CN109697524B - Method, system and equipment for optimizing matching of receiving and dispatching task and resource - Google Patents
Method, system and equipment for optimizing matching of receiving and dispatching task and resource Download PDFInfo
- Publication number
- CN109697524B CN109697524B CN201710997353.7A CN201710997353A CN109697524B CN 109697524 B CN109697524 B CN 109697524B CN 201710997353 A CN201710997353 A CN 201710997353A CN 109697524 B CN109697524 B CN 109697524B
- Authority
- CN
- China
- Prior art keywords
- information
- task
- acquiring
- waybill
- attribute
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012384 transportation and delivery Methods 0.000 claims abstract description 16
- 238000004806 packaging method and process Methods 0.000 claims abstract description 11
- 230000032683 aging Effects 0.000 claims description 15
- 238000013439 planning Methods 0.000 claims description 15
- 238000004422 calculation algorithm Methods 0.000 claims description 13
- 238000005457 optimization Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 238000010845 search algorithm Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000002922 simulated annealing Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000012856 packing Methods 0.000 description 2
- 229920000433 Lyocell Polymers 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 230000001550 time effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
- G06Q10/063112—Skill-based matching of a person or a group to a task
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The method comprises the steps of S1, obtaining the quantity of the shipping slips to be dispatched and the shipping slip number of each shipping slip; s2, acquiring the area information of the receiving address of each waybill and the service attribute corresponding to each waybill according to the waybill number; s3, scheduling information of the courier is called; s4, acquiring the resource attribute of the current value courier; s5, packaging the waybills into task packages; s6, matching the task attribute in the task package with the resource attribute of the courier; and S7, dispatching the waybills in the task package to a destination. The method can ensure timeliness of receiving the client, and realize optimal matching of tasks and resources and most convenient route pushing. The system and the device are used for realizing the method. The method can realize optimal matching of tasks and resources, route pushing and the like most conveniently, and therefore the overall service quality of company delivery is improved.
Description
Technical Field
The invention relates to the field of logistics, in particular to a method, a system and equipment for optimizing matching of a receiving and dispatching task and resources.
Background
The current mode of operation within the popularity of the industry is the regional contractual business mode. The so-called regional contractual business model is: dividing the geographic position into areas, wherein each area is taken charge of a fixed employee, the employee or a group with the head of the employee is in charge of all receiving and dispatching tasks of addresses in the area, the range of each area is relatively fixed, and the areas are relatively independent. In the past, the regional contractual system improves the enthusiasm of staff to develop markets and maintain clients in the regions, but the defects of unreasonable regional division, poor coordination capability among regions and the like are increasingly remarkable along with the development of industries and the change development of market environments.
The area contractual business model has the following problems:
1. the regional division is based on the history division basis and management experience, the unreasonable contradiction of regional division is increasingly prominent, the region with more tasks is provided, and the employee business development will is not strong; in the area with high task difficulty, the personnel input cost is not in proportion to the income;
2. different types of tasks need different resources to be completed, and the same staff skill is difficult to meet the requirements of multiple types of tasks.
3. The method has the advantages that the personnel are managed and managed through timeliness achievement and customer complaint results, process supervision and in-process follow-up are lacked, and the real reasons of the abnormality are difficult to discover and improve.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method, a system and equipment for optimizing the matching of a receiving and dispatching task and resources, which can realize the optimal matching of the task and the resources, the most convenient route pushing and the like, thereby improving the overall service quality of the company dispatching.
The method for optimizing the matching of the receiving and dispatching task and the resource comprises the steps of,
s1, acquiring the number of the waybills to be dispatched and the waybill number of each waybill;
s2, acquiring the area information of the receiving address of each waybill and the service attribute corresponding to each waybill according to the waybill number;
s3, scheduling information of the courier is called;
s4, acquiring the resource attribute of the current value courier;
s5, packaging the waybills into task packages;
s6, matching the task attribute in the task package with the resource attribute of the courier;
and S7, dispatching the waybills in the task package to a destination.
The task package in the step S5 is a man-hour package for packaging the task amount in a preset time unit; or, a quantity package packed by quantity.
The waybill dispatch in step S7 needs to acquire a path in the execution process; the acquisition path comprises the following steps:
s71, acquiring basic information of a shipping bill to be dispatched, wherein the basic information comprises a receiving address and ageing attribute information;
s72, acquiring longitude and latitude information corresponding to each receiving address;
s73, calculating the navigation distance of any two shipping addresses according to the longitude and latitude information;
s74, inputting the longitude and latitude information, the navigation distance and the time-efficiency attribute information into a path planning model, and acquiring the dispatch sequence, the expected arrival time and the dispatch route of the waybill.
The aging attribute information in step S71 includes routing information and shift-to-train information.
In step S72, the acquiring latitude and longitude information corresponding to each receiving address includes:
acquiring a receiving address of a historical freight note;
acquiring longitude and latitude information corresponding to the receiving address when the historical freight bill is confirmed, and establishing a corresponding relation between the historical receiving address and the longitude and latitude information;
acquiring the receiving address of the historical shipping bill corresponding to the receiving address of the shipping bill to be dispatched by utilizing text address matching, and acquiring longitude and latitude information of the shipping bill to be dispatched; or,
the acquiring longitude and latitude information corresponding to each receiving address comprises,
and converting the freight address of the freight bill into longitude and latitude information by using a map API.
The navigation distance for any two pick-up addresses is calculated in step S73, including,
and acquiring the navigation distance of any two shipping addresses by using the map API navigation platform or using the self history shipping data.
Before acquiring the longitude and latitude information corresponding to each receiving address in step S72, the method includes grouping the shipping slips to be dispatched according to the network points and the shifts, taking all the shipping slips of each shift of each network point as a group, and acquiring the longitude and latitude of the receiving address of the shipping slips under each group.
Inputting the longitude and latitude information, the navigation distance and the time-efficiency attribute information into a path planning model to obtain the dispatch sequence and the expected arrival time of the waybill, wherein the method comprises the steps of,
generating an initial solution by using an FFD algorithm according to the input longitude and latitude information, the navigation distance and the aging attribute information;
and optimizing the initial solution to obtain an optimized solution.
The optimizing the initial solution to obtain an optimized solution, including,
s101, selecting a task stop point on an initial route;
s102, moving the task stay points to other routes, or generating a temporary route by interacting the task stay points selected by the two routes;
s103, calculating the total route distance of the temporary route, and converting the total route distance into time consumption;
s104, repeating the steps S102-S103, evaluating all the generated temporary routes, and screening out the structure with the least total time consumption as a neighborhood optimal route;
s105, comparing the initial route with the neighborhood optimal route, if the neighborhood optimal route is better than the initial route, replacing the initial route with the neighborhood optimal route to serve as an optimal solution, otherwise, not changing the initial route to serve as the optimal solution.
Before the initial route is not changed as the optimization solution in step S105, further includes,
and stopping searching if the initial solution is not updated within the preset time or the preset comparison times, and taking the initial solution as an optimized solution.
The system for optimizing the matching of the receiving and dispatching task and the resource comprises the following steps:
the information acquisition module is used for acquiring the quantity of the waybills to be dispatched and the waybill numbers of the waybills; acquiring area information of each destination of the freight bill and service attributes corresponding to each freight bill according to the freight bill numbers;
the scheduling module is used for scheduling the scheduling conditions and the scheduling people of the couriers; acquiring the resource attribute of the current value courier;
the task package generation module is used for packaging the waybills into task packages;
the resource matching module is used for matching the task attribute in the task package with the resource attribute of the courier;
the handheld terminal is used for giving a path for executing tasks by the courier.
An apparatus for optimizing matching of a delivery job to a resource, comprising a computer readable medium storing a computer program, the program being operative to perform:
s1, acquiring the number of the waybills to be dispatched and the waybill number of each waybill;
s2, acquiring the area information of the receiving address of each waybill and the service attribute corresponding to each waybill according to the waybill number;
s3, scheduling information of the courier is called;
s4, acquiring the resource attribute of the current value courier;
s5, packaging the waybills into task packages;
s6, matching the task attribute in the task package with the resource attribute of the courier;
and S7, dispatching the waybills in the task package to a destination.
The beneficial effects of the invention are as follows:
1. breaking the regional limit of the courier, and realizing globally optimal task allocation by using the model;
2. the change area is bound with staff, and the task can be matched with the optimal staff or put into a task market to be completed with the assistance of market resources;
3. optimizing the path configuration, and routing by the system instead of staff.
Drawings
FIG. 1 is a method for matching a dispatch task with a resource;
FIG. 2 is a flow chart of a method of optimizing a receipts path;
FIG. 3 is a flow chart of a search algorithm;
FIG. 4 is a route diagram before a task stop is not moved;
FIG. 5 is a roadmap after movement of a task stop;
FIG. 6 is time consuming before the task stop is moved;
the task stop point of fig. 7 is time-consuming after moving.
FIG. 8 is a schematic diagram of the path planning model.
Wherein, A B C D E F Z is the task stop point.
Detailed Description
For a better understanding of the technical solution of the present invention, the present invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the method for optimizing matching of a delivery task and a resource includes the steps of,
s1, acquiring the number of the waybills to be dispatched and the waybill number of each waybill;
s2, acquiring area information of each freight address of the freight bill and service attributes corresponding to each freight bill according to the freight bill numbers; the service attribute refers to the time-effect requirement, weight, value-added service and the like of the manifest and is bound with the express.
S3, scheduling information of the courier is called, wherein the scheduling information comprises scheduling conditions and scheduling people;
s4, acquiring the resource attribute of the current value courier in the scheduling system; the resource attributes mainly include the posts, skills, authorities and the like of the staff, and the staff attributes determine what tasks he can complete. Such as a heavy goods taker, can serve more than 20kg of express mail.
S5, packaging the waybills into task packages; the task attribute is similar to the service attribute and is a service attribute set of the waybill in the task package. For example, more than 20kg of express items are grouped into a task package, which has the property of being heavy.
The task package is a man-hour package packaged according to the task quantity in a preset time unit or an equivalent package packaged according to the quantity. The preset time unit may be a time unit artificially set every hour, every half hour, every two hours, or the like.
The waybills are packed into packets of task amounts per hour (or other set time units) according to man-hour packing.
Packing by quantity, referring to the number of experience values of the dispatch volume of the dispatcher, the waybills in the area are packed into quantity packages (typically equal quantity packages).
S6, matching the task attribute in the task package with the resource attribute of the courier;
and S7, dispatching the waybills in the task package to a destination.
As shown in fig. 2, in step S7, a path needs to be acquired during execution; the acquisition path comprises the following steps:
s71, acquiring basic information of a to-be-dispatched bill, wherein the basic information comprises a receiving address and ageing attribute information, and the ageing attribute information comprises route information and train shift information. The routing information, namely the logistics routing, means the logistics routing, the waybill must be classified according to the mesh point through the logistics routing in the follow-up, and then the waybill number arrived at the same mesh point and the same shift is obtained through the shift information of the vehicle.
S72, longitude and latitude information corresponding to the receiving addresses of the shipping labels is obtained.
Step S72 includes the steps of:
the first is: acquiring a receiving address of a historical freight note;
acquiring longitude and latitude information corresponding to the receiving address when the historical freight bill is confirmed, and establishing a corresponding relation between the historical receiving address and the longitude and latitude information;
and acquiring the receiving address of the historical shipping bill corresponding to the receiving address of the shipping bill to be dispatched by utilizing text address matching, and acquiring the longitude and latitude information of the shipping bill to be dispatched.
The second is: the goods receiving address is a Chinese address, the conversion from the Chinese address to the longitude and latitude can be realized by means of an own integrated system or a data processing platform of a third party (such as a hundred-degree map API, a Gooder map API, a Tencel map API, a Google map API and the like, which can convert the Chinese address to the longitude and latitude information and calculate the distance between different addresses), the self integrated system is taken as an example, the own integrated system stores the Chinese address of a historical freight bill and the longitude and latitude coordinates (collected by a receiving and dispatching person terminal device) when dispatching is successful, and the conversion from the Chinese address to the dispatching longitude and latitude can be accurately obtained by a text address matching technology.
And grabbing a waybill route label according to the route information of the waybill, and classifying the waybill according to the network points and the shifts. All the waybills for each shift at each site are as a group for which task allocation and path planning are performed.
And grabbing a time tag of the waybill according to the routing information of the waybill, wherein the time tag represents the timeliness requirement of the waybill, and the timeliness requirement must be sent before 10:30 am. The time tag is contained in the routing information, and the time tag comprises two kinds of information: the time of arrival at the site is expected to be route generated with the time of the planned latest dispatch, which is generated by the system to be added to the route information.
S73, calculating the navigation distance of any two shipping address by using the navigation service software and the longitude and latitude information in the step S72;
the navigation distance is represented by a distance matrix; the longitude and latitude information can be used for acquiring the navigation distance, navigation services (such as a hundred-degree map API, a Goldmap API, a Tencement map API, a Google map API and the like) provided by a third party can be used for realizing that Chinese addresses are converted into longitude and latitude information and the distance between different addresses can be calculated, or self-history shipping data or self-owned integrated systems are used for acquiring the navigation distance, and then the time distance can be calculated by the navigation distance, wherein the time distance can be empirical data after a large amount of shipping experience, can be time by a database of a third party platform, and can be the ratio between the real-time path length and the shipping speed.
S74, calculating the distribution sequence, the expected arrival time and the distribution route of the bill by using the path planning model. The path planning model is a trained path planning model and is a domain model established according to the problem of the vehicle path with a time window. The schematic diagram is shown in figure 8.
The time window vehicle path problem (vehicle routing problems with time windows, VRPTW) the Vehicle Route Problem (VRP) was first proposed by Dantzig and Ramser in 1959, which refers to a number of customers, each having a different number of cargo demands, the distribution center providing cargo to customers, a fleet of vehicles responsible for distributing the cargo, organizing the appropriate driving route, the goal being to allow the customer's demands to be satisfied, and to achieve goals such as shortest distance, minimum cost, minimum time consumption, etc., under certain constraints.
The calculation process of the path planning model in step S74 includes the steps of:
s741, inputting longitude and latitude information of the receiving addresses of all the shipping labels under each group, the time labels and the calculation result in the step S73 into a path planning model, and generating an initial solution by using an FFD algorithm according to preset screening conditions.
FFD (FirstFitDecreasing) is a typical heuristic greedy algorithm in the boxing problem, the basic idea being to order the items from large to small and then sequentially load the items from front to back into the box that can hold the item first.
FFD algorithm example: assuming A, B, C, D, E dwell points, each dwell point takes 30, 20, 10, 5, 3, respectively, and each line takes 46 at maximum.
Step 1: the time-consuming maximum dwell point a is selected.
Step 2: the most time-consuming dwell point B of the remaining dwell points is selected, and a and C are combined to one line.
Step 3: a+b takes more than the maximum 46, B is routed to the next line.
Step 4: the most time consuming C of the unselected dwell points is selected,
a and C are combined into one line. A+C takes less than 40 times than the maximum 46 times.
Step 5: the most time consuming D of the unselected dwell points is selected,
a+c and D are combined into one line. The time consumption of a+c+d is 45 less than the maximum time consumption of 46.
Step 6: the most time consuming E of the unselected dwell points is selected,
a+c+d and E are combined into one line. The time consumption of a+c+d+e is 48 greater than the maximum time consumption 46. Then E is routed to the next line.
Step 7: the above calculations are repeated for the unselected dwell points, with the end result producing two initial lines, a+c+d and b+e.
S742, optimizing the initial solution by utilizing a search algorithm to obtain an optimized result; and (3) performing field Searching by using a Tabu search algorithm (Tabu search), an alternative scheme delay Acceptance algorithm (Late Acceptance) and a simulated annealing algorithm (Simulated Annealing), wherein the constraint condition is that the time label of a waybill must be sent before, the goal is that the total time consumption of a task is minimum, and the end condition is that a better solution does not appear in a certain step number. The number of steps is configurable and can be determined according to the number of addresses and the accuracy requirement of the result.
The three algorithms (Tabu Searching algorithm, alternative delay Acceptance algorithm (Late Acceptance) and simulated annealing algorithm (Simulated Annealing)) are re-calculated for the initial variables; the tabu search algorithm can be independently operated; so-called neighborhood search (this kind of algorithm) starts from an initial solution (or a group of solutions), generates a neighborhood of the solution through a neighborhood function, searches for a more optimal solution in the neighborhood to replace the current solution, and realizes the solution through a continuous iterative process.
As shown in fig. 3, the specific process of the search algorithm includes the steps of:
s101, selecting a task stop point on an initial route; the task stop points are selected randomly at present, but the searching and converging speeds can be improved through reinforcement learning dynamic selection strategies. The initial route is the route corresponding to the initial solution.
S102, selecting a neighborhood movement scheme: the temporary route is generated by moving the task stop point described in step 101 into one route or by interacting task stop points of two routes. In this step, the initial route is the initial solution in step S741. As shown in fig. 4 to 5, fig. 4 is a route before the movement of the task stop point a, and fig. 5 is a route after the movement of the task stop point a.
S103, evaluating a neighborhood movement scheme: calculating the total route distance of the temporary route in the step S102; and converts the total route distance into time-consuming.
S104, repeating the steps 102-103 until all possible neighborhood movement schemes of the task stop points in the step S101 are evaluated, and evaluating all the generated temporary routes.
Fig. 6 and 7 are time consuming in different dispatch sequences, respectively. Wherein, fig. 6 is: a→b→c→d, with total time consumption of 50+30+30+40+50=200 min. Fig. 7 shows a→c→b→d, with a total time consumption of 50+30+30+30+50=190 min.
And (3) screening out the structure with the least total time consumption from the evaluation result in the step S104, and taking the structure as a neighborhood optimal route to be a neighborhood optimal solution.
S105, comparing the initial solution (initial route) in the step S741 with the neighborhood optimal solution (optimal route) obtained in the step S104, if the neighborhood optimal solution is better than the initial solution, replacing the initial solution with the neighborhood optimal solution, otherwise, the initial solution is not changed;
repeating the steps until the algorithm runs for a preset time or the preset search cycle times are circulated, and the initial solution is not updated, stopping searching, and taking the current initial solution as an optimization result.
And S743, giving a dispatch route according to the optimization result.
The invention also includes: the result given by the path planning model is displayed through a terminal APP of a dispatcher, and the dispatcher dispatches according to the display result of the terminal APP. The results include the order of dispatch of the waybill, the expected arrival time, and the recommended route.
Fig. 8 shows the structure of a path planning model, where x represents a plurality, 1 represents 1, and 0..1 represents the presence or absence. Arrival time = departure time from last stop point + travel time; departure time = Max (arrival time, ready time) +service duration; next customer = reversal of last stop point; the dispatcher = boolean value (whether this time is of the same class as the dispatcher set): the last stop receives the dispatcher.
The system for optimizing the matching of the receiving and dispatching task and the resource comprises the following steps:
the information acquisition module is used for acquiring the quantity of the waybills to be dispatched and the waybill numbers of the waybills; acquiring area information of each destination of the freight bill and service attributes corresponding to each freight bill according to the freight bill numbers;
the scheduling module is used for scheduling the scheduling conditions and the scheduling people of the couriers; acquiring the resource attribute of the current value courier;
the task package generation module is used for packaging the waybills into task packages;
the resource matching module is used for matching the task attribute in the task package with the resource attribute of the courier;
the handheld terminal is used for giving a path for executing tasks by the courier.
An apparatus for optimizing matching of a delivery job to a resource, comprising a computer readable medium storing a computer program, the program being operative to perform:
s1, acquiring the number of the waybills to be dispatched and the waybill number of each waybill;
s2, acquiring the area information of the receiving address of each waybill and the service attribute corresponding to each waybill according to the waybill number;
s3, scheduling information of the courier is called;
s4, acquiring the resource attribute of the current value courier;
s5, packaging the waybills into task packages;
s6, matching the task attribute in the task package with the resource attribute of the courier;
and S7, dispatching the waybills in the task package to a destination.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (10)
1. The method for optimizing the matching of the receiving and dispatching task and the resource is characterized by comprising the steps of,
s1, acquiring the number of the waybills to be dispatched and the waybill number of each waybill;
s2, acquiring the area information of the receiving address of each waybill and the service attribute corresponding to each waybill according to the waybill number; the service attributes comprise ageing requirements, weight and value-added service of the waybill;
s3, scheduling information of the courier is called;
s4, acquiring the resource attribute of the current value courier; the resource attribute comprises posts, skills and rights of the courier;
s5, packaging the waybills into task packages; the task attribute of the task package is the set of the business attributes of the waybills in the task package;
s6, matching the task attribute in the task package with the resource attribute of the courier;
s7, dispatching the waybills in the task package to a destination;
the step of obtaining a path is needed before the waybill in the task package is dispatched to a destination, and the step of obtaining the path comprises the following steps:
s71, acquiring basic information of a to-be-dispatched bill in the task package, wherein the basic information comprises a receiving address and aging attribute information; the aging attribute information comprises route information and train shift information; generating different waybill groups according to the routing information and the shift information; each waybill group is a set of the to-be-dispatched waybills of the same train arriving at the same website; the routing information is a logistics line arrangement reaching each website; the shift information is the shift of the vehicles reaching each website;
s72, acquiring longitude and latitude information corresponding to the receiving address of each to-be-dispatched bill in each bill group;
s73, calculating the navigation distance of any two shipping addresses according to the longitude and latitude information;
s74, inputting the longitude and latitude information, the navigation distance and the time-efficiency attribute information into a path planning model, and acquiring the dispatch sequence, the expected arrival time and the dispatch route of the waybill.
2. The method for matching a receiving and dispatching task with a resource according to claim 1, wherein the task package in step S5 is a man-hour package packaged according to a task amount in a preset time unit; or, a quantity package packed by quantity.
3. The method for optimizing matching between a delivery task and a resource according to claim 1, wherein the acquiring latitude and longitude information corresponding to each delivery address in step S72 includes:
acquiring a receiving address of a historical freight note;
acquiring longitude and latitude information corresponding to the receiving address when the historical freight bill is confirmed, and establishing a corresponding relation between the historical receiving address and the longitude and latitude information;
acquiring the receiving address of the historical shipping bill corresponding to the receiving address of the shipping bill to be dispatched by utilizing text address matching, and acquiring longitude and latitude information of the shipping bill to be dispatched; or,
the acquiring longitude and latitude information corresponding to each receiving address comprises,
and converting the freight address of the freight bill into longitude and latitude information by using a map API.
4. The method for optimizing delivery tasks and resource matching as claimed in claim 1, wherein the calculating the navigation distance of any two-order delivery addresses in step S73 comprises,
and acquiring the navigation distance of any two shipping addresses by using the map API navigation platform or using the self history shipping data.
5. The method for optimizing delivery task and resource matching according to claim 1, wherein before the acquiring longitude and latitude information corresponding to each delivery address in step S72 includes grouping the to-be-delivered bills according to the net points and the train shifts, and acquiring the longitude and latitude of the delivery address under each group by taking all the bills of each shift of each net point as a group.
6. The method for optimizing delivery tasks and resources according to claim 4, wherein said inputting said latitude and longitude information, navigation distance and time-efficient attribute information into a path planning model, obtaining a delivery order and expected arrival time of a waybill comprises,
generating an initial solution by using an FFD algorithm according to the input longitude and latitude information, the navigation distance and the aging attribute information;
and optimizing the initial solution to obtain an optimized solution.
7. The method for optimizing delivery tasks and resource matching of claim 6,
the optimizing the initial solution to obtain an optimized solution, including,
s101, selecting a task stop point on an initial route;
s102, moving the task stay points to other routes, or generating a temporary route by interacting the task stay points selected by the two routes;
s103, calculating the total route distance of the temporary route, and converting the total route distance into time consumption;
s104, repeating the steps S102-S103, evaluating all the generated temporary routes, and screening out the structure with the least total time consumption as a neighborhood optimal route;
s105, comparing the initial route with the neighborhood optimal route, if the neighborhood optimal route is better than the initial route, replacing the initial route with the neighborhood optimal route to serve as an optimal solution, otherwise, not changing the initial route to serve as the optimal solution.
8. The method for optimizing delivery tasks and resource matching of claim 7,
before the initial route is not changed as the optimization solution in step S105, further includes,
and stopping searching if the initial solution is not updated within the preset time or the preset comparison times, and taking the initial solution as an optimized solution.
9. The system for optimizing the matching of the receiving and dispatching task and the resource is characterized by comprising the following steps:
the information acquisition module is used for acquiring the quantity of the waybills to be dispatched and the waybill numbers of the waybills; acquiring area information of each destination of the freight bill and service attributes corresponding to each freight bill according to the freight bill numbers; the service attributes comprise ageing requirements, weight and value-added service of the waybill;
the scheduling module is used for scheduling the scheduling conditions and the scheduling people of the couriers; acquiring the resource attribute of the current value courier; the resource attribute comprises posts, skills and rights of the courier;
the task package generation module is used for packaging the waybills into task packages; the task attribute of the task package is the set of the business attributes of the waybills in the task package;
the resource matching module is used for matching the task attribute in the task package with the resource attribute of the courier;
the handheld terminal is used for giving a path for the courier to execute the task;
the system for optimizing the matching of the receiving and dispatching task and the resource is further used for acquiring a path before giving the path of the courier executing the task, and the acquiring path comprises the following steps:
acquiring basic information of a to-be-dispatched bill in the task package, wherein the basic information comprises a receiving address and ageing attribute information; the aging attribute information comprises route information and train shift information; generating different waybill groups according to the routing information and the shift information; each waybill group is a set of the to-be-dispatched waybills of the same train arriving at the same website; the routing information is a logistics line arrangement reaching each website; the shift information is the shift of the vehicles reaching each website;
acquiring longitude and latitude information corresponding to the receiving address of each to-be-dispatched bill in each bill group;
calculating the navigation distance of any two shipping addresses according to the longitude and latitude information;
and inputting the longitude and latitude information, the navigation distance and the time-efficiency attribute information into a path planning model, and acquiring the dispatch sequence, the expected arrival time and the dispatch route of the waybill.
10. An apparatus for optimizing matching of delivery tasks to resources, comprising a computer readable medium storing a computer program, the program being operative to perform:
s1, acquiring the number of the waybills to be dispatched and the waybill number of each waybill;
s2, acquiring the area information of the receiving address of each waybill and the service attribute corresponding to each waybill according to the waybill number; the service attributes comprise ageing requirements, weight and value-added service of the waybill;
s3, scheduling information of the courier is called;
s4, acquiring the resource attribute of the current value courier; the resource attribute comprises posts, skills and rights of the courier;
s5, packaging the waybills into task packages; the task attribute of the task package is the set of the business attributes of the waybills in the task package;
s6, matching the task attribute in the task package with the resource attribute of the courier;
s7, dispatching the waybills in the task package to a destination;
the acquiring path is needed before the waybill in the task package is dispatched to the destination, and the acquiring path comprises:
s71, acquiring basic information of a to-be-dispatched bill in the task package, wherein the basic information comprises a receiving address and aging attribute information; the aging attribute information comprises route information and train shift information; generating different waybill groups according to the routing information and the shift information; each waybill group is a set of the to-be-dispatched waybills of the same train arriving at the same website; the routing information is a logistics line arrangement reaching each website; the shift information is the shift of the vehicles reaching each website;
s72, acquiring longitude and latitude information corresponding to the receiving address of each to-be-dispatched bill in each bill group;
s73, calculating the navigation distance of any two shipping addresses according to the longitude and latitude information;
s74, inputting the longitude and latitude information, the navigation distance and the time-efficiency attribute information into a path planning model, and acquiring the dispatch sequence, the expected arrival time and the dispatch route of the waybill.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710997353.7A CN109697524B (en) | 2017-10-23 | 2017-10-23 | Method, system and equipment for optimizing matching of receiving and dispatching task and resource |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710997353.7A CN109697524B (en) | 2017-10-23 | 2017-10-23 | Method, system and equipment for optimizing matching of receiving and dispatching task and resource |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109697524A CN109697524A (en) | 2019-04-30 |
| CN109697524B true CN109697524B (en) | 2024-02-27 |
Family
ID=66226931
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710997353.7A Active CN109697524B (en) | 2017-10-23 | 2017-10-23 | Method, system and equipment for optimizing matching of receiving and dispatching task and resource |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109697524B (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111915119A (en) * | 2019-05-08 | 2020-11-10 | 顺丰科技有限公司 | Task distribution method and device |
| CN110458429A (en) * | 2019-07-29 | 2019-11-15 | 暨南大学 | A kind of intelligent task distribution and personal scheduling method, system for geographical site |
| CN112445804B (en) * | 2019-08-28 | 2024-05-17 | 北京京东振世信息技术有限公司 | Method and device for adjusting waybill configuration parameters |
| CN111256718B (en) * | 2020-02-12 | 2023-04-18 | 上海东普信息科技有限公司 | Dispatching route planning method, device, system, equipment and storage medium |
| CN111325504B (en) * | 2020-02-12 | 2023-07-11 | 上海东普信息科技有限公司 | Method, device, system, equipment and storage medium for recommending dispatch track |
| CN111311152B (en) * | 2020-02-12 | 2023-05-12 | 上海东普信息科技有限公司 | Method, device, system, equipment and storage medium for recommending dispatch route |
| CN112132500B (en) * | 2020-04-14 | 2024-04-26 | 上海寻梦信息技术有限公司 | Method for constructing dispatch fence, method for identifying super dispatch list and related equipment |
| CN111523720A (en) * | 2020-04-17 | 2020-08-11 | 上海中通吉网络技术有限公司 | Method, device and equipment for planning tail end dispatch path and storage medium |
| CN111861104A (en) * | 2020-06-05 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Service distribution method and device and electronic equipment |
| CN114189531A (en) * | 2020-08-25 | 2022-03-15 | 顺丰科技有限公司 | Method and device for monitoring receiving and dispatching pieces, server and storage medium |
| CN112541637A (en) * | 2020-12-21 | 2021-03-23 | 济南浪潮高新科技投资发展有限公司 | Method and system for intelligent logistics dispatching route |
| CN112766859A (en) * | 2021-01-28 | 2021-05-07 | 深圳市跨越新科技有限公司 | Waybill grouping method, system, terminal and storage medium based on road segmentation |
| CN112926924B (en) * | 2021-03-25 | 2023-07-25 | 平安科技（深圳）有限公司 | Information processing method, device, electronic equipment and storage medium |
| CN113104600A (en) * | 2021-03-29 | 2021-07-13 | 长城汽车股份有限公司 | Express delivery subpackaging method, container loading and unloading method, corresponding system and medium |
| CN113111117B (en) * | 2021-05-13 | 2024-08-13 | 上海寻梦信息技术有限公司 | Method, system, equipment and storage medium for displaying destination address based on map |
| CN114971490A (en) * | 2022-06-20 | 2022-08-30 | 上海东普信息科技有限公司 | Cross-region pickup monitoring method, device, equipment and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004238192A (en) * | 2003-02-10 | 2004-08-26 | Hitachi Ltd | Method and system for optimum shipment management by rolling vehicle allocation and meta-transportation cost |
| CN102467703A (en) * | 2010-11-10 | 2012-05-23 | 北京天德世通科技发展有限公司 | Logistics management method, equipment and system based on cloud computing |
| CN103473659A (en) * | 2013-08-27 | 2013-12-25 | 西北工业大学 | Dynamic optimal distribution method for logistics tasks based on distribution vehicle end real-time state information drive |
| CN103839199A (en) * | 2014-03-18 | 2014-06-04 | 北京倍得力商务服务有限公司 | Resource scheduling system and method |
| CN106408174A (en) * | 2016-08-31 | 2017-02-15 | 岳占峰 | Logistics information processing method and device |
| CN106600057A (en) * | 2016-12-13 | 2017-04-26 | 品骏控股有限公司 | Express distribution task scheduling algorithm and apparatus |
| CN106845732A (en) * | 2017-02-20 | 2017-06-13 | 浙江大学 | A kind of satellite-type logistics express delivery allocator |
| CN106871916A (en) * | 2017-01-19 | 2017-06-20 | 华南理工大学 | Method is sent in a kind of express delivery based on independent navigation with charge free |
| CN107230014A (en) * | 2017-05-15 | 2017-10-03 | 浙江仟和网络科技有限公司 | A kind of intelligent dispatching system of the instant logistics in end |
| CN107274033A (en) * | 2017-06-29 | 2017-10-20 | 安徽电信规划设计有限责任公司 | A kind of easy-to-use Parts supply method for optimizing route |
-
2017
- 2017-10-23 CN CN201710997353.7A patent/CN109697524B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004238192A (en) * | 2003-02-10 | 2004-08-26 | Hitachi Ltd | Method and system for optimum shipment management by rolling vehicle allocation and meta-transportation cost |
| CN102467703A (en) * | 2010-11-10 | 2012-05-23 | 北京天德世通科技发展有限公司 | Logistics management method, equipment and system based on cloud computing |
| CN103473659A (en) * | 2013-08-27 | 2013-12-25 | 西北工业大学 | Dynamic optimal distribution method for logistics tasks based on distribution vehicle end real-time state information drive |
| CN103839199A (en) * | 2014-03-18 | 2014-06-04 | 北京倍得力商务服务有限公司 | Resource scheduling system and method |
| CN106408174A (en) * | 2016-08-31 | 2017-02-15 | 岳占峰 | Logistics information processing method and device |
| CN106600057A (en) * | 2016-12-13 | 2017-04-26 | 品骏控股有限公司 | Express distribution task scheduling algorithm and apparatus |
| CN106871916A (en) * | 2017-01-19 | 2017-06-20 | 华南理工大学 | Method is sent in a kind of express delivery based on independent navigation with charge free |
| CN106845732A (en) * | 2017-02-20 | 2017-06-13 | 浙江大学 | A kind of satellite-type logistics express delivery allocator |
| CN107230014A (en) * | 2017-05-15 | 2017-10-03 | 浙江仟和网络科技有限公司 | A kind of intelligent dispatching system of the instant logistics in end |
| CN107274033A (en) * | 2017-06-29 | 2017-10-20 | 安徽电信规划设计有限责任公司 | A kind of easy-to-use Parts supply method for optimizing route |
Non-Patent Citations (1)
| Title |
|---|
| 基于多蚁群并行优化的网络路径规划研究;黄泽汉等;《计算机工程与科学》;20110915(第09期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109697524A (en) | 2019-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109697524B (en) | Method, system and equipment for optimizing matching of receiving and dispatching task and resource | |
| CN109697523A (en) | The method, system and equipment for sending part path are received in optimization | |
| CN111461624A (en) | Logistics line planning method, device, equipment and storage medium | |
| Pina-Pardo et al. | The traveling salesman problem with release dates and drone resupply | |
| Trebuna et al. | Digital value stream mapping using the tecnomatix plant simulation software | |
| US6571213B1 (en) | Router utility for a parcel shipping system | |
| CN109784820A (en) | A kind of express delivery routing intelligence solution, device, terminal device and storage medium | |
| CA2704601C (en) | Enhanced postal data modeling framework | |
| Repoussis et al. | A web-based decision support system for waste lube oils collection and recycling | |
| Crainic et al. | Service network design | |
| Jiang et al. | Picking-replenishment synchronization for robotic forward-reserve warehouses | |
| US20120158608A1 (en) | Fleet dispatch plan optimization | |
| Crainic | A survey of optimization models for long-haul freight transportation | |
| Bräysy et al. | Chapter 12: Software tools and emerging technologies for vehicle routing and intermodal transportation | |
| CN113780956B (en) | Logistics freight generation method, device, equipment and storage medium | |
| CN109978420A (en) | A kind of logistics and distribution management scheduling system | |
| CN112308280A (en) | Logistics scheduling management method and device, electronic equipment and storage medium | |
| CN113554386A (en) | Logistics distribution method, device, equipment and storage medium suitable for e-commerce orders | |
| Wolfenburg | New version of the BBS method and its usage for determining and scheduling vehicle routes | |
| Garn et al. | Smoothly Pass the Parcel: Implementing the Theory of Swift, Even Flow | |
| Drexl | A generic heuristic for vehicle routing problems with multiple synchronization constraints | |
| Rouzafzoon et al. | Developing logistics and supply chain management by using agent-based simulation | |
| PIERZCHALA et al. | Agent-Based Simulation Framework for Optimization of Deliveries in A Parcel Distribution System | |
| Chopra | Design of Lean Manufacturing with the Use of Discrete Event Simulation | |
| Adnan et al. | Information Real-Time Delivery (IRTD) System through Internet of Things (IoT): An Improvement in Truck Management Environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |