CN113112203B - Multi-distribution center vehicle routing system based on hybrid ant colony algorithm - Google Patents
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Abstract
The invention relates to the technical field of distribution, in particular to a vehicle routing system with multiple distribution centers based on a hybrid ant colony algorithm. The system comprises a basic number library unit, a distribution management unit, a planning calculation unit and a functional application unit; the basic number library unit is used for inputting information of maps, suppliers, retailers, distribution centers and the like in an area and forming a database; the distribution management unit is used for managing and distributing logistics distribution according to the order requirement and the distribution condition of terminal retailers in the area; the planning calculation unit is used for planning the distribution path through a hybrid ant colony algorithm and calculating and selecting the optimal path arrangement; the function application unit is used for completing the functions of the system through various applications. The design of the invention can better select the distribution center, and simultaneously introduces the mixed ant colony algorithm, can more quickly and optimally plan the traveling path of the distribution vehicle, and can provide various route planning choices, thereby saving time and investment cost for users and shortening distribution time.
Description
Technical Field
The invention relates to the technical field of distribution, in particular to a vehicle routing system with multiple distribution centers based on a hybrid ant colony algorithm.
Background
The distribution refers to the logistics activity of picking, processing, packaging, dividing, assembling and the like of articles within an economic and reasonable area according to the requirements of customers and delivering the articles to a specified place on time, and is a special and comprehensive activity form in logistics. The distribution center is generated after further refining social division of labor and professional division of labor in the logistics field, and aims to save transportation cost and guarantee customer satisfaction. The distribution center utilizes circulation facilities and an information system platform to flip, classify, circulate and process, match, design a transportation route and a transportation mode for goods of logistics dealers, and provides proper distribution service for customers. However, in the distribution service work covering a plurality of distribution centers having a large service area, there is a limitation in the arrangement of the distribution vehicle route. How to reasonably plan the traveling path of the delivery vehicle, and on the premise of meeting the supply capacity limit of a delivery center and the limit of the vehicle traveling distance and the load capacity, the total travel of the vehicle is shortest. A bionic algorithm designed according to the colony behavior of foraging ants is introduced to plan the distribution path well, but at present, a software program suitable for large-scale and large-quantity distribution planning cannot be established by directly utilizing a mixed ant colony algorithm.
Disclosure of Invention
The invention aims to provide a multi-distribution center vehicle routing system based on a hybrid ant colony algorithm so as to solve the problems in the background technology.
To achieve the above technical problem, one of the objectives of the present invention is to provide a hybrid ant colony algorithm-based multi-distribution center vehicle routing system, which includes
The system comprises a basic number library unit, a distribution management unit, a planning calculation unit and a function application unit; the basic number library unit, the distribution management unit, the planning calculation unit and the function application unit are sequentially connected through Ethernet communication; the basic number library unit is used for inputting information such as maps, suppliers, retailers and distribution centers in an area and forming a database; the distribution management unit is used for managing and distributing logistics distribution according to the order requirement and the distribution situation of terminal retailers in the area; the planning calculation unit is used for planning the distribution path through a hybrid ant colony algorithm and calculating and selecting the optimal path arrangement; the function application unit is used for completing the functions of the system through various applications;
the basic number library unit comprises a regional map module, a supply distribution module, a retail distribution module and a distribution and site selection module;
the delivery management unit comprises an order demand module, an on-demand distribution module, a central distribution module and a path planning module;
the planning calculation unit comprises a simulation model module, a classical ant colony module, a mixed ant colony module and an pheromone updating module;
the function application unit comprises an information acquisition module, a screening and updating module, a selection management module and a history storage module.
As a further improvement of the technical solution, a signal output end of the regional map module is connected with a signal input end of the supply distribution module, a signal output end of the supply distribution module is connected with a signal input end of the retail distribution module, and a signal output end of the retail distribution module is connected with a signal input end of the distribution addressing module; the regional map module is used for importing map data of a coverage region; the supply distribution module is used for importing various information of suppliers and marking the positions of the suppliers on a map; the retail distribution module is used for importing various information of the terminal retailer and marking the distribution and the specific position of the retailer on a map; the distribution and site selection module is used for importing the information of the existing distribution centers, planning a new distribution center according to the distribution of suppliers and retailers and marking the positions of all the distribution centers on a map.
The map data includes city map, regional topographic map, traffic route map, satellite live-action map, etc.
The supply information includes, but is not limited to, the address, specification, qualification, supply goods, transportation vehicles, etc. of the supplier.
The information of the terminal retailer includes, but is not limited to, address, size, business scope, etc.
The selection requirements of the distribution center include serving as many suppliers as possible, supplying route loop connection, reducing the distance of repeated walking in the vehicle path as possible, and the like.
As a further improvement of the technical solution, a signal output end of the order demand module is connected with a signal input end of the on-demand goods distribution module, a signal output end of the on-demand goods distribution module is connected with a signal input end of the central goods distribution module, and a signal output end of the central goods distribution module is connected with a signal input end of the path planning module; the ordering requirement module is used for receiving and integrating ordering requirements and requirements of all terminal retailers; the on-demand goods distribution module is used for managing the distribution of goods of suppliers to each distribution center according to the demand quantity and the distribution condition of retailers; the central goods distribution module is used for respectively carrying out goods distribution arrangement on each distribution center to each retailer in the service range of the distribution center according to the requirement; the path planning module is used for respectively carrying out distribution planning on the carrying capacity and the transportation path of the transportation vehicle aiming at each distribution center.
As a further improvement of the technical solution, a signal output end of the simulation model module is connected to a signal input end of the classical ant colony module, a signal output end of the classical ant colony module is connected to a signal input end of the mixed ant colony module, and a signal output end of the mixed ant colony module is connected to a signal input end of the pheromone updating module; the simulation model module is used for carrying out data simulation and creating a data model aiming at the multi-distribution center; the classical ant colony module is used for designing a bionic algorithm according to the colony behavior of ants foraging on the basis of the simulation model; the hybrid ant colony module is used for limiting the performance of the hybrid ant colony algorithm according to the process of ant constructing paths; the pheromone updating module is used for increasing more pheromones under the condition of not neglecting the side traversed by each ant.
As a further improvement of the technical solution, the calculation expression of the simulation model module is as follows:
wherein, the formula (1) is an objective function, and the total driving distance of all vehicles is the shortest; equations (2) and (3) limit the demand of all demand points served by a vehicle not to exceed the maximum load capacity of the vehicle and the driving distance not to exceed the maximum driving distance of the vehicle; equation (4) shows that the total demand of demand points served by each distribution center must not exceed the supply capacity of the distribution center; equations (5), (6) and (7) limit the service to one vehicle having only one distribution center per demand point.
As a further improvement of the technical solution, the calculation expression of the classical ant colony module is as follows:
in the formula, τ ij (t) is the edge at time t<v i ,v j >Amount of pheromone, eta ij (t)＝1/d ij For the heuristic function, alpha is an information heuristic factor, and beta is an expected heuristic factor;
τ ij (t+1)＝(1-ρ)τ ij (t)+Δτ ij (t) (9)
wherein rho is pheromone volatilization coefficient, is a real number between 0 and 1, and (1-rho) is pheromone residual factor; delta tau ij (t) leaving ants on the edge for the t-th traversal<v i ,v j >Amount of pheromone on, Δ τ ij (0)＝0。
As a further improvement of the technical solution, in the mixed ant colony module, a calculation expression for solving the mixed ant colony algorithm is as follows:
in the formula eta ij (t)＝yita/d ij (yita is a constant) and Set is v i K, is not in the taboo table of ant K, and still satisfies the set of all vertices after adding the path.
As a further improvement of the technical solution, a calculation expression of the pheromone updating module is as follows:
(ts-s)/s＜gap (11)
where gap is a small constant and every time a feasible solution is constructed and Δ τ is updated as described above ij After (t), the pheromone is updated according to the formula (9), wherein, in order to prevent the pheromone from being excessively different and the algorithm from falling into the local optimum, the amount of the pheromone on each edge is limited, so that Bd is less than or equal to delta tau ij (t) is less than or equal to Bu, i.e. if tau ij If < Bd, set τ to ij (t) is Bd, if τ ij If Bu is greater, set τ ij (t) is Bu;
the pheromone updating process is divided into two parts: after the ant colony constructs the path and uses 2-Opt optimization, the information increment delta tau on each side in the path is set ij (t)＝Δτ ij (t) + I τ (I τ is a small constant); when the feasible solution construction is completed, set to ts, the best solution currently obtained by the algorithm is s, and if equation (11) is satisfied, set to Δ τ on the side included in the feasible solution ij (t)＝Δτ ij (t) + Q/ts (Q represents pheromone strength, a constant).
As a further improvement of the technical scheme, the information acquisition module, the screening and updating module, the selection management module and the history storage module are sequentially connected through ethernet communication and operate independently; the information acquisition module is used for acquiring newly added map data, and information data of suppliers, retailers and distribution centers in real time; the cleaning and updating module is used for cleaning and screening repeated, wrong and invalid information in the database at regular time; the selection management module is used for providing a plurality of groups of paths for a user to select according to different tendencies when planning a route each time; the history storage module is used for recording and storing effective distribution work and path selection and forming a history database for backtracking.
The different tendencies of the route include, but are not limited to, shortest route, least traffic light, least traffic jam probability, least repeated route, optimal road condition, preferential delivery of fresh or cold fresh goods, and the like.
The invention also aims to provide an operation method of the vehicle routing system with the multiple distribution centers based on the hybrid ant colony algorithm, which comprises the following steps:
s1, inputting map data of a coverage area in a system, inputting data of suppliers, retailers and distribution centers to form a basic database, and correspondingly marking the positions of the suppliers, the retailers and the distribution centers on an area map;
s2, selecting a proper distribution center in an area close to the retailer set by the system according to the distribution condition of the terminal retailer;
s3, the retailer initiates an order request to the supplier, the system receives and records the order received by the supplier, all data are imported into the mixed ant colony simulation model, and the distribution path is calculated and planned;
s4, planning a plurality of distribution routes by the system and feeding the distribution routes back to a supplier, wherein the supplier selects an optimal distribution route according to the cargo condition and the distribution vehicle condition;
s5, the supplier distributes the goods of the total goods demand of all retailers in the service area to each distribution center according to the order requirement;
s6, receiving the goods by each distribution center, selecting a distribution route in the service area again by the distribution centers according to the goods condition, the distribution vehicle condition and the distribution of the terminal retailers, and distributing the goods to the retailers according to the plan;
s7, connecting the system with a network, collecting newly added information of a map, a supplier, a retailer and a distribution center in real time, and updating the newly added information into a database;
and S8, the system regularly cleans the invalid data, records effective delivery work each time, and extracts a historical record in a historical database for a user to backtrack and select according to the same order information when a new order requirement is received.
It is a further object of the present invention to provide an operating apparatus of a hybrid ant colony algorithm-based vehicle routing system for multiple distribution centers, comprising a processor, a memory and a computer program stored in the memory and operating on the processor, wherein the processor is configured to implement any one of the hybrid ant colony algorithm-based vehicle routing systems when executing the computer program.
It is a fourth object of the present invention that the computer readable storage medium stores a computer program that when executed by a processor implements any of the hybrid ant colony algorithm-based multi-distribution center vehicle routing systems described above.
Compared with the prior art, the invention has the beneficial effects that: in the multi-distribution-center vehicle path arrangement system based on the hybrid ant colony algorithm, a visual distribution diagram is established by inputting position information of a map, a supplier and a retailer in a service area, a distribution center can be better selected, the hybrid ant colony algorithm is introduced, an ant transfer strategy and a feasible solution construction method are designed in the algorithm, k neighborhood rule is introduced to limit the transfer target of ants, a 2-Opt method is used for optimizing ant colony traversal paths and feasible solutions, an pheromone updating method is designed, the performance of the algorithm is improved, distribution vehicle traveling paths can be planned faster and more optimally, in addition, factors such as traffic conditions, goods limitation, vehicle limitation and the like can be synthesized, multiple route planning choices are provided, the application range is wide, large-scale and large-quantity distribution paths can be arranged, time and investment cost are saved for users, distribution time is shortened, good economic benefits can be brought, and faster and more stable development of the logistics distribution industry is promoted.
Drawings
FIG. 1 is a block diagram of an exemplary optimization mode of the present invention;
FIG. 2 is a block diagram of the overall system apparatus of the present invention;
FIG. 3 is a diagram of one embodiment of a local system device architecture;
FIG. 4 is a second block diagram of a local system apparatus according to the present invention;
FIG. 5 is a third block diagram of a local system apparatus according to the present invention;
FIG. 6 is a fourth embodiment of the present invention;
FIG. 7 is a block diagram of an exemplary computer program product device of the present invention.
The various reference numbers in the figures mean:
1. a supplier; 2. selecting a distribution center; 3. a terminal retailer; 4. unselected distribution centers;
100. a base number library unit (100); 101. a regional map module (101); 102. a supply distribution module (102); 103. a retail distribution module (103); 104. a distribution and site selection module (104);
200. a delivery management unit (200); 201. an order demand module (201); 202. an on-demand distribution module (202); 203. a central distribution module (203); 204. a path planning module (204);
300. a planning calculation unit (300); 301. a simulation model module (301); 302. a classic ant colony module (302); 303. a mixed ant colony module (303); 304. a pheromone update module (304);
400. a function application unit (400); 401. an information acquisition module (401); 402. a screen cleaning and updating module (402); 403. a selection management module (403); 404. a store history module (404).
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
System embodiment
1-7, the present embodiment provides a hybrid ant colony algorithm-based multi-distribution center vehicle routing system, including
A basic number library unit 100, a delivery management unit 200, a plan calculation unit 300, and a function application unit 400; the basic database unit 100, the distribution management unit 200, the planning calculation unit 300 and the function application unit 400 are sequentially connected through ethernet communication; the basic number library unit 100 is used for recording information such as maps, suppliers, retailers and distribution centers in an area and forming a database; the distribution management unit 200 is used for managing and distributing logistics distribution according to the order requirement and the distribution situation of terminal retailers in the region; the planning calculation unit 300 is configured to plan a distribution path through a hybrid ant colony algorithm and calculate and select an optimal path arrangement; the function application unit 400 is used to complete the functions of the system through various applications;
the basic number library unit 100 comprises an area map module 101, a supply distribution module 102, a retail distribution module 103 and a distribution addressing module 104;
the delivery management unit 200 comprises an order demand module 201, an on-demand delivery module 202, a central delivery module 203 and a path planning module 204;
the planning calculation unit 300 comprises a simulation model module 301, a classical ant colony module 302, a mixed ant colony module 303 and an pheromone updating module 304;
the function application unit 400 includes an information acquisition module 401, a cleaning and updating module 402, a selection management module 403, and a history storage module 404.
In this embodiment, the signal output end of the area map module 101 is connected to the signal input end of the supply distribution module 102, the signal output end of the supply distribution module 102 is connected to the signal input end of the retail distribution module 103, and the signal output end of the retail distribution module 103 is connected to the signal input end of the distribution addressing module 104; the area map module 101 is configured to import map data of a coverage area; the supply distribution module 102 is used for importing various information of the suppliers and marking the positions of the suppliers on a map; the retail distribution module 103 is used for importing various information of terminal retailers and marking the distribution and specific positions of the retailers on a map; the distribution addressing module 104 is used for importing the existing distribution center information, planning a new distribution center according to the distribution of suppliers and retailers, and marking the positions of all the distribution centers on a map.
The map data includes city map, regional topographic map, traffic route map, satellite live-action map, etc.
The supply information includes, but is not limited to, the address, specification, qualification, supply goods, transportation vehicles, etc. of the supplier.
The information of the terminal retailer includes, but is not limited to, address, size, business scope, etc.
The selection requirements of the distribution center include serving as many suppliers as possible, supplying route loop connection, reducing the distance of repeated walking in the vehicle path as possible, and the like.
In this embodiment, the signal output end of the order demand module 201 is connected with the signal input end of the on-demand distribution module 202, the signal output end of the on-demand distribution module 202 is connected with the signal input end of the central distribution module 203, and the signal output end of the central distribution module 203 is connected with the signal input end of the path planning module 204; the order requirement module 201 is used for receiving and integrating the order requirements and requirements of each terminal retailer; the on-demand goods distribution module 202 is used for managing the distribution of goods to each distribution center by suppliers according to the demands of retailers and the distribution conditions of the retailers; the central distribution module 203 is used for respectively carrying out distribution arrangement on each distribution center to retailers in the service range according to the requirement; the path planning module 204 is configured to perform distribution planning on the carrying capacity and the transportation path of the transportation vehicle for each distribution center.
In this embodiment, the signal output end of the simulation model module 301 is connected to the signal input end of the classical ant colony module 302, the signal output end of the classical ant colony module 302 is connected to the signal input end of the mixed ant colony module 303, and the signal output end of the mixed ant colony module 303 is connected to the signal input end of the pheromone updating module 304; the simulation model module 301 is used for performing data simulation and creating a data model for the multi-distribution center; the classic ant colony module 302 is used for designing a bionic algorithm according to the colony behavior of ants foraging on the basis of the simulation model; the hybrid ant colony module 303 is used to define the performance of the hybrid ant colony algorithm according to the course of ant construction paths; pheromone update module 304 is used to add more pheromones without ignoring the edges traversed by each ant.
In this embodiment, the calculation expression of the simulation model module 301 is as follows:
wherein, the formula (1) is an objective function, and the total driving distance of all vehicles is the shortest; equations (2) and (3) limit the demand of all demand points served by a vehicle not to exceed the maximum load capacity of the vehicle and the driving distance not to exceed the maximum driving distance of the vehicle; formula (4) shows that the total demand of demand points served by each distribution center must not exceed the supply capacity of the distribution center; equations (5), (6) and (7) limit the service to one vehicle having only one distribution center per demand point.
Specifically, the basis of the classical multiple-distribution-center MDVRP model is set as follows:
g = (V, E) is a directed graph with m + n vertices, where V is its set of vertices and E is its set of directed edges; v = V c ∪V d Wherein V is c ＝{v 1 ，···，v n Denotes n demand points, V d ＝{v n+1 ，···，v n+m Denotes m distribution centers; the demand of each demand point isw i (1. Ltoreq. I.ltoreq.n) and the supply capacity of each center is C i (n+1≤i≤n+m)；
Assuming that all distribution centers use the same vehicle, the number of the vehicles used by the distribution center i is D i The maximum load of the vehicle is W, and the maximum driving distance is L, q i (i is more than or equal to 1 and less than or equal to n) is the demand of the customer i; r ik (n+1≤i≤n+m，1≤k≤D i ) The path of travel of the k-th carriage of the distribution centre i (i.e. the sequence of vertices traversed by this carriage), N ik Is the number of customers on the route, N ik =0 represents an unused vehicle k; if a client is in R ik If the sequence number in (1) is j, the client is recorded as
Set the vertex v i Has the coordinates of (x, y) i ) Defining a vertex v i And y i A distance d between ij ＝(x i -x j ) 2 +(y i -y j ) 2 And d is ij ＝d ji 。
Further, the computational expression of classical ant colony module 302 is as follows:
in the formula, τ ij (t) is the edge at time t<v i ,v j >Amount of pheromone, eta ij (t)＝1/d ij For the heuristic function, alpha is an information heuristic factor, and beta is an expected heuristic factor;
τ ij (t+1)＝(1-ρ)τ ij (t)+Δτ ij (t) (9)
wherein rho is pheromone volatilization coefficient, is a real number between 0 and 1, and (1-rho) is pheromone residual factor; delta tau ij (t) leaving ants on the edge for the t-th traversal<v i ,v j >Amount of pheromone on, Δ τ ij (0)＝0。
Further, in the mixed ant colony module 303, the calculation expression for solving the mixed ant colony algorithm is as follows:
in the formula eta ij (t)＝yita/d ij (yita is a constant) and Set is v i The K field of (a), is not in the taboo table of ant K, and still satisfies the set of all vertices of the constraint after joining the path.
In order to improve the solving performance of the algorithm, the following three strategies are designed in the algorithm: 1) Introducing k fields for each vertex, and limiting a transfer target vertex when the ants are transferred; 2) Optimizing the path and the feasible solution of each ant by using a 2-Opt optimization strategy; 3) And designing an pheromone updating strategy.
Further, the calculation expression of the pheromone updating module 304 is as follows:
(ts-s)/s＜gap (11)
where gap is a small constant and every time a feasible solution is constructed and Δ τ is updated as described above ij After (t), the pheromone is updated according to the formula (9), wherein, in order to prevent the pheromone from being excessively different and the algorithm from falling into the local optimum, the amount of the pheromone on each edge is limited, so that Bd is less than or equal to delta tau ij (t) Bu, i.e., if τ ij If < Bd, set τ to ij (t) is Bd, if τ ij If Bu is greater, set τ ij (t) is Bu;
the pheromone updating process is divided into two parts: after the ant colony constructs the path and uses 2-Opt optimization, the information increment delta tau on each side in the path is set ij (t)＝Δτ ij (t) + I τ (I τ is a small constant); when the feasible solution construction is completed, set to ts, the best solution currently obtained by the algorithm is s, and if equation (11) is satisfied, set to Δ τ on the side included in the feasible solution ij (t)＝Δτ ij (t) + Q/ts (Q represents pheromone strength, a constant).
In this embodiment, the information acquisition module 401, the screening and updating module 402, the selection management module 403, and the history storage module 404 are sequentially connected through ethernet communication and operate independently; the information acquisition module 401 is used for acquiring newly added map data, and information data of suppliers, retailers and distribution centers in real time; the cleaning and updating module 402 is used for cleaning and screening repeated, wrong and invalid information in the database at regular time; the selection management module 403 is configured to provide multiple groups of paths according to different tendencies when planning a route for a user to select; the store history module 404 is used to record and store the effective delivery work and routing and form a history database for backtracking.
The different tendencies of the route include, but are not limited to, shortest route, least traffic lights, least traffic jam probability, least repeated route, optimal road conditions, preferential delivery of fresh or cold fresh goods, and the like.
Method embodiment
An object of the present embodiment is to provide an operation method of a hybrid ant colony algorithm-based multi-distribution center vehicle routing system, comprising the following steps:
s1, recording map data of a coverage area in a system, recording data of suppliers, retailers and distribution centers to form a basic database, and correspondingly marking the positions of the suppliers, the retailers and the distribution centers on an area map;
s2, selecting a proper distribution center in an area close to the retailer set by the system according to the distribution condition of the terminal retailer;
s3, the retailer initiates an order request to the supplier, the system receives and records the order received by the supplier, all data are led into the mixed ant colony simulation model, and a distribution path is calculated and planned;
s4, planning a plurality of distribution routes by the system and feeding the distribution routes back to a supplier, wherein the supplier selects an optimal distribution route according to the cargo condition and the distribution vehicle condition;
s5, the supplier distributes the goods of the total goods demand of all retailers in the service area to each distribution center according to the order requirement;
s6, each distribution center receives goods, and the distribution centers select distribution routes in the service area again according to the goods condition, the distribution vehicle condition and the distribution of the terminal retailers and distribute the goods to the retailers according to the plan;
s7, connecting the system with a network, collecting newly added information of a map, a supplier, a retailer and a distribution center in real time, and updating the newly added information into a database;
and S8, the system regularly cleans the invalid data, records effective delivery work each time, and extracts a historical record in a historical database for a user to backtrack and select according to the same order information when a new order requirement is received.
Computer program product embodiment
Referring to fig. 1, a block diagram of an exemplary optimization mode of multi-distribution center vehicle routing is shown, including a supplier 1, a plurality of end retailers 3, and a plurality of distribution centers, determining selected distribution centers 2 in an area where the end retailers 3 are centrally distributed, screening out unselected distribution centers 4, and developing distribution routing around the selected distribution centers 2.
Referring to fig. 7, a schematic diagram of an operating device of a hybrid ant colony algorithm-based multi-distribution center vehicle routing system is shown, the device including a processor, a memory, and a computer program stored in and executed on the memory.
The processor includes one or more processing cores, the processor is connected with the processor through a bus, the memory is used for storing program instructions, and the hybrid ant colony algorithm-based multi-distribution center vehicle routing system is realized when the processor executes the program instructions in the memory.
Alternatively, the memory may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
Furthermore, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the hybrid ant colony algorithm-based multi-distribution center vehicle routing system described above.
Optionally, the present invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the above aspects of the hybrid ant colony algorithm-based multi-distribution center vehicle routing system.
It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by hardware related to instructions of a program, which may be stored in a computer-readable storage medium, such as a read-only memory, a magnetic or optical disk, and the like.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A multi-distribution center vehicle routing system based on a hybrid ant colony algorithm is characterized in that: comprises that
A basic number library unit (100), a delivery management unit (200), a planning calculation unit (300) and a function application unit (400); the basic number library unit (100), the distribution management unit (200), the planning calculation unit (300) and the function application unit (400) are sequentially connected through Ethernet communication; the basic number library unit (100) is used for recording information such as maps, suppliers, retailers and distribution centers in an area and forming a database; the distribution management unit (200) is used for managing and distributing logistics distribution according to order requirements and distribution conditions of terminal retailers in the area; the planning calculation unit (300) is used for planning the distribution path through a hybrid ant colony algorithm and calculating and selecting the optimal path arrangement; the function application unit (400) is used for completing the functions of the system through various applications;
the basic number library unit (100) comprises an area map module (101), a supply distribution module (102), a retail distribution module (103) and a distribution addressing module (104);
the delivery management unit (200) comprises an order demand module (201), an on-demand distribution module (202), a central distribution module (203) and a path planning module (204);
the planning calculation unit (300) comprises a simulation model module (301), a classical ant colony module (302), a mixed ant colony module (303) and a pheromone updating module (304);
the function application unit (400) comprises an information acquisition module (401), a screening and updating module (402), a selection management module (403) and a history storage module (404);
the signal output end of the simulation model module (301) is connected with the signal input end of the classical ant colony module (302), the signal output end of the classical ant colony module (302) is connected with the signal input end of the mixed ant colony module (303), and the signal output end of the mixed ant colony module (303) is connected with the signal input end of the pheromone updating module (304); the simulation model module (301) is used for carrying out data simulation and creating a data model aiming at a multi-distribution center; the classical ant colony module (302) is used for designing a bionic algorithm according to the colony behavior of ant foraging on the basis of the simulation model; the hybrid ant colony module (303) is used for limiting the performance of the hybrid ant colony algorithm according to the ant construction path process; the pheromone updating module (304) is used for increasing more pheromones under the condition of not neglecting the side traversed by each ant; the computational expression of the simulation model module (301) is as follows:
st
wherein, the formula (1) is an objective function, and the total driving distance of all vehicles is the shortest; equations (2) and (3) limit the demand of all demand points served by a vehicle not to exceed the maximum load capacity of the vehicle and the driving distance not to exceed the maximum driving distance of the vehicle; equation (4) shows that the total demand of demand points served by each distribution center must not exceed the supply capacity of the distribution center; equations (5), (6) and (7) limit the service provided by one vehicle with only one distribution center at each demand point;
the operation method of the system comprises the following steps:
s1, recording map data of a coverage area in a system, recording data of suppliers, retailers and distribution centers to form a basic database, and correspondingly marking the positions of the suppliers, the retailers and the distribution centers on an area map;
s2, selecting a proper distribution center in an area close to the retailer set by the system according to the distribution condition of the terminal retailer;
s3, the retailer initiates an order request to the supplier, the system receives and records the order received by the supplier, all data are led into the mixed ant colony simulation model, and a distribution path is calculated and planned;
s4, planning a plurality of distribution routes by the system and feeding back the distribution routes to a supplier, and selecting an optimal distribution route by the supplier according to the cargo condition and the distribution vehicle condition;
s5, the supplier distributes the goods of the total goods demand of all retailers in the service area to each distribution center according to the order requirement;
s6, each distribution center receives goods, and the distribution centers select distribution routes in the service area again according to the goods condition, the distribution vehicle condition and the distribution of the terminal retailers and distribute the goods to the retailers according to the plan;
s7, connecting the system with a network, collecting newly added information of a map, a supplier, a retailer and a distribution center in real time, and updating the newly added information into a database;
and S8, the system regularly cleans the invalid data, records effective delivery work each time, and extracts a historical record in a historical database for a user to backtrack and select according to the same order information when a new order requirement is received.
2. The hybrid ant colony algorithm-based multi-distribution center vehicle routing system of claim 1, wherein: the signal output end of the regional map module (101) is connected with the signal input end of the supply distribution module (102), the signal output end of the supply distribution module (102) is connected with the signal input end of the retail distribution module (103), and the signal output end of the retail distribution module (103) is connected with the signal input end of the distribution addressing module (104); the area map module (101) is used for importing map data of a coverage area; the supply distribution module (102) is used for importing various types of information of suppliers and marking the positions of the suppliers on a map; the retail distribution module (103) is used for importing various information of terminal retailers and marking the distribution and specific positions of the retailers on a map; the distribution site selection module (104) is used for importing the information of the existing distribution centers, planning a new distribution center according to the distribution of suppliers and retailers and marking the positions of all the distribution centers on a map.
3. The hybrid ant colony algorithm-based multi-distribution center vehicle routing system of claim 1, wherein: the signal output end of the order demand module (201) is connected with the signal input end of the goods distribution module (202) as required, the signal output end of the goods distribution module (202) as required is connected with the signal input end of the central goods distribution module (203), and the signal output end of the central goods distribution module (203) is connected with the signal input end of the path planning module (204); the order demand module (201) is used for receiving and integrating order demands and requirements of all terminal retailers; the on-demand goods distribution module (202) is used for managing the distribution of goods of suppliers to various distribution centers according to the demands of retailers and the distribution conditions of the suppliers; the central distribution module (203) is used for respectively carrying out distribution arrangement on each distribution center to retailers in the service range according to the requirement; the path planning module (204) is used for respectively carrying out distribution planning on the carrying capacity and the transportation path of the transportation vehicle aiming at each distribution center.
4. The hybrid ant colony algorithm-based multi-distribution center vehicle routing system of claim 1, wherein: the computational expression of the classical ant colony module (302) is as follows:
in the formula, τ ij (t) is the side at time t<v i ,v j >Amount of pheromone, eta ij (t)＝1/d ij For the heuristic function, alpha is an information heuristic factor, and beta is an expected heuristic factor;
τ ij (t+1)＝(1-ρ)τ ij (t)+Δτ ij (t) (9)
wherein rho is pheromone volatilization coefficient and is a real number of 0-1, and (1-rho) is pheromoneA residual factor; delta tau ij (t) leaving ants on the edge for the t-th traversal<v i ,v j >Amount of pheromone on, Δ τ ij (0)＝0。
5. The hybrid ant colony algorithm-based multi-distribution center vehicle routing system of claim 4, wherein: in the mixed ant colony module (303), the calculation expression for solving the mixed ant colony algorithm is as follows:
in the formula eta ij (t)＝yita/d ij Where yita is a constant and Set is v i K, is not in the taboo table of ant K, and still satisfies the set of all vertices after adding the path.
6. The hybrid ant colony algorithm-based multi-distribution center vehicle routing system of claim 5, wherein: the computational expression of the pheromone update module (304) is as follows:
(ts-s)/s＜gap (11)
where gap is a small constant and every time a feasible solution is constructed and Δ τ is updated as described above ij After (t), the pheromone is updated according to the formula (9), wherein, in order to prevent the pheromone from being excessively different and the algorithm from falling into the local optimum, the amount of the pheromone on each edge is limited, so that Bd is less than or equal to delta tau ij (t) Bu, i.e., if τ ij If < Bd, set τ to ij (t) is Bd, if τ ij If Bu is greater, then put τ ij (t) is Bu;
the pheromone updating process is divided into two parts: after the ant colony constructs the path and uses 2-Opt optimization, the information increment delta tau on each side in the path is set ij (t)＝Δτ ij (t) + I τ, I τ being a small constant; when the feasible solution construction is completed, set as ts, the best solution currently obtained by the algorithm is s, and if equation (11) is satisfied, set as Δ τ on the side included in the feasible solution ij (t)＝Δτ ij (t) + Q/ts, Q represents pheromone strength, and is a constant.
7. The hybrid ant colony algorithm-based multi-distribution center vehicle routing system of claim 1, wherein: the information acquisition module (401), the screening updating module (402), the selection management module (403) and the history storage module (404) are sequentially connected through Ethernet communication and operate independently; the information acquisition module (401) is used for acquiring newly-added map data, and information data of suppliers, retailers and distribution centers in real time; the cleaning and screening updating module (402) is used for cleaning and screening repeated, wrong and invalid information in the database at regular time; the selection management module (403) is used for providing a plurality of groups of paths for a user to select according to different tendencies when planning a route each time; the history storage module (404) is used for recording and storing effective distribution work and routing and forming a history database for backtracking.
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