CN115829451A - Logistics path planning method and device, computer equipment and storage medium - Google Patents
Logistics path planning method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application discloses a method and a device for planning a logistics path, computer equipment and a storage medium, wherein the method comprises the following steps: responding to a logistics path planning request of a user, and acquiring delivery service information and pickup service information of a target network point in a target time period; acquiring vehicle transport capacity information of a target network point; and planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information. According to the method and the system, the delivery service and the pickup service under the single network point path planning scene are comprehensively planned to carry out vehicle path planning, so that the logistics transportation efficiency can be improved; the cargo can be loaded in the return process of the transport vehicle, so that the no-load condition of the vehicle is reduced, and the loading rate of the vehicle is improved; the starting point of the delivery service and the target point of the pickup service are all network points, so that the calculation efficiency can be improved, and the calculation time can be reduced.
Description
Technical Field
The invention relates to the technical field of logistics, in particular to a method and a device for planning a logistics path, computer equipment and a storage medium.
Background
In a logistics scene, reverse logistics services such as repair, return, recycling and the like are frequently used, a service from a warehouse to a network point is called a delivery service, a service from the network point to the warehouse is called a pickup service, and the reverse logistics service is a common pickup service. The current Vehicle Route (VRP) route planning model has the defects of low Vehicle loading rate, overlong driving distance and the like when respectively planning delivery routes and pickup routes.
Disclosure of Invention
The embodiment of the application provides a logistics path planning method, a logistics path planning device, computer equipment and a storage medium, and can improve the logistics transportation efficiency; the cargo can be loaded in the return process of the transport vehicle, so that the no-load condition of the vehicle is reduced, and the loading rate of the vehicle is improved; the starting point of the delivery service and the target point of the pickup service are all network points, so that the calculation efficiency can be improved, and the calculation time can be reduced.
In a first aspect, the present application provides a method for planning a logistics path, where the method includes:
responding to a logistics path planning request of a user, and acquiring delivery service information and pickup service information of a target network point in a target time period;
acquiring vehicle transport capacity information of the target network point;
and planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information.
In some embodiments of the present application, the planning a route of a vehicle at the target site according to the delivery service information, the pickup service information, and the vehicle transportation capacity information includes:
constructing a linked list structure of the vehicle object and the task object of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information;
constructing a path planning constraint condition for path planning and an objective function for path planning;
and planning the path of the vehicle of the target network point according to the linked list structure, the path planning constraint condition and the target function of the path planning.
In some embodiments of the present application, the performing path planning on the vehicle at the target site according to the linked list structure, the path planning constraint condition, and the target function of path planning includes:
adjusting the neighborhood structure of the linked list structure to obtain a new linked list structure;
calculating whether the new linked list structure violates a preset path planning constraint condition;
if not, calculating an objective function value of the objective function of the path planning;
and judging whether the path planning scheme corresponding to the new chain table structure is better than the existing path planning scheme or not according to the objective function value until the optimal vehicle path planning scheme is found.
In some embodiments of the present application, the planning a route of a vehicle at the target site according to the delivery service information, the pickup service information, and the vehicle transportation capacity information further includes:
judging whether the current path planning termination condition is met;
and if the path planning termination condition is not met, continuously adjusting the neighborhood structure of the current linked list structure, and searching a more optimal vehicle path planning scheme.
In some embodiments of the present application, the adjusting the neighborhood structure of the linked list structure to obtain a new linked list structure includes:
and selecting one linked list structure from the various linked list structures according to the selection probability of each linked list structure in the various preset linked list structures to adjust the neighborhood structure of the linked list structure, so as to obtain the new linked list structure.
In some embodiments of the present application, the constructing a linked list structure of a vehicle object and a task object at the target site according to the delivery service information, the pickup service information, and the vehicle transportation capacity information includes:
constructing vehicle objects in the target network points according to the vehicle transport capacity information;
constructing a task object in the target network point according to the delivery service information and the pickup service information, wherein the task object comprises front and rear object attributes, and the front and rear object attributes comprise a front object number and a rear object number;
describing the sequence of vehicle access tasks in a linked list mode, and constructing a linked list structure of vehicle objects and task objects of the target network point, wherein the vehicle objects are used as a header, the former object number in the task objects is a vehicle number or a task number, and the latter object number is a task number.
In some embodiments of the present application, the constructing a vehicle object in the target website according to the vehicle capacity information includes:
constructing an initial vehicle object in the target network point, wherein the vehicle object comprises vehicle attribute information, and the vehicle attribute information comprises a place information table, an order information table, a vehicle flow direction price table, a vehicle information table and a distance time matrix;
and updating the vehicle attribute information according to the vehicle transport capacity information to obtain the vehicle object of the vehicle in the target network point.
In some embodiments of the present application, the vehicle object and the task object each include a location information table, an order information table, a vehicle flow price table, a vehicle information table, and a distance time matrix;
the network information comprises network number, whether the network is a distribution center, network service and waiting time, and network limitation vehicle type;
the order information table comprises an order number, an order starting point number, an order destination point number, an order goods weight, an order left time window and an order right time window;
the vehicle flow direction price list comprises vehicle type numbers, vehicle fixed cost, vehicle variable cost and vehicle lifting and unloading cost;
the vehicle information table comprises vehicle numbers, vehicle type numbers, vehicle loads, vehicle distribution point number limits, vehicle distribution distance limits, vehicle working time limits and vehicle quantity;
the distance time matrix table comprises a starting point number, a destination point number, transportation time between every two network points and transportation mileage between every two network points.
In a second aspect, the present application provides a logistics path planning apparatus, which includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for responding to a logistics path planning request of a user and acquiring delivery service information and pickup service information of a target network point in a target time period;
the second acquisition module is used for acquiring the vehicle transport capacity information of the target network point;
and the planning module is used for planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information.
In some embodiments of the present application, the planning module is specifically configured to:
constructing a linked list structure of the vehicle object and the task object of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information;
constructing a path planning constraint condition for path planning and an objective function for path planning;
and planning the path of the vehicle of the target network point according to the linked list structure, the path planning constraint condition and the target function of the path planning.
In some embodiments of the present application, the planning module is specifically configured to:
adjusting the neighborhood structure of the linked list structure to obtain a new linked list structure;
calculating whether the new linked list structure violates a preset path planning constraint condition or not;
if not, calculating an objective function value of the objective function of the path planning;
and judging whether the path planning scheme corresponding to the new chain table structure is better than the existing path planning scheme or not according to the objective function value until the optimal vehicle path planning scheme is found.
In some embodiments of the present application, the planning module is further specifically configured to:
judging whether the current path planning termination condition is met;
and if the path planning termination condition is not met, continuously adjusting the neighborhood structure of the current linked list structure, and searching a more optimal vehicle path planning scheme.
In some embodiments of the present application, the planning module is further specifically configured to:
and selecting one linked list structure from the multiple linked list structures according to the selection probability of each linked list structure in the multiple linked list structures to adjust the neighborhood structure of the linked list structure, so as to obtain the new linked list structure.
In some embodiments of the present application, the planning module is specifically configured to:
constructing vehicle objects in the target network points according to the vehicle transport capacity information;
constructing a task object in the target network point according to the delivery service information and the pickup service information, wherein the task object comprises front and rear object attributes, and the front and rear object attributes comprise a front object number and a rear object number;
describing the sequence of vehicle access tasks in a linked list mode, and constructing a linked list structure of vehicle objects and task objects of the target network point, wherein the vehicle objects are used as a header, the former object number in the task objects is a vehicle number or a task number, and the latter object number is a task number.
In some embodiments of the present application, the planning module is specifically configured to:
constructing an initial vehicle object in the target network point, wherein the vehicle object comprises vehicle attribute information, and the vehicle attribute information comprises a place information table, an order information table, a vehicle flow direction price table, a vehicle information table and a distance time matrix;
and updating the vehicle attribute information according to the vehicle transport capacity information to obtain the vehicle object of the vehicle in the target network point.
In some embodiments of the present application, the vehicle object and the task object each include a location information table, an order information table, a vehicle flow price table, a vehicle information table, and a distance time matrix;
the network information comprises network number, whether the network is a distribution center, network service and waiting time, and network limitation vehicle type;
the order information table comprises an order number, an order starting point number, an order destination point number, an order goods weight, an order left time window and an order right time window;
the vehicle flow direction price list comprises vehicle type numbers, vehicle fixed cost, vehicle variable cost and vehicle lifting and unloading cost;
the vehicle information table comprises vehicle numbers, vehicle type numbers, vehicle loads, vehicle distribution point number limits, vehicle distribution distance limits, vehicle working time limits and vehicle quantity;
the distance time matrix table comprises a starting point number, a destination point number, transportation time between every two network points and transportation mileage between every two network points.
The method comprises the steps that delivery service information and pickup service information of a target network point in a target time period are obtained by responding to a logistics path planning request of a user; acquiring vehicle transport capacity information of the target mesh point; and planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information. According to the method and the system, the delivery service and the pickup service under the single network route planning scene are integrated to plan the vehicle route, so that the logistics transportation efficiency can be improved; the cargo can be loaded in the return process of the transport vehicle, so that the no-load condition of the vehicle is reduced, and the loading rate of the vehicle is improved; the starting point of the delivery service and the target point of the pickup service are all network points, so that the calculation efficiency can be improved, and the calculation time can be reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of a scenario of a logistics path planning system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an embodiment of a logistics path planning method provided in an embodiment of the invention;
FIG. 3 is a flowchart of one embodiment of step 203 provided in embodiments of the present invention;
fig. 4 is a schematic flow chart of an embodiment of a logistics path planning apparatus provided in an embodiment of the present invention;
FIG. 5 is a flow diagram of one embodiment of a computer device provided in embodiments of the present invention.
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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the invention. In the following description, details are set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and processes are not shown in detail to avoid obscuring the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
Some basic concepts involved in the embodiments of the present application are first described below:
OptaPlanner is a lightweight, embeddable planning and scheduling engine, 100% written in Java, that runs on any Java Virtual Machine (JVM). The Optapanner may optimize business resource planning problems such as vehicle Path planning (VRP), employee Scheduling (Employee Scheduling), cloud computing resource Scheduling (Cloud Optimization), task Assignment (Task Assignment), shop Scheduling (JSP), and knapsack (Bin Packing). Many companies face such a scheduling challenge: a limited set of resources (staff, assets, time and money) is allocated to provide a product or service. The very thing that OptaPlanner does is to provide a more efficient planning solution to improve quality of service and reduce costs. Optapanner allows a common Java engineer to efficiently solve the optimization problem, and is also compatible with other JVM languages (e.g., kotlin and Scala). In terms of problem modeling, the constraints of OptaPlanner act on generic domain objects and existing code can be reused without typing in complex mathematical formulas. In terms of problem solving, optapanner combines many complex heuristics and metaheuristics (such as tabu search, simulated annealing, overdue acceptance and variable neighborhood search) and can provide very effective optimization services.
The Vehicle Routing Problem (VRP) generally refers to: for a series of delivery and receipt points, the organization invokes certain vehicles, routes the appropriate vehicles through them in order, and returns to the delivery point.
The embodiment of the invention provides a logistics path planning method, a logistics path planning device, computer equipment and a storage medium, which are respectively described in detail below.
Referring to fig. 1, fig. 1 is a schematic view of a scenario of a logistics path planning system according to an embodiment of the present invention, where the logistics path planning system may include a computer device 100, and a logistics path planning apparatus, such as the computer device in fig. 1, is integrated in the computer device 100.
In the embodiment of the invention, the computer equipment 100 is mainly used for responding to a logistics path planning request of a user and acquiring delivery service information and pickup service information of a target network point in a target time period; acquiring vehicle transport capacity information of the target network point; and planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information.
In this embodiment, the computer device 100 may be a terminal or a server, and when the computer device 100 is a server, it may be an independent server, or may be a server network or a server cluster composed of servers, for example, the computer device 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, multiple network server sets, or a cloud server constructed by multiple servers. Among them, the Cloud server is constructed by a large number of computers or web servers based on Cloud Computing (Cloud Computing).
It is to be understood that, when the computer device 100 is a terminal in the embodiment of the present application, the terminal used may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: a cellular or other communication device having a single line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may specifically be a desktop terminal or a mobile terminal, and the computer device 100 may also specifically be one of a mobile phone, a tablet computer, a notebook computer, and the like.
Those skilled in the art can understand that the application environment shown in fig. 1 is only one application scenario related to the present application, and does not constitute a limitation on the application scenario of the present application, and that other application environments may further include more or less computer devices than those shown in fig. 1, for example, only 1 computer device is shown in fig. 1, and it is understood that the logistics path planning system may further include one or more other computer devices, which is not limited herein.
In addition, as shown in fig. 1, the logistics path planning system may further include a memory 200 for storing data, such as logistics data, for example, various data of the logistics platform, such as logistics transportation information of a transition, specifically, express information, order information (delivery order information, pickup order information, etc.), delivery vehicle information, logistics branch point information, and the like.
It should be noted that the scenario diagram of the logistics path planning system shown in fig. 1 is only an example, and the logistics path planning system and the scenario described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present invention, and do not form a limitation on the technical solution provided in the embodiment of the present invention.
In a logistics scene, reverse logistics services such as repair, return, recycling and the like are frequently used, a service from a warehouse to a network point is called a delivery service, a service from the network point to the warehouse is called a pickup service, and the reverse logistics service is a common pickup service. The current Vehicle Route (VRP) route planning model can only consider the delivery service and the pickup service respectively, but cannot consider the two service scenarios at the same time, and the respective route planning model has the defects of low Vehicle loading rate, overlong driving distance and the like. Although the Pick and Delivery (PDP) route planning model can solve the Problem, the pick and Delivery task is regarded as a warehouse-site task pair, and the task is taken as a planning variable, which means that a plurality of tasks exist on the warehouse position, so that the neighborhood search space is increased, the solution efficiency is low, and the speed is slow.
First, an embodiment of the present invention provides a method for planning a logistics path, where the method for planning a logistics path includes: responding to a logistics path planning request of a user, and acquiring delivery service information and pickup service information of a target network point in a target time period; acquiring vehicle transport capacity information of the target network point; and planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information.
Specifically, as shown in fig. 2, which is a schematic flow chart of an embodiment of a logistics path planning method in an embodiment of the present invention, the logistics path planning method includes the following steps 201 to 203:
201. and responding to a logistics path planning request of a user, and acquiring delivery service information and pickup service information of a target network point in a target time period.
The target time period may be any preset time period, for example, the target time period may be one hour, a half day, a week, or a month, and may be determined specifically according to an actual application scenario, which is not limited herein. The target site may be any one of the logistics sites, may be a transit point, and may also be a pickup and dispatch site, and the specific details are not limited herein.
In the embodiment of the present application, a service from a warehouse to a network point is referred to as a delivery service, a service from a network point to a warehouse is referred to as a pickup service, and a reverse logistics service is a common pickup service.
202. And acquiring the vehicle transport capacity information of the target network point.
The transport ability is a Chinese vocabulary, meaning ability best effort, and transport ability. Capacity is applied to the road transportation industry and generally refers to corresponding resources required by operation and production. Including the general names of operating vehicles, operating drivers and accompanying personnel.
203. And planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information.
The method comprises the steps that delivery service information and pickup service information of a target network point in a target time period are obtained by responding to a logistics path planning request of a user; acquiring vehicle transport capacity information of the target network point; and planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information. According to the method and the system, the delivery service and the pickup service under the single network route planning scene are integrated to plan the vehicle route, so that the logistics transportation efficiency can be improved; the cargo can be loaded in the return process of the transport vehicle, so that the no-load condition of the vehicle is reduced, and the loading rate of the vehicle is improved; the starting point of the delivery service and the target point of the pickup service are all network points, so that the calculation efficiency can be improved, and the calculation time can be reduced.
In some embodiments of the present application, in the step 203, performing path planning on the vehicle at the target site according to the delivery service information, the pickup service information, and the vehicle transportation capacity information, may further include: constructing a linked list structure of the vehicle object and the task object of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information; and planning the path of the vehicle of the target network point according to the linked list structure, the path planning constraint condition and the target function of the path planning.
In this embodiment of the present application, constructing the linked list structure of the vehicle object and the task object at the destination node may further be: and constructing a network linked list structure of the logistics network where the target network point is located, wherein the network linked list structure comprises a linked list structure of a vehicle object and a task object of the target network point. Namely, a linked list structure of the vehicle objects and the task objects of all nodes in the stream network is constructed first, wherein the linked list structure of the vehicle objects and the task objects of the target node is also constructed, and the linked list structure of the vehicle objects and the task objects of the target node is included.
A linked list, also known as a linked list, is a series of data structures that are linked together by links. A linked list is a series of links that contain items. Each link contains a connection to another link. The linked list is the second largest data structure next to the array.
The method comprises the following three elements:
(1) and Link: each link of the linked list may store data called an element.
(2) Next: each link of the linked list contains a link to a next link, referred to as a next link.
(3) LinkedList: the linked list contains the connection links to the first link named "first".
The linked list may be visualized as a chain of nodes, where each node points to the next node. The linked list is characterized as follows:
(1) the linked list contains a link element named first.
(2) Each link contains a data field and a link field called next.
(3) Each link is linked to its next link using its next link.
(4) The link of the last link is empty to mark the end of the list.
In the embodiment of the application, each node in the linked list is related information of a vehicle object and a task object, for example, the vehicle object is used as a header, the nodes in the linked list are task objects, and the task objects comprise a previous object number and a next object number, wherein the previous object number in the task objects of the task objects is the vehicle number or the task number, and the next object number is the task number.
Wherein, the path planning of the vehicle of the target network point according to the linked list structure, the path planning constraint condition and the target function of the path planning comprises: adjusting the neighborhood structure of the linked list structure to obtain a new linked list structure; calculating whether the new linked list structure violates a preset path planning constraint condition or not; if not, calculating an objective function value of the objective function of the path planning; and judging whether the path planning scheme corresponding to the new chain table structure is better than the existing path planning scheme or not according to the objective function value until the optimal vehicle path planning scheme is found.
In this embodiment of the application, the path planning function includes a plurality of objective functions, each objective function corresponds to an objective function value, and the objective functions may include a first objective function and a second objective function, where the first objective function minimizes the number of used vehicles, and the second objective function minimizes the cost, including the vehicle driving cost, the vehicle fixed cost, and the website service cost.
In the embodiment of the present application, there are various ways for the first objective function and the second objective function, and the following examples are given, for example, in a specific embodiment of the present application, the first objective function for defining the number of minimized vehicles may be the following formula:
for another example, in a specific embodiment of the present application, the second objective function is used to define a minimization cost, wherein the minimization cost includes a vehicle driving cost, a vehicle fixed cost, and a website service cost, and specifically, the second objective function may be the following formula:
wherein:
n d : number of delivery task customers; n is p : the number of customers for the picking task; n: total number of customers, n = n d +n p ；
Q k : a maximum load capacity of the vehicle; l ik : load when k vehicle leaves the dot i (0 is less than or equal to l) ik ≤Q k )；
P k : maximum access point number limitation；x kij : k cars pass from i to j,
k≠φ，x∈{0，1}；
T k : a vehicle maximum travel time limit; c: set of client nodes, C = {1,2, 3.., n }, C = {1,2, 3., d ∪C p ；
D k : a maximum travel distance of the vehicle; c p : set of pick-up nodes, C p ＝{n d +1，n d +2，n d +3，...，n d +n p }
y ki : vehicle k visits i, y ki ∈{0,1}；C d : set of delivery nodes, C d ＝{1,2,3，...，n d }；
z k : the k cars are used and,
z k ∈{0,1}；
as a total vehicle set
A: all the customer points and distribution centers are integrated, wherein A = C $ D, D = {0,n +1} is a distribution center; m: the total number of vehicles;
c ij : cost of the path passing through i, j; c. C k : k cost of cars; c. C i : i point service cost;
t ij : the travel time at point i, j; s i : the service time of the point i; w is a i : latency at customer site i;
[a i ，b i ]: a time window at point i, i ∈ A; t is t ik : the time at which vehicle k arrives at customer i;
q i : the demand at the ith customer site, i ∈ C, q i ∈R，
More than 0 is the goods receiving requirement at the point i, less than 0 is the goods taking requirement at the point i,
the pick demand starting at 0 is the sum of all delivery demands in the link,
the final delivery requirement at 0 is the sum of all the pick-up requirements in the link, and the service time is 0 when the requirement is 0.
It should be noted that, the parameters in the first objective function and the second objective function are only examples, and it is understood that, in a practical application scenario, the parameters in the first objective function and the second objective function may be more or less, for example, in some specific embodiments, the parameters of the limitations on the number of pickup customers and the number of delivery customers may be removed, and the specific embodiments are not limited herein.
In the embodiment of the present application, the path planning constraint conditions include the following formulas (1) to (12), specifically:
t n+1，k -t 0k ≤T k (8)
l ik ≤Q k (11)
wherein, formula (1) indicates that each network point can be serviced by one vehicle only once; formula (2) indicates that all vehicles must start from the distribution center; formula (3) shows that the vehicle must leave after the vehicle has served the network; formula (4) shows that all transport vehicles must return to the distribution center after serving the network points to form a loop; formula (5) shows that in a section of path, the sum of the arrival time of the front point, the service time of the front point and the vehicle running time on the path is less than or equal to the arrival time of the rear point, so that the correctness of the time sequence is ensured; formula (6) shows that the sum of the time of the vehicle reaching the website and the waiting time of the vehicle at the website falls within the time window of the website; equation (7) limits the maximum travel distance of the vehicle; equation (8) limits the maximum travel time of the vehicle; equation (9) limits the number of vehicle service customers; the formula (10) limits the load when the vehicle starts; formula (11) limits the load of the vehicle during driving; the formula (12) is only used in the first delivery and then delivery scene, and some service scenes require that the vehicle complete all delivery tasks first and then complete the pick-up task, and the formula shows that for any path with a front point as a pick-up point and a rear point as a delivery point, the value is 0, namely no vehicle passes through the path.
It should be noted that the above-mentioned path planning constraint conditions are only examples, and it is understood that, in practical applications, the constraint conditions may be increased or decreased according to practical situations, specifically, in the embodiment of the present application, the path planning constraint conditions and the objective function (including the first objective function and the second objective function) are matched, for example, parameters appearing in the first objective function and the second objective function may be set in the constraint conditions, and when there is no corresponding parameter in the first objective function and the second objective function, for example, there is no limitation on the number of pickup customers and the number of delivery customers, the corresponding constraint conditions may not need to be set, and may be set according to practical scenarios, which is not limited herein.
In some embodiments of the present application, the planning a route of a vehicle at the target site according to the delivery service information, the pickup service information, and the vehicle transportation capacity information further includes: judging whether the current path planning termination condition is met; and if the path planning termination condition is not met, continuously adjusting the neighborhood structure of the current linked list structure, and searching a more optimal vehicle path planning scheme.
The adjusting of the neighborhood structure of the linked list structure to obtain a new linked list structure may include multiple types, for example, randomly adjusting the neighborhood structure of the linked list structure to obtain the new linked list structure, and for example, adjusting the neighborhood structure of the linked list structure to obtain the new linked list structure may further include: and selecting one linked list structure from the multiple linked list structures according to the selection probability of each linked list structure in the multiple linked list structures to adjust the neighborhood structure of the linked list structure, so as to obtain the new linked list structure. In the embodiment of the application, the selection probability is set in advance for each preset linked list structure according to application requirements and the like, and the application with high probability is probably large, so that the selection probability of each preset linked list structure in multiple linked list structures can be used in the later period, and one linked list structure is selected from the multiple linked list structures to adjust the neighborhood structure of the linked list structure to obtain the new linked list structure.
In addition, in the embodiment of the application, an optiplaner planning engine can be used for performing neighborhood operations to obtain a new chain table structure, wherein the neighborhood operations include single task movement, task exchange, task chain movement and the like. The starting point of the delivery service and the target point of the pickup service are warehouses, so that the neighborhood searching space is reduced, the calculation efficiency can be improved, and the calculation time can be reduced.
In some embodiments of the present application, as shown in fig. 3, the constructing a linked list structure of the vehicle object and the task object at the target site according to the delivery service information, the pickup service information, and the vehicle transportation capability information may further include the following steps 301 to 303:
301. and constructing the vehicle object in the target network point according to the vehicle transport capacity information.
Further, the building of the vehicle object in the target website according to the vehicle transportation capacity information includes: constructing an initial vehicle object in the target network point, wherein the vehicle object comprises vehicle attribute information, and the vehicle attribute information comprises a location information table, an order information table, a vehicle flow direction price table, a vehicle information table and a distance time matrix; and updating the vehicle attribute information according to the vehicle transport capacity information to obtain the vehicle object of the vehicle in the target network point.
302. And constructing a task object in the target network point according to the delivery service information and the pickup service information.
The task object comprises front and back object attributes, and the front and back object attributes comprise a front object number and a back object number.
303. Describing the sequence of the vehicle access tasks in a linked list form, and constructing a linked list structure of the vehicle objects and the task objects of the target website.
The vehicle object is used as a header, the number of the former object in the task objects is a vehicle number or a task number, and the number of the latter object is a task number.
In some embodiments of the present application, the vehicle object and the task object each include a location information table, an order information table, a vehicle flow price table, a vehicle information table, and a distance time matrix;
the network information comprises network number, whether the network is a distribution center, network service and waiting time, and network limitation vehicle type; the order information table comprises an order number, an order starting point number, an order destination point number, an order goods weight, an order left time window and an order right time window; the vehicle flow direction price list comprises vehicle type numbers, vehicle fixed cost, vehicle variable cost and vehicle lifting and unloading cost; the vehicle information table comprises vehicle numbers, vehicle type numbers, vehicle loads, vehicle distribution point number limits, vehicle distribution distance limits, vehicle working time limits and vehicle quantity; the distance time matrix table comprises a starting point number, a destination point number, transportation time between every two network points and transportation mileage between every two network points.
In the embodiment of the application, a path planning is performed on the vehicles at the target site, and the final output path planning scheme includes: a route table and a statistical table, wherein when planning a route of the vehicle at the target node, according to the objective function and the route planning constraint conditions described in the above embodiments, a driving route of the vehicle can be determined, including a stopped node, a driving mileage, a load, arrival time information of each node, and the like, and the route table of the vehicle can be determined by adding the initially acquired information such as the vehicle route starting point, the vehicle number, the route number, the starting point position information, the node arrival time information, and the like.
In the embodiment of the application, the route table may include a route starting point, a route number, a vehicle number, a stop code, a position code of the current year, a current position arrival time, a current position departure time, an operation and waiting time, mileage, a current load, a loading code, a unloading code and the like, and in an actual application scenario, specific parameters may be increased or decreased according to an actual application situation except for necessary parameters such as a starting point, a stop point, a load and the like.
Meanwhile, in the embodiment of the application, when the path planning scheme is determined, the driving path of the vehicle can be determined, and similarly, after the planned driving path of the vehicle is determined, corresponding parameters can be determined, so that a statistical table of the vehicle can be output, and the statistical table can include a running route number, a number of times of the vehicle, a number of types of the vehicle, the number of points of network passing and stopping of the vehicle, a total load of the vehicle, a loading rate of the vehicle, a starting time of the path, a finishing time of the path, a total mileage of the vehicle, a driving cost of the vehicle, and a total time of the vehicle.
In order to better implement the method for planning a logistics path in the embodiment of the present invention, based on the method for planning a logistics path, an embodiment of the present invention further provides a device for planning a logistics path, as shown in fig. 4, which is a schematic structural diagram of an embodiment of the device for planning a logistics path, and the device 400 for planning a logistics path includes: a first acquisition module 401, a second acquisition module 402, and a planning module 403, wherein:
a first obtaining module 401, configured to obtain delivery service information and pickup service information of a target website in a target time period in response to a logistics path planning request of a user;
a second obtaining module 402, configured to obtain vehicle transportation capability information of the target node;
and the planning module 403 is configured to plan a route of the vehicle at the target site according to the delivery service information, the pickup service information, and the vehicle transportation capacity information.
In the embodiment of the application, the first obtaining module 401 responds to the logistics path planning request of the user, and obtains the delivery service information and the pickup service information of the target network point in the target time period; a second obtaining module 402 obtains vehicle transportation capacity information of the target network point; the planning module 403 plans a route of the vehicle at the target site according to the delivery service information, the pickup service information and the vehicle transportation capacity information. According to the method and the system, the delivery service and the pickup service under the single network route planning scene are integrated to plan the vehicle route, so that the logistics transportation efficiency can be improved; the cargo can be loaded in the return process of the transport vehicle, so that the no-load condition of the vehicle is reduced, and the loading rate of the vehicle is improved; the starting point of the delivery service and the target point of the pickup service are all network points, so that the calculation efficiency can be improved, and the calculation time can be reduced.
In some embodiments of the present application, the planning module 403 is specifically configured to:
constructing a linked list structure of the vehicle object and the task object of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information;
constructing a path planning constraint condition for path planning and an objective function for path planning;
and planning the path of the vehicle of the target network point according to the linked list structure, the path planning constraint condition and the target function of the path planning.
In some embodiments of the present application, the planning module 403 is specifically configured to:
adjusting the neighborhood structure of the linked list structure to obtain a new linked list structure;
calculating whether the new linked list structure violates a preset path planning constraint condition or not;
if not, calculating an objective function value of the objective function of the path planning;
and judging whether the path planning scheme corresponding to the new chain table structure is better than the existing path planning scheme or not according to the objective function value until the optimal vehicle path planning scheme is found.
In some embodiments of the present application, the planning module 403 is further specifically configured to:
judging whether the current path planning termination condition is met;
and if the path planning termination condition is not met, continuously adjusting the neighborhood structure of the current linked list structure, and searching a more optimal vehicle path planning scheme.
In some embodiments of the present application, the planning module 403 is further specifically configured to:
and selecting one linked list structure from the multiple linked list structures according to the selection probability of each linked list structure in the multiple linked list structures to adjust the neighborhood structure of the linked list structure, so as to obtain the new linked list structure.
In some embodiments of the present application, the planning module 403 is specifically configured to:
constructing vehicle objects in the target network points according to the vehicle transport capacity information;
constructing a task object in the target network point according to the delivery service information and the pickup service information, wherein the task object comprises front and rear object attributes, and the front and rear object attributes comprise a front object number and a rear object number;
describing the sequence of vehicle access tasks in a linked list mode, and constructing a linked list structure of vehicle objects and task objects of the target network point, wherein the vehicle objects are used as a header, the former object number in the task objects is a vehicle number or a task number, and the latter object number is a task number.
In some embodiments of the present application, the planning module is specifically configured to:
constructing an initial vehicle object in the target network point, wherein the vehicle object comprises vehicle attribute information, and the vehicle attribute information comprises a location information table, an order information table, a vehicle flow direction price table, a vehicle information table and a distance time matrix;
and updating the vehicle attribute information according to the vehicle transport capacity information to obtain the vehicle object of the vehicle in the target network point.
In some embodiments of the present application, the vehicle object and the task object each include a location information table, an order information table, a vehicle flow price table, a vehicle information table, and a distance time matrix;
the network information comprises network number, whether the network is a distribution center, network service and waiting time, and network limitation vehicle type;
the order information table comprises an order number, an order starting point number, an order destination point number, an order goods weight, an order left time window and an order right time window;
the vehicle flow direction price list comprises vehicle type numbers, vehicle fixed cost, vehicle variable cost and vehicle lifting and unloading cost;
the vehicle information table comprises vehicle numbers, vehicle type numbers, vehicle loads, vehicle distribution point number limits, vehicle distribution distance limits, vehicle working time limits and vehicle quantity;
the distance time matrix table comprises a starting point number, a destination point number, transportation time between every two network points and transportation mileage between every two network points.
The embodiment of the present invention further provides a server, which integrates any one of the logistics path planning apparatuses provided by the embodiments of the present invention, and the server includes:
one or more processors;
a memory; and
one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor for performing the steps of the logistics path planning method described in any of the logistics path planning method embodiments above.
The embodiment of the invention also provides computer equipment which integrates any logistics path planning device provided by the embodiment of the invention. Fig. 5 is a schematic diagram showing a structure of a computer device according to an embodiment of the present invention, specifically:
the computer device may include components such as a processor 501 of one or more processing cores, memory 502 of one or more computer-readable storage media, a power supply 503, and an input unit 504. Those skilled in the art will appreciate that the computer device configuration illustrated in FIG. 5 does not constitute a limitation of computer devices, and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. Wherein:
the processor 501 is a control center of the computer device, connects various parts of the entire computer device by using various interfaces and lines, and performs various functions of the computer device and processes data by running or executing software programs and/or modules stored in the memory 502 and calling data stored in the memory 502, thereby monitoring the computer device as a whole. Optionally, processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 501.
The memory 502 may be used to store software programs and modules, and the processor 501 executes various functional applications and data processing by operating the software programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 502 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 access to the memory 502.
The computer device further comprises a power supply 503 for supplying power to the various components, and preferably, the power supply 503 may be logically connected to the processor 501 through a power management system, so that functions of managing charging, discharging, power consumption, and the like are realized through the power management system. The power supply 503 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
The computer device may also include an input unit 504, and the input unit 504 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the computer device may further include a display unit and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 501 in the computer device loads the executable file corresponding to the process of one or more application programs into the memory 502 according to the following instructions, and the processor 501 runs the application programs stored in the memory 502, so as to implement various functions as follows:
responding to a logistics path planning request of a user, and acquiring delivery service information and pickup service information of a target network point in a target time period; acquiring vehicle transport capacity information of the target network point; and planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information. It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, an embodiment of the present invention provides a computer-readable storage medium, which may include: read Only Memory (ROM), random Access Memory (RAM), magnetic or optical disks, and the like. The logistics path planning method comprises a computer program and a processor, wherein the computer program is loaded by the processor to execute the steps of any logistics path planning method provided by the embodiment of the invention. For example, the computer program may be loaded by a processor to perform the steps of:
responding to a logistics path planning request of a user, and acquiring delivery service information and pickup service information of a target network point in a target time period; acquiring vehicle transport capacity information of the target network point; and planning a path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information. In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to the above detailed descriptions of other embodiments, which are not described herein again.
In a specific implementation, each unit or structure may be implemented as an independent entity, or may be combined arbitrarily to be implemented as one or several entities, and the specific implementation of each unit or structure may refer to the foregoing method embodiment, which is not described herein again.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
The logistics path planning method, the logistics path planning device, the computer equipment and the storage medium provided by the embodiment of the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A logistics path planning method is characterized by comprising the following steps:
responding to a logistics path planning request of a user, and acquiring delivery service information and pickup service information of a target network point in a target time period;
acquiring vehicle transport capacity information of the target network point;
and planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information.
2. The method for planning logistics path according to claim 1, wherein the planning a path of the vehicle at the target site according to the delivery service information, the pickup service information and the vehicle transportation capacity information comprises:
constructing a linked list structure of the vehicle object and the task object of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information;
constructing a path planning constraint condition for path planning and an objective function for path planning;
and planning the path of the vehicle of the target network point according to the linked list structure, the path planning constraint condition and the target function of the path planning.
3. The method for planning logistics path according to claim 2, wherein the path planning for the vehicle at the target site according to the linked list structure, the path planning constraint condition and the target function of the path planning comprises:
adjusting the neighborhood structure of the linked list structure to obtain a new linked list structure;
calculating whether the new linked list structure violates a preset path planning constraint condition;
if not, calculating an objective function value of the objective function of the path planning;
and judging whether the path planning scheme corresponding to the new chain table structure is better than the existing path planning scheme or not according to the objective function value until the optimal vehicle path planning scheme is found.
4. The method for planning logistics path according to claim 2 or 3, wherein the planning of the path of the vehicle at the target site according to the delivery service information, the pickup service information and the vehicle transportation capacity information further comprises:
judging whether the current path planning termination condition is met;
and if the path planning termination condition is not met, continuously adjusting the neighborhood structure of the current linked list structure, and searching a more optimal vehicle path planning scheme.
5. The method according to claim 2, wherein the adjusting the neighborhood structure of the linked list structure to obtain a new linked list structure comprises:
and selecting one linked list structure from the various linked list structures according to the selection probability of each linked list structure in the various preset linked list structures to adjust the neighborhood structure of the linked list structure, so as to obtain the new linked list structure.
6. The method for planning logistics path according to claim 5, wherein the constructing a linked list structure of the vehicle object and the task object of the target site according to the delivery service information, the pickup service information and the vehicle capacity information comprises:
constructing vehicle objects in the target network points according to the vehicle transport capacity information;
constructing a task object in the target network point according to the delivery service information and the pickup service information, wherein the task object comprises front and rear object attributes, and the front and rear object attributes comprise a front object number and a rear object number;
describing the sequence of the vehicle access tasks in a linked list mode, and constructing a linked list structure of the vehicle objects and the task objects of the target website, wherein the vehicle objects are used as a header, the former object number in the task objects is a vehicle number or a task number, and the latter object number is a task number.
7. The logistics path planning method of claim 6, wherein the constructing of the vehicle objects in the target network points according to the vehicle capacity information comprises:
constructing an initial vehicle object in the target network point, wherein the vehicle object comprises vehicle attribute information, and the vehicle attribute information comprises a location information table, an order information table, a vehicle flow direction price table, a vehicle information table and a distance time matrix;
and updating the vehicle attribute information according to the vehicle transport capacity information to obtain the vehicle object of the vehicle in the target network point.
8. A logistics path planning device, characterized in that, the logistics path planning device includes:
the first acquisition module is used for responding to a logistics path planning request of a user and acquiring delivery service information and pickup service information of a target network point in a target time period;
the second acquisition module is used for acquiring the vehicle transport capacity information of the target network point;
and the planning module is used for planning the path of the vehicle of the target network point according to the delivery service information, the pickup service information and the vehicle transport capacity information.
9. A computer device, characterized in that the computer device comprises:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the logistics path planning method of any of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which is loaded by a processor to perform the steps of the logistics path planning method of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116894620A (en) * | 2023-09-08 | 2023-10-17 | 北京京东乾石科技有限公司 | Logistics routing method and device, electronic equipment and storage medium |
| CN116911711A (en) * | 2023-07-25 | 2023-10-20 | 重庆工程职业技术学院 | Logistics transportation planning method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116911711A (en) * | 2023-07-25 | 2023-10-20 | 重庆工程职业技术学院 | Logistics transportation planning method |
| CN116911711B (en) * | 2023-07-25 | 2024-04-05 | 重庆工程职业技术学院 | Logistics transportation planning method |
| CN116894620A (en) * | 2023-09-08 | 2023-10-17 | 北京京东乾石科技有限公司 | Logistics routing method and device, electronic equipment and storage medium |
| CN116894620B (en) * | 2023-09-08 | 2024-02-06 | 北京京东乾石科技有限公司 | Logistics routing method and device, electronic equipment and storage medium |
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