A Mixed Integer Linear Programming Model for End of Life Vehicles Recycling Network Design

Automotive industry, with both its contributions to the technology and values added to the economy, has been indisputably one of the leading sectors. As the demand and interest in automobile grow, the environmental pollution caused by the automobiles increases correspondingly. In addition to automobiles’ carbon emissions, also the vehicles which have completed their life cycle, namely scrap vehicles, cause environmental pollution due to their solid and liquid waste. In developed countries, a regulation has been made in order to prevent the situation from getting worse. According to this regulation, in ord support product management, manufacturers are obliged to take back and recycle all their vehicles which have completed their life cycle. The regulation started to be implemented after being adapted to the national law. Upon its adaptation to our national regulations, it has been enforced in our country as well. In the study, Mixed Integer Linear Programming (MILP) model has been presented to design end of life vehicles recycling network. The proposed model has minimized the total network cost as well as to determine the amount of material transported between the facilities and to decide whether to open the dismantling and shredding facilities. The presented model has been applied to end of life vehicles recycling network design problem in Istanbul. The proposed model gives suitable and cost effective results for end of life vehicles recycling network in Istanbul. leading trade, Significant to become 5th largest developed or developing countries brought strict rules to environmental standards [2]. The EU Directive has developed a regulation mandating automobile manufacturers all over the world to take financial responsibility for appropriate environmental management of vehicles that completed their life cycle [3]. With this regulation, it is considered that the aim of the producer companies is to produce eco and to take the responsibility of the products that completed their life cycle for the sake of customer awareness, social responsibility and economic benefits [4]. The regulation developed by the EU harmonized with the national legislation şağıInönü


INTRODUCTION
The automotive sector is one of the world's economic activities with its high share in global production and job creation capacity [1].growth in Turkish automotive sector led Turkey the world's 15th largest and Europe's automotive producer by the end of 2015 [2].
Increasing environmental awareness and threat of global warming are among important dynamics that will determine the direction of the automotive industry.In recent years, it has been observed that a number of world's leading global trade, [1].Significant Turkey to become 5th largest areness and threat of global warming are among important dynamics that will determine the direction of the automotive industry.In recent years, it has been observed that a number of developed or developing countries brought strict rules to environmental standards [2].
The EU Directive has developed a regulation mandating automobile manufacturers all over the world to take financial responsibility for appropriate environmental management of vehicles that completed their life cycle [3].With this regulation, it is considered that the aim of the producer companies is to produce eco and to take the responsibility of the products that completed their life cycle for the sake of customer awareness, social responsibility and economic benefits [4].The regulation developed by the EU harmonized with the national legislation A Mixed Integer Linear Programming Model for End of Life Vehicles Istanbul Commerce University, Graduate School of Natural and Applied Sciences, Küçükyalı E5 KavşağıInönü Automotive industry, with both its contributions to the technology and has been indisputably one of the leading sectors.As the demand and interest in automobile grow, the environmental pollution caused by the automobiles increases correspondingly.In addition to automobiles' carbon emissions, also the pleted their life cycle, namely scrap vehicles, cause environmental pollution due to their solid and liquid waste.In developed countries, a regulation has been made in order to prevent the situation from getting worse.According to this regulation, in order to support product management, manufacturers are obliged to take back and recycle all their vehicles which have completed their life cycle.The regulation started to be implemented after being adapted to the national onal regulations, it has been enforced In the study, Mixed Integer Linear Programming (MILP) model has been presented to design end of life vehicles recycling network.The proposed as to determine the amount of material transported between the facilities and to decide whether to open the dismantling and shredding facilities.The presented model has been applied to end of life vehicles recycling network design he proposed model gives suitable and cost effective results for end of life vehicles recycling network in Istanbul.

developed or developing countries brought strict rules to
The EU Directive has developed a regulation mandating automobile manufacturers all over the world to take financial responsibility for appropriate environmental management of vehicles that completed their life cycle [3].
it is considered that the aim of the producer companies is to produce eco-friendly products and to take the responsibility of the products that completed their life cycle for the sake of customer awareness, social responsibility and economic benefits [4].
EU Directive has been legislation in our country.
The regulation harmonized with the national legislation has been published by the Ministry of Environment and Urbanization in the official newspaper in number 27448 under the name of the Directive on the Control of End of Life Vehicles (ELV).The directive has been issued to prevent the generation of waste caused by vehicles, reduce the amount of waste that is emitted to the environment through reusing, recycling and recovery operations of vehicles and parts of vehicles that completed their life cycle [5].
When the amount of hazardous waste produced by the industry is analyzed, it is measured that the amount of hazardous waste is 11 kg per automobile, 326 kg per bus, 77 kg per truck [6].
The EU directive on ELVs has determined targets for 2006 and 2015 years that contain recycling-reuse and recovery-reuse rate of ELV's.The target for energy recovery in the directive has also been determined.It has been aimed that 5% of ELVs for 2006 and 10% of ELVs for 2015 will not be used for energy recovery.These targets are listed in Directive on the Control of ELVs in Turkey, by the 2020, aims to achieve that the reuse-recovery rates of ELVs are at least 95% of the average vehicle weight and the reuse-recycling rate is at least 85% of the average vehicle weight.
In order to increase the fuel efficiency of a typical vehicle, changes have been made in the materials used in production.The light weight plastic components found in the vehicle have replaced the metal components over time.These trend lines are shown in Table 2.The economic recovery of ELVs and ELV materials and components or the proper disposal of ineffective ELV materials and components, are achieved through reverse logistics.
In the scope of this study, it took the basis of carrying out efficient recovery processes of the vehicles that have completed their life cycles in Istanbul.A model has been developed to minimize the total system cost, to determine the amount of material transported between the facilities, to decide whether to open the reprocess/disassembly and rescue facilities.This model is formulated by using the Mixed Integer Linear Program (MILP).
The second part of the study summarizes the studies in the literature about ELVs reverse logistics.In the third part, the problem is introduced, the methodology of study is mentioned and the mathematical formulation is realized.In the fourth part, performed sample implementation is summarized.Finally, in the fifth part, the results of the study are presented and sensitivity analysis is included.

2.LITERATURE REVIEW
The environmental impacts of ELVs and ELV residues are worrying around the world.The laws for the recycling of ELVs are applied in many countries.Therefore, with the implementation of the law, the number of researches on the recycling of ELVs has increased.
Schultmann, Zumkeller, &Rentz [13,14], proposed a closed-loop supply chain model for the end-of-life vehicle treatment in Germany.They focused on vehicle routing planning especially.Cruz-Rivera &Ertel [15], presented an incapacitated facility location problem for the collection of End-of-Life Vehicles in Mexico.Zarei, Mansour, Kashan, &Karimi [16], designed a reverse logistics network for the ELVs recovery process.The aim of the proposed model is to minimize he costs of collecting the ELVs and flow of materials between facilities.Genetic algorithm approach is used for solving the model.Harraz&Galal [17], presented a mixed integer goal programming model to design a sustainable recovery network for ELVs in Egypt.The proposed model includes locations for the different facilities and the amount of allocation to the different end of life options.Vidovic, Dimitrijevic, Ratkovic, &Simic [18], presented a maximal covering location model to establish a reverse logistics network for ELVs by defining optimum locations for collection points.The developed model was illustrated on the Belgrade city area.Farel, Yannou, Ghaffari, & Leroy [19], used system dynamics simulation approach to model ELV glazing recycling network in France under different scenarios.Gołebiewski, Trajer, Jaros, &Winiczenko [20], developed a model for end-of-life vehicles (ELVs) by defining the optimum locations for dismantling facilities.The proposed model is applied to Mazovia province in Poland.Because of the high complexity of the presented model, a genetic algorithm has been used for solving the model.
Mahmoudzadeh, Mansour, &Karimi [21], proposed MILP model to determine optimal locations of scrap yards over the Iran as well as their optimal allocations and material flows.In this study, ELVs are categorized in three quality levels with different output material streams.Farel et al. [22], propose linear programming model to determine configuration and material flow sizing of the future ELV glazing recycling network in France Ene&Öztürk [5], presented a mathematical programming model for managing reverse flows disassembly, refurbishing, shredding, recycling, disposal and reuse of vehicle parts.The scope of the network model is to determine the numbers and locations of facilities in the network and the material flows between these facilities.Simic [23], proposed a two-stage interval stochastic programming model for management of ELV allocation under uncertainty.It is made various policy scenarios dealing with different levels of economic penalties in terms of ELV allocation targets.The proposed model was applied to a hypothetical case study.Ch [24], apply dynamic modelling and cost-benefit analysis to investigate how polices may affect recycling of ELVs in China and outline that parameter uncertainty should be further explored.Demirel, Demirel, &Gökçen [25], proposed a mixed integer linear programming model for ELV network design.The proposed framework is applied to a real case study in Ankara.Simic [26], presented a multi-stage interval-stochastic programming model for planning end-of-life vehicles allocation.The developed model is able to reflect dynamics in terms of decisions for ELV allocation from a multi-region waste management system to multiple vehicle recycling factories within a multi-period context.Uncertain parameters are expressed by using probability distributions and discrete intervals.Özceylan, Demirel, Çetinkaya&Demirel [27], has developed a linear programming model to reintegrate the backward material flow with the forward supply chain and investigated the performance and applicability of the model by creating various scenarios.
Starting from these researches, a mixed linear programming model was developed for the efficient management of ELV recycling processes in Istanbul and the required responsibilities were met with minimum cost and complied with the related regulations, and reverse logistics network design was made.
Mahmoudzadeh, Mansour, &Karimi [21], proposed a MILP model to determine optimal locations of scrap yards over the Iran as well as their optimal allocations and material flows.In this study, ELVs are categorized in three quality levels with different output material streams.Farel e linear programming model to determine configuration and material flow sizing of the future ELV glazing recycling network in France.
Ene&Öztürk [5], presented a mathematical programming model for managing reverse flows g, recycling, disposal and reuse of vehicle parts.The scope of the network model is to determine the numbers and locations of facilities in the network and the material flows between these stage intervalrogramming model for management of ELV allocation under uncertainty.It is made various policy scenarios dealing with different levels of economic penalties in terms of ELV allocation targets.The proposed model was applied to a hypothetical case study.Chen et al. benefit analysis to investigate how polices may affect recycling of ELVs in China and outline that parameter uncertainty should be further explored.Demirel, Demirel, &Gökçen [25], linear programming model for ELV network design.The proposed framework is applied to a real case study in Ankara.Simic [26], presented a stochastic programming model for life vehicles allocation.The developed s able to reflect dynamics in terms of decisions for region waste management system to multiple vehicle recycling factories within a period context.Uncertain parameters are expressed discrete intervals.Özceylan, Demirel, Çetinkaya&Demirel [27], has developed a linear programming model to reintegrate the backward material flow with the forward supply chain and investigated the performance and applicability of the Starting from these researches, a mixed linear programming model was developed for the efficient management of ELV recycling processes in Istanbul and the required responsibilities were met with minimum cost gulations, and reverse In this study, we used mixed integer linear programming model because of the fact that some of variables which will be used in the model are integer values (0,1) and some are continuous variable values.
In order to implement a mixed integer linear programming model, stages that need to be completed are shown in Figure 1.It is shown that x is the vector of continuous variables, a is the coefficient vector in the objective function of x and c is the coefficient matrix in the constrains of This study addresses a network design problem for cost minimization as well determining to open or not to open the facilities, and materials are transferred from the opened facilities.The distances between the facilities is estimated via Euclidean Relation Method.Facility locations are mapped with scalable integrated geographic information system software which is called ArcGIS 10.For the solution model, GAMS 23.5.1 (General Algebraic Modeling System) program was used and the optimal result was reached.Sensitivity analysis has been performed for the problem that has been solved.It has been researched how the solutions will be affected depends on the system parameters changes.

Problem Definition
The reverse logistics network for ELVs begins with the transfer of vehicles whose life has been completed, from ELV source points to licensed vehicle collectors or authorized dismantling facilities.For the ELVs arriving at the authorized dismantling facility or the licensed vehicle collector, the registration deletion and disposal form nteger linear programming because of the fact that some of variables which will be used in the model are integer values (0,1) and some In order to implement a mixed integer linear programming model, stages that need to be completed are Installation Phases of Mixed Integer Linear mixed integer programming It is shown that x is the vector of continuous variables, the coefficient vector in the objective function of x and c is the coefficient matrix in the constrains of x.
This study addresses a network design problem for cost minimization as well determining to open or not to open transferred from the opened facilities.The distances between the facilities is estimated via Euclidean Relation Method.Facility locations are mapped with scalable integrated geographic information system software which is called ArcGIS 10.For the on model, GAMS 23.5.1 (General Algebraic Modeling System) program was used and the optimal result was reached.Sensitivity analysis has been performed for the problem that has been solved.It has been researched how the solutions will be affected depends The reverse logistics network for ELVs begins with the transfer of vehicles whose life has been completed, from ELV source points to licensed vehicle collectors or For the ELVs arriving at the authorized dismantling facility or the licensed vehicle collector, the registration deletion and disposal form approved by the traffic registration is filled out.So the vehicle is legally deregistered from the traffic.
ELVs arriving at licensed vehicle collectors are sent directly to the authorized dismantling facility without any processing.Fluids (fuel, engine oil, transmission oil, hydraulic oil, coolant, air conditioning fluid, brake fluid, steering fluid etc.) of the ELVs coming to the authorized dismantling facility are drained and then the dismantling process is carried out.Materials such as the plastic, glass obtained as a result of the dismantling process are sent to the recycling facility and the ELV body is called a hulk and sent to reprocessing/shredding facility to be broken into pieces.As a result of the dismantling process, some valuable and reusable parts also emerge.Engines, differentials, transmissions, body panels (covers, doors, bumpers), wheels are reusable and valuable pieces and can be sold in secondary markets.Ferrous and non-ferrous metals and ASRs which are obtained as a result of the shredding process applied to the vehicle body, which is called Hulk.ASRs, referred to as vehicle parts residues, are transferred to the disposal facility for disposal.Ferrous and non-ferrous metals are transported to the recycling facility.Materials that have been transferred from the competent dismantling facilities and the reprocess/shredding facilities to the recycling facilities are subjected to the recycling process.The recycled raw materials are transferred to the suppliers.Hazardous and toxic wastes that cannot be recovered are transferred to the disposal facilities like ASRs and disposal of them.The recycling process of ELVs is shown in Figure 2.
The proposed mathematical model includes the following assumptions: • All ELVs must be collected.
• ELV source points must send their ELV to licensed vehicle collection centers or authorized dismantling facilities.
• The capacities of all facilities are limited and stable.
• The materials to be transported are divisible quantities which can be applied in units of weight.
• Inventory or accumulated order is not allowed due to the capacity which is sufficient for requests.
• The coefficients of the objective function are deterministic and known in advance.
• Weight percentages related to reverse flow are known.
• 39 district of Istanbul Province are accepted as ELV source points.
• The authorized dismantling facility and the reprocessing/shredding facility are considered as candidate areas.The selection of the final location is made from among the potential locations.‫ܤ‬ .

݈ܿ [7]
Constraints: ܺ , ܻ , ܹ , ܵ , ‫ܣ‬ , ‫ܤ‬ , ‫ܩ‬ , ‫ܧ‬ > 0 ∀݅, ݆, ݇, ݉, ‫,ݎ‬ ݈, ‫,‬ [21] ݁ .݁ = ሼ0,1ሽ ∀݇, ݈ [22] The Constraint [11] is a restriction that the amount of reusable component/material transported from the authorized dismantling facilities to the secondary markets is equal to the amount of reusable component/material generated after disassembly of the ELVs transported from the licensed vehicle collectors and ELV source points to the authorized dismantling facilities.The constraint [12] is a restriction that the amount of non-reusable component/material transported from the authorized dismantling facilities to the recycling facilities is equal to the amount of nonreusable component/material generated after disassembly of the ELVs transported from the licensed vehicle collectors and ELV source points to the authorized dismantling facilities.The constraint [13] is a restriction that the amount of ASR transported from the reprocessing/shredding facilities to the disposal facilities is equal to that of ASR generated after shredding of hulk transported from the authorized dismantling facilities to the reprocessing/shredding facilities.The constraint [14] is a restriction that the amount of ferrous and non-ferrous metal transported from the reprocessing/shredding facilities to the recycling facilities is equal to the amount of ferrous and non-ferrous metal generated after shredding of hulk transported from the authorized dismantling facilities to the reprocessing/shredding facilities.The constraint [15] is a restriction that the amount of hazardous and toxic waste transported from the recycling facilities to the disposal facilities is equal to the that of hazardous and toxic waste generated after recycling of component/material transported from authorized dismantling facilities and the reprocessing/shredding facilities to the recycling facilities.Constraints [16][17][18][19][20] stipulate that the transportation amounts must not exceed the capacity of licensed vehicle collectors, authorized dismantling facilities, reprocessing/shredding facilities, recycling facilities and disposal facilities at each period, respectively.Constraint [21] enforces the non-negativity restriction on the decision variables.Finally, the constraint [22] ensures that the values that the facilities opening decision variable can take are 0 or 1.

APPLICATION
The aim of this study is to define the recycling network parameters of vehicles which completed their lifecycle and to solve them with minimum cost within the scope of the ELV Directive in İstanbul.The required data for ELV reverse logistics network model is provided by Turkish Statistical Institute and the Ministry of Environment and Urban Planning of the Republic of Turkey.For recycling of end of life vehicles, the total number of automobiles in category M1 has been taken into account.It is assumed that the average weight of M1 ELVs is 1000 kg.
Istanbul is located in the north-west of Turkey between 280 01' and 290 55' east longitudes and 410 33' and 400 28' north latitudes.It is the most crowded and important city economicallyand socio-culturally in the country.According to the Turkish Statistical Institute, as of the end of 2016, the population of Istanbul is 14.804.116.Due to the large population of people residing in Istanbul, the number of vehicles registered in traffic in Istanbul and deregistered from traffic is higher than that in other cities.According to data of Turkish Statistical Institute, as of the end of 2016 in Istanbul, the number of vehicles registered to traffic is 3.875.145.69% of this number belongs to automobiles.It is 2.669.296.In Figure 3, graphically on the total number of vehicles and automobiles deregistered from traffic in Istanbul between 2005 and 2016 is shown.There are 39 districts belonging to the province of Istanbul and all of them are determined as ELV source.Organized industrial zones and auto industry sites operating in Istanbul were taken into consideration as secondary markets.Therefore, 29 secondary markets were assumed in Istanbul.Table 4 shows ELV numbers for the districts of Istanbul according to the proportion to the district population in Istanbul.
The average number of automobile numbers deregistered from traffic in Istanbul covering the years 2012-2016 is given in Figure 4.In İstanbul, number of automobile numbers deregistered from traffic covering the years 2012-2016 is 14.379.Based on the year 2016 districts population data, the average ELV numbers for the last 5 years have been distributed to the counties.These data can be accessed from Table 3.
According to data of the Ministry of Environment and Urban Planning of the Republic of the Turkey in 2016, there are 52 licensed vehicle collectors, 5 authorized dismantling facilities, 4 reprocessing/shredding facilities, 3 recycling facilities and 2 disposal centers.
In Figure 4, it is seen that in the recycling network for ELVs in Istanbul, licensed vehicle collectors, authorized dismantling facilities, reprocessing/shredding facilities, recycling facilities and disposal facilities.The locations of the facilities are mapped with the scalable integrated geographic information system software which is named ArcGIS 10.
Euclidean distance method was used for distances between facilities and distances were calculated.The weight ratios of the components present in the ELV with a total weight of 1000 kilograms are 0,65; 0,09; 0,12; 0,03; 0,03; 0,013; 0,017; 0,05.These ratios are ferrous metal (n1), non-ferrous metal (n2), plastic (n3), rubber (n4), glass (n5), battery (n6), fluids (n6) and other materials (n7) respectively.In addition, the hulk weight percentage (a1) in the ELV is 0.810, the weight percentage (a2) of the ASR in the hulk is 0.185, the reusable weight percentage (a3) of the material n in the ELV is 0.137, The nonrecyclable weight percentage (a4) of the material n in the ELV is 0.864, the recycled weight percentage (a5) of the material n in the hulk is 0.815, and the percent disposal weight (a6) in the recycled n material is 0.15.The opening costs of the authorized dismantling facilities and the reprocessing/shredding facilities are set at 630.000 TL and 2.500.000TL respectively.Transaction cost for all authorized dismantling facilities; 980 TL/ton, transaction costs for reprocessing /shredding facilities; 135 TL/ton, transaction costs of recycling plants; 500 TL/ton and the transaction costs of the disposal centers are 250 TL/ton..000tons for reprocessing/shredding facilities and 10.000 tons for disposal facilities.For the recycling facilities, 1,726 tons of capacity was allocated to the battery, 5,910 tons of capacity was reserved for the fluids, and 2,364 tons of capacities were allocated to the tires.

SOLUTIONS
The problem was solved using GAMS 23.5.1 and processed with a server with 2.40 GHz Intel Core Processor and 8 GB RAM.The calculation time required for the optimal solution was determined to be 15 CPU seconds.
The optimal solution value for the problem was determined to be 96.526.930TL.It has been decided that 3 of the authorized dismantling facilities (2nd, 4th and 5th) and 2 of the reprocessing/shredding facilities (2nd and 3rd) should be opened for the most appropriate solution.The optimal values of the decision variables are shown in Table 5.It is also possible to see from Table 4 that ELVs and ELV parts and components are subjected to the processing through the which facilities in the recycling network.As shown in  5.

Sensitivity Analysis
In this section of the study, we conduct some analysis to determine effecting of some parameters on results.

i. Sensitivity to Number of Vehicles Deregistered from Traffic:
It is thought that there was an increase in the number of vehicles deregistered from traffic.How this increase will affect the number of authorized dismantling facilities and The increase in the number of vehicles deregistered traffic did not change the number of dismantling facilities; but it increased the reprocessing/shredding facilities.In addition, in the number of vehicles deregistered from increased the optimal value of the objective function.is shown in Figure 6.In the next studies, it can be developed stochastic mixed integer programming model which is some parameters (etc.quantity of ELV and transportation cost) on uncertain.In addition, close loop network designed to include manufacturers and suppliers actor in network.

Figure 1 :
Figure 1: Installation Phases of Mixed Integer Linear Programming Model [31] The representation of the mixed integer programming problem is as follows: min: ax + by constraints: Cx + Dy ≥ b x ≥ 0 y ≥ 0 and is integer.

Figure 2 .
Figure 2. ELV Recycling Process3.2.The ProposedMathematical ModelThe objective function of the proposed MILP model consists of the transportation cost of ELVs and ELV parts, the transaction cost (collection, dismantling, reprocessing/shredding, recycling, disposal) and the facility constant opening costs.The sale of reusable metals and recycled raw materials has not been evaluated in the objective function.Transportation costs of ELVs transported from ELV source points to licensed vehicle collectors or authorized dismantling facilities are ignored in the objective function.The proposed model is formulated as following: Indexes: n: Component/Material series n:1,2,3…N i:ELV source point i:1,2,3…I j:Licensed vehicle collection center j:1,2,3…J objective function has seven components.The first component represents the fixed costs associated with locating authorized dismantling facility and reprocessing/shredding facilities [1].The second component represents the cost of transportation on each arc of the network [2].The third component represents the collection cost of the ELVs [3].The fourth component represents the disposal cost of the ELVs sent to the authorized dismantling facility [4].The fifth component represents the shredding cost of the Hulk sent to the reprocessing/shredding facility [5].The sixth component represents the recycling cost of materials sent to the recycling facility [6].Finally, the seventh component represents the disposal cost of ASRs sent to the disposal facility [7].Constraint [8] determines the returned quantities of ELV from ELV source points to the licensed vehicle collection centers and the authorized dismantling facilities.Constraint [9] is the balance equation for licensed vehicle collection centers.The constraint [10] is the restriction that the amount of hulk transported from the authorized dismantling facilities to the reprocessing/shredding facilities is equal to the amount of Hulk generated after the dismantling of the ELVs transported from the licensed vehicle collectors and ELV source points to the authorized dismantling facilities.

Figure 3 .
Figure 3. Number of Vehicle and Automobile Deregistered from Traffic between 2005 and 2016

Figure 6 .
Figure 6.The Relationship Between Number Deregistered from Traffic and Objective Function

Figure 7 .
Figure 7.The Relationship Between the Number

Table 1 .
Targets Determined in the European Union's End of Life Vehicles Directive [7]

Table 2 .
Composition of a Typical ELV Over Time

Table 3 .
Districts of Istanbul Province and Population-Based ELV Amounts

Table 4 .
Table 5, the ELVs are transported to licensed vehicle collectors with number 11, 25, 26, 28, 35, 37, 39, 41, 44, 49 and 50.There is no transportation between ELV source points and other licensed vehicle collectors.ELVs were transported to the licensed vehicle collectors numbered 26, 35, 41, 44 from the ELV source points and 1000 of ELV have been transported to each one.The minimum amount of ELV has been transferred to the licensed vehicle collector with number 11. Results for GAMS 23.5.1 Program's ELV Recycling Network 29. S Balci, B. Ayvaz/ Southeast Europe Journal of Soft Computing Vol.6 No.1 March 2017 reprocessing/shredding facilities and value function has been researched.As a result investigations made, the values in Table 6 were

Table 6 .
The Effect of Increase in Number of Vehicles

Table 7 .
The Effect of Change in the Number Transported

Number of Vehicles deregistreted from Traffic (%)
(20)(21)(22)(23)(24)(25)(26)(27)ft Computing Vol.6 No.1 March 2017(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)It was assumed that the amount of ELV source point to the licensed changed.It has been researched affect the number of authorized dismantling reprocessing/shredding facility and function.As a result of the investigations in Table7were obtained.Change in the amount of ELV carried source point to the licensed vehicle change the number of authorized dismantling reprocessing/shredding facilities optimal value of objective function.illustrated in Figure7.The number of scrap vehicles has increased with the rapid growth experienced automotive sector.Ensuring that ELVs proper conditions or disposal with crucial in protecting the environment.