CN104866911B - For optimizing the device and method of logistics prestowage - Google Patents
For optimizing the device and method of logistics prestowage Download PDFInfo
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Abstract
It provides a kind of for optimizing the equipment of logistics prestowage, comprising: data capture unit is configured as obtaining data related with logistics prestowage；Loading pattern generates unit, is configured as generating loading pattern based on data related with logistics prestowage, and obtains the set of the least loading pattern of required vehicle number；Energy consumption calculation unit is configured as calculating the smallest Distribution path of energy consumption for each of set loading pattern；And output unit, it is configured as output optimal packing scheme and corresponding Distribution path, wherein the energy consumption of the Distribution path of the optimal packing scheme is the smallest in the energy consumption of the Distribution path of all loading patterns.It additionally provides a kind of for optimizing the method for logistics prestowage.
Description
Technical field
This application involves data analysis fields, and in particular to a kind of for optimizing the device and method of logistics prestowage.
Background technique
With the fast development of e-commerce and the continuous upgrading of modern consumption mode, logistics distribution demand increasingly increases
It is long.At the same time, as the continuous of resource is reduced and the aggravation of environmental pollution, the environmental pollution generated in delivery process and the energy
The problems such as consumption, is increasingly subject to the attention of people.
Logistics prestowage is an important link in delivery process, and the quality of stowage method not only influences whether required vehicle
Quantity, also will affect distribution time and Distribution path, to play large effect to the energy consumption generated in delivery process.It is logical
Cross the optimization method of research logistics prestowage, it is possible to reduce the energy consumption generated in logistics progress not only reduces the cost of enterprise,
It can reduce environmental pollution.
Prestowage problem can be described as: be made of multiple clients that receive, its kinds of goods volume and quality are respectively less than the specified load of bicycle
Weight and load volume make to load kinds of goods using which kind of assembly form to realize vehicle loading efficiency, reducing transportation cost
Vehicle number is as few as possible, and the utilization rate of vehicle is maximum, or makes the delivery profit of vehicle maximum.
However, during existing logistics prestowage, only with the volume of cargo, weight, stability and given
The conditions such as goods delivery sequence are as constraint, using least haulage vehicle number as objective function.So that existing method
Vehicle cost can only be optimized, do not consider the transportation cost such as oil consumption in transportational process.
Therefore, vehicle cost and oil consumption problem how are comprehensively considered to optimize logistics prestowage, are urgently to be resolved ask
Topic.
Summary of the invention
From it is traditional at least different for the logistics prestowage problem of optimization aim with vehicle number, the present invention with distribution vehicle number and
Energy consumption minimum guarantees that vehicle cost is minimum as dual-layer optimization target (wherein vehicle number is at least the first optimization aim), to realize
In the case where reduce transportation cost to greatest extent so that the lowest cost.As shown in figure 4, equally using two cars
It completes in the case that cargo loads (vehicle cost is identical), it can be fully loaded by combination to distribution point in vehicle and each vehicle
The adjustment of rate reduces dispatching distance and reduces distribution cost, to realize the reduction of Logistics Total Cost.
According to the first aspect of the invention, it provides a kind of for optimizing the equipment of logistics prestowage, comprising: data acquisition list
Member is configured as obtaining data related with logistics prestowage；Loading pattern generates unit, is configured as being based on matching with logistics being loaded with
The data of pass generate loading pattern, and the set of vehicle number least loading pattern needed for obtaining；Energy consumption calculation unit is matched
It is set to and calculates the smallest Distribution path of energy consumption for each of set loading pattern；And output unit, it is configured
For output optimal packing scheme and corresponding Distribution path, wherein the energy consumption of the Distribution path of the optimal packing scheme is institute
Have the smallest in the energy consumption of the Distribution path of loading pattern.
In one embodiment, the data related with logistics prestowage include goods information, client's point information, vehicle letter
Breath and dispatching order.
In one embodiment, the loading pattern generates unit and is configured as: first, in accordance with first descending the website filled afterwards suitable
Sequence generates initial load scheme, then generates loading pattern adjusted and being adjusted to cargo.
In one embodiment, the adjustment includes following one or more: sequence adjustment is put in placement position adjustment
It is adjusted with vehicle combination.
In one embodiment, the energy consumption calculation unit is configured as: the place to be passed through based on vehicle and road network letter
Corresponding traffic condition when breath, vehicle departure time, vehicle pass through respective stretch, passage path algorithm are calculated for the collection
The smallest Distribution path of energy consumption of each of conjunction loading pattern.
In one embodiment, the energy consumption calculation unit is configured as: according to the difference of speed and vehicle load, will be matched
Path is sent to be divided into several segments；Calculate the revised Energy consumption factor in each segment；And based in each segment
The corresponding length of revised Energy consumption factor and each segment calculates the energy consumption of the Distribution path.
In one embodiment, the energy consumption calculation unit is configured as: speed and load-carrying based on vehicle, is calculated each
Revised Energy consumption factor in a segment.
According to the second aspect of the invention, it provides a kind of for optimizing the method for logistics prestowage, comprising: acquisition and logistics
The related data of prestowage；Loading pattern is generated based on data related with logistics prestowage, and it is least to obtain required vehicle number
The set of loading pattern；The smallest Distribution path of energy consumption is calculated for each of set loading pattern；And output
Optimal packing scheme and corresponding Distribution path, wherein the energy consumption of the Distribution path of the optimal packing scheme is all loadings
It is the smallest in the energy consumption of the Distribution path of scheme.
In one embodiment, the data related with logistics prestowage include goods information, client's point information, vehicle letter
Breath and dispatching order.
In one embodiment, generating loading pattern based on data related with logistics prestowage includes: first, in accordance with elder generation
The website sequence filled after lower generates initial load scheme, then generates loading side adjusted and being adjusted to cargo
Case.
In one embodiment, the adjustment includes following one or more: sequence adjustment is put in placement position adjustment
It is adjusted with vehicle combination.
In one embodiment, the place to be passed through based on vehicle and road network information, vehicle departure time, vehicle are by phase
Corresponding traffic condition when answering section, passage path algorithm calculate the energy consumption for each of set loading pattern
The smallest Distribution path.
In one embodiment, this method comprises: according to the difference of speed and vehicle load, Distribution path is divided into several
Segment；Calculate the revised Energy consumption factor in each segment；And based on the revised energy consumption in each segment because
The corresponding length of son and each segment, calculates the energy consumption of the Distribution path.
In one embodiment, speed and load-carrying based on vehicle, calculate revised energy consumption in each segment because
Son.
Using the present invention, it can be effectively improved the deficiency of existing loading pattern, it can be on the basis of optimizing vehicle cost
The optimization for carrying out transportation cost, to further reduced Logistics Total Cost and reduce environmental pollution.
Detailed description of the invention
By the detailed description below in conjunction with attached drawing, above and other feature of the invention be will become more apparent,
In:
Fig. 1 is to show the block diagram of the equipment for optimizing logistics prestowage of an example embodiment according to the present invention.
Fig. 2 is to show the schematic diagram of the vehicle combination adjustment of an example embodiment according to the present invention.
Fig. 3 is to show the time relationship between the distribution point position and distribution point of an example embodiment according to the present invention
Schematic diagram.
Fig. 4 is to show the schematic diagram of the principle of an example embodiment according to the present invention.
Fig. 5 is to show the flow chart of the method for optimizing logistics prestowage of an example embodiment according to the present invention.
Specific embodiment
In the following, will become to the description of specific embodiments of the present invention, the principle of the present invention and realization in conjunction with the accompanying drawings
It obtains obviously.It should be noted that the present invention should not be limited to specific embodiments described below.In addition, for simplicity saving
The detailed description of well-known technique unrelated to the invention is omited.
Fig. 1 is to show the block diagram of the equipment for optimizing logistics prestowage of an example embodiment according to the present invention.Such as
Shown in Fig. 1, equipment 10 includes that data capture unit 110, loading pattern generation unit 120, energy consumption calculation unit 130 and output are single
Member 140.In the following, the operation of each unit in equipment 10 of the detailed description for optimizing logistics prestowage.
Data capture unit 110 is configured as obtaining data 101 related with logistics prestowage.For example, the logistics prestowage number
According to 101 may include goods information (size, weight etc.), client's point information, information of vehicles (such as size, maximum load etc.) and
Dispense order.
Loading pattern generates unit 120 and is configured as generating loading pattern based on data related with logistics prestowage, and
The set of the least loading pattern of vehicle number needed for obtaining.For example, after loading pattern generation unit 120 can be first, in accordance with first descending
The website sequence of dress generates initial load scheme, then generates loading pattern adjusted and being adjusted to cargo.So
Afterwards, the smallest vehicle number is found from all vehicle numbers recorded, and filters out corresponding to the minimum vehicle number and owns
Loading pattern forms the set of the least loading pattern of vehicle number, includes one or more loading patterns in the set.
In this application, above-mentioned adjustment may include following one or more: sequence adjustment is put in placement position adjustment
It is adjusted with vehicle combination.Hereinafter, these three adjustment modes are introduced in detail.
(1) placement position adjusts: placement position adjustment is carried out for certain a kind of cargo of some distribution point, often into
Placement position adjustment of row, i.e., by the existing number+1 for putting orientation.For example, it is assumed that putting orientation now is 1, then it is enabled to add
Become 2 after 1；Assume that putting orientation now is 6, then becomes 1 after enabling it add 1 again.Specific cargo puts bearing definition such as the following table 1
It is shown.Note that placement position adjustment is carried out as unit of type of merchandize.
| Put orientation | The length in corresponding three direction of container length |
| 1 | Length |
| 2 | It is wide, long, high |
| 3 | It is long, high, wide |
| 4 | High, length and width |
| 5 | It is wide, high, long |
| 6 | It is high, wide, long |
Table 1
(2) put sequence to adjust: putting sequence adjustment is operated between the variety classes cargo to a certain distribution point,
Sequence is put in every variation for carrying out a cargo and putting sequence, the two class cargos exchange that some distribution point is randomly generated.Note that pendulum
Putting forward sequence adjustment is carried out as unit of type of merchandize.
(3) vehicle combination adjusts: vehicle combination adjustment is changed to the distribution point combination in vehicle.The tool of the operation
Body method is to select two adjacent vehicles every time, is adjusted to the vehicle where the combination and distribution point of distribution point.Vehicle
Combination adjustment specific method it is as shown in Figure 2.As shown in Fig. 2, 5 distribution points can produce after vehicle combination adjusts
A variety of vehicle combinations.
Preferably, after the operating process of above-mentioned 3 kinds of adjustment modes may is that vehicle combination adjustment of every progress, etc.
Placement position adjustment is performed a plurality of times to probability and puts sequence adjustment operation.
Energy consumption calculation unit 130 is configured as calculating the smallest dispatching road of energy consumption for each of set loading pattern
Diameter.Preferably, energy consumption calculation unit 130 can be based on the place and road network information that vehicle to be passed through, and passage path algorithm is counted
Calculate the smallest Distribution path of energy consumption for each of set loading pattern.
For example, energy consumption calculation unit 130 can be based on the customer site that each car to be passed through, passage path algorithm is (such as
Dijkstra, A* algorithm etc.) calculate each car Distribution path.Then believed according to the road network of input, departure time, road conditions etc.
Breath, calculates the dispatching energy consumption of each car.The energy consumption is the dynamic energy consumption with speed and vehicle load variation and variation.
For example, the carbon balance method formula according to listed by GB/T19233-2003, the available Fuel consumption factor
Formula:
FC=f(V)
In above formula, FC is the fuel consumption factor (unit is kg/100km), and V is car speed (unit is km/h).
In this application, to basal energy expenditure consumption models carry out quality amendment, obtain revised Fuel consumption because
The formula of son is as follows:
FCM, V=α(M-M0)+FC
Wherein, FCM, VTo pass through the revised comprehensive fuel consumption factor (unit is kg/100km) of quality, α is amendment system
Number (value 1.09E-3/100km), M are vehicle actual mass (unit is kg), and M0 is vehicle gross (unit is kg).
Preferably, according to speed and vehicle load difference, the dispatching route of distribution project k can be divided into n segment, segment
The length of w is L (w), and the speed and load-carrying in same segment are fixed values, and corresponding Energy consumption factor is also fixed value, and different
The speed of segment or load-carrying are different, and corresponding Energy consumption factor is also different.Therefore, the meter of the total energy consumption of loading pattern k
It is as follows to calculate formula:
Wherein, FCm,VFor the Energy consumption factor on segment w；L (w) is the length of segment w, EkFor the energy consumption of section w.
Fig. 1 is returned to, output unit 140 is configured as output optimal packing scheme and corresponding Distribution path 102.Wherein,
The energy consumption of the Distribution path of optimal packing scheme is the smallest in the energy consumption of the Distribution path of all loading patterns.
In the following, describing the operation of equipment 10 shown in FIG. 1 by a specific example.
First, it is assumed that the demand of information of vehicles, goods information and each distribution point that data capture unit 1 10 obtains
And arrival time window is required as shown in following table 2-4:
| Vehicle name | Long (m) | Wide (m) | High (m) | It is self-possessed (kg) | Payload ratings (kg) |
| East wind Little Caesar | 3 | 2 | 2 | 2000 | 1000 |
Table 2- information of vehicles
| Type of merchandize | Long (m) | Wide (m) | High (m) | Weight (kg) |
| 1 | 0.75 | 0.3 | 0.5 | 3.0 |
| 2 | 0.6 | 0.45 | 0.6 | 8.5 |
| 3 | 0.825 | 0.5 | 0.8 | 18 |
Table 3- goods information
Demand and arrival time the window requirement of each distribution point of table 4-
Fig. 3 is to show the time relationship between the distribution point position and distribution point of an example embodiment according to the present invention
Schematic diagram.With reference to Fig. 3, it is assumed that the distance between distribution point relationship is (unit is km) as shown in Table 5 below:
The distance between table 5- distribution point
Traffic condition is assumed: due to more crowded in morning peak period road, Vehicle Speed is slower.Therefore, simple false
If from the Vehicle Speed of distribution point being 40km/h before 9 points, it is from the Vehicle Speed of distribution point after 9 points
45km/h.
Loading pattern, which generates unit 120, can generate loading pattern based on above-mentioned data related with logistics prestowage, and
The set of the least loading pattern of vehicle number needed for obtaining.In this example, according to first descend the website filled afterwards sequence, loading pattern
It is as shown in Table 6 below to generate the initial load scheme that unit 120 generates:
Table 6- loading pattern
After obtaining initial load scheme, after loading pattern generation unit 120 generates adjustment and being adjusted to cargo
Loading pattern.Then, the smallest vehicle number is found from all vehicle numbers recorded, and filters out the minimum vehicle number
Corresponding all loading patterns form the set of the least loading pattern of vehicle number, include one or more in the set
Loading pattern.
Energy consumption calculation unit 130 calculates the smallest Distribution path of energy consumption for each of above-mentioned set loading pattern.
Specifically, energy consumption calculation unit 130 utilizes Fuel consumption factor calculation formula, can be in conjunction with road dynamic information
The total energy consumption of each scheme is calculated.
By taking medium transporter as an example,
FC=103.633/V-0.104V+8.560 × 10-4V2+6.163
In this example, the revised Fuel consumption of quality is because of subformula are as follows:
FCM, V=α(M-M0)+103.633/V-0.104V+8.560 × 10-4V2+6.163
Wherein, FCm, v are by the revised comprehensive fuel consumption factor (unit is kg/100km) of quality, and α is amendment
Coefficient (value 1.09E-3/100km), M are vehicle actual mass (unit is kg), M0For vehicle gross, (unit is
Kg), V is average speed (unit is km/h).The energy consumption for each loading pattern that energy consumption calculation unit 130 is calculated is such as
Shown in the following table 7:
| Serial number | Loading pattern adjusted | Scheme energy consumption (kg) |
| 1 | A-B-C-E-A；A-D-A | 16.585 |
| 2 | A-B-D-E-A；A-C-A | 15.033 |
| 3 | A-C-D-E-A；A-B-A | 13.298 |
| 4 | A-B-C-A；A-D-E-A | 15.558 |
| 5 | A-B-D-A；A-C-E-A | 11.9573 |
| 6 | A-B-E-A；A-C-D-A | 14.398 |
Table 7- loading pattern adjusted
For least energy consumption 11.9573 (the 5th row) in the above table, the calculating process of energy consumption is described in detail.
First car:
Second car:
The total energy consumption of the distribution project are as follows: 8.10+3.8573=11.9573 (kg)
Finally, output unit 140 exports optimal packing scheme and corresponding Distribution path, as shown in Table 8 below:
Table 8- optimal packing scheme
That is, in optimal packing scheme, the load-carrying of vehicle 1 is 295kg, the load of vehicle 2 after optimization by equipment 10
It is 334kg again, the two relative equilibrium, and also total travel distance thus is also reduced.
Using the present embodiment, the optimization of transportation cost can be carried out on the basis of optimizing vehicle cost, is further decreased
Logistics Total Cost simultaneously reduces environmental pollution.
Fig. 5 is to show the flow chart of the method for optimizing logistics prestowage of an example embodiment according to the present invention.
As shown in figure 5, method 50 starts at step S510.
In step S520, data related with logistics prestowage are obtained.For example, the data may include goods information (size,
Weight etc.), client's point information, information of vehicles (such as size, maximum load etc.) and dispatching order.
In step S530, loading pattern is generated based on data related with logistics prestowage, and obtains required vehicle number most
The set of few loading pattern.For example, can then lead to first, in accordance with first descending the website filled afterwards sequence to generate initial load scheme
It crosses and cargo is adjusted and generates loading pattern adjusted.Then, minimum is found from all vehicle numbers recorded
Vehicle number, and filter out all loading patterns corresponding to the minimum vehicle number, form the least loading pattern of vehicle number
Gather, includes one or more loading patterns in the set.In this application, above-mentioned adjustment may include with next or more
Multiple: sequence adjustment and vehicle combination adjustment are put in placement position adjustment.
In step S540, the smallest Distribution path of energy consumption is calculated for each of set loading pattern.It is preferred that
Ground, can be based on the place and road network information that vehicle to be passed through, and passage path algorithm (such as Dijkstra, A* algorithm) calculates
For the smallest Distribution path of energy consumption of each of set loading pattern.
In step S550, optimal packing scheme and corresponding Distribution path are exported, wherein the optimal packing scheme is matched
Send path energy consumption be all loading patterns Distribution path energy consumption in it is the smallest.
Finally, method 50 terminates at step S560.
It should be understood that the above embodiment of the present invention can pass through the combination of both software, hardware or software and hardware
To realize.For example, the various assemblies in equipment in above-described embodiment can realize that these devices include by a variety of devices
But it is not limited to: analog circuit, digital circuit, general processor, Digital Signal Processing (DSP) circuit, programmable processor, dedicated
Integrated circuit (ASIC), field programmable gate array (FPGA), programmable logic device (CPLD), etc..
In addition, it will be understood to those skilled in the art that initial parameter described in the embodiment of the present invention can store
In local data base, it also can store in distributed data base or can store in remote data base.
In addition, the embodiment of the present invention disclosed herein can be realized on computer program product.More specifically, should
Computer program product is a kind of following product: having computer-readable medium, coding has calculating on computer-readable medium
Machine program logic, when being performed on the computing device, it is of the invention to realize which provides relevant operation
Above-mentioned technical proposal.When executing at least one processor in computing system, computer program logic holds processor
Operation described in the row embodiment of the present invention (method).This set of the invention is typically provided as being arranged or encode in such as light
Software, code and/or other data structures on the computer-readable medium of medium (such as CD-ROM), floppy disk or hard disk etc.,
Or other media or one or more moulds of such as one or more ROM or the firmware on RAM or PROM chip or microcode
Downloadable software image, shared data bank in block etc..Software or firmware or this configuration are mountable on the computing device, with
So that the one or more processors calculated in equipment execute technical solution described in the embodiment of the present invention.
Although having been combined the preferred embodiment of the present invention above shows the present invention, those skilled in the art will
It will be appreciated that without departing from the spirit and scope of the present invention, can carry out various modifications, replace and change to the present invention
Become.Therefore, the present invention should not be limited by above-described embodiment, and should be limited by appended claims and its equivalent.
Claims (12)
1. a kind of for optimizing the equipment of logistics prestowage, comprising:
Data capture unit is configured as obtaining data related with logistics prestowage；
Loading pattern generates unit, is configured as generating loading pattern based on data related with logistics prestowage, and obtain institute
Need the set of the least loading pattern of vehicle number；
Energy consumption calculation unit is configured as calculating the smallest dispatching road of energy consumption for each of set loading pattern
Diameter；And
Output unit is configured as output optimal packing scheme and corresponding Distribution path, wherein the optimal packing scheme
The energy consumption of Distribution path is the smallest in the energy consumption of the Distribution path of all loading patterns；
Wherein, the energy consumption calculation unit is configured as: according to the difference of speed and vehicle load, Distribution path being divided into several
Segment；Calculate the revised Energy consumption factor in each segment；And based on the revised energy consumption in each segment because
The corresponding length of son and each segment, calculates the energy consumption of the Distribution path,
Revised Energy consumption factor formula are as follows:
FCM, V=α (M-M0)+103.633/V-0.104V+8.560×10-4V2+6.163
Wherein, FCm, v are revised Energy consumption factors, and unit is kg/100km；α is correction factor, value 1.09E-3/
100km；M is vehicle actual mass, and unit is kg；M0For vehicle gross, unit is kg；V is average speed, and unit is
km/h。
2. equipment according to claim 1, wherein the data related with logistics prestowage include goods information, client
Point information, information of vehicles and dispatching order.
3. equipment according to claim 1, wherein the loading pattern generates unit and is configured as: under first
The website sequence filled afterwards generates initial load scheme, then generates loading pattern adjusted and being adjusted to cargo.
4. equipment according to claim 3, wherein the adjustment includes following one or more: placement position adjustment,
Put sequence adjustment and vehicle combination adjustment.
5. equipment according to claim 1, wherein the energy consumption calculation unit is configured as: being passed through based on vehicle
Place and road network information, vehicle departure time, vehicle pass through corresponding traffic condition when respective stretch, and passage path algorithm is counted
Calculate the smallest Distribution path of energy consumption for each of set loading pattern.
6. equipment according to claim 1, wherein the energy consumption calculation unit is configured as: speed based on vehicle and
Load-carrying calculates the revised Energy consumption factor in each segment.
7. a kind of for optimizing the method for logistics prestowage, comprising:
Obtain data related with logistics prestowage；
Loading pattern is generated based on data related with logistics prestowage, and obtains the collection of the least loading pattern of required vehicle number
It closes；
The smallest Distribution path of energy consumption is calculated for each of set loading pattern；And
Export optimal packing scheme and corresponding Distribution path, wherein the energy consumption of the Distribution path of the optimal packing scheme is
It is the smallest in the energy consumption of the Distribution path of all loading patterns；
Wherein, according to the difference of speed and vehicle load, Distribution path is divided into several segments；Calculate repairing in each segment
Energy consumption factor after just；And the corresponding length based on revised Energy consumption factor and each segment in each segment,
The energy consumption of the Distribution path is calculated,
Revised Energy consumption factor formula are as follows:
FCm,V=α (M-M0)+103.633/V-0.104V+8.560×10-4V2+6.163
Wherein, FCm, v are revised Energy consumption factors, and unit is kg/100km；α is correction factor, value 1.09E-3/
100km；M is vehicle actual mass, and unit is kg；M0For vehicle gross, unit is kg；V is average speed, and unit is
km/h。
8. according to the method described in claim 7, wherein, the data related with logistics prestowage include goods information, client
Point information, information of vehicles and dispatching order.
9. according to the method described in claim 7, wherein, generating loading pattern packet based on data related with logistics prestowage
It includes: first, in accordance with first descending the website sequence filled afterwards to generate initial load scheme, then generating tune and being adjusted to cargo
Loading pattern after whole.
10. according to the method described in claim 9, wherein, the adjustment includes following one or more: placement position tune
It is whole, put sequence adjustment and vehicle combination adjustment.
11. according to the method described in claim 7, wherein, when the place to be passed through based on vehicle and road network information, vehicle are set out
Between, vehicle corresponding traffic condition when passing through respective stretch, passage path algorithm calculates for each of described set
The smallest Distribution path of the energy consumption of loading pattern.
12. according to the method described in claim 7, wherein, speed and load-carrying based on vehicle calculate repairing in each segment
Energy consumption factor after just.
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| CN108960747B (en) * | 2018-08-21 | 2021-10-01 | 安吉汽车物流股份有限公司 | Logistics scheduling optimization method and device, storage medium and terminal |
| CN108846623B (en) * | 2018-09-17 | 2021-02-19 | 安吉汽车物流股份有限公司 | Whole vehicle logistics scheduling method and device based on multi-target ant colony algorithm, storage medium and terminal |
| CN109740910A (en) * | 2018-12-27 | 2019-05-10 | 秒针信息技术有限公司 | Haulage vehicle determines method and apparatus |
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| CN113450005B (en) * | 2021-07-02 | 2022-06-07 | 北京科技大学 | Cluster management scheduling method and device for unmanned vehicles in closed area |
| CN115809842B (en) * | 2022-12-12 | 2024-05-17 | 中外运空运有限公司 | Implementation method and system for intelligent allocation of air freight |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101673382A (en) * | 2009-10-21 | 2010-03-17 | 北京交通大学 | Combined optimization method for agricultural chain-operation logistics delivering and loading-distribution |
| CN102496096A (en) * | 2011-11-25 | 2012-06-13 | 深圳市赛格导航科技股份有限公司 | High-efficient logistic scheduling system and method |
-
2014
- 2014-02-21 CN CN201410059945.0A patent/CN104866911B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101673382A (en) * | 2009-10-21 | 2010-03-17 | 北京交通大学 | Combined optimization method for agricultural chain-operation logistics delivering and loading-distribution |
| CN102496096A (en) * | 2011-11-25 | 2012-06-13 | 深圳市赛格导航科技股份有限公司 | High-efficient logistic scheduling system and method |
Non-Patent Citations (2)
| Title |
|---|
| "Optimisation of MSW collection routes for minimum fuel consumption using 3D GIS modelling";Tavares G等;《Waste Management》;20081231;第29卷(第3期);第1176-1185页 * |
| "Optimizing the VRP by minimizing fuel consumption";KUO Y等;《Management Of Environmental Quality: An International Journal》;20111231;第22卷(第4期);第440-450页 * |
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