CN111160609A - Road network reachability-based vehicle scheduling method with time window - Google Patents
Road network reachability-based vehicle scheduling method with time window Download PDFInfo
- Publication number
- CN111160609A CN111160609A CN201911200234.XA CN201911200234A CN111160609A CN 111160609 A CN111160609 A CN 111160609A CN 201911200234 A CN201911200234 A CN 201911200234A CN 111160609 A CN111160609 A CN 111160609A
- Authority
- CN
- China
- Prior art keywords
- point
- customer
- chromosome
- population
- chromosomes
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
Abstract
A vehicle scheduling method with a time window based on road network accessibility comprises the steps of firstly, creating a network data set of a research area, and acquiring a real road distance between research object points; then, the client point performs coding operation by adopting a natural number coding mode and completes the initialization of the population; after the population is crossed and mutated, selecting chromosomes with higher fitness according to the fitness to form a new population, and repeating the operations of crossing, mutating and selecting until the termination condition of iteration is met; and finally, outputting the chromosome with the highest fitness in the population, and after the decoding operation is completed, obtaining the optimal path scheme for vehicle scheduling. The invention provides a vehicle scheduling method with a time window based on road network accessibility, which is more suitable for practical application.
Description
Technical Field
The invention relates to the fields of GIS technology, logistics distribution, intelligent calculation and computer application, in particular to a vehicle scheduling method with a time window based on road network accessibility.
Background
The logistics industry has gradually become an important support for national economy, and the industry is a composite service production and dyeing which integrates the industries of transportation, storage, freight transportation agent, information and the like. The space for obtaining economic benefits by reducing the commodity cost and expanding the market has become smaller and smaller, and numerous enterprises aim at reducing the logistics cost of the enterprises. However, compared with developed countries, the overall level of the logistics industry in China is not high, and under the background, the government has a series of policy measures for supporting the promotion of the third-party logistics development so as to promote the healthy and stable development of the whole logistics industry. Logistics distribution is a very important place in third-party logistics systems, with more than half and even higher costs arising from the distribution process. The rational planning of logistics distribution routes, i.e. vehicle routing problems, has attracted extensive attention from the academic community.
The vehicle scheduling problem, also called vehicle routing problem, is one of the core problems in transportation organization optimization, and it optimizes the transportation route of the vehicle to deliver the goods to the destination with the lowest transportation cost and expense as possible on the premise of meeting the customer demand. In practice, the problem is not only limited to the field of logistics distribution, but also has certain application in the fields of aviation, ocean shipping, industrial management and the like, and research results of the problem are used for various combined optimization problems of ship companies for transporting goods through ports, optimization design of goods arrangement, transportation vehicle routing arrangement and the like. The research on the vehicle path problem influences the development of multiple application fields such as logistics, supply chain management, enterprise resource planning and the like, and still attracts the attention of broad scholars.
The research of the vehicle scheduling problem with the time window is more suitable for the practical life and has higher application value compared with the traditional vehicle scheduling problem. At present, most researchers are developing the problem of vehicle scheduling with a time window by abstracting the positions and roads of client points into intersection points and edges in graph theory through a simple modeling mode of graph theory, and when calculating the distance between each client point, the distance is developed by applying a distance formula between two points based on the conversion of position coordinates of the client points. The data processing mode completely ignores the accessibility of a road network between client points under a real road, the Euclidean distance between two points is not equal to the actual road distance between two client points under the real road, and the real road distance is regressed to the actual application, so that the precision loss is large.
Therefore, the existing vehicle scheduling method with the time window has a defect in the accuracy of application research of the distribution scheme under the real road, and needs to be improved.
Disclosure of Invention
In order to solve the defect that the existing vehicle scheduling method with a time window has low accuracy of a scheme for solving the distribution problem under a real road, the invention provides a vehicle scheduling method with a time window based on road network accessibility.
The technical scheme adopted by the invention to solve the technical problem is as follows:
a vehicle scheduling method with a time window based on road network accessibility comprises the following steps:
1) the following objective function is established with the goal of minimizing the total cost of all delivery vehicles:
where C is the transportation cost per unit distance of the delivery vehicles, K is the number of delivery vehicles, V is the set of all customer points,
the decision variable is 0 or 1, when the delivery vehicle k goes from the client point i to j, the value is 1, otherwise, the value is 0, and dijRepresenting the actual road distance between customer points i and j, eiAnd liIndicating the start and end times, M, of the customer point i receiving service1Is shown at eiThe time to wait until reaching client point i becomes the penalty factor, M2Indicates later than liTime cost coefficient, t, of accepting penalty to reach customer point iiIndicating the time of arrival at customer point i; the constraint conditions are as follows: only one distribution center is provided, all distribution vehicles of the distribution center are provided with a starting point and an ending point, the demand of a customer point is known and is smaller than the maximum carrying capacity of the vehicle, each vehicle can distribute a plurality of customer points, but each customer point can be distributed by only one vehicle;
2) setting parameters: the unit distance transportation cost C, the number N of customer points, the maximum vehicle load Q, a demand list T of the customer points, a time window TW of the customer points for receiving service, service time H and a time cost penalty coefficient M1And M2Cross probability PC, variation probability PM, population size NP and iteration number G;
3) importing a geographic information map of a target area by means of an ArcMap platform, creating a corresponding road line graph layer according to real road information, and completing road vectorization and geographic registration operation of the target area;
4) constructing a network data set of a target area based on the created road line graph layer, and newly constructing a point feature graph layer in the network data set to represent the geographic positions of a distribution center and a customer point;
5) creating a distance cost analysis model between a distribution center and a client point, and acquiring a distance cost matrix D between the distribution center and the client point based on the accessibility of a road network, wherein
Wherein the diagonal element d in the matrix00,d11,…,dNNA value of 0, d0jRepresents the actual road distance, d, from the distribution center 0 to the customer point ji0Representing the actual road distance, d, at which customer point i returns to distribution center 0ijRepresenting the real road distance from a client point i to a client point j, wherein i, j belongs to V and i is not equal to j;
6) determining the number of delivery vehicles required
Wherein q isiIndicating the cargo demand at the ith customer site, α is [0,1]]The random restriction factor of (a) is,
represents rounding down;
7) and (3) encoding: the customer point is coded into 1,2,3, … by adopting a natural number coding mode, and N, 0 represents a distribution center;
8) population initialization: randomly arranging natural number codes of the client points into a row to form an initial chromosome, repeating the operation for 2NP times to obtain a sample population formed by the 2NP chromosomes, calculating the fitness of each chromosome in the sample population, wherein the fitness is the reciprocal of an objective function, and reserving NP strips with higher fitness to form the initial population;
9) the cross operation, the process is as follows:
9.1) randomly and repeatedly selecting two chromosomes from the population as parent chromosomes, generating a random number r in the interval of [0,1], if r is less than PC, executing the step 9.2), otherwise, directly keeping the two chromosomes to the next generation, and turning to the step 9.4);
9.2) adopting a partial matching and crossing mode: generating two random integers p less than the length of the chromosome1And p2And needs to satisfy p1＜p2Locating the two parent chromosomes at p1And p2The client point code segment exchange position between; if repeated conflict occurs in the chromosome codes after the exchange, replacing the codes repeated in the exchange segment with the allelic codes of the other chromosome corresponding to the exchange segment;
9.3) calculating the fitness of the two parent chromosomes and the two crossed chromosomes, and reserving the two chromosomes with higher fitness as the result of the crossing operation;
9.4) iterating steps 9.1) to 9.3) until all chromosomes are traversed;
10) mutation operation: generating a random number r' in the interval [0,1 ]; if r' is less than PM, randomly selecting two client point codes in the chromosome to carry out position exchange; if r' is not less than PM, directly keeping the current chromosome to the next generation;
11) selecting operation: calculating the fitness of all chromosome individuals of the parent population and the offspring population obtained after crossing and mutation operations, arranging the chromosomes according to the fitness in an ascending order, and selecting NP chromosomes with high fitness to form a new population;
12) iterating the steps 9) to 11) to the maximum iteration number G, and selecting the chromosome with the highest fitness in the current population to perform decoding operation, namely qiIndicating the demand of the ith customer point if satisfied
And is
Then 0 is inserted behind the a-th position of the chromosome, then repeated calculation is started until K-1 0 is inserted, and finally 0 is added to the first position and the last position of the chromosome respectively to obtain the decoded chromosome, namely the optimal solution of the objective function.
The invention has the beneficial effects that: and constructing a road network model by means of the ArcMap platform, and creating a corresponding network data set to acquire the real road network distance between the distribution center and the customer point. On the basis, a genetic algorithm with strong optimizing capability is used for solving the problem of vehicle scheduling; in the solving process of the genetic algorithm, the real road distance considering the accessibility of the real road network is adopted, and compared with the Euclidean distance in the traditional method, the real road distance has more practical significance and is more suitable for practical application.
Drawings
FIG. 1 is a flow chart of a method for scheduling vehicles with time windows based on road network reachability.
Fig. 2 is a distribution diagram of a research area network model and distribution centers and customer sites.
Fig. 3 is a path diagram of an optimal distribution scheme.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 3, a vehicle path optimization method based on road network reachability includes the following steps:
1) the following objective function is established with the goal of minimizing the total cost of all delivery vehicles:
where C is the transportation cost per unit distance of the delivery vehicles, K is the number of delivery vehicles, V is the set of all customer points,
the decision variable is 0 or 1, when the delivery vehicle k goes from the client point i to j, the value is 1, otherwise, the value is 0, and dijRepresenting the actual road distance between customer points i and j, eiAnd liIndicating the start and end times, M, of the customer point i receiving service1Is shown at eiThe time to wait until reaching client point i becomes the penalty factor, M2Indicates later than liTime cost coefficient, t, of accepting penalty to reach customer point iiIndicating the time of arrival at customer point i; the constraint conditions are as follows: only one distribution center is provided, all distribution vehicles of the distribution center are provided with a starting point and an ending point, the demand of a customer point is known and is smaller than the maximum carrying capacity of the vehicle, each vehicle can distribute a plurality of customer points, but each customer point can be distributed by only one vehicle;
2) setting parameters: the unit distance transportation cost C, the number N of customer points, the maximum vehicle load Q, a demand list T of the customer points, a time window TW of the customer points for receiving service, service time H and a time cost penalty coefficient M1And M2Cross probability PC, variation probability PM, population size NP and iteration number G;
3) importing a geographic information map of a target area by means of an ArcMap platform, creating a corresponding road line graph layer according to real road information, and completing road vectorization and geographic registration operation of the target area;
4) constructing a network data set of a target area based on the created road line graph layer, and newly constructing a point feature graph layer in the network data set to represent the geographic positions of a distribution center and a customer point;
5) creating a distance cost analysis model between a distribution center and a client point, and acquiring a distance cost matrix D between the distribution center and the client point based on the accessibility of a road network, wherein
Wherein the diagonal element d in the matrix00,d11,…,dNNA value of 0, d0jRepresents the actual road distance, d, from the distribution center 0 to the customer point ji0Representing the actual road distance, d, at which customer point i returns to distribution center 0ijRepresenting the real road distance from a client point i to a client point j, wherein i, j belongs to V and i is not equal to j;
6) determining the number of delivery vehicles required
Wherein q isiIndicating the cargo demand at the ith customer site, α is [0,1]]The random restriction factor of (a) is,
represents rounding down;
7) and (3) encoding: the customer point is coded into 1,2,3, … by adopting a natural number coding mode, and N, 0 represents a distribution center;
8) population initialization: randomly arranging natural number codes of the client points into a row to form an initial chromosome, repeating the operation for 2NP times to obtain a sample population formed by the 2NP chromosomes, calculating the fitness of each chromosome in the sample population, wherein the fitness is the reciprocal of an objective function, and reserving NP strips with higher fitness to form the initial population;
9) the cross operation, the process is as follows:
9.1) randomly and repeatedly selecting two chromosomes from the population as parent chromosomes, generating a random number r in the interval of [0,1], if r is less than PC, executing the step 9.2), otherwise, directly keeping the two chromosomes to the next generation, and turning to the step 9.4);
9.2) adopting a partial matching and crossing mode: generating two random integers p less than the length of the chromosome1And p2And needs to satisfy p1＜p2Locating the two parent chromosomes at p1And p2Exchanging positions of the client point coding segments, and replacing repeated codes in the exchange segments with allele codes of the other chromosome corresponding to the exchange segments if repeated conflicts occur in the chromosome codes after exchange;
9.3) calculating the fitness of the two parent chromosomes and the two crossed chromosomes, and reserving the two chromosomes with higher fitness as the result of the crossing operation;
9.4) iterating steps 9.1) to 9.3) until all chromosomes are traversed;
10) mutation operation: generating a random number r' in the interval [0,1 ]; if r' is less than PM, randomly selecting two client point codes in the chromosome to carry out position exchange; if r' is not less than PM, directly keeping the current chromosome to the next generation;
11) selecting operation: calculating the fitness of all chromosome individuals of the parent population and the offspring population obtained after crossing and mutation operations, arranging the chromosomes according to the fitness in an ascending order, and selecting NP chromosomes with high fitness to form a new population;
12) iterating the steps 9) to 11) to the maximum iteration number G, and selecting the chromosome with the highest fitness in the current population to perform decoding operation, namely qiIndicating the demand of the ith customer point if satisfied
And is
Then insert 0 after the a-th position of the chromosome and then repeat the calculation untilInserting K-1 0, and finally adding 0 to the first and last chromosome bits respectively to obtain the decoded chromosome, namely the optimal solution of the objective function.
In this embodiment, taking the delivery of 20 client points in the coastal river area in hangzhou city as an example, a vehicle scheduling method with a time window based on the reachability of a road network includes the following steps:
1) the following objective function is established with the goal of minimizing the total cost of all delivery vehicles:
where C is the transportation cost per unit distance of the delivery vehicles, K is the number of delivery vehicles, V is the set of all customer points,
the decision variable is 0 or 1, when the delivery vehicle k goes from the client point i to j, the value is 1, otherwise, the value is 0, and dijRepresenting the actual road distance between customer points i and j, eiAnd liIndicating the start and end times, M, of the customer point i receiving service1Is shown at eiThe time to wait until reaching client point i becomes the penalty factor, M2Indicates later than liTime cost coefficient, t, of accepting penalty to reach customer point iiIndicating the time of arrival at customer point i; the constraint conditions are as follows: only one distribution center is provided, all distribution vehicles of the distribution center are provided with a starting point and an ending point, the demand of a customer point is known and is smaller than the maximum carrying capacity of the vehicle, each vehicle can distribute a plurality of customer points, but each customer point can be distributed by only one vehicle;
2) setting parameters: the transportation cost C of unit distance is 10, the number N of customer points is 20, the maximum load Q of the vehicle is 3, and the time cost penalty coefficient M15 and M2The time window TW of the customer site receiving the service, the crossover probability PC 0.9, the mutation probability PM 0.05, the population size NP 30, the number of iterations G3000, the demand list T of the customer site, the time window TW, and the service time H are shown in the following table:
| customer points | Demand volume | Time window | Service time | Customer points | Demand volume | Time window | Service time |
| 1 | 0.4 | [6:00,7:00] | 0.5 | 11 | 0.4 | [7:00,8:00] | 0.7 |
| 2 | 0.5 | [7:00,8:00] | 0.3 | 12 | 0.5 | [9:00,10:00] | 0.6 |
| 3 | 0.4 | [8:00,10:00] | 0.3 | 13 | 0.5 | [6:00,7:00] | 0.6 |
| 4 | 0.3 | [13:00,14:00] | 0.3 | 14 | 0.4 | [8:00,10:00] | 0.5 |
| 5 | 0.5 | [11:00,12:00] | 0.2 | 15 | 0.3 | [8:00,9:00] | 0.2 |
| 6 | 0.3 | [9:00,11:00] | 0.5 | 16 | 0.5 | [13:00,14:00] | 0.3 |
| 7 | 0.3 | [6:00,8:00] | 0.4 | 17 | 0.3 | [12:00,14:00] | 0.5 |
| 8 | 0.5 | [13:00,14:00] | 0.4 | 18 | 0.2 | [13:00,15:00] | 0.3 |
| 9 | 0.4 | [7:00,9:00] | 0.5 | 19 | 0.5 | [7:00,9:00] | 0.7 |
| 10 | 0.3 | [10:00,11:00] | 0.7 | 20 | 0.4 | [7:00,9:00] | 0.5 |
3) Importing a geographic information map of a target area by means of an ArcMap platform, creating a corresponding road line graph layer according to real road information, and completing road vectorization and geographic registration operation of the target area;
4) constructing a network data set of a target area based on the created road line graph layer, and newly constructing a point feature graph layer in the network data set to represent the geographic positions of a distribution center and a customer point;
5) creating a distance cost analysis model between a distribution center and a client point, and acquiring a distance cost matrix D between the distribution center and the client point based on the accessibility of a road network, wherein
Wherein the diagonal element d in the matrix00,d11,…,dNNA value of 0, d0jRepresents the actual road distance, d, from the distribution center 0 to the customer point ji0Representing the actual road distance, d, at which customer point i returns to distribution center 0ijRepresenting the real road distance from a client point i to a client point j, wherein i, j belongs to V and i is not equal to j;
6) determining the number of delivery vehicles required
Wherein q isiIndicating the cargo demand at the ith customer site, α is [0,1]]The random restriction factor of (a) is,
represents rounding down;
7) and (3) encoding: the customer point is coded into 1,2,3, … by adopting a natural number coding mode, and N, 0 represents a distribution center;
8) population initialization: randomly arranging natural number codes of the client points into a row to form an initial chromosome, repeating the operation for 2NP times to obtain a sample population formed by the 2NP chromosomes, calculating the fitness of each chromosome in the sample population, wherein the fitness is the reciprocal of an objective function, and reserving NP strips with higher fitness to form the initial population;
9) the cross operation, the process is as follows:
9.1) randomly and repeatedly selecting two chromosomes from the population as parent chromosomes, generating a random number r in the interval of [0,1], if r is less than PC, executing the step 9.2), otherwise, directly keeping the two chromosomes to the next generation, and turning to the step 9.4);
9.2) adopting a partial matching and crossing mode: generating two random integers p less than the length of the chromosome1And p2And needs to satisfy p1＜p2Locating the two parent chromosomes at p1And p2Exchanging positions of the client point coding segments, and replacing repeated codes in the exchange segments with allele codes of the other chromosome corresponding to the exchange segments if repeated conflicts occur in the chromosome codes after exchange;
9.3) calculating the fitness of the two parent chromosomes and the two crossed chromosomes, and reserving the two chromosomes with higher fitness as the result of the crossing operation;
9.4) iterating steps 9.1) to 9.3) until all chromosomes are traversed;
10) mutation operation: generating a random number r' in the interval [0,1 ]; if r' is less than PM, randomly selecting two client point codes in the chromosome to carry out position exchange; if r' is not less than PM, directly keeping the current chromosome to the next generation;
11) selecting operation: calculating the fitness of all chromosome individuals of the parent population and the offspring population obtained after crossing and mutation operations, arranging the chromosomes according to the fitness in an ascending order, and selecting NP chromosomes with high fitness to form a new population;
12) iterating the steps 9) to 11) to the maximum iteration number G, and selecting the chromosome with the highest fitness in the current population to perform decoding operation, namely qiIndicating the demand of the ith customer point if satisfied
And is
Then 0 is inserted behind the a-th position of the chromosome, then repeated calculation is started until K-1 0 is inserted, and finally 0 is added to the first position and the last position of the chromosome respectively to obtain the decoded chromosome, namely the optimal solution of the objective function.
Taking a distribution center in the coastal region of Hangzhou city as an example to distribute 20 customer points, a flow chart of a research method is shown in FIG. 1, a network model of a research area and a distribution chart of the distribution center and the customer points are shown in FIG. 2, and a path of an optimal distribution scheme is shown in FIG. 3. The optimal distribution route scheme is obtained by applying the method, namely the distribution scheme of three vehicles is as follows in sequence: [0,15,4,10,20,2,6,17,3,0], [0,8,1,9,11,19,12,0] and [0,3,5,18,16,7,14,0 ].
The foregoing is a predictive effect of one embodiment of the present invention, which may be adapted not only to the above-described embodiments, but also to various modifications thereof without departing from the basic idea of the invention and without exceeding the essence of the invention.
Claims (1)
1. A vehicle scheduling method with a time window based on road network accessibility is characterized by comprising the following steps:
1) the following objective function is established with the goal of minimizing the total cost of all delivery vehicles:
where C is the transportation cost per unit distance of the delivery vehicles, K is the number of delivery vehicles, V is the set of all customer points,
the decision variable is 0 or 1, the value is 1 when the delivery vehicle k goes from the customer point i to the point j, otherwise, the value is 0, dijRepresenting the actual road distance between customer points i and j, eiAnd liIndicating the start and end times, M, of the customer point i receiving service1Is shown at eiTime cost penalty factor, M, of waiting until customer site i is reached2To representLater than liTime cost coefficient, t, of accepting penalty to reach customer point iiIndicating the time of arrival at customer point i; the constraint conditions are as follows: only one distribution center is provided, all distribution vehicles of the distribution center are provided with a starting point and an ending point, the demand of a customer point is known and is smaller than the maximum carrying capacity of the vehicles, each vehicle can distribute a plurality of customer points, but each customer point can be distributed by only one vehicle;
2) setting parameters: transportation cost C of unit distance, number N of customer points, maximum loading capacity Q of vehicles, demand quantity list T of customer points, time window TW of customer points for receiving service, service time H and time cost penalty coefficient M1And M2Cross probability PC, variation probability PM, population size NP and iteration number G;
3) importing a geographic information map of a target area by means of an ArcMap platform, creating a corresponding road line graph layer according to real road information, and completing road vectorization and geographic registration operation of the target area;
4) constructing a network data set of a target area based on the created road line graph layer, and newly constructing a point feature graph layer in the network data set to represent the geographic positions of a distribution center and a customer point;
5) creating a distance cost analysis model between a distribution center and a client point, and acquiring a distance cost matrix D between the distribution center and the client point based on the accessibility of a road network, wherein
Wherein, the diagonal element d in the matrix00,d11,…,dNNA value of 0, d0jRepresents the actual road distance, d, from the distribution center 0 to the customer point ji0Representing the actual road distance, d, at which customer point i returns to distribution center 0ijRepresenting the real road distance from a client point i to a client point j, wherein i, j belongs to V and i is not equal to j;
6) determining the number of delivery vehicles required
Wherein q isiRepresenting the ith customer siteCargo demand, α is [0,1]]The random restriction factor of (a) is,
represents rounding down;
7) and (3) encoding: the customer point is coded into 1,2,3, … by adopting a natural number coding mode, and N, 0 represents a distribution center;
8) population initialization: randomly arranging natural number codes of the client points into a row to form an initial chromosome, repeatedly operating for 2NP times to obtain a sample population formed by the 2NP chromosomes, calculating the fitness of each chromosome in the sample population, wherein the fitness is the reciprocal of a target function, and reserving NP strips with higher fitness to form the initial population;
9) the cross operation, the process is as follows:
9.1) randomly and repeatedly selecting two chromosomes from the population as parent chromosomes, generating a random number r in the interval of [0,1], if r is less than PC, executing the step 9.2), otherwise, directly keeping the two chromosomes to the next generation, and turning to the step 9.4);
9.2) adopting a partial matching and crossing mode: generating two random integers p less than the length of the chromosome1And p2And needs to satisfy p1＜p2Locating the two parent chromosomes at p1And p2Exchanging positions of the client point coding segments, and replacing repeated codes in the exchange segments with allele codes of the other chromosome corresponding to the exchange segments if repeated conflicts occur in the chromosome codes after exchange;
9.3) calculating the fitness of the two parent chromosomes and the two crossed chromosomes, and reserving the two chromosomes with higher fitness as the result of the crossing operation;
9.4) iterating steps 9.1) to 9.3) until all chromosomes are traversed;
10) mutation operation: generating a random number r' in the interval [0,1 ]; if r' is less than PM, randomly selecting two client point codes in the chromosome to carry out position exchange; if r' is not less than PM, directly keeping the current chromosome to the next generation;
11) selecting operation: calculating the fitness of all chromosome individuals of the parent population and the offspring population obtained after crossing and mutation operations, arranging the chromosomes according to the fitness in an ascending order, and selecting NP chromosomes with high fitness to form a new population;
12) iterating the steps 9) to 11) to the maximum iteration number G, and selecting the chromosome with the highest fitness in the current population to perform decoding operation, namely qiIndicating the demand of the ith customer point if satisfied
And is
Then 0 is inserted after the a-th position of the chromosome, then the calculation is repeated until K-1 0 is inserted, and finally 0 is added to the first position and the last position of the chromosome respectively to obtain the decoded chromosome, namely the optimal solution of the objective function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911200234.XA CN111160609A (en) | 2019-11-29 | 2019-11-29 | Road network reachability-based vehicle scheduling method with time window |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911200234.XA CN111160609A (en) | 2019-11-29 | 2019-11-29 | Road network reachability-based vehicle scheduling method with time window |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111160609A true CN111160609A (en) | 2020-05-15 |
Family
ID=70556229
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911200234.XA Pending CN111160609A (en) | 2019-11-29 | 2019-11-29 | Road network reachability-based vehicle scheduling method with time window |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111160609A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111708369A (en) * | 2020-07-17 | 2020-09-25 | 武汉科技大学 | Route planning method for transformer substation inspection robot |
| CN112132317A (en) * | 2020-08-21 | 2020-12-25 | 浙江工业大学 | Vehicle path optimization method based on multi-population coevolution |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030084011A1 (en) * | 2001-04-26 | 2003-05-01 | Honeywell International Inc. | Methods for solving the traveling salesman problem |
| CN109919376A (en) * | 2019-03-01 | 2019-06-21 | 浙江工业大学 | Multi-field model and multi-vehicle-type vehicle route dispatch control method |
-
2019
- 2019-11-29 CN CN201911200234.XA patent/CN111160609A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030084011A1 (en) * | 2001-04-26 | 2003-05-01 | Honeywell International Inc. | Methods for solving the traveling salesman problem |
| CN109919376A (en) * | 2019-03-01 | 2019-06-21 | 浙江工业大学 | Multi-field model and multi-vehicle-type vehicle route dispatch control method |
Non-Patent Citations (4)
| Title |
|---|
| 全斌等: "《ArcGIS 10.2 地理信息系统软件与应用》", 31 July 2017, 中国矿业大学出版社 * |
| 李征宇等: "《人工智能导论》", 31 December 2016, 哈尔滨工业大学出版社 * |
| 杨浩雄: "《城市环境中虚拟共同配送系统构建研究》", 31 July 2012, 中国物资出版社 * |
| 王荃菲: "快餐外卖配送路径方案研究", 《中国优秀博硕士学位论文全文数据库（硕士）经济与管理科学辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111708369A (en) * | 2020-07-17 | 2020-09-25 | 武汉科技大学 | Route planning method for transformer substation inspection robot |
| CN111708369B (en) * | 2020-07-17 | 2021-07-23 | 武汉科技大学 | Route planning method for transformer substation inspection robot |
| CN112132317A (en) * | 2020-08-21 | 2020-12-25 | 浙江工业大学 | Vehicle path optimization method based on multi-population coevolution |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111178582B (en) | Logistics distribution optimization method based on improved genetic algorithm | |
| Ma et al. | Distribution path robust optimization of electric vehicle with multiple distribution centers | |
| CN110689165A (en) | Vehicle path optimization method based on road network reachability | |
| CN107833002B (en) | Multi-stage low-carbon logistics distribution network planning method based on cooperative multi-objective algorithm | |
| CN111311158B (en) | Electric logistics vehicle path planning method under limited charging facility condition | |
| CN112132317A (en) | Vehicle path optimization method based on multi-population coevolution | |
| CN112836892A (en) | Multi-target vehicle distribution path determining method and system based on improved genetic algorithm | |
| CN107578132B (en) | Garbage truck path obtaining method based on immune optimization | |
| Zhou et al. | Electric vehicle handling routing and battery swap station location optimisation for automotive assembly lines | |
| CN111428931A (en) | Logistics distribution line planning method, device, equipment and storage medium | |
| CN111160609A (en) | Road network reachability-based vehicle scheduling method with time window | |
| CN111461402B (en) | Logistics scheduling optimization method and device, computer-readable storage medium and terminal | |
| Lu et al. | The fourth-party logistics routing problem using ant colony system-improved grey wolf optimization | |
| CN110705741B (en) | Multi-distribution center vehicle path optimization method based on improved ant colony algorithm | |
| Vaira | Genetic algorithm for vehicle routing problem | |
| CN114022014A (en) | Intelligent vehicle scheduling method and device, electronic equipment and storage medium | |
| Madleňák et al. | Multiple approaches of solving allocation problems on postal transportation network in conditions of large countries | |
| CN111445094B (en) | Express vehicle path optimization method and system based on time requirement | |
| Tan et al. | The optimization of bus scheduling based on genetic algorithm | |
| CN108416482B (en) | Logistics distribution path planning method based on regional multiple strategies | |
| CN116882883A (en) | Power grid material distribution path planning method utilizing big data | |
| Visutarrom et al. | An evolutionary algorithm with heuristic longest cycle crossover for solving the capacitated vehicle routing problem | |
| Jakara et al. | VEHICLE ROUTING PROBLEM-CASE STUDY ON LOGISTICS COMPANY IN CROATIA. | |
| CN116307330A (en) | Multi-mode intermodal organization mode dynamic optimization method based on big data processing | |
| Qian et al. | A hyper heuristic algorithm for low carbon location routing problem |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200515 |