WO2022245311A1

WO2022245311A1 - Vehicle routing and optimization system and a method thereof

Info

Publication number: WO2022245311A1
Application number: PCT/TR2021/051110
Authority: WO
Inventors: Burak ERDEM
Original assignee: Basarsoft Bilgi Teknolojileri Anonim Sirketi
Priority date: 2021-05-21
Filing date: 2021-11-01
Publication date: 2022-11-24

Abstract

The present invention relates to a vehicle routing and optimizations system and a method thereof that allows for determining the routing obtained from the most optimal ordering for the vehicles/teams that will bring goods or services to customers from one or a plurality of depots.

Description

VEHICLE ROUTING AND OPTIMIZATION SYSTEM AND A METHOD THEREOF

Technical Field of the Invention

The present invention relates to a vehicle routing and optimization system and a method thereof that allows for determining the routing obtained from the most optimal ordering for the vehicle/teams that will bring goods or services to customers from one or a plurality of depots

State of the Art

Vehicle routing problem is a combinational optimization problem that aims to determine routing obtained from the most optimal ordering for vehicles/teams that will bring goods or services to customers from one or a plurality of depots. It is a more general form of problems known as the traveling salesman problem. The first article on the vehicle routing problem was published by George Dantzig and Ramser John in 1959 and includes the first algorithm-based method developed in USA in order to solve the problems arising in the fuel delivery of tankers.

The objective-cost function of the vehicle routing problem is to minimize the total road cost. It performs constraint function verification by considering all constraints while minimizing the cost function, thereby minimizing the number of vehicles/teams to be used and minimizing the total distance or total time. It is possible to add secondary objectives in addition to the main objective, for example, maximizing customer satisfaction by means of reducing travel times.

Vehicle routing problems (VRP) are classified as NP-hard. The class of problems that are at least as difficult as each NP problem is called NP-Hard. If any problem in NP- Hard class can be solved in polynomial time, then all problems in NP class can be solved in polynomial time. Np, on the other hand, is a complexity class that includes decision problems, which can be solved in polynomial time with an indefinite Turing Machine. Problems in this class can be verified in polynomial time with a deterministic Turing Machine, and any problem that can be verified in this manner is in the NP class. Therefore, NP can also be defined as the class of problems that can be verified in polynomial time (with a deterministic Turing Machine). All problems in class P are also in NP since a deterministic Turing machine is also a non-determ inistic Turing machine. It is not possible to find the best theoretical solution for the problems in large scale since vehicle routing problems are in the NP-hard class.

There are various types of VRPs according to the constraints in the distribution system.

The capacitated vehicle routing problem (CVRP) is a route planning problem for vehicles with limited loading capacity such that a company with one or more business units (depot) can reach n customers. In the literature, classical VRP and CVRP are considered as equal, and there is generally a capacity constraint in all VRP applications. The total demand of customers on a route in CVRP should not exceed vehicle capacity C.

Each customer must be reached within a certain time interval in the vehicle routing problem with time windows (VRPTW). In this type of problems, each vehicle will leave the operating unit at 0 time, and when it visits an i customer, it will serve the customer as long as the si service time. In real life, such problems can be encountered in fast cargo transportation. This problem is generally encountered in cases where the service must be provided within a certain time period to the addresses, in which the goods will be received or distributed.

In the vehicle routing problem with pick-up and delivering (VRPPD), delivering product to customers and picking-up a product from customers are performed. In this regard, each customer has a supply and demand value. Therefore, it is checked that if there is enough product in the vehicle before the vehicle reaches the customer. As a different version of this type of problem, there is a problem of delivering the product received from a certain customer only to a certain customer, as matched pairs.

There are two groups of customers in the vehicle routing problem with pick-up and delivering (VRPPD), and while products are delivered to some customers (group L), product picking-up from other customers (group B) is performed. A vehicle can serve both customers, but it must first visit L group customers and then B group customers (it should deliver the goods first, then it should pick-up the goods). In the model, the demands of the customers to whom the product will be picked up are indicated as negative while the demand of the customers who will deliver the product is indicated as positive.

The multi-depot vehicle routing problem (MDVRP) includes the main idea of providing services to all customers of a company with more than one depot and vehicles by means of creating the routes with least cost in line with the determined constraints. In this problem, there is a relationship between customers and depots. Therefore, first of all zoning should be made in order to solve the problem.

Heterogeneous vehicle routing problem (HVRP) is the problem of creating the least costly routes of different types of vehicles according to the requirements of the customers. Therefore, Heterogeneous Vehicle Routing Problem is a much better adaptable field to real-life scenarios than Homogeneous Vehicle Routing Problem.

In the state of the art, the application numbered “US7660651 B2” was examined. The invention that is the subject of the application relates to a route planning and optimization system that promotes driver familiarity while meeting the efficiency requirements of a stochastic demand. The invention comprises that a good framework of driver service territories is established, which is essential for making consistent route plans while at the same time leaving enough flexibility to accommodate the varying customer demand, which in turn results in better driver utilization.

In the state of the art, the application numbered “CN108182499A” was examined. The invention that is subject to the application describes a hybrid ant colony algorithm for vehicle routing problem and an implementation system thereof. The invention comprises providing a hybrid ant colony algorithm for vehicle routing problems and implementation system thereof, which have the advantages of both he ant colony algorithm and the simulated annealing algorithm.

In the state of the art, the application numbered “TR2018/05307” was examined. The invention that is the subject of the application is a route planning system used in the sectors having field and sales teams such as information technologies, geographic information systems, logistics companies, health sector, transportation sector, primary areas and sectors, and retail sector, fast moving consumer goods, real estate, consultancy, media. In the state of the art, exact solution algorithms can reach the absolute optimal value of the cost function of the problem that is desired to be optimized. They are not preferred for large-sized problems due to the long solution times thereof. Heuristic solution algorithms, on the other hand, are criteria or computer methods defined with the aim of deciding which of various alternative actions are effective in order to achieve any objective or to arrive the target. These algorithms are also called algorithms whose convergence cannot be proven to the optimum solution in the solution space. Such algorithms have the property of convergence; however, they do not guarantee the exact solution, they only guarantee the solution close to the exact solution. Thus, a balance can be established between the calculation time and the solution quality. The inadequacy of these methods is that there is not enough time in commercial applications in order to solve large-scale problems with exact solution algorithms, and classical heuristic algorithms become distanced from reaching the absolute optimal result in most cases. In general, one of the most important problems of optimization algorithms is that while operating to reach the absolute optimal, they cannot climb these hills by denigrating themselves in order to reach a better result by hanging up to the local optimum point.

In the studies conducted in the state of the art, there is a need for a method that can perform calculation suitable for many types of vehicles/teams routing problems, requiring both less hardware in terms of computing power and produces the closest result to the optimal value.

Consequently, the disadvantages disclosed above and the inadequacy of available solutions in this regard necessitated making an improvement in the relevant technical field.

Objects of the Invention

The present invention relates to a vehicle routing and optimizations system and a method thereof that allows for determining the routing obtained from the most optimal ordering for the vehicles/teams that will bring goods or services to customers from one or a plurality of depots. The most important object of the present invention is to provide calculation suitable for many types of vehicles/teams routing problems.

Another important object of the present invention is to provide requiring less hardware in terms of computing power and producing the closest result to the optimal value. Another important object of the present invention is to create a hybrid approach by collating different techniques.

Another important object of the present invention is to meet the need for a differentiated approach and methodology required for solving different types of vehicles/teams routing problems. Structural and characteristic features of the present invention as well as all advantages thereof will be understood more clearly from figures disclosed below and the detailed description written by making references to these figures. Therefore, the assessment should be made by taking these figures and the detailed description into consideration.

Description of the Figures Figure -1 is a drawing that gives the schematic view of the system according to the present invention.

Reference Numerals:

100. Vehicle routing and optimization system

110. Application 120. Server

121. Planning module

122. Zoning module

123. Reporting module

124. Work order assignment module Description of the Invention

This problem that is especially encountered during the distribution of products in companies causes quite high costs in some sectors. Therefore, effective solution of the vehicle/team routing problem is important in terms of providing great savings.

In case the algorithm works with less technique, problems of getting stuck and not producing better results may occur since the solution of different types of VPR problems requires different techniques and approaches. On the other hand, the vehicle/team routing method creates a hybrid approach by means of collating different techniques. It can meet the need for a differentiated approach and methodology required in the solution of different VRP types. It performs less calculations in the process leading to the solution, and less calculation means that less computing power is required.

Vehicle routing and optimization system (100) comprises server (120) and application (110) comprising planning module (121 ), zoning module (122), reporting module (123), and work order assignment module (124).

The application (110) is executed in a server that exchanges data over a data network and enables users to log in with a username and password. The application (110) can be used both on the web and on a mobile device. Application (110) provides user management of information such as distribution, collection points, depot, and starting point. The application (110) enables the user to upload information such as distribution, collection points, depot, and starting point. The application (110) enables the user to define and update information such as the depot, where the distribution or collection will begin, and the starting point. Application (110) enables users to set constraints for all data. In an embodiment of the invention, the application (110) allows users to set constraints such as trucks cannot enter the distribution point or collection can be performed only by passenger car from this depot. The application (110) enables that the user can define the vehicle/teams and can add and update the file upload function and vehicle/team’s information (vehicle/team name, id, capacity information, responsible web user, region of responsibility, departure or arrival depot etc.). The application (110) enables presenting routes with navigation properties over the numerical footing map data of the list and the ordering between the points created in the planning module (121) to the user. The application (110) provides that the region clustering information calculated in the zoning module (122) is presented to the user over an interface.

The planning module (121 ) provides the calculations that the user needs and desires to make by running on the server (120). Planning module (121 ) enables that TSP planning is performed in case the user has defined which vehicle/team made each distribution or collection data and desires to determine the order between distribution/collection points. The planning module (121 ) allows that the user can calculate and list the distribution or collection order over the data defined to the vehicle/team that he/she is selected from the application (110) interface in TSP planning by means of the TSP algorithm. Users can receive these calculations as reports in excel format. In TSP (Traveling salesman problem), the planning module (121) allows the user to calculate the distance of each point from another point and the time information by enabling that a starting point is determined (depot, vehicle/team location etc.) and the usage status information of options such as time window and start/end point is entered over the application (110). The planning module (121 ) allows for listing and ordering by taking into consideration the proximity (distance) calculated according to the start/end point by starting from the farthest point to the starting point (or any defined point) by means of using a remote adding algorithm in TSP planning. The planning (121 ) module provides the ordering list created with the shortest distance/time between the points from the defined starting point by running the algorithm until the best solution is reached or according to the time and number of cycles, by means of using 2-opt algorithm in TSP planning, by extracting two connections between the points from the order list in the initial solution, instead of adding two different alternative connections to the existing points, trying to reach a better solution (shorter distance and time) in different combinations. Planning module (121) enables that user can make cluster planning in accordance with how many regions and vechicles/teams will be used by the user by means of making the clustering planning first, if the user has not defined each distribution/collection data will be made by which vehicle/team and desires to define it by clustering. The planning module (121 ) primarily allows for calculating the distance of each point to another point and the time information in cluster planning. Subsequently, the planning module (121 ) determines as many random points as the cluster center as the number of clusters according to the number of clusters (region or vehicle/team number) from the constraints that user enters in the application (110) by means of using k-means algorithm in cluster planning, points that are not designated as cluster centers are assigned to the closest cluster center point, and it provides the most suitable (shortest distance or time) list of clustered points based on vehicle-region by repeating the point assignments and cluster center calculations until it becomes stable according to the new cluster center created by calculating the cluster center over the assigned points again.

If it is not defined that each distribution/collection data added through the application (110) will be made by which vehicle/teams, the planning module (121) allows for assigning distribution/collection points to the vehicles/teams and calculating the order of distribution/collection by means of the VRP algorithms over the depot/depots, defined vehicles/teams, and data that the user selects over the application (110) by means of the VRP (vehicle routing problem) planning. The planning module (121 ) primarily allows for calculating the distance of each point to another point and the time information in VRP planning. Then, the planning module (121 ) allows for calculating the shortest distance/time for the optimum solution by means of using the ant colony algorithm in VRP planning. The planning module (121 ) allows for trying to reach a better solution (shorter distance and time) in different combinations and trying to obtain a better result (shorter distance and / or time) until the best solution is reached or continuing to run the algorithm according to the time and number of cycles, by extracting two connections between points using 2-opt algorithm from the ordering list found by using the ant colony algorithm in VRP planning and by adding two different alternative connections to the existing points over it. If a result that is worse than the result found by the ant colony algorithm in VRP planning is found by using the 2-opt algorithm, the planning module (121) ensures that the results are sent back to the 2- opt algorithm by using the threshold acceptance algorithm in order to find a better result and increase the maximum acceptable solution value of these results. If a result that is better than the result found by the ant colony algorithm in VRP planning is found by using the 2-opt algorithm, the planning module (121 ) ensures that these results are executed on the tabu algorithm in order to find a better result from these results. The planning module (121 ) allows for reducing the number of vehicles/teams found in the results in tabu algorithm by means of using the extraction pool algorithm in VPR planning. The planning module (121 ) provides improvement in the results calculated in the VRP planning by means of using the search space algorithm.

Planning module (121 ) allows for running TSP planning according to data receiving from the clustering planning if the user has performed clustering planning and desires to calculate the order between points in the cluster per vehicle/team. Planning module (121) allows for performing TSP planning according to data receiving from VRP planning if the user has performed VRP planning and desires to calculate the order between points per vehicle/team.

Planning module (121) allows for performing CRP planning according to data receiving from clustering planning if the user has performed clustering planning and desires to calculate the points in the cluster per vehicle/team and vehicle/team matching.

If the user does not desire to calculate any route and course and desires to manage and monitor the resulting clusters as a result of cluster planning in the planning module (121) by executing on the server (120), the zoning module (122) provides the use of cluster planning only for zone management and cluster recalculation.

The work order assignment module (124) ensures that the orders and lists resulting from the planning module (121 ) are transmitted to the application (110) instantly. The work order assignment module (124) allows for receiving the status information of completing or canceling the works of the teams in the field, over the application (110). The reporting Module (123), the zoning module (122), and the planning module (121) allow users to freely filter and inquire over the data and the results calculated, and as a result, get report outputs such as excel.

The remote adding algorithm allows for ordering by listing starting from the farthest point from the starting point (or any defined point), considering their proximity (distance) to the start/end point. The steps of R-opt (2-opt) algorithm are as follows:

Step 1 : r (2) edges are deleted from a route and new r (2) edges are added from other parts of the route as long as the result remains a complete route. In case this change leads to a shorter route, the change is preserved; otherwise, other changes are attempted by deleting/adding different edges.

Step 2: Step 1 is repeated as long as there are improvements after the attempted changes. If all possible changes are tried and no further improvement can be made, i.e. a local minimum point is reached, it is considered a result and the process is terminated.

The process steps of K-means algorithm are as follows:

Step 1 : number of clusters, k is determined step 2: k number of random points are determined as cluster centers

Step 3: The remaining points are assigned to their closest cluster center point. step 4: a center is calculated again for newly formed clusters.

Step 5: the optimal (shortest distance) clustering solution is found by repeating step 3 and 4 until it becomes stable.

The following steps are followed in the Ant Colony Algorithm:

Step 1 : The pheromone value of the initial route is determined.

Step 2: Artificial ants are randomly placed at each collection/distribution point.

Step 3: Each ant completes its route by selecting the next collection/distribution point. Step 4: The length of the routes completed by each ant is calculated and the information of local pheromones is updated.

Step 5: The best solution (shortest distance, fastest route) solution is calculated and if necessary, information of global pheromones is updated.

Step 6: The process is repeated from step 2 until the maximum number of iterations, time or adequacy criteria are met.

The following steps are followed for tabu search:

1 . Start: A suitable solution (shortest distance or fastest route) is selected among the starting/depot points and distribution/collection points. At the beginning, no moves are prohibited. 2. Stop: A move between the depot/distribution/collection points in the current solution is considered as prohibited and a new solution is sought.

3. Move: A non-prohibited move is selected, and it is controlled whether the new solution is better than the previous solution/s.

4. Current Solution: If better, this solution is considered the available best, if not, a new solution is sought.

5. Tabu List: Moves that are expired are removed from the prohibition list and added to the trying list.

6. Move: If the maximum time determined for the solution is not reached, the process is repeated by returning to the 1st step between the non-prohibited moves.

7. Stop: If the maximum time for the solution is reached, the best result is considered the local best.

The steps of threshold accepting (BATA) algorithm are as follows:

Step 1 : An initial applicable solution (S), and an initial threshold (T, T>0) are established.

Step 2: (Start of outer loop)

If the outer loop stop criterion is not met, the following steps are followed. a) (start of inner loop)

If the inner loop stop criterion is not met, the following steps are followed. b) an S1 solution is selected from the solution space, if the difference between the selected solution S1 and the selection S is less than the accepted threshold, this solution meets the acceptance criterion.

(end of inner loop) if the threshold acceptance criterion is met at least once by the inner loop, the threshold is reduced if not met, the threshold increases (trackback).

(end of inner loop)

Step 3: Returning to the best solution found.

The extraction pool algorithm process steps are as follows:

1 : The algorithm starts with clearing the route with the least number of points and adding it to the extraction pool. 2: In the simple adding step, on the other hand, only processes that meet the appropriate conditions, in addition to processes that do not meet the appropriate conditions, was recorded in the short-term memory.

3: If appropriate addition cannot be made, the objective function value is updated by giving a penalty over the problem parameters that do not provide the appropriate condition.

4: The local scanning algorithm is activated by applying the operation selected according to the lowest penalty value, and a predetermined time / number of iterations is performed to reset the penalty value due to inappropriate condition.

5: If the penalty value is reset, it means that the whole solution also meets the problem criteria and one more point from the extraction pool was successfully added to another route. If this step is also not successful, this inappropriate condition is regulated by passing to the extraction step as introduced in the first version.

The search space smoothing algorithm changes the topographic structure of the search space and reduces the number of local best points. Graphically, local best points are filled temporarily, avoiding being stuck on local bests. In this smoothing process, only the metric properties of a search space are changed, and topological structure thereof is left untouched.

Vehicle routing and optimization method comprises the following process steps:

• User accessing the application (110) through the application (110) and inputting data,

• User viewing the current data of distribution/collection points by means of the application (110), and uploading the data that users work with,

• Users updating on the data by the application (110),

• Adding and updating users' vehicle/team and information by the application

(110),

• Users creating constraints for data by the application (110),

• tsp planning to order the distribution/collection points according to the shortest distance and time if the planning module (121 ) running on the server (120) is defined for a vehicle/team in the application (110), • cluster planning for clustering the more than one distribution/collection point based on region/vehicle entered into the application (110) by the planning module (121) running on the server (120),

• vrp planning to distribute multiple distribution/collection points entered into the application (110) by the planning module (121) running on the server (120) to the more than one vehicle/team and to order them according to the shortest distance and time,

• displaying the order between the points and the list created by the planning module (121 ) running on the server (120) as routing with navigation properties on the application (110),

• instantly transmitting the orderings and lists resulting from the planning module to the application (110), and receiving status information of completing or canceling the works of the teams in the field over the application (110) by means of the work order assignment module (121 ) running on the server (120),

• displaying the calculations and route courses made in the planning module running on the server (120) by the application (110) via an interface,

• users filtering and inquiring and getting report outputs over the data and results calculated by the reporting module (123) running on the server (120) with the zoning module (122) and the planning module (121 ).

The process step of tsp planning to order the distribution/collection points according to the shortest distance and time if the planning module (121 ) running on the server (120) is defined for a vehicle/team in the application (110) comprises; user calculating the distance of each point from another point and the time information by means of the planning module (121 ) by providing determining a starting point over the application (110), entering the usage status information of options such as time window and start/end point, generating the initial solution by listing and ordering according to the start/end point, starting from the farthest point to the starting point by using the remote adding algorithm by means of the planning module (121 ), extracting the optimum ordering list created with the shortest distance/time between the points from the starting point to the results found in the remote adding algorithm by using 2-opt algorithm by means of the planning module (121 ). The process step of cluster planning for clustering the more than one distribution/collection point based on region/vehicle entered into the application (110) by the planning module (121 ) running on the server (120) comprises;

- calculating the distance and time information of each point from another point by the planning module (121 ),

-finding the list of clustered points based on the vehicle region according to the shortest distance and/or time by using k-means algorithm by means of the planning module (121).

The process step of vrp planning to distribute multiple distribution/collection points entered into the application (110) by the planning module (121) running on the server

(120) to the more than one vehicle/team and to order them according to the shortest distance and time, comprises calculating the distance and time information of each point from another point by the planning module (121 ), generating the initial solution by calculating the shortest distance/time for the optimum solution by using the ant colony algorithm by means of the planning module

(121), obtaining a shorter distance and/or time by using 2-opt algorithm from ordering list found by using the ant colony algorithm by means of the planning module (121 ), sending the results back to the 2-opt algorithm by using the threshold acceptance algorithm in order to find a better result and to increase the acceptable maximum solution value by these results by means of the planning module (121 ) if a result that is worse than the result found with the ant colony algorithm is found by using the 2-opt algorithm, executing these results on tabu algorithm to find a better result by means of the planning module (121 ) if a result that is better than the result found by the ant colony algorithm is found by using 2-opt algorithm, reducing the number of vehicles/teams found in the results in tabu algorithm by using the extraction pool algorithm by means of the planning module (121), making improvement in the results calculated by using the search space algorithm by means of the planning module (121 ).

Claims

1. A vehicle routing and optimization system (100) that allows for determining the routing obtained from the most optimal ordering for the vehicles/teams that will bring goods or services to customers from one or a plurality of depots, characterized in that, it comprises;

• a server (120) comprising at least one application (110) that exchanges data over data network, that can work on web and mobile device, that allows users to register and log in with a username and password by executing in a server, entering the information of vehicle/team and distribution point and presenting the routes and lists created to the user over an interface,

• at least one planning module (121 ) that allows for making tsp planning to order the distribution/collection points defined for a vehicle/team in the application (110) according to the shortest distance and time by running on server (120), making cluster planning for clustering the multiple distribution/collection points entered into the application (110) based on region/vehicle, and making vrp planning to distribute multiple distribution/collection points entered into the application (110) to more than one vehicle/team and ordering them according to the shortest distance and time, at least one zoning module (122) that allows for managing and monitoring the clusters by using clustering planning for zone management and cluster recalculation in case the user does not desire to calculate any route and course as a result of clustering planning in the planning module (121 ) by being executed on the server (120), at least one work order assignment module (124) that provides instantly transmitting the orderings and lists resulting from the planning module (121 ) to the application (110), and

At least one reporting module (123) that allows users to filter and inquire and get report outputs over the data and results calculated with the zoning module (122) and the planning module (121).

2. Vehicle routing method according to Claim 1 characterized by comprising the process steps of: • User accessing the application (110) through the application (110) and inputting data,

• Users updating on the data by the application (110),

• Adding and updating users' vehicle/team and information by the application (110),

• Users creating constraints for data by the application (110),

• tsp planning to order the distribution/collection points according to the shortest distance and time if the planning module (121) running on the server (120) is defined for a vehicle/team in the application (110),

• cluster planning for clustering the more than one distribution/collection point based on region/vehicle entered into the application (110) by the planning module (121) running on the server (120),

• displaying the order between the points and the list created by the planning module (121) running on the server (120) as routing with navigation properties on the application (110),

• instantly transmitting the orderings and lists resulting from the planning module to the application (110), and receiving status information of completing or canceling the works of the teams in the field over the application (110) by means of the work order assignment module (121) running on the server (120),

• users filtering and inquiring and getting report outputs over the data and results calculated by the reporting module (123) running on the server (120) with the zoning module (122) and the planning module (121).

3. Vehicle routing method according to Claim 2, characterized in that, in the process step of tsp planning to order the distribution/collection points according to the shortest distance and time if the planning module (121) running on the server (120) is defined for a vehicle/team in the application (110),

- user calculating the distance of each point from another point and the time information by means of the planning module (121) by providing determining a starting point over the application (110), entering the usage status information of options such as time window and start/end point,

- generating the initial solution by listing and ordering according to the start/end point, starting from the farthest point to the starting point by using the remote adding algorithm by means of the planning module (121 ),

- extracting the optimum ordering list created with the shortest distance/time between the points from the starting point to the results found in the remote adding algorithm by using 2-opt algorithm by means of the planning module (121 ).

4. Vehicle routing method according to Claim 2, characterized in that, in the process step of cluster planning for clustering the more than one distribution/collection point based on region/vehicle entered into the application (110) by the planning module (121 ) running on the server (120),

-finding the list of clustered points based on the vehicle region according to the shortest distance and/or time by using k-means algorithm by means of the planning module (121 ).

5. Vehicle routing method according to Claim 2, in the process step of vrp planning to distribute multiple distribution/collection points entered into the application (110) by the planning module (121 ) running on the server (120) to the more than one vehicle/team and to order them according to the shortest distance and time,

- calculating the shortest distance/time for the optimum solution by using the ant colony algorithm by means of the planning module (121 ),

- obtaining a shorter distance and/or time by using 2-opt algorithm from ordering list found by using the ant colony algorithm by means of the planning module (121 ),

- sending the results back to the 2-opt algorithm by using the threshold acceptance algorithm in order to find a better result and to increase the acceptable maximum solution value by these results by means of the planning module (121 ) if a result that is worse than the result found with the ant colony algorithm is found by using the 2-opt algorithm,

- executing these results on tabu algorithm to find a better result by means of the planning module (121) if a result that is better than the result found by the ant colony algorithm is found by using 2-opt algorithm,

- reducing the number of vehicles/teams found in the results in tabu algorithm by using the extraction pool algorithm by means of the planning module (121 ),

- making improvement in the results calculated using the search space algorithm by means of the planning module (121 ).

6. Vehicle routing and optimization system (100) according to Claim 1 , characterized in that it comprises application (110) that allows users to enter information of distribution and collection points, depots and starting point.

7. Vehicle routing and optimization system (100) according to Claim 1 , characterized in that it comprises application (110) that allows users to enter information of distribution and collection points, depot and starting point.

8. Vehicle routing and optimization system (100) according to Claim 1 , characterized in that it comprises application (110) that allows users to define constraints for time window, start/end point, number of vehicles/teams to be planned, number of regions, capacity, function of collecting and distributing, multiple depots selection, vehicle type.

9. Vehicle routing and optimization system (100) according to Claim 1 , characterized in that it comprises application (110) that allows users to enter information of distribution and collection points, depot and starting point.

10. Vehicle routing and optimization system (100) according to Claim 1 , characterized in that, it comprises application (110) that enables presenting routes with navigation properties over the numerical footing map data of the list and the ordering between the points created in the planning module (121 ) to the user.

11. Vehicle routing and optimization system (100) according to Claim 1 , characterized in that, it comprises application (110) that enables presenting the region clustering information calculated in the zoning module (122) to the user over an interface.

12. Vehicle routing and optimization system (100) according to Claim 1 , characterized in that, it comprises planning module (121 ) that allows for assigning distribution/collection points to the vehicles/teams, and making VRP planning by calculating the order of distribution/collection by means of the VRP algorithms over the depot/depots, defined vehicles/teams, and data that the user selects over the application (110) in case it is not defined that each one of the distribution/collection data added through the application (110) will be made by which vehicle/teams.

13. Vehicle routing and optimization system (100) according to Claim 1, characterized in that, it comprises planning module (121) that allows for calculating the distance of each point to another point and the time information in VRP planning.

14. Vehicle routing and optimization system (100) according to Claim 1 and Claim 13, characterized in that, it comprises planning module (121 ) that allows for calculating the shortest distance/time for the optimum solution by means of using the ant colony algorithm in VRP planning.

15. Vehicle routing and optimization system (100) according to Claim 1 and Claim 14, characterized in that, it comprises planning module (121) that allows for obtaining a shorter distance and/or time by using 2-opt algorithm from ordering list found by using the ant colony algorithm in VRP planning.

16. Vehicle routing and optimization system (100) according to Claim 1 and Claim 15, characterized in that, it comprises planning module (121) that allows for sending back the results again to the 2-opt algorithm by using the threshold acceptance algorithm in order to find a better result and increase the maximum acceptable solution value of these results if a result that is worse than the result found by the ant colony algorithm in VRP planning is found by using the 2-opt algorithm.

17. Vehicle routing and optimization system (100) according to Claim 1 and Claim 15, characterized in that, it comprises planning module (121) that allows for executing these results on the tabu algorithm in order to find a better result from these results if a result that is better than the result found by the ant colony algorithm in VRP planning is found by using the 2-opt algorithm.

18. Vehicle routing and optimization system (100) according to Claim 1 and Claim 17, characterized in that, it comprises planning module (121) that allows for reducing the number of vehicles/teams found in the results in tabu algorithm by means of using the extraction pool algorithm in VPR planning.

19. Vehicle routing and optimization system (100) according to Claim 1, characterized in that, it comprises planning module (121) that provides making improvement in the results calculated in VRP planning by means of using the search space algorithm.

20. Vehicle routing and optimization system (100) according to Claim 1, characterized in that, it comprises planning module (121) that allows the user to calculate and list the distribution or collection order over the data defined to the vehicle/team that he/she is selected from the application (110) interface in TSP planning by means of the TSP algorithm.

21. Vehicle routing and optimization system (100) according to Claim 1, characterized in that, it comprises planning module (121) that allows the user to calculate the distance of each point from another point and the time information by providing determining a starting point over the application (110), entering the usage status information of options such as time window and start/end point in TSP planning.

22. Vehicle routing and optimization system (100) according to Claim 1 and Claim 21, characterized in that, it comprises planning module (121) that allows for listing and ordering by taking into consideration the proximity (distance) calculated according to the start/end point by starting from the farthest point to the starting point (or any defined point) by means of using a remote adding algorithm in TSP planning.

23. Vehicle routing and optimization system (100) according to Claim 1 and Claim 22, characterized in that, it comprises planning module (121) that allows for extracting the optimum ordering list created with the shortest distance/time between the points from the starting point to the results found in the remote adding algorithm by using 2-opt algorithm.

24. Vehicle routing and optimization system (100) according to Claim 1, characterized in that, it comprises planning module (121) that enables the user to make cluster planning in accordance with how many regions and vehicles/teams will be used according to the need of the user, if the user has not defined each distribution/collection data will be made by which vehicle/team and desires to define it by clustering.

25. Vehicle routing and optimization system (100) according to Claim 1 and Claim 24, characterized in that, it comprises planning module (121 ) that allows for calculating the distance of each point to another point and the time information in clustering planning.

26. Vehicle routing and optimization system (100) according to Claim 1 and Claim 25, characterized in that, it comprises planning module (121) that allows for finding the list of clustered points based on the vehicle region according to the shortest distance and/or time by using k-means algorithm.

27. Vehicle routing and optimization system (100) according to Claim 1, characterized in that, it comprises work order assignment module (124) that allows for receiving the status information of completing or canceling the works of the teams in the field, over the application (110).

28. Vehicle routing and optimization system (100) according to Claim 1 , characterized in that, it comprises planning module (121) that allows for running TSP planning according to data receiving from the clustering planning in case the user has performed clustering planning and desires to calculate the order between points in the cluster per vehicle/team.

29. Vehicle routing and optimization system (100) according to Claim 1 , characterized in that, it comprises planning module (121) that allows for performing TSP planning according to data receiving from VRP planning in case the user has performed VRP planning and desires to calculate the order between points per vehicle/team.

30. Vehicle routing and optimization system (100) according to Claim 1, characterized in that, it comprises planning module (121) that allows for performing VRP planning according to data from cluster planning if the user has performed clustering planning and desires to calculate the points in the cluster per vehicle/team and vehicle/team matching.