CN112418584A - Task planning method and device, computer equipment and storage medium - Google Patents
Task planning method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application discloses a task planning method, a task planning device, computer equipment and a storage medium. The task planning method comprises the following steps: acquiring a plurality of transportation tasks of a path to be planned in a target area; acquiring the transport capacity resource information of all logistics nodes participating in planning in a target area; counting constraint data for planning a plurality of transportation tasks according to the transportation capacity resource information; and planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planned vehicles for executing the plurality of transportation tasks, the types of the planned vehicles and the transportation path. On the basis that the prior art can only consider the line combination between two liang of logistics network points, can participate in the planning together with all logistics network points that participate in the planning of target area, from the global visual angle overall planning transportation technique of target area, can once plan a large amount of transportation tasks for the vehicle transportation route of planning is more excellent, has improved the efficiency of transportation task planning, has reduced the running cost of planning later stage.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a task planning method and device, computer equipment and a storage medium.
Background
The main line capacity resource planning refers to the planning of vehicle lines in main line road transportation. Specifically, given a series of tasks, the starting and ending points and the time of the tasks are determined, and on the basis, the task connection of the trunk vehicle needs to be planned under the condition that the constraint condition of the actual situation is met, and finally the driving path of the vehicle and the type of the vehicle are output.
The planning of trunk transportation capacity resources is very important in a large-scale trunk road transportation network, and in the case of a certain express company in China, 1000 tasks among 40 stations are required to be planned every day in the range of only one province. The planning results directly affect the resource investment and cost of the transportation network.
Aiming at the problem, the current vehicle planning of the trunk road transportation mainly depends on manual calculation and is limited by the complexity which can be considered by people, the planning of the transportation capacity resources can only reduce the planning range, partial lines, single or a few logistics nodes are considered, the number of tasks is less, the output vehicle lines are generally shorter, and the condition of connection of multiple sections of tasks cannot be considered. The method starting from the local view cannot seek the optimal scheme from the global view, so that the proportion of unilateral lines in actual operation is high, the number of vehicles is large, and the operation cost of the whole line is high.
Disclosure of Invention
The embodiment of the invention provides a task planning method, a task planning device, computer equipment and a storage medium, which can participate in planning of all logistics nodes participating in planning in a target area, integrally plan a transportation technology from the global perspective of the target area, plan a large number of transportation tasks at one time, enable the planned vehicle transportation path to be more optimal, improve the planning efficiency of the transportation tasks and reduce the operation cost in the later period of planning. .
In a first aspect, the present application provides a mission planning method, including:
acquiring a plurality of transportation tasks of a path to be planned in a target area;
acquiring the transport capacity resource information of all logistics nodes participating in planning in the target area;
counting constraint data for planning the plurality of transportation tasks according to the transportation capacity resource information;
and planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the plurality of transportation tasks, the type of the planning vehicles and a transportation path.
In some embodiments of the present application, the counting constraint data for planning the transportation tasks according to the transportation capacity resource information includes:
acquiring position information of all logistics nodes participating in planning in the target area;
counting the number of usable vehicles in all parking lots participating in planning;
counting the vehicle model of each vehicle in the usable vehicles;
counting the number of bayonets of different vehicle types which can be loaded and unloaded in all the logistics outlets participating in planning;
the constraint data comprises position information of all logistics sites participating in planning, the number of usable vehicles in all parking lots participating in planning, the vehicle model of each vehicle in the usable vehicles and the number of checkpoints of different vehicle models for loading and unloading goods in all logistics sites participating in planning.
In some embodiments of the present application, said planning a transportation plan for said plurality of transportation tasks according to said constraint data comprises:
constructing a spatio-temporal network model according to the constraint data;
planning a transportation plan for the plurality of transportation tasks using the spatio-temporal network model.
In some embodiments of the present application, the constructing a spatiotemporal network model according to the historical transportation task data includes:
determining constraint conditions for planning the plurality of transportation tasks according to the constraint data;
acquiring an objective function for planning the plurality of transportation tasks;
and establishing a space-time network model according to the objective function and the constraint condition.
In some embodiments of the present application, each of the plurality of transportation tasks includes a transportation cargo amount, a task start time, a start location, a task end time, and an end location;
the space-time network model comprises a task arc line, a waiting arc line, a free driving arc line, a driving-out arc line, a driving-in arc line and a circulating arc line; the system comprises a task arc line, a task control module and a control module, wherein the task arc line refers to an input transportation task, the starting point of the task arc line represents the starting point of the task and the starting time including loading time and waiting time, and the end point of the task arc line represents the end point of the task and the ending time including unloading time; the waiting arc line refers to the time for the vehicle to stay at the logistics point; the idle driving arc line refers to an arc line corresponding to the idle driving section between two connected task arc lines in the space-time network; the outgoing arc line refers to an arc line which corresponds to the space-time network when the vehicle is driven out of the parking lot and then performs the first task; the driving-in arc line refers to a corresponding arc line in a space-time network when the vehicle drives back to the parking lot in an empty state after the last task is executed; a circular arc refers to an arc from the end of a time window back to the beginning of the time window at a point in the same space in the spatio-temporal network.
In some embodiments of the present application, planning a transportation plan for the plurality of transportation tasks using the spatiotemporal network model includes:
inputting the plurality of transportation tasks into the spatio-temporal network model to respectively establish a first spatio-temporal network for each logistics branch point and each vehicle type in the target area to obtain planning vehicle information required in each first spatio-temporal network;
and establishing a second space-time network for each planning vehicle according to the planning vehicle information required in all the first space-time networks so as to output the transportation path, the transportation task connection sequence and the planning vehicle type of each planning vehicle and obtain the transportation plans of the multiple transportation tasks.
In some embodiments of the present application, the mission planning method further includes:
determining terminal equipment corresponding to a target logistics network point of a planning vehicle in the target area;
and sending the transportation plans of the plurality of transportation tasks to the terminal equipment.
In a second aspect, the present application provides a mission planning apparatus, comprising:
the first acquisition unit is used for acquiring a plurality of transportation tasks of a path to be planned in a target area;
the second acquisition unit is used for acquiring the transport capacity resource information of all logistics nodes participating in planning in the target area;
the statistical unit is used for counting the constraint data for planning the plurality of transportation tasks according to the transportation capacity resource information;
and the planning unit is used for planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the plurality of transportation tasks, and the planning vehicle type and the transportation path.
In some embodiments of the present application, the statistical unit is specifically configured to:
acquiring position information of all logistics nodes participating in planning in the target area;
counting the number of usable vehicles in all parking lots participating in planning;
counting the vehicle model of each vehicle in the usable vehicles;
counting the quantity of bayonets of different vehicle models, which can be loaded and unloaded, in all the logistics outlets participating in planning;
the constraint data comprises position information of all logistics sites participating in planning, the number of usable vehicles in all parking lots participating in planning, the vehicle model of each vehicle in the usable vehicles and the number of checkpoints of different vehicle models for loading and unloading goods in all logistics sites participating in planning.
In some embodiments of the present application, the planning unit is specifically configured to:
constructing a spatio-temporal network model according to the constraint data;
planning a transportation plan for the plurality of transportation tasks using the spatio-temporal network model.
In some embodiments of the present application, the planning unit is specifically configured to:
determining constraint conditions for planning the plurality of transportation tasks according to the constraint data;
acquiring an objective function for planning the plurality of transportation tasks;
and establishing a space-time network model according to the objective function and the constraint condition.
In some embodiments of the present application, each of the plurality of transportation tasks includes a transportation cargo amount, a task start time, a start location, a task end time, and an end location;
the space-time network model comprises a task arc line, a waiting arc line, a free driving arc line, a driving-out arc line, a driving-in arc line and a circulating arc line; the system comprises a task arc line, a task control module and a control module, wherein the task arc line refers to an input transportation task, the starting point of the task arc line represents the starting point of the task and the starting time including loading time and waiting time, and the end point of the task arc line represents the end point of the task and the ending time including unloading time; the waiting arc line refers to the time for the vehicle to stay at the logistics point; the idle driving arc line refers to an arc line corresponding to the idle driving section between two connected task arc lines in the space-time network; the outgoing arc line refers to an arc line which corresponds to the space-time network when the vehicle is driven out of the parking lot and then performs the first task; the driving-in arc line refers to a corresponding arc line in a space-time network when the vehicle drives back to the parking lot in an empty state after the last task is executed; a circular arc refers to an arc from the end of a time window back to the beginning of the time window at a point in the same space in the spatio-temporal network.
In some embodiments of the present application, the planning unit is specifically configured to:
inputting the plurality of transportation tasks into the spatio-temporal network model to respectively establish a first spatio-temporal network for each logistics branch point and each vehicle type in the target area to obtain planning vehicle information required in each first spatio-temporal network;
and establishing a second space-time network for each planning vehicle according to the planning vehicle information required in all the first space-time networks so as to output the transportation path, the transportation task connection sequence and the planning vehicle type of each planning vehicle and obtain the transportation plans of the multiple transportation tasks.
In some embodiments of the present application, the mission planning apparatus further includes a sending unit, where the sending unit is configured to:
determining terminal equipment corresponding to a target logistics network point of a planning vehicle in the target area;
and sending the transportation plans of the plurality of transportation tasks to the terminal equipment.
In a third aspect, the present application further provides a computer device, including:
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 to implement the mission planning method of any one of the first aspects.
In a fourth aspect, the present application further provides a computer-readable storage medium having stored thereon a computer program, which is loaded by a processor to perform the steps of the mission planning method of any one of the first aspect.
The method comprises the steps of obtaining a plurality of transportation tasks of a path to be planned in a target area; acquiring the transport capacity resource information of all logistics nodes participating in planning in a target area; counting constraint data for planning a plurality of transportation tasks according to the transportation capacity resource information; and planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planned vehicles for executing the plurality of transportation tasks, the types of the planned vehicles and the transportation path. Aiming at the planning problem of a plurality of transportation tasks, all the logistics nodes participating in planning in the target area can participate in the planning together on the basis that only the line combination between every two logistics nodes can be considered manually in the prior art, the transportation technology is integrally planned from the global perspective of the target area, a large number of transportation tasks can be planned at one time, the planned vehicle transportation path is enabled to be more excellent, the transportation task planning efficiency is improved, and the operation cost in the later planning stage is 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 mission planning system according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram illustrating an embodiment of a mission planning method provided in an embodiment of the present invention;
FIG. 3 is a flowchart illustrating an embodiment of step 203 in an embodiment of the present invention;
FIG. 4 is a flowchart illustrating an embodiment of step 204 in an embodiment of the present invention;
FIG. 5 is a schematic diagram of a scenario of mission planning in an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of an embodiment of a mission planning apparatus provided in an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an embodiment of a computer device provided in the embodiment 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", "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.
Embodiments of the present invention provide a task planning method, a task planning apparatus, a computer device, and a storage medium, which are described in detail below.
Referring to fig. 1, fig. 1 is a schematic view of a scenario of a task planning system according to an embodiment of the present invention, where the task planning system may include a computer device 100, and a task 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 acquiring a plurality of transportation tasks of a path to be planned in a target area; acquiring the transport capacity resource information of all logistics nodes participating in planning in the target area; counting constraint data for planning the plurality of transportation tasks according to the transportation capacity resource information; and planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the plurality of transportation tasks, the type of the planning vehicles and a transportation path.
In this embodiment of the present invention, the computer device 100 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 of the present invention includes, but is not limited to, a computer, a network host, a single network server, a plurality of network server sets, or a cloud server composed of a plurality of servers. Among them, the Cloud server is constituted by a large number of computers or web servers based on Cloud Computing (Cloud Computing).
Those skilled in the art will appreciate 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 task planning system may further include one or more other services, which are not limited herein.
In addition, as shown in fig. 1, the mission planning system may further include a storage 200 for storing data, such as logistics data, for example, various data of the logistics platform, logistics transportation information of the logistics network, such as a transit point, and specifically, express information, delivery vehicle information, logistics network information, and the like.
It should be noted that the scenario diagram of the task planning system shown in fig. 1 is only an example, and the task 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.
First, an embodiment of the present invention provides a task planning method, where an execution subject of the task planning method is a task planning device, the task planning device is applied to a computer device, and the task planning method includes: acquiring a plurality of transportation tasks of a path to be planned in a target area; acquiring the transport capacity resource information of all logistics nodes participating in planning in the target area; counting constraint data for planning the plurality of transportation tasks according to the transportation capacity resource information; and planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the plurality of transportation tasks, the type of the planning vehicles and a transportation path.
As shown in fig. 2, which is a schematic flow chart of an embodiment of a task planning method in an embodiment of the present invention, the task planning method includes:
201. and acquiring a plurality of transportation tasks of the path to be planned in the target area.
In the embodiment of the present invention, the target area may be an area in which a user specifies a path to be planned in advance, and the range of the target area may be large or small, and may be province, city, district, village, town, and the like, which may be specifically set according to an actual situation, and is not limited herein.
Each of the plurality of transportation tasks includes a transportation cargo amount, a task start time, a start location, a task end time, and an end location, specifically, the start location may be a start logistics point of the task, and the end location may be an end logistics point of the task, that is, the transportation task starts at the start logistics point, and the transportation task ends at the end logistics point.
The logistics network is a distribution node in the logistics network, and the basic function is to distribute and transport express items. The logistics network generally comprises a transfer station, a distribution station, a transfer center, an express delivery point and the like. From the viewpoint of the logistics network, the logistics network is also a network node. The logistics network is an important node for sorting, collecting and distributing express items, and mainly collects, exchanges and transports express items collected from other logistics network to realize the flow of the express items from distribution to concentration and then to dispersion in the whole network.
The computer equipment can acquire a plurality of transportation tasks of the path to be planned in the target area when the user selects a plurality of transportation tasks of the path to be planned in the target area when the user plans the tasks.
202. And acquiring the transport capacity resource information of all logistics network points participating in planning in the target area.
The transportation capacity resource information may include location information of all logistics sites participating in the planning, vehicle information (number of vehicles and types of vehicles) of each parking lot in all parking lots participating in the planning, and information of the number of checkpoints available for loading and unloading goods at each logistics site in all logistics sites participating in the planning.
Because some logistics nodes in the target area may not be suitable for the logistics planning (for example, a transportation task of a certain logistics node is saturated, or loading and unloading gates of the logistics nodes are saturated, etc.), the number of all the logistics nodes participating in the planning in the target area may be less than or equal to the number of all the logistics nodes in the target area, specifically, if the target area includes 5 logistics nodes and only 3 logistics nodes can participate in the planning currently, the vehicle information and the number information of the gates available for loading and unloading of the 3 logistics nodes participating in the planning are obtained.
Specifically, the acquiring the transportation capacity resource information of all logistics nodes participating in planning in the target area may include: determining logistics points which can participate in planning in the target area; and acquiring the transport capacity resource information of all logistics nodes participating in planning. The mode of determining the logistics network points which can participate in planning in the target area can be determined by acquiring the number information of the card slots which can be used for loading and unloading goods in all the logistics network points in the target area, and determining the logistics network points which can participate in planning in all the logistics network points in the target area as the logistics network points which can participate in planning in the target area.
203. And counting constraint data for planning the plurality of transportation tasks according to the transportation capacity resource information.
Specifically, as shown in fig. 3, the counting constraint data for planning the transportation tasks according to the transportation resource information may include:
301. and acquiring the position information of all logistics network points participating in planning in the target area.
302. And counting the number of usable vehicles in all the parking lots participating in planning.
The method for counting the number of usable vehicles in all parking lots participating in planning may be: and acquiring vehicle information in all parking lots participating in planning in the target area, determining parking lots in all parking lots in the target area to be available for vehicles, determining the parking lots in the target area to be available for planning, and counting the number of the available vehicles in all the parking lots participating in planning.
303. And counting the vehicle model of each vehicle in the usable vehicles.
Wherein, the vehicle model can be bread, golden cup, small van, large van, etc. In the embodiment of the present invention, when the model of the vehicle is determined, the capacity (cargo capacity) of the corresponding vehicle is determined.
304. And counting the quantity of the bayonets of different vehicle models which can be loaded and unloaded in all the logistics network points participating in the planning.
The constraint data comprises position information of all logistics sites participating in planning, the number of usable vehicles in all parking lots participating in planning, the vehicle model of each vehicle in the usable vehicles and the number of checkpoints of different vehicle models for loading and unloading goods in all logistics sites participating in planning.
204. And planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the plurality of transportation tasks, the type of the planning vehicles and a transportation path.
The method comprises the steps of obtaining a plurality of transportation tasks of a path to be planned in a target area; acquiring the transport capacity resource information of all logistics nodes participating in planning in a target area; counting constraint data for planning a plurality of transportation tasks according to the transportation capacity resource information; and planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planned vehicles for executing the plurality of transportation tasks, the types of the planned vehicles and the transportation path. Aiming at the planning problem of a plurality of transportation tasks, all the logistics nodes participating in planning in the target area can participate in the planning together on the basis that only the line combination between every two logistics nodes can be considered manually in the prior art, the transportation technology is integrally planned from the global perspective of the target area, a large number of transportation tasks can be planned at one time, the planned vehicle transportation path is enabled to be more excellent, the transportation task planning efficiency is improved, and the operation cost in the later planning stage is reduced.
In some embodiments of the present invention, as shown in fig. 4, the step of planning a transportation plan for the plurality of transportation tasks according to the constraint data further includes:
401. and constructing a space-time network model according to the constraint data.
Specifically, the constructing a spatiotemporal network model according to the historical transportation task data may include: determining constraint conditions for planning the plurality of transportation tasks according to the constraint data; acquiring an objective function for planning the plurality of transportation tasks; and establishing a space-time network model according to the objective function and the constraint condition.
In some embodiments of the present application, each of the plurality of transportation tasks includes a transportation cargo amount, a task start time, a start location, a task end time, and an end location;
the space-time network model comprises a task arc line, a waiting arc line, a free driving arc line, a driving-out arc line, a driving-in arc line and a circulating arc line; the system comprises a task arc line, a task control module and a control module, wherein the task arc line refers to an input transportation task, the starting point of the task arc line represents the starting point of the task and the starting time including loading time and waiting time, and the end point of the task arc line represents the end point of the task and the ending time including unloading time; the waiting arc line refers to the time for the vehicle to stay at the logistics point; the idle driving arc line refers to an arc line corresponding to the idle driving section between two connected task arc lines in the space-time network; the outgoing arc line refers to an arc line which corresponds to the space-time network when the vehicle is driven out of the parking lot and then performs the first task; the driving-in arc line refers to a corresponding arc line in a space-time network when the vehicle drives back to the parking lot in an empty state after the last task is executed; a circular arc refers to an arc from the end of a time window back to the beginning of the time window at a point in the same space in the spatio-temporal network.
Specifically, the objective function is as follows:
the constraint conditions may include the following (2) to (8):
wherein the decision variables are
The method is characterized in that the method starts from a parking lot k, has a vehicle type of f, and covers an edge (i, j) epsilon A in a space-time networkkfThe number of vehicles. The objective function (1) ensures that the total cost of the current mission plan is minimum, the constraint condition (2) ensures that each transportation mission is executed, the constraint condition (3) ensures that the total capacity of vehicles for executing the transportation mission in the current mission plan is larger than the total cargo capacity of a plurality of transportation missions in the current mission plan, the constraint condition (4) limits the number of vehicles of which the vehicle types are f starting from a parking lot k, the constraint condition (5) specifies the in-out balance of each parking lot participating in the planning, the constraint conditions (6) and (7) limit the number of loading and unloading vehicle screens of logistics network points participating in the planning, and the constraint condition (8) limits the running time of the vehicles.
Where K represents the set of available parking lots (all parking lots participating in the planning);
m represents a planning task set;
f represents a set of available vehicle types;
t represents a set of discrete time points within a time window;
h represents the set of available logistics points (possibly coinciding with the set K);
n represents a set of spatio-temporal points in the spatio-temporal network;
a represents the set of spatio-temporal edges (i.e., arcs) in a spatio-temporal network;
Ase＝{(ms,me)∈N*N|ms＝(ssm,stm),ie＝(esm,etm) Denotes a set of task arcs in a spatio-temporal network, where msAnd meRespectively representing a start space-time point and an end space-time point of task m, each space-time point including both a location and a time. ss and st represent a start point (start state) and a start time (start time), and es and et represent an end point (end state) and an end time (end time).
Await＝{(me,os)∈N*N|me＝(esm,etm),os＝(sso,sto),esm＝sso,sto>etmRepresenting a set of waiting arcs in the spatio-temporal network, and representing a starting spatio-temporal point of the transportation task n by Os;
Adh＝{(me,os)∈N*N|me＝(esm,etm),os＝(sso,sto),esm≠sso,sto>etmrepresents a set of null driving arcs in the spatio-temporal network;
Apout＝{(o1,ms)∈|K|*N|o1＝(k,t),ms＝(ssm,stm),k∈K,stm>t}，
representing the set of outgoing arcs in the spatio-temporal network, o1 refers to the spatio-temporal point of the logistics node k at the beginning of the time window;
Apin＝{(me,o2)∈N*|K||me＝(esm,etm),o2＝(k,t),k∈K,t>etirepresenting a set of driving arcs in the space-time network, wherein O2 refers to a space-time point of a logistics network point k at the end time of a time window;
Ac＝{(o2,o1)∈|K|*|K||o2＝(k,t2),o1＝(k,t1),k∈K,t2≥t1represents a set of circular arcs in the spatio-temporal network;
qfrepresenting the load of the vehicle type f;
dijrepresents an arc (i, j) e.g. AseThe volume of task inventory;
nkfrepresenting the number of vehicle types f in the available parking lot k;
ulhthe number of unloading bayonets in the logistics network point h is represented;
llhthe quantity of loading bayonets in the logistics network point h is represented;
tp represents the maximum allowable line duration.
In order to effectively analyze the temporal order (time-dependent constraints) and spatial relationships (scatter point distributions) in the problem, the logistics network is modeled by a 'space-time network' model. The spatio-temporal network is an extension of the static network, and is discretized at certain intervals through continuous distribution of time, each node represents a position and a certain time point, and the spatio-temporal network has two types of arcs (edges connecting the nodes).
In the embodiment of the invention, a space-time network model can be established by using an integer programming algorithm according to the objective function and the constraint condition. The integer programming refers to that variables (all or part of the variables) in the programming are limited to integers, and if the variables are limited to integers in the linear model, the integer programming is called integer linear programming. The currently popular methods for solving integer programming are often only suitable for integer linear programming.
The integer programming algorithm is divided into:
1. pure integer programming algorithm: all decision variables require integer programming of integers;
2. mixed integer programming algorithm: part of decision variables are required to be integer programming of integers;
3. pure 0-1 integer programming algorithm: all decision variables require integer programming of 0-1;
4. hybrid 0-1 planning algorithm: part of decision variables are required to be integer programming of 0-1;
therefore, it should be noted that, in the embodiment of the present invention, the integer programming algorithm may be an existing integer programming algorithm, for example, a pure integer programming algorithm, a mixed integer programming algorithm, a pure 0-1 integer programming algorithm, or a mixed 0-1 programming algorithm, and it is understood that, in the embodiment of the present invention, the integer programming algorithm may also be an integer programming algorithm that newly appears in the future, and the specific details are not limited herein.
402. Planning a transportation plan for the plurality of transportation tasks using the spatio-temporal network model.
In the embodiment of the invention, a transportation plan is planned for a plurality of transportation tasks by using the space-time network model through solving the space-time network in two stages, wherein in the first stage, a first space-time network is established for each logistics network and each vehicle type, the number of vehicles required in each space-time network is obtained through modeling and solving, and in the second stage, a second space-time network is established for each vehicle according to the number of vehicles obtained through solving in the first stage, so that the driving path, transportation task connection and vehicle type of each vehicle are finally output.
Wherein, in the second stage, the variables are decided
0/1 variable, expressed as if the vehicle type f covers the edge (i, j) epsilon A in the space-time network from the logistics network point kkfOn the basis of the first stage model, the constraints (6) (7) representing the truck screens are deleted, and the rest is similar to the first stage.
Specifically, in some embodiments of the present application, the planning a transportation plan for the plurality of transportation tasks by using the spatiotemporal network model may include: inputting the plurality of transportation tasks into the spatio-temporal network model to respectively establish a first spatio-temporal network for each logistics branch point and each vehicle type in the target area to obtain planning vehicle information required in each first spatio-temporal network; and establishing a second space-time network for each planning vehicle according to the planning vehicle information required in all the first space-time networks so as to output the transportation path, the transportation task connection sequence and the planning vehicle type of each planning vehicle and obtain the transportation plans of the multiple transportation tasks.
In the embodiment of the invention, the transportation capacity resource can be planned from the perspective of the overall task planning, a systematic method and the perspective of cost, and compared with the existing solution of manual arrangement, the transportation capacity resource planning method has the advantages that the cost, the number of vehicles and the mileage of the output transportation plan and the speed of completing the whole planning process are greatly improved.
After the initial measurement and investigation of the actual scene under the line by the natural people, under the condition of the data volume of 1000 lines of about 40 logistics nodes in a province, the planning result (i.e. the transportation plan) output by the systematic task planning method provided by the embodiment of the invention can bring about 10% of cost saving under the line.
In addition, the working efficiency of the first-line personnel is greatly improved in the embodiment of the invention, the working time of two first-line planners in one day is consumed for adjusting and replanning the transport capacity resources of about 500 lines each time, and the planning results can hardly be output once when the number of lines exceeds 500. The planning result output by the systematic task planning method provided by the embodiment of the invention can output the capacity resource planning result of more than 3000 lines at one time, the time is about 1 hour, and the planning efficiency and the planning speed are greatly improved.
Fig. 5 is a schematic diagram of a specific scenario of one-time task planning in the embodiment of the present invention.
The present embodiment considers the lines within one day, disperses the time according to 30 minutes, and thus disperses the spatio-temporal network model into 48 time points, each of 30 minutes, as one departure time interval, and establishes spatio-temporal networks as shown in fig. 5 with different sites (including parking lots and transit stations) A, B, C and different time points, each representing a combination of a vehicle type and a logistics site, representing that all lines in this network must start and end from the logistics site where the logistics site is located, and must be executed by a specified vehicle type. The space-time network comprises the following arc lines:
the task arc refers to a planning task for inputting a question, the starting point of the arc represents the starting point of the task and the starting time including the card leaning time, and the end point of the arc represents the ending point of the task and the ending time including the unloading time. As shown in fig. 5, an arc line is shown by a solid arrow from the parking lot a to the transit B. The card leaning time refers to the time (including loading time and waiting time) before the vehicle arrives at the logistics network point in advance before the task starts, and the ending time includes unloading time after the vehicle arrives at the logistics network point.
The waiting arc line refers to the time of the vehicle staying at a logistics point, and the starting and arriving spatial positions of the waiting arc line are positions. As shown in fig. 5, the arc from time 3 to 5 of the transition point B represents that the vehicle stays at the transition point B for 1 hour. Such arcs allow the vehicle to reduce unnecessary vehicles by waiting in the time dimension.
The idle arc refers to the space-time edge of the vehicle during idle running between two connected task arcs. As shown in fig. 5, the arc from transition B to transition C, time 4 to 6, represents the vehicle traveling from transition B for 1 hour to transition C, thereby providing more possibilities for the engagement between the tasks. In the present embodiment, the travel time from the intermediate transition B to the intermediate transition C is calculated using GIS data according to the vehicle type.
The exit arc refers to a space-time edge between the exit of the vehicle from the logistics point and the first task. As shown in fig. 2, the arc from parking lot a to transition point B, time 0-1, represents the vehicle that started to depart from parking lot a on the route, went empty to transition point B over half an hour, and could engage the task behind. In this example, the travel time from the parking lot a to the transition point B is calculated by using GIS data according to the vehicle type.
The approach arc refers to the space-time edge between the vehicle from the last task and the approach to the logistics point. As shown in fig. 5, the arc from transition C to parking lot a, time 0-2, represents the vehicle leaving transition C at the end of the line, leaving parking lot a over an hour of empty travel, so that the previous mission-end line can be engaged. In the case, the driving time from the transition point C to the transition point a is calculated by using GIS data according to different vehicle types.
Finally, the circular arcs represent the space-time edges of each space point from the end point of the time window back to the start point of the time window, the circular arcs have the same space position, and the time forms a closed loop from back to front. As shown in fig. 5, the time when parking lot a returns from time 6 to time 0 is a circular arc. The circular arcs ensure that both the start and the end of the line are one point, and allow the spatio-temporal network model to account for the fixed cost of the vehicle.
In the embodiment of the invention, after the transportation plans of the plurality of transportation tasks are determined, the calculated result can be output to off-line equipment, so that a decision basis is provided for daily operation of trunk transportation of a business party. Specifically, in some embodiments of the present application, the mission planning method may further include: determining terminal equipment corresponding to a target logistics network point of a planning vehicle in the target area; and sending the transportation plans of the plurality of transportation tasks to the terminal equipment.
In order to better implement the task planning method in the embodiment of the present invention, based on the task planning method, an embodiment of the present invention further provides a task planning apparatus, as shown in fig. 6, where the task planning apparatus is applied to a computer device, and the task planning apparatus 600 includes:
a first obtaining unit 601, configured to obtain multiple transportation tasks of a path to be planned in a target area;
a second obtaining unit 602, configured to obtain capacity resource information of all logistics nodes participating in planning in the target area;
a statistic unit 603, configured to count constraint data for planning the multiple transportation tasks according to the transportation resource information;
a planning unit 604, configured to plan a transportation plan for the multiple transportation tasks according to the constraint data, where the transportation plan includes a number of planned vehicles for executing the multiple transportation tasks, a planned vehicle type, and a transportation path.
In some embodiments of the present application, the statistical unit 603 is specifically configured to:
acquiring position information of all logistics nodes participating in planning in the target area;
counting the number of usable vehicles in all parking lots participating in planning;
counting the vehicle model of each vehicle in the usable vehicles;
counting the number of bayonets of different vehicle types which can be loaded and unloaded in all the logistics outlets participating in planning;
the constraint data comprises position information of all logistics sites participating in planning, the number of usable vehicles in all parking lots participating in planning, the vehicle model of each vehicle in the usable vehicles and the number of checkpoints of different vehicle models for loading and unloading goods in all logistics sites participating in planning.
In some embodiments of the present application, the planning unit 604 is specifically configured to:
constructing a spatio-temporal network model according to the constraint data;
planning a transportation plan for the plurality of transportation tasks using the spatio-temporal network model.
In some embodiments of the present application, the planning unit 604 is specifically configured to:
determining constraint conditions for planning the plurality of transportation tasks according to the constraint data;
acquiring an objective function for planning the plurality of transportation tasks;
and establishing a space-time network model according to the objective function and the constraint condition.
In some embodiments of the present application, each of the plurality of transportation tasks includes a transportation cargo amount, a task start time, a start location, a task end time, and an end location;
the space-time network model comprises a task arc line, a waiting arc line, a free driving arc line, a driving-out arc line, a driving-in arc line and a circulating arc line; the system comprises a task arc line, a task control module and a control module, wherein the task arc line refers to an input transportation task, the starting point of the task arc line represents the starting point of the task and the starting time including loading time and waiting time, and the end point of the task arc line represents the end point of the task and the ending time including unloading time; the waiting arc line refers to the time for the vehicle to stay at the logistics point; the idle driving arc line refers to an arc line corresponding to the idle driving section between two connected task arc lines in the space-time network; the outgoing arc line refers to an arc line which corresponds to the space-time network when the vehicle is driven out of the parking lot and then performs the first task; the driving-in arc line refers to a corresponding arc line in a space-time network when the vehicle drives back to the parking lot in an empty state after the last task is executed; a circular arc refers to an arc from the end of a time window back to the beginning of the time window at a point in the same space in the spatio-temporal network.
In some embodiments of the present application, the planning unit 604 is specifically configured to:
inputting the plurality of transportation tasks into the spatio-temporal network model to respectively establish a first spatio-temporal network for each logistics branch point and each vehicle type in the target area to obtain planning vehicle information required in each first spatio-temporal network;
and establishing a second space-time network for each planning vehicle according to the planning vehicle information required in all the first space-time networks so as to output the transportation path, the transportation task connection sequence and the planning vehicle type of each planning vehicle and obtain the transportation plans of the multiple transportation tasks.
In some embodiments of the present application, the mission planning apparatus further includes a sending unit, where the sending unit is configured to:
determining terminal equipment corresponding to a target logistics network point of a planning vehicle in the target area;
and sending the transportation plans of the plurality of transportation tasks to the terminal equipment.
According to the embodiment of the invention, a first obtaining unit 601 is used for obtaining a plurality of transportation tasks of a path to be planned in a target area; a second obtaining unit 602 obtains the transportation capacity resource information of all logistics nodes participating in planning in the target area; the statistical unit 603 performs statistics on constraint data for planning a plurality of transportation tasks according to the transportation capacity resource information; the planning unit 604 plans a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planned vehicles for executing the plurality of transportation tasks, the type of the planned vehicles and the transportation path. Aiming at the planning problem of a plurality of transportation tasks, all the logistics nodes participating in planning in the target area can participate in the planning together on the basis that only the line combination between every two logistics nodes can be considered manually in the prior art, the transportation technology is integrally planned from the global perspective of the target area, a large number of transportation tasks can be planned at one time, the planned vehicle transportation path is enabled to be more excellent, the transportation task planning efficiency is improved, and the operation cost in the later planning stage is reduced.
The embodiment of the present invention further provides a computer device, which integrates any one of the task planning apparatuses provided by the embodiments of the present invention, and the computer device 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 mission planning method described in any of the above embodiments of the mission planning method.
The embodiment of the invention also provides computer equipment which integrates any task planning device provided by the embodiment of the invention. Fig. 7 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 701 of one or more processing cores, memory 702 of one or more computer-readable storage media, a power supply 703, and an input unit 704. Those skilled in the art will appreciate that the computer device configuration illustrated in FIG. 7 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 701 is a control center of the computer apparatus, connects various parts of the entire computer apparatus using various interfaces and lines, and performs various functions of the computer apparatus and processes data by running or executing software programs and/or modules stored in the memory 702 and calling data stored in the memory 702, thereby monitoring the computer apparatus as a whole. Optionally, processor 701 may include one or more processing cores; preferably, the processor 701 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 701.
The memory 702 may be used to store software programs and modules, and the processor 701 executes various functional applications and data processing by operating the software programs and modules stored in the memory 702. The memory 702 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 by 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 702 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 702 may also include a memory controller to provide the processor 701 with access to the memory 702.
The computer device further includes a power supply 703 for supplying power to the various components, and preferably, the power supply 703 is logically connected to the processor 701 through a power management system, so that functions of managing charging, discharging, and power consumption are implemented through the power management system. The power supply 703 may also include any component including one or more of a dc or ac power source, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
The computer device may also include an input unit 704, the input unit 704 being operable 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 701 in the computer device loads the executable file corresponding to the process of one or more application programs into the memory 702 according to the following instructions, and the processor 701 runs the application program stored in the memory 702, thereby implementing various functions as follows:
acquiring a plurality of transportation tasks of a path to be planned in a target area;
acquiring the transport capacity resource information of all logistics nodes participating in planning in the target area;
counting constraint data for planning the plurality of transportation tasks according to the transportation capacity resource information;
and planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the plurality of transportation tasks, the type of the planning vehicles and a transportation path.
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. Stored thereon, is a computer program which is loaded by a processor to perform the steps of any of the methods of task planning provided by the embodiments of the present invention. For example, the computer program may be loaded by a processor to perform the steps of:
acquiring a plurality of transportation tasks of a path to be planned in a target area;
acquiring the transport capacity resource information of all logistics nodes participating in planning in the target area;
counting constraint data for planning the plurality of transportation tasks according to the transportation capacity resource information;
and planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the plurality of transportation tasks, the type of the planning vehicles and a transportation path.
In the above 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, and 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 task planning method, the task planning device, the computer device and the storage medium according to the embodiments of the present invention are described in detail, and a specific example is applied to illustrate the principle and the implementation manner of the present invention, and the description of the embodiments is only used to help understanding the method and the core idea of the present 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 mission planning method, characterized in that the mission planning method comprises:
acquiring a plurality of transportation tasks of a path to be planned in a target area;
acquiring the transport capacity resource information of all logistics nodes participating in planning in the target area;
counting constraint data for planning the plurality of transportation tasks according to the transportation capacity resource information;
and planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the plurality of transportation tasks, the type of the planning vehicles and a transportation path.
2. The mission planning method of claim 1, wherein said counting constraint data for planning said plurality of transportation missions based on said capacity resource information comprises:
acquiring position information of all logistics nodes participating in planning in the target area;
counting the number of usable vehicles in all parking lots participating in planning;
counting the vehicle model of each vehicle in the usable vehicles;
counting the number of bayonets of different vehicle types which can be loaded and unloaded in all the logistics outlets participating in planning;
the constraint data comprises position information of all logistics sites participating in planning, the number of usable vehicles in all parking lots participating in planning, the vehicle model of each vehicle in the usable vehicles and the number of checkpoints of different vehicle models for loading and unloading goods in all logistics sites participating in planning.
3. A mission planning method according to claim 2, wherein said planning a transportation plan for said plurality of transportation missions according to said constraint data comprises:
constructing a spatio-temporal network model according to the constraint data;
planning a transportation plan for the plurality of transportation tasks using the spatio-temporal network model.
4. The mission planning method of claim 3, wherein said constructing a spatiotemporal network model from said historical transportation mission data comprises:
determining constraint conditions for planning the plurality of transportation tasks according to the constraint data;
acquiring an objective function for planning the plurality of transportation tasks;
and establishing a space-time network model according to the objective function and the constraint condition.
5. The mission planning method of claim 3, wherein each of said plurality of transportation missions comprises a transportation cargo volume, a mission start time, a start location, a mission end time, and an end location;
the space-time network model comprises a task arc line, a waiting arc line, a free driving arc line, a driving-out arc line, a driving-in arc line and a circulating arc line; the system comprises a task arc line, a task control module and a control module, wherein the task arc line refers to an input transportation task, the starting point of the task arc line represents the starting point of the task and the starting time including loading time and waiting time, and the end point of the task arc line represents the end point of the task and the ending time including unloading time; the waiting arc line refers to the time for the vehicle to stay at the logistics point; the idle driving arc line refers to an arc line corresponding to the idle driving section between two connected task arc lines in the space-time network; the outgoing arc line refers to an arc line which corresponds to the space-time network when the vehicle is driven out of the parking lot and then performs the first task; the driving-in arc line refers to a corresponding arc line in a space-time network when the vehicle drives back to the parking lot in an empty state after the last task is executed; a circular arc refers to an arc from the end of a time window back to the beginning of the time window at a point in the same space in the spatio-temporal network.
6. The mission planning method of claim 5, wherein said planning a transportation plan for said plurality of transportation missions using said spatiotemporal network model comprises:
inputting the plurality of transportation tasks into the spatio-temporal network model to respectively establish a first spatio-temporal network for each logistics branch point and each vehicle type in the target area to obtain planning vehicle information required in each first spatio-temporal network;
and establishing a second space-time network for each planning vehicle according to the planning vehicle information required in all the first space-time networks so as to output the transportation path, the transportation task connection sequence and the planning vehicle type of each planning vehicle and obtain the transportation plans of the multiple transportation tasks.
7. The mission planning method according to any one of claims 1 to 6, further comprising:
determining terminal equipment corresponding to a target logistics network point of a planning vehicle in the target area;
and sending the transportation plans of the plurality of transportation tasks to the terminal equipment.
8. A mission planning apparatus, comprising:
the first acquisition unit is used for acquiring a plurality of transportation tasks of a path to be planned in a target area;
the second acquisition unit is used for acquiring the transport capacity resource information of all logistics nodes participating in planning in the target area;
the statistical unit is used for counting the constraint data for planning the plurality of transportation tasks according to the transportation capacity resource information;
and the planning unit is used for planning a transportation plan for the plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the plurality of transportation tasks, and the planning vehicle type and the transportation path.
9. A computer device, characterized in that the computer device comprises:
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 to implement the mission planning method of any one 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 mission planning method according to any one of claims 1 to 7.
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