CN113761458A - Logistics carrier selection scheme generation method and device, cargo compartment reservation method and system - Google Patents
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Abstract
The disclosure provides a method and a device for generating a logistics carrier selection scheme, and a method and a system for reserving a cargo hold, and relates to the technical field of logistics. The logistics carrier selection scheme generation method comprises the following steps: acquiring the total shipment volume, the shipment time limit, the source address and the destination address of the article; determining a logistics carrier with an origin, a destination and a navigation date respectively matched with a source address, a destination address and a delivery time limit, and acquiring parameter information of the logistics carrier; and determining a set of the selected logistics carriers and the cargo hold amount occupied by each logistics carrier in the set respectively through a solver operated by a computer based on the constraint conditions and the objective function according to the total delivery amount, the delivery time limit and the parameter information of the logistics carriers. By the method, the formulation efficiency of the logistics carrier selection scheme can be improved, and the formulated 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 for supporting enterprise development. Since it relates to the long-distance transportation of goods, conventional logistics carriers may include airplanes, ships, trains, trucks, etc., wherein the airplanes, ships, and trains have independent operators, respectively, and when such vehicles are used to carry goods, ordering is required at the corresponding operators. Through reserving proper flights, trains or cargo ships, the goods can be efficiently transported, and the order time limit requirement is met.
Taking flight reservation as an example, when selecting a transportation flight, at present, a worker needs to arrange the transportation volume of each flight one to two days in advance according to the estimated performance time of each batch of goods and the flight taking-off and landing time, and order a cargo hold to an airline company.
Disclosure of Invention
One purpose of this disclosure is to improve the efficiency of formulating a logistics carrier selection scheme and optimize the logistics carrier selection scheme.
According to an aspect of some embodiments of the present disclosure, there is provided a method for generating a logistics carrier selection scheme, including: acquiring the total shipment volume, the shipment time limit, the source address and the destination address of the article; determining a logistics carrier with an origin, a destination and a navigation date respectively matched with a source address, a destination address and a delivery time limit, and acquiring parameter information of the logistics carrier; determining a set of selected logistics carriers and the cargo hold quantity respectively occupied by each logistics carrier in the set by a solver operated by a computer based on constraint conditions and an objective function according to the total shipping quantity, the shipping time limit and the parameter information of the logistics carriers; wherein the objective function includes: the sum of the costs is minimal when each logistics carrier in the collection orders a corresponding cargo capacity.
In some embodiments, the logistics carrier includes at least one of a ship, a train, or an airplane for each shift.
In some embodiments, determining the logistics carrier with the origin, the destination and the navigation date respectively matched with the source address, the destination address and the delivery time limit, and acquiring the parameter information of the logistics carrier comprises: searching matched logistics carriers according to the sending time limit, the source address and the destination address through a data interface between the logistics carriers and an operator of the logistics carriers; 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 occupied by the logistics carriers in the collection is not less than the total delivery volume of the goods; the time of the article in the delivery time limit to the starting station of the logistics carrier is not later than the goods collection deadline of the logistics carrier; the time limit of the delivery time for the performance of the article is not earlier than the time limit of the physical distribution carrier reaching the destination address; and the cargo capacity occupied by each logistics carrier is less 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 occupied by the logistics carriers belonging to the priority logistics carriers in the collection to the total delivery volume is larger than or equal to a preset ratio.
In some embodiments, the objective function is represented by a linear function with the amount of cargo hold usage at each cargo hold ordering cost as a variable; determining a set of selected logistics carriers through a solver operated by a computer according to the total delivery volume, the delivery time limit and the parameter information of the logistics carriers and based on the constraint conditions and the objective function, wherein the cargo hold volume occupied by each logistics carrier in the set respectively comprises the following steps: and processing the linear function and the constraint condition by utilizing a solver of a mixed integer linear problem, and determining the selected logistics carrier set and the cargo hold amount respectively occupied by each logistics carrier in the set.
In some embodiments, the objective function comprises: respectively acquiring the sum of the product of the cargo hold usage amount of each cargo hold ordering cost and the corresponding cargo hold ordering cost for each logistics carrier in the set, and taking the sum as the cost of a single logistics carrier; determining a sum of costs according to the cost of the single logistics carrier of each logistics carrier in the set, and minimizing the sum of costs; the constraints further include: the usage amount of the cargo hold at the cargo hold ordering cost is less than or equal to the cargo hold amount occupied by the logistics carrier; the usage amount of the cargo hold ordering cost is less than or equal to the product of a preset constant and an ordering cost usage identifier, wherein the preset constant is not less 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, and the ordering cost usage identifiers of other cargo hold ordering costs are 0 for each logistics carrier; the cargo hold usage amount of the cargo hold ordering cost is more than or equal to the sum of the product of the preset constant and the ordering cost usage identifier and the cargo hold amount occupied by the logistics carrier minus the preset constant; the usage amount of the cargo hold at 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 carrier.
In some embodiments, the unit price of ordering the cargo space of the logistics carrier is reduced in steps along with the increase of the ordered cargo space amount; the constraints further include: the sum of the order cost use identifiers of the single logistics carriers is 1; and the cargo capacity corresponding to the logistics carrier is more than or equal to the product of the ordering unit price use identifier and the lower limit of the cargo capacity of the corresponding cargo ordering unit price, and is less than or equal to the product of the ordering unit price use identifier and the upper limit of the cargo capacity of the corresponding cargo ordering unit price.
In some embodiments, obtaining the total shipment volume, the shipment time limit, the source address, and the destination address for the item comprises: predicting a total shipment 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 for generating a logistics vehicle selection plan further comprises: the cargo hold of the logistics carrier is predetermined according to the collection of logistics carriers and the respective occupied cargo hold amount of each logistics carrier in the collection.
In some embodiments, determining the logistics carrier having an origin, a destination, and a voyage date that match the source address, the destination address, and the departure time 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 conform to the originating site set and the destination site set and are matched with the travel time and the delivery time limit.
By the method, the logistics carrier selection scheme meeting the constraint condition can be obtained through data collection and matching and calculation and executed objective function operation, 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 compartment reservation method, including any one of the above logistics carrier selection scheme generation methods; and generating a set of logistics carriers according to the logistics carrier selection scheme, and reserving the cargo hold of the logistics carriers for the cargo hold amount occupied by each logistics carrier in the set.
By the method, the formulating efficiency of the logistics carrier selection scheme can be improved, the formulated logistics carrier selection scheme is optimized, and the reserving 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 generation apparatus, including: an article information acquisition unit configured to acquire a total shipment volume, a shipment time limit, a source address, and a destination address of an article; a carrier information acquisition unit configured to determine a physical distribution carrier whose origin, destination and navigation date respectively match with a source address, a destination address and a delivery time limit, and acquire parameter information of the physical distribution carrier; a scheme generation unit configured to determine a set of selected logistics carriers and the amount of cargo holds respectively occupied by each logistics carrier in the set by a solver operated by a computer based on a constraint condition and an objective function according to the total shipment volume, the shipment time limit and the parameter information of the logistics carriers; wherein the objective function includes: the sum of the costs is minimal when each logistics carrier in the collection orders a corresponding cargo capacity.
In some embodiments, the logistics carrier selection scheme generating means further comprises: and the predetermined unit is configured to reserve the cargo hold of the logistics carrier according to the set of logistics carriers and the cargo hold amount occupied by each logistics carrier in the set.
According to an aspect of some embodiments of the present disclosure, there is provided a logistics carrier selection scheme generation apparatus, including: a memory; and a processor coupled to the memory, the processor configured to perform the method for logistics carrier selection scheme generation as any one of the above based on instructions stored in the memory.
The device can obtain the logistics carrier selection scheme meeting the constraint condition through data collection and matching and calculation and executed objective function operation, 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 compartment reservation system including any one of the above logistics carrier selection scheme generation apparatus; and a predetermined unit configured to predetermine the cargo holds of the logistics carriers according to the set of logistics carriers and the respective occupied cargo hold amounts of each logistics carrier in the set.
The system can improve the formulation efficiency of the logistics carrier selection scheme, optimize the formulated logistics carrier selection scheme and improve the efficiency and rationality of cargo hold reservation.
According to an aspect of some embodiments of the present disclosure, a computer-readable storage medium is proposed, on which computer program instructions are stored, which instructions, when executed by a processor, implement the steps of the logistics carrier selection scheme generation method as any of the above.
By executing the instructions on the storage medium, a logistics carrier selection scheme meeting the constraint condition can be obtained through data collection and matching and calculation and executed objective function operation, 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 and are incorporated in and constitute a part of this disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 is a flow diagram of some embodiments of a method of generating a logistics carrier selection scheme of the present disclosure.
Fig. 2A is a flow diagram of further embodiments of a method of generating a logistics carrier selection scheme of the present disclosure.
Figure 2B is a flow chart of some embodiments of the cargo compartment reservation method of the present disclosure.
Fig. 3 is a schematic diagram of some examples of the selection of the carrier site of the carrier in the method of generating the selection scheme of the carrier of the present disclosure.
Fig. 4 is a schematic diagram of some embodiments of shipments and flight take-offs in the method for generating a logistics carrier selection scheme of the present disclosure.
Fig. 5 is a schematic diagram of some embodiments of discounted prices in a logistics carrier selection scheme generation method of the present disclosure.
Fig. 6 is a schematic diagram of some embodiments of a logistics carrier selection option generation apparatus of the present disclosure.
Fig. 7 is a schematic view of further embodiments of a stream carrier selection scheme generating device of the present disclosure.
Fig. 8 is a schematic diagram of still further embodiments of a logistics carrier selection option generation apparatus of the present disclosure.
Figure 9 is a schematic view of some embodiments of the cargo compartment reservation system of the present disclosure.
Detailed Description
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
The inventor finds that during the selection of the logistics carrier in the related art, the express delivery can be satisfied as much as possible, but due to the influences of high complexity, insufficient residual carrying capacity and the like, the 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 an airline company and the like is difficult to obtain an optimal selection scheme, the controllability degree of a logistics enterprise on the logistics carrier selection scheme is reduced, and the formulation efficiency of the scheme is reduced.
For convenience of description, the logistics carrier is taken as an airplane and the logistics carrier selection scheme is taken as a shipping ordering scheme.
A flow diagram of some embodiments of a method of generating a logistics carrier selection scheme of the present disclosure is shown in fig. 1.
In step 101, the total shipment volume, shipment time limit, source address and destination address of the item are obtained. In some embodiments, aggregation may be performed based on the order information, for each combination of items (source address and destination address), the order with the same source address and destination address and the equivalent delivery time limit is obtained, the delivery volume of the order is counted, and the total delivery volume is obtained. In other embodiments, for each combination of items (source address, destination address), the total delivery volume for the time interval corresponding to the delivery time limit may be estimated based on the historical delivery volumes.
In some embodiments, the source address and the destination address may be in a city granularity, or may be in a plurality of cities, such as a region within a predetermined range of distance. In some embodiments, the granularity of the source address and the destination address may be different for different regions, 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 multiple airports, the granularity of the source address is the city; when there is no airport in the city corresponding to the source address, the granularity of the source address is a set of multiple cities, and the set at least comprises one airport.
In some embodiments, the ship time limit may include the earliest ship time, the performance age (i.e., the latest arrival time).
In step 102, determining the logistics carrier with the origin, the destination and the navigation date respectively matched with the source address, the destination address and the delivery time limit, and acquiring the parameter information of the logistics carrier.
In some embodiments, the matched logistics carrier can be searched according to the sending time limit, the source address and the destination address through a data interface with the airline company, and then the parameter information of the searched logistics carrier is extracted.
In step 103, determining a set of selected logistics carriers and the cargo hold amount respectively occupied by each logistics carrier in the set through a solver operated by a computer based on constraint conditions and an objective function according to the total delivery amount, the delivery time limit and the parameter information of the logistics carriers; wherein the objective function includes: the sum of the costs is minimal when each logistics carrier in the collection orders a corresponding cargo capacity.
In some embodiments, an objective function and a constraint function may be set in a solver, so as to bring in parameter information of total shipment volume, shipment time limit, and logistics carriers, and a set of selected logistics carriers and a cargo hold volume occupied by each logistics carrier in the set are obtained through a solving operation of the solver. In some embodiments, the solver may be a scip solver.
In some embodiments, the objective function may be a linear function; the constraint may be a set of a plurality of inequalities.
By the method, the logistics carrier selection scheme meeting the constraint condition can be obtained through data collection and matching and calculation of the calculated and executed objective function, so that the formulation efficiency of the logistics carrier selection scheme is improved, and the formulated logistics carrier selection scheme is optimized.
A flow diagram of further embodiments of the disclosed method of generating a logistics carrier selection scheme is shown in fig. 2A.
In step 201, a total shipment 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 delivery volume of the article may be estimated according to the delivery volume between the source address and the destination address in the period corresponding to the history and according to the recent delivery volume change trend. In some embodiments, the predetermined length of time may be a fixed value, such as one month, thereby anticipating a flight one month later.
In step 202, a time-to-ship limit is determined based on a predetermined length of time. In some embodiments, the shipping date is determined based on the predetermined length of time and the current date, and the shipping time, the performance age (i.e., the latest arrival time) at the shipping date is determined as the shipping time limit based on the daily article shipping schedule, such as the daily article shipping time, the time to destination, and the like.
In some embodiments, the shipping volume related information may be as shown in tables 1 and 2. The following table is exemplary only and not limiting as to the specific case.
TABLE 1 detailed information example of shipping volume
TABLE 2 freight volume per shipment to destination cities
In step 203, the originating site set of the logistics carrier is obtained according to the source address. In some embodiments, the source address may be a city corresponding to the delivery point of the item, as shown in FIG. 3, such as Foshan. Goods from the Buddha mountain can select the Guangzhou Baiyun airport as an originating airport, and can also select the Shenzhen Baoan airport as an originating airport, so that the originating site set comprises the Guangzhou Baiyun 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 a shipping point of the item, as shown in fig. 3, such as kunsha, suzhou, or jiaxing. The cargo destined for Kunshan, Suzhou, or Jiaxing may be selected from Shanghai Rainbow airport or Shanghai Pudong International airport, and thus the set of destination sites includes Shanghai Rainbow airport or Shanghai Pudong International airport, Guangzhou.
In step 205, the logistics carriers that match the originating site set and the destination site set and have travel time matching with the delivery time limit are screened out. In some embodiments, an airline interface may be invoked to enter the ship date, origination site, and destination site, and screen out the appropriate flights.
In step 206, parameter information of the screened flights is extracted. In some embodiments, the details of the flight may be as shown in tables 3, 4. The following table is exemplary only and not limiting as to the specific case.
TABLE 3 flight details example
| Flight number | Departure airport | Departure time | End time of collection | To an airport | Time of arrival | Available cargo capacity (ton) |
| Flight 1 | Guangzhou white cloud | 5:00 | 5:30 | Shanghai Pudong | 7:45 | 5 |
| Flight 2 | Shenzhen Baoan (Shenzhen medicine for treating diseases) | 5:15 | 5:45 | Shanghai rainbow bridge | 7:30 | 8 |
| Flight 3 | Guangzhou white cloud | 5:30 | 5:00 | Shanghai rainbow bridge | 8:00 | 4 |
| Flight 4 | Shenzhen Baoan (Shenzhen medicine for treating diseases) | 5:30 | 5:00 | Shanghai Pudong | 7:50 | 7 |
| Flight 5 | Shenzhen Baoan (Shenzhen medicine for treating diseases) | 6:00 | 5:30 | Shanghai Pudong | 8:15 | 8 |
| Flight 6 | Shenzhen Baoan (Shenzhen medicine for treating diseases) | 6:05 | 5:35 | Shanghai rainbow 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 rainbow bridge | 9:30 | 3 |
| Flight 9 | Shenzhen Baoan (Shenzhen medicine for treating diseases) | 15:30 | 15:00 | Shanghai Pudong | 17:50 | 8 |
| Flight 10 | Guangzhou white cloud | 15:35 | 15:05 | Shanghai rainbow bridge | 16:15 | 6 |
| Flight 11 | Shenzhen Baoan (Shenzhen medicine for treating diseases) | 16:00 | 15:30 | Shanghai rainbow bridge | 18:00 | 4 |
TABLE 4 earliest timeliness of each flight to meet each city
From the table, the delivery volume, flight schedule as shown in fig. 4 can be obtained. For example, shipment volume 4 may use flights 3, 4, and 5 without regard to performance age.
In step 207, a set of selected logistics carriers and the amount of cargo space occupied by each logistics carrier in the set are determined by a solver run by a computer based on the constraint conditions and an objective function according to the total shipment volume, the shipment time limit and the parameter information of the logistics carriers.
In some embodiments, the constraint includes that the sum of the cargo hold amounts occupied by the logistics carriers in the set is not less than the total shipment of the items, taking into account that the scheduled flight is to satisfy the requirement of sending all the items corresponding to the total shipment; in order to ensure that the goods can arrive before the collection of the goods on the flight, the time of the goods to the starting station of the logistics carrier in the delivery time limit is not later than the collection stop time of the logistics carrier; in order to ensure that the time for the article to reach the destination does not exceed the performance time limit, the constraint condition may further include that the performance time limit of the article in the delivery time limit is not earlier than the time limit for the logistics carrier to reach the destination; considering that the remaining cargo capacity of the flight is limited, the constraint condition further includes that the cargo capacity occupied by each logistics carrier is less than or equal to the remaining load capacity of the corresponding logistics carrier. In addition, in view of the higher cost and lower efficiency of delivering items to multiple origination sites, the origination sites of selected logistics carriers can be set to be the same so that all items can be shipped to the same airport for distribution to different airline transports.
By means of the constraint conditions, the calculation result based on the solver can meet the requirements of article transportation and performance, and convenience of transporting the articles to the airport is improved.
In some embodiments, a portion of the airline flights may need to be used preferentially due to a collaborative relationship with that portion of the airline flights. The constraints may further include: the ratio of the sum of the cargo hold occupied by the logistics carriers belonging to the priority logistics carriers in the collection to the total delivery volume is larger than or equal to a preset ratio. The proportion of the cargo volume carried by the priority flight in the total delivery volume is controlled by setting the proportion value, and the proportion value is adjusted as required, so that scheme adjustment is realized.
By the method, the flight cargo hold can be reserved for a long time in advance, on one hand, the sufficient bearing capacity of the rest flights can be ensured, the optional range is expanded, the success rate of scheme generation is improved, and all goods are ensured to be transported; on the other hand, historical data and the discount price reserved in advance provided by the airline company can be fully used, and the freight cost can be reduced.
A flow chart of some embodiments of the cargo compartment reservation method of the present disclosure is shown in fig. 2B.
In step 211, a set of selected logistics carriers and the amount of cargo space occupied by each logistics carrier in the set respectively are determined by the logistics carrier selection scheme generation method of any one of the above. In some embodiments, the set and the corresponding cargo capacity for each item in the set may be displayed to a worker, or output to a table, or output to a predetermined port.
In step 212, the cargo space of the logistics carriers is predetermined according to the determined set of logistics carriers and the respective occupied cargo space amount of each logistics carrier in the set. In some embodiments, the reservation may be initiated by a worker in accordance with the output information; in other embodiments, an interface with the airline may be invoked, triggering a predetermined platform of the airline to perform a predetermined operation.
By the method, the formulation efficiency of the logistics carrier selection scheme can be improved, the formulated logistics carrier selection scheme is optimized, the cargo compartment reservation efficiency 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 flight is temporarily cancelled, the flight may be set as unavailable, the logistics carrier selection scheme generation method disclosed in the present disclosure is executed again, so that the information of the flight is no longer obtained in the above steps 205 and 206, and then a new temporary decision scheme is obtained through the re-operation of the solver, thereby improving the capability of coping with an abnormal situation and improving the reliability of the scheme generation method.
In some embodiments, the relationship of the symbols and meanings below is shown in table 5 for convenience of the subsequent description.
TABLE 5 symbol-meaning correspondence
In some embodiments, a mixed integer programming model is created first:
with gibIndicating whether the ith shipment selects a flight at the b-th airport, gib∈ {0，1}，i∈I，b∈B；
By xivjThe cargo capacity proportion of the jth flight used by the ith delivery volume to the vth destination city is represented, and x is more than or equal to 0ivj≤1，i∈I，v∈V，j∈J；
By yjIndicating the cargo volume, y, of the jth flight orderedj∈[0，gj]
The model objective function is:
the model constraints include:
each shipment can only go to one origin airport:
∑b∈Bgib＝1，i∈I， (1)
the ith shipment selects the b-th airport to use the flight at that airport:
zij≤gib，b∈B，j∈Jb，i∈I， (2)
when the shipping volume is determined for an airport, the goods can only be transported by the flights of the airport:
when the ith shipment uses the jth flight, the shipment of the ith shipment can be transported by the jth flight:
xivj≤zij，i∈I，b∈B，v∈V， (4)
the proportion of the amount of each shipment transported by its corresponding priority flight should not be less than α
The time of arrival of each shipment at airport b should be earlier than the terminal deadline for the collection of its selected flight, where M represents a significant number, which in some embodiments may be gj：
The performance age for each shipment is less than the latest age for the city whose selected flight covers the destination, where M represents a significant number, which in some embodiments may be gj：
The load capacity of each flight does not exceed the hold order capacity of the flight:
the value range of the variable is as follows:
further, since the discount price of the flight is often a step function with the amount of the ordered cargo as a variable, as shown in FIG. 5, the discount price is used separately
And
the lower and upper bounds of the cargo hold quantity of the kth discounted price segment of the jth flight are represented, k is the segment serial number of the price segment, and the following relations exist:
by using hj(yj) Indicating the functional relationship between the order price of the cargo hold and the order quantity of the cargo hold, e.g. when
Time (q is the sequence number of the price segment), the order price of the cargo hold is hj(yj)＝ cjq. Thus, h in the objective functionj(yj) Is a piecewise function, which results in the objective function being a non-linear function as described above.
In some embodiments, the constraint is set:
fjk∈{0，1}，v∈V，j∈Kj. (12)
fjkthe discounted price interval representing the jth flight used. Equation (9) indicates that the ordered cargo quantity is only within 1 discounted interval, equation (10) indicates that the ordered cargo quantity must be between the upper and lower bounds of the selected interval, and equation (11) indicates that the variable (f)jk) The value range of (a). For example, when
Due to the limitations of equation (10),
k is more than 1; will be provided with
After substituting the value of (C) into the formula (11), is
Since the solvers of the present disclosure can only represent cases less than or equal to,
yjthe value of the bound cannot be taken. Thus, by using eps, which is a small number, can be the minimum precision of a computer, strictly less than is converted to equal to or less than is convenient for the solver to solve.
By the method, the problem of nonlinearity caused by stepped 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 into:
due to yjAnd fjkAre variables, the objective function is still a non-linear function. In some embodiments, a variable γ is setjkThe physical meaning of the variable is the cargo hold usage at the order price per cargo hold, i.e. gammajk＝yj*fjk. After setting the variables, the objective function is converted to a linear function:
wherein, γjkIs a variable, cjkIs a constant. Corresponding gammajkSatisfy the formulas (13) - (16)
The formulas (13) and (14) are γjkIs γ in equation (15)jkIs γ in equation (16)jkIs constrained by the value range of (a). M is one largerFor improving the solution efficiency, the number of (c) may be gjInstead of M.
When f isjkWhen 1, formula (11) ensures yjThe value range of (2) and (14) can ensure gammajk＝yj(ii) a When f isjkWhen 0, the formula (13) can ensure γ jk0, thus proving that such a conversion mode is effective.
By the above method, the nonlinear objective function is linearized, and the model is converted into an MILP (Mix Integer Linear Programming). The model is solved by bringing the objective function (17) and the constraints (1) - (16) into a solver for mixed integer linear functions, such as an open source solver (scip).
Variable x output by solverivjCan determine the flight option. In some embodiments, the results of the solution may be displayed, for example, exported into a table for viewing and performing predetermined operations by an operator.
In some embodiments, the value of α may also be adjusted, and an open source solver is used to solve the value, so as to obtain a relationship between the cost of the selected flight and the value of α, which is expressed by a polygonal line graph. After the environment changes, the graph can provide reference for a decision maker to select the optimal alpha value, and further optimize the subsequent scheme generation process.
Reading the information of the predicted delivery volume and the flight from the database by the method, and processing data required by a subsequent mixed integer programming model; establishing a mixed integer programming model by taking the minimization of the order cost as a target, the time efficiency requirement of the delivery volume, the cargo hold volume of each flight as a limit and the discount price of the cargo hold volume 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; the optimal scheme is solved by utilizing a solver, the optimal scheme under different parameter levels is observed by adjusting parameters in the model, historical data and flight discounts are fully utilized, the operation model is linearized, and the reliability and the operation efficiency of the solution result are improved.
A schematic diagram of some embodiments of the disclosed logistics carrier selection scheme generation apparatus is shown in fig. 6.
The article information acquisition unit 601 can acquire the total shipment volume, shipment time limit, source address, and destination address of the article. In some embodiments, aggregation may be performed based on the order information, for each combination of items (source address and destination address), the order with the same source address and destination address and the equivalent delivery time limit is obtained, the delivery volume of the order is counted, and the total delivery volume is obtained. In other embodiments, for each combination of items (source address, destination address), the total delivery volume for the time interval corresponding to the delivery time limit may be estimated based on the historical delivery volumes.
The carrier information acquisition unit 602 can determine a physical distribution carrier whose origin, destination, and navigation date respectively match the source address, destination address, and shipment time limit, and acquire parameter information of the physical distribution carrier. In some embodiments, the matched logistics carrier can be searched according to the sending time limit, the source address and the destination address through a data interface with the airline company, and then the parameter information of the searched logistics carrier is extracted.
The plan generating unit 603 determines a set of selected logistics carriers and the cargo hold amount respectively occupied by each logistics carrier in the set by a solver operated by a computer based on a constraint condition and an objective function according to the total shipment amount, the shipment time limit and the parameter information of the logistics carriers; wherein the objective function includes: the sum of the costs is minimal when each logistics carrier in the collection orders a corresponding cargo capacity. In some embodiments, an objective function and a constraint function may be set in a solver, so as to bring in parameter information of total shipment volume, shipment time limit, and logistics carriers, and a set of selected logistics carriers and a cargo hold volume occupied by each logistics carrier in the set are obtained through a solving operation of the solver. In some embodiments, the solver may be a scip solver.
The device can obtain the logistics carrier selection scheme meeting the constraint condition through data collection and matching and calculation of the objective function 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 schematic structural diagram of one embodiment of the flow carrier selection scheme generation apparatus of the present disclosure is shown in fig. 7. The logistics carrier selection scheme generation device comprises a memory 701 and a processor 702. Wherein: the memory 701 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory is for storing instructions in the corresponding embodiments of the method of generating a logistics carrier selection scheme hereinbefore. 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, so as to improve the efficiency of formulating the logistics carrier selection scheme and optimize the formulated logistics carrier selection scheme.
In one embodiment, as also shown in fig. 8, the logistics carrier selection scheme generation apparatus 800 comprises 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 generation apparatus 800 can also be connected to an external storage device 805 through a storage interface 804 to call external data, and can also be connected to a network or another computer system (not shown) through a network interface 806. And will not be described in detail herein.
In the embodiment, the data instructions are stored in the memory, and the instructions are processed by the processor, so that the formulation efficiency of the logistics carrier selection scheme can be improved, and the formulated logistics carrier selection scheme is optimized.
A schematic diagram of some embodiments of the cargo compartment reservation system of the present disclosure is shown in fig. 9. The cargo hold reservation system includes any of the above-mentioned logistics vehicular selection scheme generating means 91. The cargo space reservation system further comprises a reservation unit 92 capable of reserving the cargo space of the logistics carriers according to the set of logistics carriers acquired from the logistics carrier selection scheme generation device 91 and the respective occupied cargo space amount of each logistics carrier in the set.
The system can improve the formulation efficiency of the logistics carrier selection scheme, optimize the formulated logistics carrier selection scheme, improve the preset 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 steps of the method in the corresponding embodiment of the logistics carrier selection scheme generation method, cargo compartment reservation method. As will be appreciated by one skilled in the art, 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, and the like) 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
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, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied 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 examples are intended only to illustrate the technical solutions of the present disclosure and not to limit them; although the present disclosure has been described in detail with reference to preferred embodiments, those of ordinary skill in the art will understand that: modifications to the specific embodiments of the disclosure or equivalent substitutions for parts of the technical features may still be made; all such modifications are intended to be included within the scope of the claims of this disclosure without departing from the spirit thereof.
Claims (15)
1. A method for generating a logistics carrier selection scheme, comprising:
acquiring the total shipment volume, the shipment time limit, the source address and the destination address of the article;
determining a logistics carrier with an origin, a destination and a navigation date respectively matched with the source address, the destination address and the delivery time limit, and acquiring parameter information of the logistics carrier;
determining, by a solver run by a computer, a set of the selected logistics carriers and the amount of cargo holds respectively occupied by each of the logistics carriers in the set, based on constraints and objective functions, according to the total shipment volume, the shipment time limit and the parameter information of the logistics carriers;
wherein the objective function comprises: the sum of the costs is minimal when each logistics carrier in the set orders a corresponding cargo capacity.
2. The method of claim 1, wherein the logistics carrier comprises at least one of a ship, a train, or an airplane for each shift.
3. The method according to claim 1, wherein the determining of the logistics carrier with the origin, the destination and the navigation date respectively matched with the source address, the destination address and the delivery time limit, and the obtaining of the 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 an operator of the logistics carriers;
and extracting the searched parameter information of the logistics carrier.
4. The method of claim 1, wherein the constraints comprise:
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 delivery amount of the goods;
the time of the article in the delivery time limit to the starting station of the logistics carrier is not later than the goods collection deadline of the logistics carrier;
the time limit of the delivery time is not earlier than the time limit of the delivery time for the logistics carriers to reach the destination addresses; and
the cargo capacity occupied by each logistics carrier is less than or equal to the residual carrying 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 occupied by the logistics carriers belonging to the priority logistics carriers in the set to the total delivery volume is larger than or equal to a preset ratio.
6. The method of claim 4 or 5, wherein the objective function is represented by a linear function with the cargo hold usage as a variable at each cargo hold ordering cost;
determining, by a solver operated by a computer, a set of the selected logistics carriers according to the total shipment volume, the shipment time limit, and the parameter information of the logistics carriers based on a constraint condition and an objective function, and the cargo hold volume occupied by each logistics carrier in the set respectively comprises:
and processing the linear function and the constraint condition by utilizing a solver of a mixed integer linear problem, and determining the selected set of the logistics carriers and the cargo hold quantity respectively occupied by each logistics carrier in the set.
7. The method of claim 6, wherein the objective function comprises:
respectively acquiring the sum of the product of the cargo hold usage amount of each cargo hold ordering cost and the corresponding cargo hold ordering cost for each logistics carrier in the set, and taking the sum as the cost of a single logistics carrier;
determining the sum of the costs according to the cost of the single stream carrier of each stream carrier in the set, and minimizing the sum of the costs;
the constraint further comprises:
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 carrier;
the cargo hold usage amount of the cargo hold ordering cost is less than or equal to the product of a predetermined constant and an ordering cost usage identifier, wherein the predetermined constant is not less 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, and the ordering cost usage identifiers of other cargo hold ordering costs are 0 for each logistics carrier;
the cargo hold usage amount of the cargo hold ordering cost is more 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;
the cargo hold usage 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 carrier.
8. The method of claim 7, wherein the cargo ordering unit price of the logistics carrier is stepped down as the ordered cargo volume increases;
the constraint further comprises:
the sum of the order cost use identifiers of the single logistics carriers is 1; and
the cargo capacity corresponding to the logistics carrier is larger than or equal to the product of the ordering unit price use identifier and the lower limit of the cargo capacity of the corresponding cargo ordering unit price, and is smaller than or equal to the product of the ordering unit price use identifier and the upper limit of the cargo capacity of the corresponding cargo ordering unit price.
9. The method of claim 1, wherein the obtaining a total shipment volume, a shipment time limit, a source address, and a destination address for an item comprises:
predicting a total shipment volume of the item between the source address and the destination address after a predetermined length of time;
determining the shipment time limit based on the predetermined length of time.
10. The method of claim 1, wherein the determining the logistics carrier having an origin, a destination, and a voyage date that match the source address, the destination address, and the departure time 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 conform to the originating site set and the destination site set and have travel time matched with the shipping time limit.
11. A cargo hold reservation method comprising: a method for generating a logistics carrier selection scheme according to any one of claims 1 to 10; and
the method comprises the steps of generating a set of logistics carriers according to the logistics carrier selection scheme, and reserving the cargo hold of the logistics carriers for the cargo hold amount occupied by each logistics carrier in the set.
12. A logistics carrier selection scheme generation apparatus comprising:
an article information acquisition unit configured to acquire a total shipment volume, a shipment time limit, a source address, and a destination address of an article;
a carrier information acquisition unit configured to determine a logistics carrier whose origin, destination and navigation date respectively match the source address, the destination address and the delivery time limit, and acquire parameter information of the logistics carrier;
a plan generating unit configured to determine, by a solver operated by a computer, a set of the selected logistics carriers and the amount of cargo holds occupied by each of the logistics carriers in the set, respectively, based on the total shipment volume, the shipment time limit and the parameter information of the logistics carriers, based on a constraint condition and an objective function;
wherein the objective function comprises: the sum of the costs is minimal when each logistics carrier in the set orders a corresponding cargo capacity.
13. A logistics carrier selection scheme generation apparatus comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of any of claims 1-10 based on instructions stored in the memory.
14. A cargo compartment reservation system comprising:
the logistics carrier selection scheme generation apparatus of claim 12 or 13; and
and the predetermined unit is configured to reserve the cargo hold of the logistics carrier according to the set of logistics carriers and the cargo hold amount occupied by each logistics carrier in the set.
15. 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 one of claims 1 to 11.
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