CN113761458B - Logistics carrier selection scheme generation method and device, cargo hold reservation method and system - Google Patents
Logistics carrier selection scheme generation method and device, cargo hold reservation method and system Download PDFInfo
- Publication number
- CN113761458B CN113761458B CN202110278204.1A CN202110278204A CN113761458B CN 113761458 B CN113761458 B CN 113761458B CN 202110278204 A CN202110278204 A CN 202110278204A CN 113761458 B CN113761458 B CN 113761458B
- Authority
- CN
- China
- Prior art keywords
- logistics
- cargo hold
- carriers
- logistics carrier
- amount
- 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 66
- 239000000969 carrier Substances 0.000 claims abstract description 82
- 230000006870 function Effects 0.000 claims description 47
- 238000004590 computer program Methods 0.000 claims description 10
- 238000012886 linear function Methods 0.000 claims description 8
- 230000032683 aging Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 3
- 230000007423 decrease Effects 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 17
- 230000010006 flight Effects 0.000 description 13
- 239000000203 mixture Substances 0.000 description 8
- 238000009472 formulation Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 238000013480 data collection Methods 0.000 description 5
- 101150082690 Pou3f1 gene Proteins 0.000 description 3
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 244000179970 Monarda didyma Species 0.000 description 1
- 235000010672 Monarda didyma Nutrition 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012887 quadratic function Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- 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
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Mathematical Physics (AREA)
- Operations Research (AREA)
- Mathematical Analysis (AREA)
- Marketing (AREA)
- Quality & Reliability (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Development Economics (AREA)
- Computational Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Mathematical Optimization (AREA)
- Tourism & Hospitality (AREA)
- Pure & Applied Mathematics (AREA)
- Algebra (AREA)
- Game Theory and Decision Science (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The disclosure provides a method and a device for generating a logistics carrier selection scheme, a method and a system for reserving a cargo hold, and relates to the technical field of logistics. The method for generating the logistics carrier selection scheme comprises the following steps: acquiring the total shipping volume, shipping time limit, source address and destination address of the article; determining a logistics carrier of which the origin, the destination and the sailing date are respectively matched with the source address, the destination address and the shipping time limit, and acquiring parameter information of the logistics carrier; and determining a set of selected logistics carriers and the amount of cargo space occupied by each logistics carrier in the set respectively by a solver operated by a computer based on the constraint condition and the objective function according to the total shipping amount, the shipping time limit and the parameter information of the logistics carriers. By the method, the making efficiency of the logistics carrier selection scheme can be improved, and the made logistics carrier selection scheme is optimized.
Description
Technical Field
The disclosure relates to the technical field of logistics, in particular to a method and a device for generating a logistics carrier selection scheme, and a method and a system for reserving a cargo hold.
Background
In the field of logistics, high efficiency and service are important factors in supporting enterprise development. As far as the remote transport of items is concerned, conventional logistics carriers may include airplanes, ships, trains, trucks, etc., where each of the airplanes, ships and trains has an independent operator, and when such vehicles are used to carry cargo, ordering at the corresponding operator is required. By reserving proper flights, trains or cargo ships, the cargo can be transported efficiently, and the order time limit requirement is met.
Taking flight reservation as an example, currently, when selecting a transport flight, a worker needs to schedule the traffic of each flight one to two days in advance and order the cargo hold from the airline company according to the predicted performance age of each lot of goods and the flight take-off and landing time.
Disclosure of Invention
An object of the present disclosure is to improve the efficiency of formulation of a logistics carrier selection scheme and optimize the logistics carrier selection scheme.
According to an aspect of some embodiments of the present disclosure, a method for generating a logistics carrier selection scheme is provided, including: acquiring the total shipping volume, shipping time limit, source address and destination address of the article; determining a logistics carrier of which the origin, the destination and the sailing date are respectively matched with the source address, the destination address and the shipping time limit, and acquiring parameter information of the logistics carrier; determining a set of selected logistics carriers and the amount of cargo hold each logistics carrier occupies in the set respectively by a solver operated by a computer based on constraint conditions and objective functions according to the total shipping amount, the shipping time limit and parameter information of the logistics carriers; wherein the objective function includes: the sum of costs is minimized when each logistics carrier in the collection orders a corresponding hold amount.
In some embodiments, the logistics carrier comprises at least one of a ship, train, or plane for each shift.
In some embodiments, determining a logistics carrier whose origin, destination and date of voyage match the source address, destination address and time of shipment limit, respectively, and obtaining parameter information for the logistics carrier comprises: searching matched logistics carriers according to the delivery time limit, the source address and the destination address through a data interface between the logistics carriers and operators; and extracting the searched parameter information of the logistics carrier.
In some embodiments, the constraints include: the originating sites of the selected logistics carriers are the same; the sum of the cargo hold amounts occupied by the logistics carriers in the collection is not less than the total shipping amount of the articles; the time from the article to the starting site of the logistics carrier in the delivery time limit is not later than the goods collection cut-off time of the logistics carrier; the performance aging of the articles in the delivery time limit is not earlier than the aging of the logistics carrier to the destination address; and the occupied cargo hold amount of each logistics carrier is smaller than or equal to the residual carrying capacity of the corresponding logistics carrier.
In some embodiments, the constraints further include: the ratio of the sum of the cargo hold amounts occupied by the logistics carriers belonging to the priority logistics carriers in the collection to the total shipping amount is equal to or greater than a predetermined ratio.
In some embodiments, the objective function is represented by a linear function that varies with cargo usage at the cost of each cargo order; determining, by a solver operated by a computer, a collection of selected logistics carriers based on constraint conditions and objective functions based on the total shipping volume, shipping time limit, and parameter information of the logistics carriers, and the amount of cargo space each logistics carrier in the collection occupies separately comprises: and (3) processing the linear function and the constraint condition by utilizing a solver of the mixed integer linear problem, and determining a selected collection of logistics carriers and the cargo hold quantity occupied by each logistics carrier in the collection.
In some embodiments, the objective function includes: for each logistics carrier in the collection, respectively obtaining the sum of products of the cargo hold using amount of each cargo hold ordering cost and the corresponding cargo hold ordering cost to be used as a single logistics carrier cost; determining a cost sum according to the single-logistics carrier cost of each logistics carrier in the collection, and minimizing the cost sum; constraints also include: the cargo hold using amount of the cargo hold ordering cost is smaller than or equal to the cargo hold amount occupied by the logistics carriers; the cargo hold using amount of the cargo hold ordering cost is smaller than or equal to the product of a preset constant and an ordering cost using mark, wherein the preset constant is not smaller than the cargo hold amount occupied by the logistics carriers, the ordering cost using mark of the cargo hold ordering cost corresponding to the cargo hold amount is 1 for each logistics carrier, and the ordering cost using marks of other cargo hold ordering costs are 0; the cargo hold usage amount of the cargo hold ordering cost is greater than or equal to a sum of a product of the predetermined constant and the ordering cost usage identifier and the cargo hold amount occupied by the logistics carrier minus the predetermined constant; and the cargo hold using amount of the cargo hold ordering cost is more than or equal to 0 and less than or equal to the cargo hold amount occupied by the logistics carriers.
In some embodiments, the shipping bay ordering unit price of the logistics carriers decreases stepwise with increasing amount of shipping bay ordered; constraints also include: the sum of the ordering cost using identifiers of the single logistics carriers is 1; the cargo hold amount corresponding to the logistics carriers is equal to or greater than the product of the ordering unit price use identifier and the lower limit of the cargo hold amount corresponding to the cargo hold ordering unit price, and is equal to or less than the product of the ordering unit price use identifier and the upper limit of the cargo hold amount corresponding to the cargo hold ordering unit price.
In some embodiments, obtaining the total shipping volume, shipping time limit, source address, and destination address of the item comprises: predicting a total shipping volume of the item between the source address and the destination address after a predetermined length of time; the shipping time limit is determined based on the predetermined length of time.
In some embodiments, the method of generating a logistics carrier selection scheme further comprises: the cargo holds of the logistics carriers are predetermined according to the collection of logistics carriers and the amount of cargo holds each logistics carrier occupies in the collection.
In some embodiments, determining a logistics carrier having an origin, destination and date of voyage that match the source address, destination address and time of shipment limit, respectively, comprises: acquiring an originating site set of the logistics carrier according to the source address; acquiring a destination site set of the logistics carrier according to the destination address; and screening the logistics carriers which accord with the originating site set and the destination site set and are matched with the delivery time limit in travel time.
By the method, the logistics carrier selection scheme meeting the constraint conditions can be obtained through data collection and matching and objective function operation by calculation and execution, so that the formulation efficiency of the logistics carrier selection scheme is improved, and the formulated logistics carrier selection scheme is optimized.
According to an aspect of some embodiments of the present disclosure, there is provided a cargo hold reservation method including any one of the above logistics carrier selection scheme generating methods; and a collection of the logistics carriers generated according to the logistics carrier selection scheme generation method, and a cargo hold of the logistics carrier is reserved for each cargo hold amount occupied by each logistics carrier in the collection.
By the method, the making efficiency of the logistics carrier selection scheme can be improved, the made logistics carrier selection scheme is optimized, and the preset efficiency and rationality of the cargo hold are improved.
According to an aspect of some embodiments of the present disclosure, there is provided a logistics carrier selection scheme generating apparatus, including: an item information acquisition unit configured to acquire a total shipping volume, a shipping time limit, a source address, and a destination address of an item; a carrier information acquisition unit configured to determine a logistics carrier whose origin, destination and travel date are respectively matched with the source address, destination address and travel time limit, and acquire parameter information of the logistics carrier; a scenario generation unit configured to determine, based on the constraint condition and the objective function, a set of selected logistics carriers and a cargo hold amount respectively occupied by each logistics carrier in the set by a solver operated by a computer according to the total shipping amount, the shipping time limit, and parameter information of the logistics carriers; wherein the objective function includes: the sum of costs is minimized when each logistics carrier in the collection orders a corresponding hold amount.
In some embodiments, the logistic carrier selection scheme generating device further comprises: a reservation unit configured to reserve cargo holds of the logistics carriers according to the collection of the logistics carriers and the amount of cargo holds respectively occupied for each logistics carrier in the collection.
According to an aspect of some embodiments of the present disclosure, there is provided a logistics carrier selection scheme generating apparatus, including: a memory; and a processor coupled to the memory, the processor configured to perform any one of the logistics carrier selection scheme generation methods described above based on instructions stored in the memory.
The device can obtain the logistics carrier selection scheme meeting the constraint conditions through data collection and matching and objective function operation by calculation and execution, thereby improving the formulation efficiency of the logistics carrier selection scheme and optimizing the formulated logistics carrier selection scheme.
According to an aspect of some embodiments of the present disclosure, there is provided a cargo hold reservation system including any one of the above logistics carrier selection scheme generating devices; and a reservation unit configured to reserve cargo holds of the logistics carriers according to the collection of the logistics carriers and the amount of cargo holds respectively occupied for each logistics carrier in the collection.
The system can improve the making efficiency of the logistics carrier selection scheme, optimize the made logistics carrier selection scheme and improve the preset efficiency and rationality of the cargo hold.
According to an aspect of some embodiments of the present disclosure, a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of any of the logistics carrier selection scheme generation methods as described above is presented.
By executing the instructions on the storage medium, the logistics carrier selection scheme meeting the constraint conditions can be obtained through data collection and matching and objective function operation of calculation and execution, so that the formulation efficiency of the logistics carrier selection scheme is improved, and the formulated logistics carrier selection scheme is optimized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the present disclosure, and together with the description serve to explain the present disclosure. In the drawings:
fig. 1 is a flow chart of some embodiments of a method of generating a logistics carrier selection scheme of the present disclosure.
Fig. 2A is a flow chart of further embodiments of a method of generating a logistics carrier selection scheme of the present disclosure.
FIG. 2B is a flow chart of some embodiments of the cargo hold reservation method of the present disclosure.
Fig. 3 is a schematic diagram of some embodiments of logistics carrier site selection in a logistics carrier selection scheme generation method of the present disclosure.
Fig. 4 is a schematic diagram of some embodiments of shipping volume and flight take-off and landing in a method of generating a logistics carrier selection scheme of the present disclosure.
Fig. 5 is a schematic diagram of some embodiments of discounted prices in the logistics carrier selection scheme generation method of the present disclosure.
Fig. 6 is a schematic diagram of some embodiments of a logistics carrier selection scheme generating apparatus of the present disclosure.
Fig. 7 is a schematic diagram of further embodiments of a logistics carrier selection scheme generating apparatus of the present disclosure.
Fig. 8 is a schematic diagram of yet further embodiments of a logistics carrier selection scheme generating apparatus of the present disclosure.
FIG. 9 is a schematic diagram of some embodiments of the cargo hold reservation system of the present disclosure.
Detailed Description
The technical scheme of the present disclosure is described in further detail below through the accompanying drawings and examples.
The inventor finds that when the logistics carrier in the related technology is selected, express items meet the performance aging as far as possible, but due to the influence of high complexity, insufficient residual carrying capacity and the like, an optimal selection scheme is difficult to obtain in the aspects of transportation efficiency, price and the like; the strategy of coordination and configuration by operators such as airlines is difficult to obtain an optimal selection scheme, the controllable degree of logistics enterprises on the logistics carrier selection scheme is reduced, and the scheme making efficiency is reduced.
For convenience of description, the following takes the logistics carrier as an airplane as an example, and the logistics carrier selection scheme is a scattered navigation ordering scheme as an example.
A flow chart of some embodiments of the logistics carrier selection scheme generation method of the present disclosure is shown in fig. 1.
In step 101, the total shipping volume, shipping time limit, source address, and destination address of the item are obtained. In some embodiments, the order information may be summarized, and for each combination of items (source address, destination address), the same order is obtained for both the source address and the destination address, and the shipping time is limited, and the shipping volume of the order is counted to obtain the total shipping volume. In other embodiments, for each combination of items (source address, destination address), the total shipping volume for the time interval corresponding to the shipping time limit may be estimated based on the historical shipping volume.
In some embodiments, the source address and the destination address may be at granularity of a city, or may be at granularity of a plurality of cities, such as a region within a predetermined range. In some embodiments, the granularity of the source address and the destination address may be different from region to region, and the granularity may be determined according to the number of airports and the deployment location of the corresponding region. For example, when a city corresponding to a source address (or a destination address) includes a plurality of airports, then the granularity of the source address is a city; when there is no airport in the city corresponding to the source address, the granularity of the source address is a set of a plurality of cities, and the set at least comprises one airport.
In some embodiments, the shipment time limit may include earliest shipment time, performance age (i.e., latest arrival time).
In step 102, a logistics carrier with origin, destination and navigation date matched with the source address, destination address and shipping time limit is determined, and parameter information of the logistics carrier is obtained.
In some embodiments, the matched logistics carriers can be searched according to the delivery time limit, the source address and the destination address through a data interface with an airline company, and then the parameter information of the searched logistics carriers is extracted.
In step 103, determining, by a solver operated by a computer, a set of selected logistics carriers and the amount of cargo space each logistics carrier occupies in the set, respectively, based on the constraint condition and the objective function, based on the total shipping volume, the shipping time limit and the parameter information of the logistics carriers; wherein the objective function includes: the sum of costs is minimized when each logistics carrier in the collection orders a corresponding hold amount.
In some embodiments, an objective function and a constraint function may be set in the solver, so as to bring in the total shipping volume, the shipping time limit and the parameter information of the logistics carriers, and a set of selected logistics carriers and the cargo hold volume occupied by each logistics carrier in the set respectively are obtained through the solving operation of the solver. In some embodiments, the solver may be scip solver.
In some embodiments, the objective function may be a linear function; the constraint may be a set of multiple inequalities.
By the method, the logistics carrier selection scheme meeting the constraint conditions can be obtained through data collection and matching and through objective function operation through calculation and execution, so that the formulation efficiency of the logistics carrier selection scheme is improved, and the formulated logistics carrier selection scheme is optimized.
A flow chart of further embodiments of the method of generating a logistics carrier selection scheme of the present disclosure is illustrated in fig. 2A.
In step 201, the total shipping volume of the item between the source address and the destination address after a predetermined length of time is predicted. In some embodiments, the data in the database may be retrieved, and the total shipping volume of the item may be estimated based on the shipping volume between the source address and the destination address for the historical corresponding time period, in combination with recent shipping volume change trends. In some embodiments, the predetermined length of time may be a fixed value, such as one month, thereby reserving a one month later flight in advance.
In step 202, a shipping time limit is determined based on a predetermined length of time. In some embodiments, the date of shipment is determined based on a predetermined length of time and a current date, and the time of shipment, the time of performance (i.e., the latest arrival time) at the date of shipment is determined as a time of shipment limit based on daily item shipping laws, such as time of day of shipment of the item, time of arrival at the destination, etc.
In some embodiments, the shipping volume related information may be as shown in tables 1, 2. The following table is merely exemplary and is not intended to be limiting in any way.
TABLE 1 shipping quantity details examples
TABLE 2 amount of goods for each delivery to reach each destination city
In step 203, a collection of originating sites for the logistics carrier is obtained from the source address. In some embodiments, the source address may be a city corresponding to the shipping point of the item, as shown in FIG. 3, such as Buddha. Goods starting from the bergamot can be selected as an original airport or a Shenzhen baoan airport, and the original site set comprises the Guangzhou white cloud airport and the Shenzhen baoan airport.
In step 204, a destination site set of the logistics carrier is obtained according to the destination address. In some embodiments, the destination address may be a city corresponding to the point of receipt of the item, as shown in FIG. 3, such as Kunskia, suzhou, or Jiaxing. Goods destined for qunshan, su zhou or jiaxing may be selected from the Shanghai siphon bridge airport or Shanghai Pudong international airport, so the set of destination sites includes Guangzhou Shanghai siphon bridge airport or Shanghai Pudong international airport.
In step 205, the logistics carriers which meet the set of originating sites and the set of destination sites and the travel time matches the limit of shipping time are screened out. In some embodiments, the airline interface may be invoked, the date of delivery entered, the origination site and the destination site entered, and the appropriate flights screened out.
In step 206, the parameter information of the screened flights is extracted. In some embodiments, the flight details may be as shown in tables 3, 4. The following table is merely exemplary and is not intended to be limiting in any way.
TABLE 3 flight details example
| Flight number | Departure airport | Departure time | Collection expiration time | Arriving at airport | Time of arrival | Available cargo hold capacity (ton) |
| Flight 1 | Guangzhou white cloud | 5:00 | 5:30 | Shanghai Pudong | 7:45 | 5 |
| Flight 2 | Shenzhen Baoan | 5:15 | 5:45 | Shanghai siphon bridge | 7:30 | 8 |
| Flight 3 | Guangzhou white cloud | 5:30 | 5:00 | Shanghai siphon bridge | 8:00 | 4 |
| Flight 4 | Shenzhen Baoan | 5:30 | 5:00 | Shanghai Pudong | 7:50 | 7 |
| Flight 5 | Shenzhen Baoan | 6:00 | 5:30 | Shanghai Pudong | 8:15 | 8 |
| Flight 6 | Shenzhen Baoan | 6:05 | 5:35 | Shanghai siphon bridge | 8:45 | 3 |
| Flight 7 | Guangzhou white cloud | 6:05 | 5:35 | Shanghai Pudong | 8:35 | 7 |
| Flight 8 | Guangzhou white cloud | 6:30 | 6:00 | Shanghai siphon bridge | 9:30 | 3 |
| Flight 9 | Shenzhen Baoan | 15:30 | 15:00 | Shanghai Pudong | 17:50 | 8 |
| Flight 10 | Guangzhou white cloud | 15:35 | 15:05 | Shanghai siphon bridge | 16:15 | 6 |
| Flight 11 | Shenzhen Baoan | 16:00 | 15:30 | Shanghai siphon bridge | 18:00 | 4 |
TABLE 4 earliest aging for each city for each flight
From the table, the traffic, flight timing diagram as shown in fig. 4 can be obtained. For example, flights 3, 4, and 5 may be used for shipment 4 without regard to performance aging.
In step 207, the collection of selected logistics carriers, and the respective amount of cargo hold occupied by each logistics carrier in the collection, is determined by a solver run by a computer based on the constraints and objective functions, based on the total shipping volume, shipping time limit, and parameter information of the logistics carriers.
In some embodiments, the constraints include that the sum of the cargo holds occupied by the logistics carriers in the collection is not less than the total shipping volume of the items, considering that the scheduled flights are to meet the demand for all items corresponding to the total shipping volume to be sent; in order to ensure that the article can arrive before the off-flight goods collection, the time from the article to the starting site of the logistics carrier in the delivery time limit is not later than the goods collection off-time of the logistics carrier; in order to ensure that the time for the article to reach the destination does not exceed the performance age, the constraint may further include that the performance age of the article in the shipping time limit is not earlier than the time for the logistics carrier to reach the destination address; the constraint further includes that the amount of cargo space occupied at each logistics carrier is less than or equal to the remaining carrying capacity of the corresponding logistics carrier, considering the limited amount of cargo space remaining for the flights. In addition, given the higher cost and lower efficiency of delivering items to multiple origin sites, the origin sites of the selected logistics carriers can be set to be the same, so that all items can be transported to the same airport and distributed to different flights for transportation.
Through the constraint condition, the calculation result based on the solver can meet the requirement of article transportation and performance, and the convenience of transporting articles to an airport is improved.
In some embodiments, partial airlines, partial flights need to be used with priority due to their partnership. Constraints may also include: the ratio of the sum of the cargo hold amounts occupied by the logistics carriers belonging to the priority logistics carriers in the collection to the total shipping amount is equal to or greater than a predetermined ratio. The proportion of the cargo quantity carried by the priority flight in the total shipping volume is controlled by setting the proportion value, and the proportion value is adjusted according to the requirement, so that scheme adjustment is realized.
By the method, the cargo holds of the flights can be reserved for a long time in advance, on one hand, the sufficient bearing capacity of the remaining flights can be ensured, the optional range is enlarged, the success rate of scheme generation is improved, and all the objects are ensured to be transported; on the other hand, historical data and the preset discount price provided by the airlines can be fully applied, and the freight cost is reduced.
A flowchart of some embodiments of the cargo hold reservation method of the present disclosure is shown in fig. 2B.
In step 211, a collection of selected logistics carriers and the amount of cargo space each logistics carrier occupies in the collection are determined by the logistics carrier selection scheme generating method of any one of the above. In some embodiments, the aggregate and the corresponding cargo hold amounts for each of the aggregate may be presented to a worker, or output to a table, or output to a predetermined port.
In step 212, the cargo holds of the logistics carriers are predetermined based on the determined collection of logistics carriers and the amount of cargo holds each occupies for each logistics carrier in the collection. In some embodiments, the reservation may be initiated by the staff member in accordance with the output information; in other embodiments, an interface with the airline may be invoked to trigger the airline's reservation platform to complete the reservation operation.
By the method, the making efficiency of the logistics carrier selection scheme can be improved, the making logistics carrier selection scheme is optimized, the preset efficiency of the cargo hold is improved, the cost is reduced, and the transportation efficiency is improved.
In some embodiments, when the actual situation of a flight changes after the cargo hold is reserved, for example, a certain flight is temporarily cancelled, the flight may be set as unavailable, and the method for generating the logistics carrier selection scheme of the present disclosure is re-executed, so that the information of the flight is not obtained in the steps 205 and 206, and further, a new temporary decision scheme is obtained through re-operation of the solver, so that the capability of coping with abnormal situations is improved, and the reliability of the scheme generating method is improved.
In some embodiments, for convenience of the following description, the relationship of symbols and meanings below is shown in table 5.
TABLE 5 symbol-meaning correspondence
In some embodiments, a mixed integer programming model is first created:
G ib denotes whether the ith delivery volume selects the flight of the B-th airport, g ib epsilon {0,1}, I epsilon I, B epsilon B;
X ivj is adopted to represent the cargo quantity proportion of the ith delivery quantity to the jth destination city and the jth flight, wherein x ivj is more than or equal to 0 and less than or equal to 1, I epsilon I, V epsilon V and J epsilon J;
Using y j to denote the cargo hold volume for ordering the jth flight, y j∈[0,gj ]
The model objective function is:
Model constraints include:
each shipment can only go to one originating airport:
∑b∈Bgib＝1，i∈I， (1)
the ith shipment selects the b-th airport to use the flights of that airport:
zij≤gib,b∈B,j∈Jb，i∈I， (2)
after determining the airport to which the shipping volume is to be directed, its cargo can only be transported by flights at this airport:
when the ith shipment volume uses the jth flight, the goods of the ith shipment volume can be transported by the jth flight:
xivj≤zij，i∈I，b∈B，v∈V， (4)
The proportion of the volume of each delivery volume transported by its corresponding priority flight should be not less than alpha
Each shipment should arrive at airport b earlier than the pickup deadline for its selected flight, where M represents a significant number, which in some embodiments may be g j:
The performance age for each delivery is less than the latest age for its selected flight to cover the destination city, where M represents a significant number, which in some embodiments may be g j:
the load per flight does not exceed the hold order for that flight:
the range of values of the variables:
further, since the discounted price of flights is often a step function with the ordered hold amount as a variable, as shown in FIG. 5, respectively AndThe lower bound and the upper bound of the cargo hold quantity of the kth discount price segment of the jth flight are represented, k is the segment number of the price segment, and the following relation exists:
let h j(yj) denote the functional relationship between the hold order price and the hold order quantity, e.g. when When (q is the segment number of the price segment), the hold order price is h j(yj)＝ cjq. Thus, h j(yj in the objective function) is a piecewise function, which also results in the objective function above being a nonlinear function.
In some embodiments, constraints are set:
fjk∈{0,1},v∈V,j∈Kj. (12)
f jk denotes a discount price zone for the j-th flight used. Equation (9) indicates that the ordered hold amount can only be located in 1 discount zone, equation (10) indicates that the hold ordered amount must be located between the upper and lower bounds of the selected zone, and equation (11) indicates the range of values of variable (f jk). For example, when In this case, due to the limitation of the formula (10),K is more than 1; will beThe value of (2) is given in the equation (11)
Since the solver of the present disclosure can only represent cases of less than or equal to,Y j cannot take on the value of the lower bound. Thus, by using eps, which is a small number, it is possible to minimize the accuracy of the computer, a strict less than is converted to less than or equal to that which would facilitate invoking the solver solution.
By the method, the problem of nonlinearity caused by step price is solved, the nonlinearity degree is reduced, the operation difficulty is reduced, and the operation efficiency of the solver is improved.
In some embodiments, after linearizing the discounted price, the objective function of the model is converted to:
since y j and f jk are both variables, the objective function is still a nonlinear function. In some embodiments, a variable γ jk is set, which has the physical meaning of the cargo space usage for each cargo space ordering price, γ jk＝yj*fjk. After setting the variables, the objective function is converted into a linear function:
Wherein γ jk is a variable and c jk is a constant. The corresponding gamma jk should satisfy formulas (13) - (16)
Equation (13) and equation (14) are upper bound constraints of γ jk, equation (15) is lower bound constraint of γ jk, and equation (16) is a range constraint of γ jk. M is a larger number, and g j can be used for replacing M in order to improve the solving efficiency.
When f jk =1, equation (11) ensures the value range of y j, and equations (12) and (14) ensure γ jk＝yj; when f jk =0, equation (13) can guarantee γ jk =0, thus proving that such a conversion manner is effective.
By the method, the nonlinear objective function is linearized, and the model is converted into an MILP (Mix INTEGER LINEAR Programming, mixed integer linearity problem). The objective function (17) and constraint conditions (1) - (16) are brought into a solver of mixed integer linear functions, such as an open source solver (e.g., scip) to solve the model.
The selected flight can be determined by the value of variable x ivj output by the solver. In some embodiments, the solution results may be displayed, e.g., exported into a table, for the operator to view and perform the predetermined operations.
In some embodiments, the value of α may also be adjusted, and solved using an open source solver to obtain a relationship between the cost of the selected flight and the value of α, and expressed using a line graph. After the environment changes, the graph can provide a reference for a decision maker to select the optimal alpha value, and further optimize the subsequent scheme generation process.
Reading information of the predicted delivery volume and the flight from a database by the method, and processing data required by a subsequent mixed integer programming model; establishing a mixed integer programming model with the aim of minimizing the order cost, with the timeliness requirement of the shipping quantity and the cargo hold quantity of each flight as limits and with the discount price of the cargo hold quantity as a step function; two linearization modes are adopted in the model, and a step function and a quadratic function are respectively converted into linear functions; and the optimal scheme is solved by using a solver, the optimal scheme under different parameter levels is observed by adjusting parameters in the model, the historical data and the flight discount are fully utilized, the operation model is linearized, and the reliability and the operation efficiency of a solving result are improved.
A schematic diagram of some embodiments of the logistic carrier selection scheme generating device of the present disclosure is shown in fig. 6.
The item information acquiring unit 601 can acquire the total shipping volume, shipping time limit, source address, and destination address of the item. In some embodiments, the order information may be summarized, and for each combination of items (source address, destination address), the same order is obtained for both the source address and the destination address, and the shipping time is limited, and the shipping volume of the order is counted to obtain the total shipping volume. In other embodiments, for each combination of items (source address, destination address), the total shipping volume for the time interval corresponding to the shipping time limit may be estimated based on the historical shipping volume.
The carrier information acquiring unit 602 can determine a logistics carrier whose origin, destination and date of voyage are matched with the source address, destination address and time limit of shipment, respectively, and acquire parameter information of the logistics carrier. In some embodiments, the matched logistics carriers can be searched according to the delivery time limit, the source address and the destination address through a data interface with an airline company, and then the parameter information of the searched logistics carriers is extracted.
The scheme generating unit 603 determines a set of selected logistics carriers and the amount of cargo hold each logistics carrier occupies in the set, respectively, by a solver operated by a computer based on constraint conditions and objective functions according to the total shipping amount, shipping time limit and parameter information of the logistics carriers; wherein the objective function includes: the sum of costs is minimized when each logistics carrier in the collection orders a corresponding hold amount. In some embodiments, an objective function and a constraint function may be set in the solver, so as to bring in the total shipping volume, the shipping time limit and the parameter information of the logistics carriers, and a set of selected logistics carriers and the cargo hold volume occupied by each logistics carrier in the set respectively are obtained through the solving operation of the solver. In some embodiments, the solver may be scip solver.
The device can obtain the logistics carrier selection scheme meeting the constraint conditions through data collection and matching and objective function operation through calculation and execution, thereby improving the formulation efficiency of the logistics carrier selection scheme and optimizing the formulated logistics carrier selection scheme.
A schematic structural diagram of one embodiment of the logistic carrier selection scheme generating device of the present disclosure is shown in fig. 7. The logistics carrier selection scheme generating device comprises a memory 701 and a processor 702. Wherein: memory 701 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory is used for storing instructions in the corresponding embodiment of the above logistics carrier selection scheme generation method. Processor 702 is coupled to memory 701 and may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 702 is configured to execute instructions stored in the memory, thereby improving efficiency of formulation of the logistics carrier selection scheme and optimizing the formulated logistics carrier selection scheme.
In one embodiment, as also shown in fig. 8, the logistics carrier selection scheme generating apparatus 800 includes a memory 801 and a processor 802. The processor 802 is coupled to the memory 801 by a BUS 803. The logistics carrier selection scheme generating apparatus 800 can be further connected to an external storage device 805 via a storage interface 804 for invoking external data, and can be further connected to a network or another computer system (not shown) via a network interface 806. And will not be described in detail herein.
In this embodiment, the data instruction is stored in the memory, and the processor processes the instruction, so that the efficiency of making the logistics carrier selection scheme can be improved, and the made logistics carrier selection scheme can be optimized.
A schematic diagram of some embodiments of the cargo hold reservation system of the present disclosure is shown in fig. 9. The cargo hold reservation system includes any of the logistics carrier selection scheme generating means 91 mentioned hereinabove. The cargo hold reservation system further includes a reservation unit 92 capable of reserving cargo holds of the logistics carriers in accordance with the collection of logistics carriers acquired from the logistics carrier selection scheme generating device 91 and the amount of cargo holds respectively occupied for each logistics carrier in the collection.
The system can improve the formulating efficiency of the logistics carrier selection scheme, optimize the formulated logistics carrier selection scheme, improve the reservation efficiency of the cargo hold, reduce the cost and improve the transportation efficiency.
In one embodiment, the present disclosure proposes a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of generating a logistics carrier selection scheme, the method of reserving a cargo space, and the steps of the method in the corresponding embodiments. It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Thus far, the present disclosure has been described in detail. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
Finally, it should be noted that: the above embodiments are merely for illustrating the technical solution of the present disclosure and are not limiting thereof; although the present disclosure has been described in detail with reference to preferred embodiments, those of ordinary skill in the art will appreciate that: modifications may be made to the specific embodiments of the disclosure or equivalents may be substituted for part of the technical features; without departing from the spirit of the technical solutions of the present disclosure, it should be covered in the scope of the technical solutions claimed in the present disclosure.
Claims (14)
1. A method for generating a logistics carrier selection scheme, comprising:
Acquiring total shipping volume, shipping time limit, source address and destination address of an article, wherein the source address and the destination address take a city set as granularity, and each granularity at least comprises an airport;
Determining a logistics carrier having an origin, destination and date of voyage that match the source address, destination address and time of shipment limit, respectively, comprising: acquiring an originating site set of the logistics carrier according to the source address; acquiring a destination site set of the logistics carrier according to the destination address; screening the logistics carriers which accord with the originating site set and the destination site set and have travel time matched with the delivery time limit;
Acquiring parameter information of the logistics carrier;
Determining, by a solver run by a computer, a selected collection of said logistics carriers and the respective amount of hold occupied by each of said logistics carriers in the collection based on constraint conditions and objective functions, based on said total shipping volume, said shipping time limit and parameter information of said logistics carriers;
Wherein the objective function includes: the sum of costs is minimized when each logistics carrier in the collection orders a corresponding hold amount.
2. The method of claim 1, wherein the logistics carrier comprises at least one of a ship, train, or plane for each shift.
3. The method of claim 1, wherein the determining a logistics carrier whose origin, destination and date of voyage match the source address, destination address and time of shipment limit, respectively, and the obtaining parameter information of the logistics carrier comprises:
Searching matched logistics carriers according to the delivery time limit, the source address and the destination address through a data interface between the logistics carriers and operators;
And extracting the searched parameter information of the logistics carrier.
4. The method of claim 1, wherein the constraint comprises:
the originating sites of the selected logistics carriers are the same;
the sum of the cargo hold amounts occupied by the logistics carriers in the collection is not less than the total shipping amount of the articles;
The time from the article to the starting site of the logistics carrier in the delivery time limit is not later than the goods collection cut-off time of the logistics carrier;
the performance aging of the articles in the delivery time limit is not earlier than the aging of the logistics carrier reaching the destination address; and
And the occupied cargo hold amount of each logistics carrier is smaller than or equal to the residual bearing capacity of the corresponding logistics carrier.
5. The method of claim 4, wherein the constraints further comprise:
The ratio of the sum of the cargo hold amounts occupied by the physical distribution carriers belonging to the priority physical distribution carriers in the collection to the total shipping amount is equal to or greater than a predetermined ratio.
6. The method of claim 4 or 5, wherein the objective function is represented by a linear function that varies with cargo usage at each cargo ordering cost;
Said determining, by a solver operated by a computer, a selected collection of said logistics carriers based on said total shipping volume, said shipping time limit and said parameter information of said logistics carriers based on constraints and objective functions, and the respective occupied cargo space amounts of each of said logistics carriers in the collection comprising:
And processing the linear function and the constraint condition by using a solver of the mixed integer linear problem, and determining a selected collection of the logistics carriers and the cargo hold amount occupied by each logistics carrier in the collection respectively.
7. The method of claim 6, wherein the objective function comprises:
For each logistics carrier in the collection, respectively obtaining the sum of products of the cargo hold using amount of each cargo hold ordering cost and the corresponding cargo hold ordering cost to be used as a single logistics carrier cost;
Determining the cost sum according to the single-logistics carrier cost of each logistics carrier in the collection, and minimizing the cost sum;
the constraint further includes:
the cargo hold usage amount of the cargo hold ordering cost is less than or equal to the cargo hold amount occupied by the logistics carriers;
The cargo hold usage amount of the cargo hold ordering cost is smaller than or equal to the product of a preset constant and an ordering cost usage identifier, wherein the preset constant is not smaller than the cargo hold amount occupied by the logistics carriers, the ordering cost usage identifier of the cargo hold ordering cost corresponding to the cargo hold amount is 1 for each logistics carrier, and the ordering cost usage identifiers of other cargo hold ordering costs are 0;
the cargo hold usage amount of the cargo hold ordering cost is greater than or equal to the sum of the product of the predetermined constant and the ordering cost usage identifier and the cargo hold amount occupied by the logistics carrier minus the predetermined constant;
and the cargo hold using amount of the cargo hold ordering cost is more than or equal to 0 and less than or equal to the cargo hold amount occupied by the logistics carriers.
8. The method of claim 7, wherein the shipping bay ordering unit price of the logistics carriers decreases stepwise with increasing amount of shipping bay ordered;
the constraint further includes:
The sum of the ordering cost using identifiers of the single logistics carriers is 1; and
And the cargo hold amount corresponding to the logistics carriers is larger than or equal to the product of the ordering unit price use identification and the lower limit of the cargo hold amount of the corresponding cargo hold ordering unit price, and smaller than or equal to the product of the ordering unit price use identification and the upper limit of the cargo hold amount of the corresponding cargo hold ordering unit price.
9. The method of claim 1, wherein the acquiring the total shipping volume, shipping time limit, source address, and destination address of the item comprises:
Predicting a total shipping volume of the item between the source address and the destination address after a predetermined length of time;
And determining the shipping time limit according to the predetermined length of time.
10. A cargo hold reservation method comprising: the logistics carrier selection scheme generating method of any one of claims 1 to 9; and
And presetting the cargo holds of the logistics carriers according to the collection of the logistics carriers generated by the logistics carrier selection scheme generation method and the cargo hold amount respectively occupied by each logistics carrier in the collection.
11. A logistic carrier selection scheme generating device, comprising:
The system comprises an article information acquisition unit, a storage unit and a storage unit, wherein the article information acquisition unit is configured to acquire the total shipping volume, the shipping time limit, the source address and the destination address of an article, the source address and the destination address take a city set as granularity, and each granularity at least comprises an airport;
A carrier information acquisition unit configured to determine a logistics carrier whose origin, destination and date of voyage match the source address, destination address and time limit of shipment, respectively, comprising: acquiring an originating site set of the logistics carrier according to the source address; acquiring a destination site set of the logistics carrier according to the destination address; screening the logistics carriers which accord with the originating site set and the destination site set and have travel time matched with the delivery time limit; acquiring parameter information of the logistics carrier;
A scenario generation unit configured to determine, based on constraint conditions and objective functions, a set of selected logistics carriers and the amount of cargo hold each of the logistics carriers occupies in the set, respectively, by a solver run by a computer, based on the total shipping amount, the shipping time limit, and parameter information of the logistics carriers;
Wherein the objective function includes: the sum of costs is minimized when each logistics carrier in the collection orders a corresponding hold amount.
12. A logistic carrier selection scheme generating device, comprising:
A memory; and
A processor coupled to the memory, the processor configured to perform the method of any of claims 1-9 based on instructions stored in the memory.
13. A cargo hold reservation system comprising:
The logistics carrier selection scheme generating apparatus according to claim 11 or 12; and
A reservation unit configured to reserve cargo holds of the logistics carriers according to the collection of the logistics carriers and the amount of cargo holds respectively occupied by each logistics carrier in the collection.
14. A computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any of claims 1 to 10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110278204.1A CN113761458B (en) | 2021-03-15 | 2021-03-15 | Logistics carrier selection scheme generation method and device, cargo hold reservation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110278204.1A CN113761458B (en) | 2021-03-15 | 2021-03-15 | Logistics carrier selection scheme generation method and device, cargo hold reservation method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113761458A CN113761458A (en) | 2021-12-07 |
| CN113761458B true CN113761458B (en) | 2024-09-24 |
Family
ID=78786722
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110278204.1A Active CN113761458B (en) | 2021-03-15 | 2021-03-15 | Logistics carrier selection scheme generation method and device, cargo hold reservation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113761458B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116434235B (en) * | 2023-03-20 | 2024-07-19 | 上海昱耀信息科技有限公司 | Information processing method, device, equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110009272A (en) * | 2019-02-21 | 2019-07-12 | 深圳市北斗智能科技有限公司 | More strategies parallel logistic resources dispatching method and relevant apparatus |
| CN111382894A (en) * | 2018-12-29 | 2020-07-07 | 顺丰科技有限公司 | Cabin booking planning method, device, equipment and storage equipment |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3161529B2 (en) * | 1998-05-15 | 2001-04-25 | サントリー株式会社 | Vehicle dispatching equipment |
| US20170214701A1 (en) * | 2016-01-24 | 2017-07-27 | Syed Kamran Hasan | Computer security based on artificial intelligence |
| US10692039B2 (en) * | 2016-09-20 | 2020-06-23 | International Business Machines Corporation | Cargo logistics dispatch service with integrated pricing and scheduling |
| CN112183812B (en) * | 2020-08-25 | 2022-07-01 | 昆明理工大学 | Finished cigarette logistics vehicle scheduling method considering short-time and low-cost |
| CN112270135B (en) * | 2020-11-13 | 2023-02-03 | 吉林烟草工业有限责任公司 | Intelligent distribution method, device and equipment for logistics dispatching and storage medium |
-
2021
- 2021-03-15 CN CN202110278204.1A patent/CN113761458B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111382894A (en) * | 2018-12-29 | 2020-07-07 | 顺丰科技有限公司 | Cabin booking planning method, device, equipment and storage equipment |
| CN110009272A (en) * | 2019-02-21 | 2019-07-12 | 深圳市北斗智能科技有限公司 | More strategies parallel logistic resources dispatching method and relevant apparatus |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113761458A (en) | 2021-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109816147B (en) | Air freight route planning method, device, equipment and storage medium | |
| Smith et al. | A simulation-based evaluation of warehouse check-in strategies for improving inbound logistics operations | |
| US20120226624A1 (en) | Optimization system of smart logistics network | |
| CN111815231B (en) | Intelligent carpooling method and system for logistics platform | |
| Azadian et al. | Dynamic routing of time-sensitive air cargo using real-time information | |
| US20200327497A1 (en) | System and method for linehaul optimization | |
| Delgado et al. | Recovering from demand disruptions on an air cargo network | |
| Erera et al. | Creating schedules and computing operating costs for LTL load plans | |
| KR20210008581A (en) | System for providing logistics transportation service for multi pick up and delivery with imporved navigation algorithm | |
| Delgado et al. | A matheuristic approach to the air-cargo recovery problem under demand disruption | |
| Azadian et al. | An unpaired pickup and delivery problem with time dependent assignment costs: Application in air cargo transportation | |
| CN113761458B (en) | Logistics carrier selection scheme generation method and device, cargo hold reservation method and system | |
| Emde et al. | Scheduling personnel for the build-up of unit load devices at an air cargo terminal with limited space | |
| CN110826757B (en) | Flight scheduling method, device and equipment thereof | |
| CN115630833B (en) | Method for predicting passenger consignment baggage information and related device | |
| KR102395527B1 (en) | Computing system of managing logistics service based on big data | |
| JP2002193450A (en) | Physical distribution control method and physical distribution control system for transported cargo | |
| CN110400004A (en) | The time limit method for early warning and device of railway goods | |
| US12123724B2 (en) | Method for operating a vehicle | |
| Kuśmińska-Fijałkowska et al. | Consolidation as a key to success–an European transport enterprise case study | |
| CN117314294A (en) | Logistics line planning method, device, equipment and storage medium | |
| Schenk et al. | Intramarket optimization for express package carriers | |
| CN113902359A (en) | Logistics distribution path planning method and device, computer equipment and storage medium | |
| Schenk et al. | Intra market optimization for express package carriers with station to station travel and proportional sorting | |
| Loskot et al. | Innovative baggage delivery for sustainable air transport |
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 |