13779 19.8 13607 25.0 17200 
5 Alfalfa . 380 6 2280 15.0 5700 13.5 5139 0.0 0 
6 Sunflower 690 19 13110 15.0 10350 15.0 10350 15.0 10350 
7 Total X 76 46,178 59.0 35,303 70.7 42,751 76.0 49,510 
8 Gross margin (lei / ha) 
  
607.6 
 
598.3 
 
604.8 
 
651.4 
Processed: Evidence of technical and operative SC RENTEA SRL, Dolj County 
 
In Table 4, we can see the main resource consumption in society and in optimized variants: 
 Consumption of days time in March - from an available 132 days, the company 
consumes 76 variant 1-54, version 2 - 47.82 days and 49.61 days 3- version; 
 Normal day in July - where have all the 132 days, the company consumes 48 days, 
variant 1-24, version 2 - 3 version 14.16 and 13.9 days; 
 Regarding the consumption of days mechanized - for all five months, eating the 
largest we see society, version 1 has the lowest consumption (area less), and variant 
3 (same surface), we see greater consumption lower than the actual situation. 
 The same results are recorded and cosnumul fertilizer, water, manual and 
mechanized wages. 
It concluding that it optimized version 3 adds lei gross margin/ha on the same surface as the 
company with a much lower consumption of resources. 
 
Table 4 Consumption of the main resources in SC Rentea Srl and optimized variants (V1, V2, V3) 
No. No. Specification UM Resources 
Resources 
consumed 
Rentea 
V1 
consumed 
resources 
Resources 
consumed 
V2 
V3 
consumed 
resources 
1 ZNMAN 3 hours 132 76 54 47.82 49.61 
2 ZNMAN 7 hours 132 48 24 14.16 13.9 
3 ZNMEC 3 hours 100 102 100 108 114.56 
4 ZNMEC 4 hours 100 36 20 31 31.2 
5 ZNMEC 5 hours 100 14 10 9 9.28 
6 ZNMEC 9 hours 100 245 100 200 197.12 
7 ZNMEC10 hours 100 171 70 140 137.88 
8 CONSAZOT Kg as 14000 15570 12084 14000.00 14320 
9 CONSFOSFOR Kg as 16500 5350 4450 3801.67 3800 
10 CONSPOTA Kg as 15000 8260 7202 6729.09 6900 
11 RTOTMAN lei 6000 5513 5838 3799.80 3883 
12 RTOTMEC lei 15000 3767 2680 2943.00 2945 
Source: Own calculations 
 
For a complete interpretation of the solutions resulting from solving mathematical model 
uses dual solution that gives us the marginal contributions of resources restrictive (limiting) in four 
solution results (Table 5). 
The dual variable we can see totally consumed resources (become limiting factors) and 
marginal contribution of each objective function value, as follows: 
 In alternative 1 were totally consumed ZNMEC 3 with a marginal contribution from 10946 
lei, ZNMEC9 a marginal contribution of 18740 lei and sunflower surface with a marginal 
contribution of 30 lei. It reaches an objective function value of 35302 lei. 
 In the latter were consumed ZNMEC 3 with a contribution margin of 7674 lei, ZNMEC10 
with a contribution margin of 24285 lei, RTOTMAN with a contribution margin of 10082 
and the area of sunflower with a contribution margin of 711 lei. It reaches a value of 42,751 
lei objective function. 
 Version 3 has three limiting factors (resources consumed entirely), namely: the total area of 
76 ha, with a marginal contribution from 46360 lei. Hoe surface 40 ha, with a marginal 
contribution from 3120 lei and sunflower area 15 ha, with a contribution margin of 30 lei. 
Objective function value totaling 49,510 lei. 
 
Table 5 Dual solutions of the three variants 
N
o. 
Specificatio
n 
Option 1 Option 2 Option 3 
Resource 
Consumpt
ion 
Margi
nal 
Cost 
Marginal 
contribut
ion 
Resource 
Consumpt
ion 
Margi
nal 
Cost 
Marginal 
contribut
ion 
Resource 
Consumpt
ion 
Margi
nal 
Cost 
Marginal 
contribut
ion 
1 ZNMAN 3 53.92 0 0 55.6 00 0 49.61 0 0 
2 ZNMAN 7 24.45 0 0 25.8 00 0 13.9 0 0 
3 EC ZNM 3 100 109.46 10946 100.0 76.7 7674 114.56 0 0 
4 ZNMEC 4 20.07 0 0 25.2 00 0 31.2 0 0 
5 ZNMEC 5 10.4 0 0 106 00 0 9.28 0 0 
6 EC ZNM 9 100 187.4 18740 1,427 00 0 197.12 0 0 
7 ECLO 
ZNM 
70.45 0 0 1,000 242.8 24285 137.88 0 0 
8 CONSAZO
T 
1208.363 0 0 13409.0 00 0 14320 0 0 
9 CONSFOS
FOR 
4450.04 0 0 4886.6 00 0 3800 0 0 
10 CONSPOT
A 
7201.95 0 0 7621.0 00 0 6900 0 0 
11 RTOTMA
N 
5837.98 0 0 6000.0 1.7 10082 3883 0 0 
12 RTOTMEC 2680.06 0 0 3105.9 00 0 2945 0 0 
13 LUCTERTI 4005.45 0 0 3955.4 00 0 5000 0 0 
14 Total area 59 0 0 70.7 00 0 76 610 #### 
15 Area hoeing 35.03 0 0 34.8 00 0 40 78 3120 
16 Area straw 8.97 0 0 22.4 00 0 36 0 0 
17 Tis area 15 2 30 15.0 47.4 711 15 2 30 
18 Area 
alfalfa min 
15 186,22 2793.3 13.5 00 0 0 0 0 
19 Area 
alfalfa max 
15 186,22 2793.3 13.5 00 0 0 0 0 
20 Total 
contribution 
margin 
  35302.6   42,751   49,510 
 
 
 
 
 
 
CONCLUSIONS 
 
1. The interpretation of entries received, the mathematical model, we get the size and 
structure acreage and gross margin value 
 In Sc Rent Srl, wheat occupy the largest share of 28.9% (22ha) followed by corn and 
sunflower 25% (19ha), barley 13.2% (10ha) and alfalfa 7.9% (6ha), where gross margin 
has a value of 607.6 lei / ha, with a total of 46,178 lei; 
 In alternate crop structure optimization is changed as follows: 
 Option 1 proposes a structure with 59 hectares planted, 20ha maize (33.9%), each 15ha 
for alfalfa and sunflower (25.4%) and 9 ha barley (15.2%), where gross margin has a 
value of 35,303 lei with a gross margin of 598.3 lei / ha; 
 Version 2 uses 70,7 ha spread over 22.4 hectares barley (31.7%), 19.8 ha maize (28%), 
15 ha sunflower (21.2%) and 13.5 ha alfalfa (19.1%), where the gross margin is 
estimated at 604.8 lei / ha worth a total of 42,751 lei; 
 The third variant uses all the available area, 76 ha with three cultures - 36 ha barley 
(47.4%), 25 ha maize (32.9%) and 15 hectares of sunflower (19.7%), where the gross 
margin is estimated at 49 510 lei and a gross margin of 651.4 lei per ha / ha. 
2. The dual interpretation, namely the marginal costs of resources consumed entirely 
attached declaration that: 
 version 1 were totally consumed ZNMEC 3 with a marginal contribution from 10946 lei, 
ZNMEC9 a marginal contribution of 18740 lei and sunflower surface with a marginal 
contribution of 30 lei. It reaches an objective function value of 35302 lei; 
 in the latter were consumed ZNMEC 3 with a contribution margin of 7674 lei, 
ZNMEC10 with a contribution margin of 24285 lei, RTOTMAN with a contribution 
margin of 10082 and the area of sunflower with a contribution margin of 711 lei . It 
reaches a value of 42,751 lei objective function; 
 version 3 has three limiting factors (resources consumed entirely), namely: the total area 
of 76 ha, with a marginal contribution from 46360 lei. Hoe surface 40 ha, with a 
marginal contribution from 3120 lei and sunflower area 15 ha, with a contribution 
margin of 30 lei. Objective function value totaling 49,510 lei. 
3. Promoting the use of scientific methods of planning but especially for agricultural 
management software, namely to promote the use of information technology to support innovation 
in agriculture; using programs that can identify risk production has an important role in decision 
making, both in terms of choosing the optimal and hierarchical level that will be taken. The crowd 
variants of decision, the decision maker will choose which of them will generate a minimum of 
maximum foreseeable loss loss [3]. 
4. We believe that there is significant use of economic-mathematical methods in 
agriculture: integrated planning of activities and sustainable use of resources, optimizing the size 
and profile of farms, crop technology optimization, etc., which could help increase labor 
productivity and economic efficiency. 
 
BIBLIOGRAPHY 
 
1. Cofas E., 2009, Effective Information Systems in Analyzing the Profitability of Farms, PhD thesis, Veterinary 
Medicine, Bucharest 
2. Pătruţi F., Importance of Planning in Farm Work, https://www.scribd.com/doc/41677501/Importanța-
planificării-in-activitatea-exploatațiilor-agricole 
3. Helen Thomas, Simona Roxana PATARLAGEANU, Elena Cofas, Gheorghiţă Mircea Mircea Nastase, 2009 
Model for estimation and evaluation of production efficiency in agriculture activities trough Information 
Systems, Metalurgia International vol. XIV (2009), Special Issue No.13, pp 40-45, ISSN 1582-2214. 


Designing a mathematical model to optimize the size activities in the production plan for SC RENTEA SRL

Munich RePEc Personal Archive

Munich Personal RePEc ArchiveDesigning a mathematical model tooptimize the size activities in theproduction plan for SC RENTEA SRLVlad, Mihaela Cristina and Nitu, MihaelaResearch Institute for Agricultural Economics and RuralDevelopment, National Institute of Research Development forMachines and Installations Designed to Agriculture and FoodIndustry20 November 2015Online at https://mpra.ub.uni-muenchen.de/69380/MPRA Paper No. 69380, posted 10 Feb 2016 16:17 UTCDESIGNING A MATHEMATICAL MODEL TO OPTIMIZE THE SIZE ACTIVITIES IN THE PRODUCTION PLAN FOR SC RENTEA SRL  VLAD CRISTINA MIHAELA1, NITU MIHAELA2   Summary: Planning is the process of identifying the present situation, setting goals and define ways to achieve the objectives. Planning is the primary function of business management because of time preparing space for all other aspects of management, is considered a link between past and present [2]. Use of computer technology in agriculture will have a significant effect, provided achieving integrated information systems at macro (national agriculture, ministry) and micro (farm, farms etc.). Expanding use of computers in agriculture will ensure rational use of resources, their savings, the provision of complete and operative information on the conditions of production, the supply-sales market, the cost on profitability, etc. Thus, this work demonstrates the need and applicability of computer systems at the level of companies.  Keywords: informatics, planning, goals  INTRODUCTION  To enhance management practices to ensure high quality products and competitive skills, farms will need to adopt methods of decision support and proper management. Accordingly, holdings want to become and/or remain competitive they need adequate quality resources: financial, material, energy, human and not least information. In order to increase competitiveness of agricultural products on domestic and foreign markets it is imperative to improve existing information flows at the branch level and to provide methods for grounding the business and the use of information technology to innovate the field, taking into account the strategies defined at European level[1].  MATERIAL AND METHODS  AGR4 program allows developing production and business plan at farm level (vegetable farms) by calculating the alternatives on:  plan culture, technical and economic indicators at the level of the holding as a whole cultures and component technologies for each crop, the necessary material resources, manpower and financial resources annually, months and decades each culture and the whole farm;   demand for tractors and agricultural machinery months and decades;   calculating the normal plowing hectares (hantri) to mechanized farming operations executed AGR4 program can update all the indicators monthly or at any time, information which is realistic, "Today" in the act of operative management, increasing the effectiveness of the decisions. Production plans and budgets, revenues and expenditures have several sections and include objectives that must be completed in a year. These are the main tools in the work of management in agricultural companies and farms components. The main indicators calculated using the program AGR4 are: list of crops in the plan and the main indicators of materials necessary technical-economic indicators at farm, vegetable farms, calculate the necessary amount per calendar month assignment requisition means calendar month calculating the necessary mechanical means, the necessary operating hours on Monday and decades of agricultural machinery. In compiling mathematical model was used matrix coefficients characterizing techniques technological alternatives, the crops developed using AGR4 program product, and the product-PROCSMS_SIMPLEX.prg Program, which gives us the opportunity to add interactive                                                  1 Dr.ec. Cristina Mihaela Vlad Research Institute for Agricultural Economics and Rural Development, cristina.vlad@iceadr.ro 2 Drd.ing. Nitu Mihaela, National Institute of Research Development for Machines and Installations Designed to Agriculture and Food Industry, rosumihaelan@yahoo.com mathematical model of linear programming. Simplex method for solving the model was used Prog_lin.exe program in Matlab library (subroutine fd_prog_xls_9).  RESULTS AND DISCUSSIONS  Starting from technologies used in SC RENTEA SRL in Dolj County were developed technological options for crops they practice: winter wheat, winter barley, barley, maize, sunflower, alfalfa. Technological descriptions were drawn on the experience of farmers and literature data. These technologies have been included in a linear programming model that offered us by solving solutions for crop structure. Calculation of technical and economic elements specific to each culture was performed with the Software AGR4, which allows a quick understanding of the economic indicators representative for each technology. The program allows calculating technical key indicators - economic characterizing production technology based on the data sheet mechanical technology on the work, the workmanship, materials and material consumption. These expenses are spread over months, depending on when they were scheduled to be executed.    With AGR4 program was compiled and calculated by calendar month, consumption of mechanical force (mechanical ZN), manual labor (manual ZN) expenditure.  In compiling mathematical model on optimizing gross margin in SC RENTEA SRL, Dolj county, to use technical coefficients matrix characterizing technological alternatives, the crops developed using AGR4 program product, and the product-PROCSMS_SIMPLEX.prg Program, which gives us the possibility of completing interactive mathematical model of linear programming. We PROCSMS_SIMPLEX product-schedule finalized linear programming model as follows: - Including economic function (object) (FE) [Max] Gross margin (lei) = 528x1 + 610x2 + 464x3 + 688x4 + 380x5 + 690x6; In this way it obtained mathematical model for SC Rent Srl, which is stored in a text file in order to deal with the method simplex. The three model variants corresponding to each of the economic functions (Table 1).   Simplex method for solving the model was used Prog_lin.exe program in Matlab library (subroutine fd_prog_xls_9). This program allows you to retrieve data from a text file generated by AGR4 envelope, address it and display the results in a xls file type. The model that includes enrollment in xls spreadsheet type (Table 1) ("Sheet1"), resolution, and playback model results, respectively dual solution and the solution first. The first model interpreting solution for SC RENTEA SRL, Dolj County, get the size and structure of acreage and gross margin value. Following the data in Table 2 following notice:  In Sc Rentea Srl, wheat occupies the largest share 28.9% (22 ha) followed by corn and sunflower 25% (19 ha), barley 13.2% (10 ha) and alfalfa 7.9% (6ha);  The variants crop structure optimization is changed as follows:  Option 1 proposes a structure with 59 hectares planted, 20 ha maize (33.9%), by 15 ha for alfalfa and sunflower (25.4%) and 9 ha barley (15.2%);  Version 2 uses 70.7 ha spread over 22.4 hectares barley (31.7%), 19.8 ha maize (28%), 15 ha sunflower (21.2%) and 13.5 ha alfalfa (19.1%); The third variant uses all the available area, 76 ha with three crops - barley 36 ha (47.4%), 25 ha maize (32.9%) and 15 hectares of sunflower (19.7%).      Table 1 model, transcribed for solving the Prog_lin.exe 6 19  1  0  0  0  0 0  528 610  464  688  380  690     3002 3115  2805  3510  2950  2310     1,125 0,267  0,267  1  1.1  1    132    ZNMAN 3 < 1263 0,234  0,234  0,137  1.17  0,137  132   ZNMAN 7 < 0.059 0.06  0.06  2.81  0,069  2.81  200 ZNMEC 3 < 0.35 0.4  0.4  042  0,118  0.42  100 ZNMEC 4 < 0.22 0.04  0.04  0.196  0.212  0.196  100 ZNMEC 5 < 3.22 3.22  3.22  2.03  0  2.03  200 ZNMEC 9 < 2.23 2.23  2.23  1.44  0  1.44  200 ZNMEC10 < 1.20 120  64  250  150  250  15000 CONSAZOT < 50 50  50  50  150  50  16500 CONSFOSFOR < 50 50  64  120  150  140  15000 CONSPOTA < 38 38  38  73  223  46  6000 RTOTMAN < 36 40  40  41  68  32  15000 RTOTMEC < 0 0  0  200  0  0  15000 LUCTERTI < 1 1  1  1  1  1  76 Total area < 0 0  0  1  0  1  40 Horing area < 1 1  1  0  0  0  50 Straw area < 0 0  0  0  0  1  15 Sunflower < 0 0  0  0  1  0  15 Alfalfa < 0 0  0  0  1  0  15 Alfalfa > Corn Barley Barley Maize Alfalfa Sunflower    Processed: Evidence of technical and operative SC RENTEA SRL, Dolj County  Table 2 The size and structure of cultivated areas in SC RENTEA SRL in optimized variants (V1, V2) V3) No. No.  SC RENTEA SRL V1 V2 V3 ha % ha % ha % ha % 1 Corn 22 28.9 0.0 0.0 0.0 0.0 0.0 0.0 2 Barley 10 13.2 9.0 15.2 22.4 31.7 36.0 47.4 3 Barley  0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 Corn consumption 19 25.0 200 33.9 19.8 28.0 25.0 32.9 5 Alfalfa 6 7.9 15.0 25.4 13.5 19.1 0.0 0.0 6 Sunflower 19 25.0 15.0 25.4 15.0 21.2 15.0 19.7 7 Total 76 100.0 59.0 100.0 70.7 100.0 76.0 100.0 Processed: Evidence of technical and operative SC RENTEA SRL, Dolj Couty  In terms of size and structure Gross margin Sc Rentea Srl it has a value of 607.6 lei / ha, with a total of 46,178 lei (table 3).  Option 1, the proposed structure brings a total of 35,303 lei, with a gross margin of 598.3 lei/ha;  Option 2 is at a level of gross margin 604.8 lei / ha worth a total of 42,751 lei;  An important contribution shown in the third embodiment, a plurality of 49 510 lei and a gross margin of 651.4 lei per ha / ha. From the analysis it is observed that Alternative 3 is the most advantageous.  Table 3 Gross margin size and structure of SC Rentea Srl, and optimized variants (V1, V2) V3) No. No. Specification MB (euro / ha) SC RENT SRL V1 V2 V3 ha lei ha lei ha lei ha lei 1 Corn 528 22 11616 0.0 0 0.0 0 0.0 0 2 Barley 610 10 6100 9.0 5474 22.4 13655 36.0 21960 3 Barley 464  0 0.0 0 0.0 0 0.0 0 4 Corn consumption 688 19 13072 200 13779 19.8 13607 25.0 17200 5 Alfalfa . 380 6 2280 15.0 5700 13.5 5139 0.0 0 6 Sunflower 690 19 13110 15.0 10350 15.0 10350 15.0 10350 7 Total X 76 46,178 59.0 35,303 70.7 42,751 76.0 49,510 8 Gross margin (lei / ha)   607.6  598.3  604.8  651.4 Processed: Evidence of technical and operative SC RENTEA SRL, Dolj County  In Table 4, we can see the main resource consumption in society and in optimized variants:  Consumption of days time in March - from an available 132 days, the company consumes 76 variant 1-54, version 2 - 47.82 days and 49.61 days 3- version;  Normal day in July - where have all the 132 days, the company consumes 48 days, variant 1-24, version 2 - 3 version 14.16 and 13.9 days;  Regarding the consumption of days mechanized - for all five months, eating the largest we see society, version 1 has the lowest consumption (area less), and variant 3 (same surface), we see greater consumption lower than the actual situation.  The same results are recorded and cosnumul fertilizer, water, manual and mechanized wages. It concluding that it optimized version 3 adds lei gross margin/ha on the same surface as the company with a much lower consumption of resources.  Table 4 Consumption of the main resources in SC Rentea Srl and optimized variants (V1, V2, V3) No. No. Specification UM Resources Resources consumed Rentea V1 consumed resources Resources consumed V2 V3 consumed resources 1 ZNMAN 3 hours 132 76 54 47.82 49.61 2 ZNMAN 7 hours 132 48 24 14.16 13.9 3 ZNMEC 3 hours 100 102 100 108 114.56 4 ZNMEC 4 hours 100 36 20 31 31.2 5 ZNMEC 5 hours 100 14 10 9 9.28 6 ZNMEC 9 hours 100 245 100 200 197.12 7 ZNMEC10 hours 100 171 70 140 137.88 8 CONSAZOT Kg as 14000 15570 12084 14000.00 14320 9 CONSFOSFOR Kg as 16500 5350 4450 3801.67 3800 10 CONSPOTA Kg as 15000 8260 7202 6729.09 6900 11 RTOTMAN lei 6000 5513 5838 3799.80 3883 12 RTOTMEC lei 15000 3767 2680 2943.00 2945 Source: Own calculations  For a complete interpretation of the solutions resulting from solving mathematical model uses dual solution that gives us the marginal contributions of resources restrictive (limiting) in four solution results (Table 5). The dual variable we can see totally consumed resources (become limiting factors) and marginal contribution of each objective function value, as follows:  In alternative 1 were totally consumed ZNMEC 3 with a marginal contribution from 10946 lei, ZNMEC9 a marginal contribution of 18740 lei and sunflower surface with a marginal contribution of 30 lei. It reaches an objective function value of 35302 lei.  In the latter were consumed ZNMEC 3 with a contribution margin of 7674 lei, ZNMEC10 with a contribution margin of 24285 lei, RTOTMAN with a contribution margin of 10082 and the area of sunflower with a contribution margin of 711 lei. It reaches a value of 42,751 lei objective function.  Version 3 has three limiting factors (resources consumed entirely), namely: the total area of 76 ha, with a marginal contribution from 46360 lei. Hoe surface 40 ha, with a marginal contribution from 3120 lei and sunflower area 15 ha, with a contribution margin of 30 lei. Objective function value totaling 49,510 lei.  Table 5 Dual solutions of the three variants No. Specification Option 1 Option 2 Option 3 Resource Consumption Marginal Cost Marginal contribution Resource Consumption Marginal Cost Marginal contribution Resource Consumption Marginal Cost Marginal contribution 1 ZNMAN 3 53.92 0 0 55.6 00 0 49.61 0 0 2 ZNMAN 7 24.45 0 0 25.8 00 0 13.9 0 0 3 EC ZNM 3 100 109.46 10946 100.0 76.7 7674 114.56 0 0 4 ZNMEC 4 20.07 0 0 25.2 00 0 31.2 0 0 5 ZNMEC 5 10.4 0 0 106 00 0 9.28 0 0 6 EC ZNM 9 100 187.4 18740 1,427 00 0 197.12 0 0 7 ECLO ZNM 70.45 0 0 1,000 242.8 24285 137.88 0 0 8 CONSAZOT 1208.363 0 0 13409.0 00 0 14320 0 0 9 CONSFOSFOR 4450.04 0 0 4886.6 00 0 3800 0 0 10 CONSPOTA 7201.95 0 0 7621.0 00 0 6900 0 0 11 RTOTMAN 5837.98 0 0 6000.0 1.7 10082 3883 0 0 12 RTOTMEC 2680.06 0 0 3105.9 00 0 2945 0 0 13 LUCTERTI 4005.45 0 0 3955.4 00 0 5000 0 0 14 Total area 59 0 0 70.7 00 0 76 610 #### 15 Area hoeing 35.03 0 0 34.8 00 0 40 78 3120 16 Area straw 8.97 0 0 22.4 00 0 36 0 0 17 Tis area 15 2 30 15.0 47.4 711 15 2 30 18 Area alfalfa min 15 186,22 2793.3 13.5 00 0 0 0 0 19 Area alfalfa max 15 186,22 2793.3 13.5 00 0 0 0 0 20 Total contribution margin   35302.6   42,751   49,510       CONCLUSIONS  1. The interpretation of entries received, the mathematical model, we get the size and structure acreage and gross margin value  In Sc Rent Srl, wheat occupy the largest share of 28.9% (22ha) followed by corn and sunflower 25% (19ha), barley 13.2% (10ha) and alfalfa 7.9% (6ha), where gross margin has a value of 607.6 lei / ha, with a total of 46,178 lei;  In alternate crop structure optimization is changed as follows:  Option 1 proposes a structure with 59 hectares planted, 20ha maize (33.9%), each 15ha for alfalfa and sunflower (25.4%) and 9 ha barley (15.2%), where gross margin has a value of 35,303 lei with a gross margin of 598.3 lei / ha;  Version 2 uses 70,7 ha spread over 22.4 hectares barley (31.7%), 19.8 ha maize (28%), 15 ha sunflower (21.2%) and 13.5 ha alfalfa (19.1%), where the gross margin is estimated at 604.8 lei / ha worth a total of 42,751 lei;  The third variant uses all the available area, 76 ha with three cultures - 36 ha barley (47.4%), 25 ha maize (32.9%) and 15 hectares of sunflower (19.7%), where the gross margin is estimated at 49 510 lei and a gross margin of 651.4 lei per ha / ha. 2. The dual interpretation, namely the marginal costs of resources consumed entirely attached declaration that:  version 1 were totally consumed ZNMEC 3 with a marginal contribution from 10946 lei, ZNMEC9 a marginal contribution of 18740 lei and sunflower surface with a marginal contribution of 30 lei. It reaches an objective function value of 35302 lei;  in the latter were consumed ZNMEC 3 with a contribution margin of 7674 lei, ZNMEC10 with a contribution margin of 24285 lei, RTOTMAN with a contribution margin of 10082 and the area of sunflower with a contribution margin of 711 lei . It reaches a value of 42,751 lei objective function;  version 3 has three limiting factors (resources consumed entirely), namely: the total area of 76 ha, with a marginal contribution from 46360 lei. Hoe surface 40 ha, with a marginal contribution from 3120 lei and sunflower area 15 ha, with a contribution margin of 30 lei. Objective function value totaling 49,510 lei. 3. Promoting the use of scientific methods of planning but especially for agricultural management software, namely to promote the use of information technology to support innovation in agriculture; using programs that can identify risk production has an important role in decision making, both in terms of choosing the optimal and hierarchical level that will be taken. The crowd variants of decision, the decision maker will choose which of them will generate a minimum of maximum foreseeable loss loss [3]. 4. We believe that there is significant use of economic-mathematical methods in agriculture: integrated planning of activities and sustainable use of resources, optimizing the size and profile of farms, crop technology optimization, etc., which could help increase labor productivity and economic efficiency.  BIBLIOGRAPHY  1. Cofas E., 2009, Effective Information Systems in Analyzing the Profitability of Farms, PhD thesis, Veterinary Medicine, Bucharest 2. Pătruţi F., Importance of Planning in Farm Work, https://www.scribd.com/doc/41677501/Importanța-planificării-in-activitatea-exploatațiilor-agricole 3. Helen Thomas, Simona Roxana PATARLAGEANU, Elena Cofas, Gheorghiţă Mircea Mircea Nastase, 2009 Model for estimation and evaluation of production efficiency in agriculture activities trough Information Systems, Metalurgia International vol. XIV (2009), Special Issue No.13, pp 40-45, ISSN 1582-2214. 

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Designing a mathematical model to optimize the size activities in the production plan for SC RENTEA SRL

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