Disclosure of Invention
The embodiment of the invention provides a task planning method, a device, computer equipment and a storage medium, which can participate in planning all logistics network points of a target area, integrally plan a transportation technology from the global view of the target area, and plan a large number of transportation tasks at one time, so that the planned vehicle transportation path is better, the transportation task planning efficiency is improved, and the operation cost in the later stage of planning is reduced. .
In a first aspect, the present application provides a task planning method, including:
Acquiring a plurality of transportation tasks of a path to be planned in a target area;
acquiring capacity resource information of all logistics network points participating in planning in the target area;
according to the transport capacity resource information, constraint data for planning the transport tasks are counted;
And planning a transportation plan for the transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the transportation tasks, the types of the planning vehicles and the transportation paths.
In some embodiments of the present application, the calculating constraint data for planning the plurality of transportation tasks according to the capacity resource information includes:
acquiring position information of all logistics network points participating in planning in the target area;
Counting the number of available 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 available for loading and unloading in all logistics network points participating in planning;
The constraint data comprise position information of all logistics network points participating in planning, the number of available vehicles in all parking lots participating in planning, the vehicle types of all vehicles in the available vehicles and the number of bayonets of different vehicle types available for loading and unloading in all logistics network points participating in planning.
In some embodiments of the present application, the planning a transportation plan for the plurality of transportation tasks according to the constraint data includes:
constructing a space-time network model according to the constraint data;
and planning a transportation plan for the transportation tasks by using the space-time network model.
In some embodiments of the present application, the constructing a space-time 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 volume, 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, an empty driving arc line, a driving-out arc line, a driving-in arc line and a circulating arc line; the method comprises the steps that a task arc line refers to an input transportation task, a starting point of the task arc line represents a starting point of the task and a starting time comprising loading time and waiting time, and an end point of the task arc line represents an end point of the task and an end time comprising unloading time; waiting arc refers to the time that the vehicle stays at the logistics network point; the empty driving arc line refers to an arc line of the space-time network, corresponding to an empty driving section between two sections of task arc lines, of the vehicle; the outgoing arc refers to an arc corresponding to a space-time network under the condition that the vehicle is driven out of the parking lot and the first section of task is executed; the driving-in arc line refers to a corresponding arc line in the space-time network between the vehicles and the empty parking lots after the vehicles execute the last section of task; a circular arc refers to an arc in the spatio-temporal network from the end of the time window back to the start of the time window at a point in the same space.
In some embodiments of the application, said planning a transportation plan for said plurality of transportation tasks using said spatio-temporal network model comprises:
Inputting the transportation tasks into the space-time network model to respectively establish a first space-time network for each logistics network point and each vehicle type in the target area, so as to obtain planning vehicle information required in each first space-time 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 types of each planning vehicle and obtain the transportation plans of the transportation tasks.
In some embodiments of the present application, the task planning method further includes:
Determining a terminal device corresponding to a target logistics network point of a planning vehicle in the target area;
And sending the transportation plans of the 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 capacity resource information of all the logistics network points participating in planning in the target area;
The statistical unit is used for counting constraint data for planning the transportation tasks according to the transportation resource information;
And the planning unit is used for planning a transportation plan for the transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the transportation tasks, the type of the planning vehicles and the transportation path.
In some embodiments of the present application, the statistics unit is specifically configured to:
acquiring position information of all logistics network points participating in planning in the target area;
Counting the number of available 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 for loading and unloading in all logistics network points participating in planning;
The constraint data comprise position information of all logistics network points participating in planning, the number of available vehicles in all parking lots participating in planning, the vehicle types of all vehicles in the available vehicles and the number of bayonets of different vehicle types available for loading and unloading in all logistics network points participating in planning.
In some embodiments of the present application, the planning unit is specifically configured to:
constructing a space-time network model according to the constraint data;
and planning a transportation plan for the transportation tasks by using the space-time 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 volume, 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, an empty driving arc line, a driving-out arc line, a driving-in arc line and a circulating arc line; the method comprises the steps that a task arc line refers to an input transportation task, a starting point of the task arc line represents a starting point of the task and a starting time comprising loading time and waiting time, and an end point of the task arc line represents an end point of the task and an end time comprising unloading time; waiting arc refers to the time that the vehicle stays at the logistics network point; the empty driving arc line refers to an arc line of the space-time network, corresponding to an empty driving section between two sections of task arc lines, of the vehicle; the outgoing arc refers to an arc corresponding to a space-time network under the condition that the vehicle is driven out of the parking lot and the first section of task is executed; the driving-in arc line refers to a corresponding arc line in the space-time network between the vehicles and the empty parking lots after the vehicles execute the last section of task; a circular arc refers to an arc in the spatio-temporal network from the end of the time window back to the start of the time window at a point in the same space.
In some embodiments of the present application, the planning unit is specifically configured to:
Inputting the transportation tasks into the space-time network model to respectively establish a first space-time network for each logistics network point and each vehicle type in the target area, so as to obtain planning vehicle information required in each first space-time 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 types of each planning vehicle and obtain the transportation plans of the transportation tasks.
In some embodiments of the present application, the task planning apparatus further includes a sending unit, where the sending unit is configured to:
Determining a terminal device corresponding to a target logistics network point of a planning vehicle in the target area;
And sending the transportation plans of the transportation tasks to the terminal equipment.
In a third aspect, the present application also provides a computer apparatus comprising:
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 task planning method of any one of the first aspects.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program to be loaded by a processor for performing the steps of the task planning method of any of the first aspects.
The method comprises the steps of obtaining a plurality of transportation tasks of a path to be planned in a target area; acquiring capacity resource information of all logistics network points participating in planning in a target area; according to the transport capacity resource information, constraint data for planning a plurality of transport tasks are counted; and planning a transportation plan for a plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the transportation tasks, the types of the planning vehicles and the transportation paths. According to the embodiment of the invention, aiming at the planning problem of a plurality of transportation tasks, on the basis that only line combinations between every two logistics network points can be considered manually in the prior art, all logistics network points participating in planning in a target area can be involved in planning, and the transportation technology can be planned in an overall way from the global view of the target area, so that a large number of transportation tasks can be planned at one time, the planned vehicle transportation path is better, the planning efficiency of the transportation tasks is improved, and the running cost in the later stage of planning is reduced.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
In the description of the present invention, it should 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 the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present application, the term "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described as "exemplary" in this disclosure 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 application. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes have not been described in detail so as not to obscure the description of the application with unnecessary detail. Thus, the present application 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.
The embodiment of the invention provides a task planning method, a task planning device, computer equipment and a storage medium, and the task planning method, the task planning device, the computer equipment and the storage medium are respectively described in detail below.
Referring to fig. 1, fig. 1 is a schematic view 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.
The computer device 100 in the embodiment of the present invention is mainly used for acquiring a plurality of transportation tasks of a path to be planned in a target area; acquiring capacity resource information of all logistics network points participating in planning in the target area; according to the transport capacity resource information, constraint data for planning the transport tasks are counted; and planning a transportation plan for the transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the transportation tasks, the types of the planning vehicles and the transportation paths.
In the embodiment of the present invention, the computer device 100 may be an independent server, or may be a server network or a server cluster formed by servers, for example, the computer device 100 described in the 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 formed by a plurality of servers. Wherein the Cloud server is composed of a large number of computers or web servers based on Cloud Computing (Cloud Computing).
It will be appreciated by those skilled in the art that the application environment shown in fig. 1 is merely an application scenario of the present application, and is not limited to the application scenario of the present application, and that other application environments may include more or fewer computer devices than those shown in fig. 1, for example, only 1 computer device is shown in fig. 1, and it will be appreciated that the task planning system may also include one or more other services, and is not limited thereto.
In addition, as shown in fig. 1, the mission planning system may further include a memory 200 for storing data, such as logistics data, for example, various data of a logistics platform, such as logistics transportation information of logistics sites, such as a transfer site, and the like, specifically, express information, delivery vehicle information, logistics site information, and the like.
It should be noted that, the schematic view of the task planning system shown in fig. 1 is only an example, and the task planning system and the scene described in the embodiments of the present invention are for more clearly describing the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention, and those skilled in the art can know that, with the evolution of the task planning system and the appearance of a new service scenario, the technical solutions provided by the embodiments of the present invention are equally applicable to similar technical problems.
Firstly, in an embodiment of the present invention, a task planning method is provided, 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 capacity resource information of all logistics network points participating in planning in the target area; according to the transport capacity resource information, constraint data for planning the transport tasks are counted; and planning a transportation plan for the transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the transportation tasks, the types of the planning vehicles and the transportation paths.
Fig. 2 is a schematic flow chart of an embodiment of a task planning method according to the present invention, where 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 where the user designates the 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 and town, etc., which may be specifically set according to the actual situation, and is not limited herein.
Wherein, each transportation task in the plurality of transportation tasks comprises a transportation cargo amount, a task starting time, a starting place, a task ending time and an ending place, specifically, the starting place may be a starting logistics website of the task, and the ending place may be an ending logistics website of the task, that is, the transportation task is started at the starting logistics website and the transportation task is ended at the ending logistics website.
The logistics network point is a distributed node in a logistics network, and the basic function is to distribute and transfer the express. The logistics network points generally comprise a transfer yard, a transfer station, an allocation yard, a transfer center, an express delivery point and the like. From the point of view of the logistics network, the logistics network point is also a network node. The logistics network points are important nodes for sorting and distributing the express items, and mainly concentrate, exchange and transfer the express items collected from other logistics network points, so that the express items can flow from scattered to concentrated to scattered in the whole network.
The computer device can obtain a plurality of transportation tasks of the path to be planned in the target area when the user selects the plurality of transportation tasks of the path to be planned in the target area when the user performs task planning.
202. And acquiring the capacity resource information of all logistics network points participating in planning in the target area.
The capacity resource information may include position information of all logistics sites participating in planning, vehicle information (vehicle number and vehicle type) of each parking lot in all parking lots participating in planning, and bayonet number information of each logistics site available for loading and unloading in all logistics sites participating in planning.
Because some logistics dots in the target area may not be suitable for the logistics planning (for example, a transportation task of a logistics dot is saturated, or loading and unloading openings of the logistics dots are saturated, etc.), the number of all logistics dots in the target area participating in the planning may be less than or equal to the number of all logistics dots in the target area, specifically, if the target area includes 5 logistics dots, and only 3 logistics dots can participate in the planning currently, vehicle information of the 3 logistics dots participating in the planning and loading and unloading opening number information are obtained.
Specifically, the obtaining the capacity resource information of all the logistics network points participating in planning in the target area may include: determining logistics network points which can participate in planning in the target area; and acquiring the capacity resource information of all the logistics network points participating in planning. The method for determining the logistics network points which can participate in planning in the target area can be implemented by acquiring the information of the number of the openings which can be used for loading and unloading goods in all the logistics network points in the target area, determining the logistics network points which can be used for loading and unloading goods to exist in all the logistics network points in the target area, and determining the logistics network points which can participate in planning in the target area.
203. And according to the transport capacity resource information, calculating constraint data for planning the transportation tasks.
Specifically, as shown in fig. 3, the calculating constraint data for planning the plurality of transportation tasks according to the capacity 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 available vehicles in all parking lots participating in planning.
The method for counting the number of available vehicles in all parking lots participating in planning may be as follows: and acquiring vehicle information in all the parking lots participating in planning in the target area, determining the parking lots with available vehicles in all the parking lots in the target area, determining the parking lots as the parking lots capable of participating in planning in the target area, 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.
The vehicle model can be bread, a golden cup, a small truck, a large truck and the like. In the embodiment of the present invention, when the vehicle model is determined, the capacity (cargo carrying capacity) of the corresponding vehicle is determined.
304. And counting the number of bayonets of different vehicle types available for loading and unloading in all logistics network points participating in planning.
The constraint data comprise position information of all logistics network points participating in planning, the number of available vehicles in all parking lots participating in planning, the vehicle types of all vehicles in the available vehicles and the number of bayonets of different vehicle types available for loading and unloading in all logistics network points participating in planning.
204. And planning a transportation plan for the transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the transportation tasks, the types of the planning vehicles and the transportation paths.
The method comprises the steps of obtaining a plurality of transportation tasks of a path to be planned in a target area; acquiring capacity resource information of all logistics network points participating in planning in a target area; according to the transport capacity resource information, constraint data for planning a plurality of transport tasks are counted; and planning a transportation plan for a plurality of transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the transportation tasks, the types of the planning vehicles and the transportation paths. According to the embodiment of the invention, aiming at the planning problem of a plurality of transportation tasks, on the basis that only line combinations between every two logistics network points can be considered manually in the prior art, all logistics network points participating in planning in a target area can be involved in planning, and the transportation technology can be planned in an overall way from the global view of the target area, so that a large number of transportation tasks can be planned at one time, the planned vehicle transportation path is better, the planning efficiency of the transportation tasks is improved, and the running cost in the later stage of planning 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 may further include:
401. and constructing a space-time network model according to the constraint data.
Specifically, the constructing a space-time 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 volume, 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, an empty driving arc line, a driving-out arc line, a driving-in arc line and a circulating arc line; the method comprises the steps that a task arc line refers to an input transportation task, a starting point of the task arc line represents a starting point of the task and a starting time comprising loading time and waiting time, and an end point of the task arc line represents an end point of the task and an end time comprising unloading time; waiting arc refers to the time that the vehicle stays at the logistics network point; the empty driving arc line refers to an arc line of the space-time network, corresponding to an empty driving section between two sections of task arc lines, of the vehicle; the outgoing arc refers to an arc corresponding to a space-time network under the condition that the vehicle is driven out of the parking lot and the first section of task is executed; the driving-in arc line refers to a corresponding arc line in the space-time network between the vehicles and the empty parking lots after the vehicles execute the last section of task; a circular arc refers to an arc in the spatio-temporal network from the end of the time window back to the start of the time window at a point in the same space.
Specifically, the objective function is as follows:
The constraint may include the constraint in (2) to (8) below:
wherein the decision variablesThe number of vehicles from parking lot k, vehicle model f, covering side (i, j) e Akf in the spatio-temporal network is shown. The objective function (1) ensures that the total cost of the current task plan is minimum, the constraint condition (2) ensures that each transportation task is executed, the constraint condition (3) ensures that the total capacity of vehicles for executing the transportation task in the current task plan is larger than the total cargo capacity of a plurality of transportation tasks in the current task plan, the constraint condition (4) limits the number of vehicles for starting from a parking lot k and taking the vehicle model as f, the constraint condition (5) prescribes the balance of the access of each parking lot participating in the plan, the constraint conditions (6) and (7) limit the number of loading and unloading vehicle clamping positions of logistics network points participating in the plan, 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 models;
T represents a set of discrete time points within a time window;
H represents the set of available logistics dots (possibly coincident with set K);
N represents a set of spatio-temporal points in a spatio-temporal network;
A represents a set of spatio-temporal edges (i.e., arcs) in a spatio-temporal network;
Ase＝{(ms,me)∈N*N|ms＝(ssm,stm),ie＝(esm,etm)}, Representing a set of task arcs in a spatio-temporal network, where ms and me represent a start spatio-temporal point and an end spatio-temporal point, respectively, of task m, each spatio-temporal point comprising both a location and a time. ss and st represent a start point (start station) and a start time (START TIME), and es and et represent an end point (end station) and an end time (end time).
Await＝{(me,os)∈N*N|me＝(esm,etm),os＝(sso,sto),esm＝sso,sto>etm}, Representing a set of waiting arcs in a space-time network, wherein Os represents a starting space-time point of a transport task n;
Adh＝{(me,os)∈N*N|me＝(esm,etm),os＝(sso,sto),esm≠sso,sto>etm}, Representing a set of hollow driving arcs in the space-time network;
Apout＝{(o1,ms)∈|K|*N|o1＝(k,t),ms＝(ssm,stm),k∈K,stm>t},
an aggregation of outgoing arcs in a space-time network is represented, and o1 refers to a space-time point of a logistics lattice point k at the starting moment of a time window;
Apin＝{(me,o2)∈N*|K||me＝(esm,etm),o2＝(k,t),k∈K,t>eti}, Representing a set of entering arcs in a space-time network, wherein O2 refers to a space-time point of a logistics lattice point k at the end time of a time window;
Ac＝{(o2,o1)∈|K|*|K||o2＝(k,t2),o1＝(k,t1),k∈K,t2≥t1}, Representing a set of cyclic arcs in a spatio-temporal network;
Representing that vehicle model f starts from logistics lattice point k in arc lineCost of travel;
qf represents the load of the vehicle model f;
dij represents the task volume on arc (i, j) e Ase;
nkf represents the number of vehicle models f in the available parking lot k;
ulh represents the number of discharge bayonets in the logistics network point h;
llh represents the number of loading bayonets in the logistics network point h;
tp represents the maximum length of time allowed for the line.
In order to effectively analyze the time sequence (age constraint) and the spatial relationship (distributed distribution point) in the problem, the application adopts a 'space-time network' model to model the logistics network. A spatio-temporal network is an extension of a static network in which there are two types of arcs (edges connecting nodes) by discretizing the continuous distribution of time at intervals, each node representing both a location and a point in time.
In the embodiment of the invention, a space-time network model can be established by utilizing an integer programming algorithm according to the objective function and the constraint condition. The integer programming refers to that the variables (all or part) in the programming are limited to integers, and if the variables are limited to integers in the linear model, the integer linear programming is called. The currently popular methods for solving integer programming are often only applicable to integer linear programming.
Since integer programming algorithms are in turn divided into:
1. pure integer programming algorithm: all decision variables require integer programming for integers;
2. mixed integer programming algorithm: the part of decision variables all require 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: the integer programming with the partial decision variables of 0-1 is required;
Therefore, it should be noted that the integer programming algorithm in the embodiment of the present invention may be an existing integer programming algorithm, such as 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 of course, it should be understood that the integer programming algorithm in the embodiment of the present invention may also be a new integer programming algorithm in the future, which is not limited herein.
402. And planning a transportation plan for the transportation tasks by using the space-time network model.
In the embodiment of the invention, the space-time network model is utilized to plan the transportation plan for the transportation tasks by solving the space-time network in two stages, wherein the first stage firstly establishes a first space-time network for each logistics network point and each vehicle type, the modeling solution obtains the number of vehicles required in each space-time network, and the second stage establishes a second space-time network for each vehicle according to the number of vehicles obtained in the first stage, thereby finally outputting the driving path, the transportation task connection and the vehicle type of each vehicle.
Wherein, in the second stage, the decision variableThe 0/1 variable is expressed as whether the vehicle model f covers the edge (i, j) epsilon Akf in the space-time network from the logistics network point k, and the constraint condition (6) representing the clamping position of the loading and unloading vehicle is deleted on the basis of the first stage model (7), and the other steps are similar to the first stage.
Specifically, in some embodiments of the present application, the planning a transportation plan for the plurality of transportation tasks using the space-time network model may include: inputting the transportation tasks into the space-time network model to respectively establish a first space-time network for each logistics network point and each vehicle type in the target area, so as to obtain planning vehicle information required in each first space-time 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 types of each planning vehicle and obtain the transportation plans of the transportation tasks.
In the embodiment of the invention, the capacity resource can be planned by a systematic method from the overall view of task planning, and from the view of cost, compared with the existing manual arrangement solution, the cost, the number of vehicles, the mileage of the output transportation plan and the completion speed of the whole planning process are greatly improved.
Through initial measurement and calculation of people and investigation of actual scenes off line, the planning result (namely the transportation plan) output by the systematic task planning method provided by the embodiment of the invention can bring about 10% of cost saving off line under the condition of data quantity of more than 1000 lines of about 40 logistics network points in one province.
In addition, the working efficiency of the line staff is greatly improved, the current adjustment and re-planning of the capacity resources of about 500 lines at a time requires the consumption of one day of working time of two line planners, and the planning result can not be output almost once for more than 500 lines. 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, which takes about 1 hour, thereby greatly improving the planning efficiency and the planning speed.
Fig. 5 is a schematic diagram of a specific scenario of one-time task planning in an embodiment of the present invention.
In this embodiment, the time is discretized according to 30 minutes by considering the routes within one day, so that the space-time network model is discretized into 48 time points, each of which is 30 minutes, as a departure time interval, the space-time network as shown in fig. 5 is built up with different sites (including parking lots and intermediate transitions) A, B, C and different time points, each space-time network represents a combination of a vehicle model and a logistics network point, represents that all routes in this network must start and end from the logistics network point and must be executed by a specified vehicle model. The space-time network comprises the following arcs:
the arc line of the task refers to a planning task input by a problem, the starting point of the arc line represents the starting point of the task and the starting time including the card leaning time, and the end point of the arc line represents the end point of the task and the end time including the unloading time. As shown in fig. 5, an arc is indicated by a solid arrow from parking lot a to transit lot B. The card-leaning time refers to the time (the starting time including loading time and waiting time) when the vehicle arrives at the logistics network point in advance before the task starts to load, and the ending time includes the unloading time after the vehicle arrives at the logistics network point.
The waiting arc line refers to the time that the vehicle stays at the logistics network point, and the space positions of departure and arrival of the waiting arc line are the positions. As shown in fig. 5, the arc from time 3 to 5 for transition B indicates that the vehicle has stayed in transition B for 1 hour. Such an arc allows the vehicle to reduce unnecessary vehicles by waiting in the time dimension.
The empty driving arc line refers to the space-time edge of the vehicle when the vehicle runs empty between two sections of the task arc lines. As shown in fig. 5, the arc from transition B to transition C, time 4 to 6, represents a1 hour empty vehicle travel from transition B to transition C, thereby providing more possibilities for engagement between tasks. In this embodiment, the driving time from the transition B to the transition C is calculated according to different vehicle types by using GIS data.
The exit arc refers to a space-time edge linking the exit of the vehicle from the logistics network with the first task. As shown in fig. 2, the arc from parking lot a to transition B, time 0-1, indicates that the vehicle starts to travel from parking lot a to transition B after a half hour of empty time on the road, so that the following tasks can be engaged. In this case, the driving time from the parking lot a to the transit yard B is calculated according to different vehicle types by using GIS data.
The entering arc refers to the space-time edge between the last section of task and the returning stream net point of the linking vehicle. As shown in fig. 5, the arc from transition C to parking lot a, time 0-2, indicates that the vehicle starts from transition C at the end of the route and travels through parking lot a in a small space, thereby engaging the end of the route of the previous mission. In the present case, the driving time from transition C to A is calculated according to different vehicle types by using GIS data.
Finally, the circular arc represents the space-time edge of each space point from the end point of the time window to the beginning point of the time window, the space positions of the circular arc are the same, and the time forms a closed loop from back to front. As shown in fig. 5, parking lot a returns from time 6 to time 0 to form a circular arc. The circular arc ensures that the departure and the end of the line are all one point, and the space-time network model can consider the fixed cost of the vehicle.
In the embodiment of the application, after the transportation plans of the transportation tasks are determined, the calculated result can be output to off-line equipment, so that decision basis is provided for daily operation of trunk transportation of the business party. Specifically, in some embodiments of the present application, the task planning method may further include: determining a terminal device corresponding to a target logistics network point of a planning vehicle in the target area; and sending the transportation plans of the transportation tasks to the terminal equipment.
In order to better implement the task planning method according to the embodiment of the present invention, on the basis of the task planning method, a task planning apparatus is further provided according to the embodiment of the present invention, 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 a plurality of 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 the logistics network points participating in planning in the target area;
A statistics unit 603, configured to count constraint data for planning the plurality of transportation tasks according to the capacity resource information;
And a planning unit 604, configured to plan a transportation plan for the plurality of transportation tasks according to the constraint data, where the transportation plan includes a number of planned vehicles for executing the plurality of transportation tasks, a planned vehicle type and a transportation path.
In some embodiments of the present application, the statistics unit 603 is specifically configured to:
acquiring position information of all logistics network points participating in planning in the target area;
Counting the number of available 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 available for loading and unloading in all logistics network points participating in planning;
The constraint data comprise position information of all logistics network points participating in planning, the number of available vehicles in all parking lots participating in planning, the vehicle types of all vehicles in the available vehicles and the number of bayonets of different vehicle types available for loading and unloading in all logistics network points participating in planning.
In some embodiments of the present application, the planning unit 604 is specifically configured to:
constructing a space-time network model according to the constraint data;
and planning a transportation plan for the transportation tasks by using the space-time 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 volume, 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, an empty driving arc line, a driving-out arc line, a driving-in arc line and a circulating arc line; the method comprises the steps that a task arc line refers to an input transportation task, a starting point of the task arc line represents a starting point of the task and a starting time comprising loading time and waiting time, and an end point of the task arc line represents an end point of the task and an end time comprising unloading time; waiting arc refers to the time that the vehicle stays at the logistics network point; the empty driving arc line refers to an arc line of the space-time network, corresponding to an empty driving section between two sections of task arc lines, of the vehicle; the outgoing arc refers to an arc corresponding to a space-time network under the condition that the vehicle is driven out of the parking lot and the first section of task is executed; the driving-in arc line refers to a corresponding arc line in the space-time network between the vehicles and the empty parking lots after the vehicles execute the last section of task; a circular arc refers to an arc in the spatio-temporal network from the end of the time window back to the start of the time window at a point in the same space.
In some embodiments of the present application, the planning unit 604 is specifically configured to:
Inputting the transportation tasks into the space-time network model to respectively establish a first space-time network for each logistics network point and each vehicle type in the target area, so as to obtain planning vehicle information required in each first space-time 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 types of each planning vehicle and obtain the transportation plans of the transportation tasks.
In some embodiments of the present application, the task planning apparatus further includes a sending unit, where the sending unit is configured to:
Determining a terminal device corresponding to a target logistics network point of a planning vehicle in the target area;
And sending the transportation plans of the transportation tasks to the terminal equipment.
According to the embodiment of the invention, a plurality of transportation tasks of a path to be planned in a target area are acquired through a first acquisition unit 601; the second obtaining unit 602 obtains the capacity resource information of all the logistics network points participating in planning in the target area; the statistics unit 603 counts constraint data for planning a plurality of transportation tasks according to the transportation resource information; the planning unit 604 plans a transportation plan for a plurality of transportation tasks according to the constraint data, wherein the transportation plan includes a planned vehicle number, a planned vehicle type and a transportation path for executing the plurality of transportation tasks. According to the embodiment of the invention, aiming at the planning problem of a plurality of transportation tasks, on the basis that only line combinations between every two logistics network points can be considered manually in the prior art, all logistics network points participating in planning in a target area can be involved in planning, and the transportation technology can be planned in an overall way from the global view of the target area, so that a large number of transportation tasks can be planned at one time, the planned vehicle transportation path is better, the planning efficiency of the transportation tasks is improved, and the running cost in the later stage of planning is reduced.
The embodiment of the invention also provides a computer device which integrates any one of the task planning devices provided by the embodiment of the invention, and 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 perform the steps of the mission planning method described in any of the embodiments of the mission planning method described above.
The embodiment of the invention also provides computer equipment which integrates any task planning device provided by the embodiment of the invention. As shown in fig. 7, a schematic structural diagram of a computer device according to an embodiment of the present invention is shown, specifically:
The computer device may include one or more processors 701 of a processing core, memory 702 of one or more computer readable storage media, power supply 703, and input unit 704, among other components. Those skilled in the art will appreciate that the computer device structure shown in FIG. 7 is not limiting of the computer device and may include more or fewer components than shown, or may be combined with certain components, or a different arrangement of components. Wherein:
The processor 701 is a control center of the computer device, connects various parts of the entire computer device 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 702, and calling data stored in the memory 702, thereby performing overall monitoring of the computer device. Optionally, processor 701 may include one or more processing cores; preferably, the processor 701 may integrate an application processor and a modem processor, wherein the application processor primarily handles operating systems, user interfaces, applications, etc., and the modem processor primarily 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 executing the software programs and modules stored in the memory 702. The memory 702 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created according to the use of the computer device, etc. In addition, 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 access to the memory 702 by the processor 701.
The computer device further comprises a power supply 703 for powering the various components, preferably the power supply 703 is logically connected to the processor 701 by a power management system, whereby the functions of managing charging, discharging, and power consumption are performed by the power management system. The power supply 703 may also include one or more of any component, such as a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc.
The computer device may further comprise an input unit 704, which input unit 704 may be used for receiving input numerical or character information and generating keyboard, mouse, joystick, optical or trackball signal inputs in connection with user settings and function control.
Although not shown, the computer device may further include a display unit or the like, which is not described herein. In particular, in this embodiment, the processor 701 in the computer device loads executable files corresponding to the processes of one or more application programs into the memory 702 according to the following instructions, and the processor 701 executes the application programs stored in the memory 702, so as to implement various functions, as follows:
Acquiring a plurality of transportation tasks of a path to be planned in a target area;
acquiring capacity resource information of all logistics network points participating in planning in the target area;
according to the transport capacity resource information, constraint data for planning the transport tasks are counted;
And planning a transportation plan for the transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the transportation tasks, the types of the planning vehicles and the transportation paths.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, 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, random Access Memory), magnetic or optical disk, and the like. On which a computer program is stored, which computer program is loaded by a processor for performing the steps of any of the task planning methods provided by the embodiments of the invention. For example, the loading of the computer program by the processor may perform the steps of:
Acquiring a plurality of transportation tasks of a path to be planned in a target area;
acquiring capacity resource information of all logistics network points participating in planning in the target area;
according to the transport capacity resource information, constraint data for planning the transport tasks are counted;
And planning a transportation plan for the transportation tasks according to the constraint data, wherein the transportation plan comprises the number of planning vehicles for executing the transportation tasks, the types of the planning vehicles and the transportation paths.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and the portions of one embodiment that are not described in detail in the foregoing embodiments may be referred to in the foregoing detailed description of other embodiments, which are not described herein again.
In the implementation, each unit or structure may be implemented as an independent entity, or may be implemented as the same entity or several entities in any combination, and the implementation of each unit or structure may be referred to the foregoing method embodiments and will not be repeated herein.
The specific implementation of each operation above may be referred to the previous embodiments, and will not be described herein.
The task planning method, device, computer equipment and storage medium provided by the embodiment of the invention are described in detail, and specific examples are applied to illustrate the principle and implementation of the invention, and the description of the above embodiments is only used for helping to understand the method and core ideas of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present invention, the present description should not be construed as limiting the present invention.