CN110309946A - Logistics route method and device for planning, computer-readable medium and logistics system - Google Patents
Logistics route method and device for planning, computer-readable medium and logistics system Download PDFInfo
- Publication number
- CN110309946A CN110309946A CN201910382917.5A CN201910382917A CN110309946A CN 110309946 A CN110309946 A CN 110309946A CN 201910382917 A CN201910382917 A CN 201910382917A CN 110309946 A CN110309946 A CN 110309946A
- Authority
- CN
- China
- Prior art keywords
- logistics route
- order data
- state
- logistics
- temperature
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 230000003044 adaptive effect Effects 0.000 claims abstract description 12
- 230000000763 evoking effect Effects 0.000 claims abstract description 8
- 230000000007 visual effect Effects 0.000 claims abstract description 8
- 238000002922 simulated annealing Methods 0.000 claims description 33
- 230000008569 process Effects 0.000 claims description 23
- 125000004122 cyclic group Chemical group 0.000 claims description 16
- 238000005070 sampling Methods 0.000 claims description 16
- 230000006870 function Effects 0.000 claims description 15
- 230000015654 memory Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 6
- 230000003247 decreasing effect Effects 0.000 claims description 5
- 230000032258 transport Effects 0.000 claims 1
- 238000000137 annealing Methods 0.000 abstract description 9
- 238000005457 optimization Methods 0.000 description 17
- 238000010586 diagram Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 3
- 238000012856 packing Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- 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"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman 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
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
Abstract
The present invention relates to a kind of logistics route method and device for planning, computer-readable medium and logistics systems.The logistics route planing method, comprising: the order data is cleaned in step S1, order data initialization；Step S2, pre-packaged are packaged the order data according to business constraint rule；Step S3 obtains the optimal solution of logistics route planning based on visual evoked potential estimation；Step S4, judges whether order data can continue to split, if can split, unpacks to order data, return step S2；Step S5 obtains optimal logistics route.Logistics route method and device for planning, storage medium and the logistics system provided through the invention is adjusted annealing parameter using adaptive model, and promotes search speed by being packaged and unpacking, and the logistics route planning problem of large-scale order form is successfully solved.
Description
Technical field
The present invention relates to logistics orders to dispense field, in particular to a kind of logistics route planning based on simulated annealing
Method and device, storage medium and logistics system.
Background technique
In actual logistics order dispatching problem, Path Planning is related to many constraint condition, such as hands over delivery
Time, cargo stack rule, the limitation of vehicle working out, vehicle running path limitation, time window limitation, flow on vehicle
Resource constraint and other artificial limitations etc..With the mathematical problem of total kilometrage or the minimum optimization aim of load-carrying mileage actually with it is upper
It states several factors and mixes coupling together.At this stage, in the case where meeting certain constraint condition, often by business personnel or dispatcher
Carry out rule-based and experience artificial allotment.And in huge logistics quantity on order, this artificial logistics deployment
Difficulty will substantially increase, and since scheduling rule is excessively complicated and manually the appearance of scheduling error occurs, often lead
Cause route programming result undesirable, so that Order splitting result is undesirable.Therefore, the skill based on mathematics how is introduced
Art finds a feasible optimal solution, promotes the effect and efficiency of path planning, is obtaining a large amount of research in the past.With
The rise in intelligence computation field, some optimization algorithms (such as genetic algorithm, particle swarm algorithm and simulated annealing etc.)
It is applied to solve complicated logistics route planning problem, achieves outstanding result.
But existing optimization algorithm (such as genetic algorithm, particle swarm algorithm, ant group algorithm and simulated annealing)
It is substantially based on didactic random algorithm, the complicated optimum problem that will be unable to solve is converted to seeks in huge solution space
The problem of looking for approximate optimal solution.Optimal solution is chosen relative to entire solution space is traversed, didactic method, which reduces, to need to search
Solution space, speed of searching optimization can be greatly speeded up, have preferable effect of optimization when solving complicated logistics route planning problem.When
When the larger of optimization problem, search space complexity are higher, such as in the case of huge order volume, common optimization is calculated
Method carries out a large amount of meaningless search in the initial stage, and the substantially extension of search time or algorithm is caused to fall into local optimum
Solution can not be jumped out and carry out larger range of search, will lead to the reduction of optimization precision.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of logistics route method and device for planning, computer-readable medium
And logistics system, annealing parameter is adjusted using adaptive model, and promote search speed by being packaged and unpacking, obtain
Optimal logistics route.
In order to solve the above technical problems, the present invention provides a kind of logistics route planning side based on simulated annealing
Method, comprising:
Step S1, order data initialization, cleans the order data；
Step S2, pre-packaged are packaged the order data according to business constraint rule；
Step S3 obtains the optimal solution of logistics route planning based on visual evoked potential estimation；
Step S4, judges whether the order data can continue to split, if can split, tears open to the order data
Packet, return step S2；
Step S5 obtains optimal logistics route.
According to one embodiment of present invention, step S3 is specifically included:
Step V1, definition status pond SP and its capacity n initialize n annealed initial state S0 and its corresponding temperature T0,
The initialization temperature is determined according to the variation of objective function in sampling several times；
Step V2, it is assumed that available computational resources (thread/process) are N, and each computing resource is from the state pool SP
One state S0 of middle random selection, and state S is obtained after then executing the inner cyclic process of certain lengthn, in inner cyclic process
The status criteria difference of middle basis at each temperature carries out temperature update；
Step V3, judges whether inner cyclic process meets termination condition, if not meeting, return step V2；
Step V4 chooses the optimal solution in the state pool SP.
According to one embodiment of present invention, it includes same provider that the business constraint rule, which includes the order data,
And same facility.
According to one embodiment of present invention, in step V1, temperature T is initialized0According to the following formula:
Wherein, Δ C is the mean value of objective function decreasing value in the sampling several times of init state progress, m1And m2Respectively
Objective function increases in sampling several times for this and reduced number, α receive generally for initialization time difference solution in control simulated annealing
The custom parameter of rate.
According to one embodiment of present invention, in state SnUnder temperature TnAccording to the following formula:
Wherein, δ is one customized a small amount of, σ (Tn-1) it is temperature Tn-1When status criteria it is poor, by SnIt is updated according to setting quasi-
Then the state in the state pool SP is updated.
According to one embodiment of present invention, in step V1, according to capacity random initializtion n of the state pool SP
Annealed initial state S0And its corresponding temperature T0, or according to n annealed initial state S of certain prior information initialization0And its it is right
The temperature T answered0。
According to one embodiment of present invention, in step V3, the state pool SP is updated using resource lock.
According to one embodiment of present invention, in step s 4, described if still there is the business constraint rule to be not carried out
Order data can continue to split.
According to one embodiment of present invention, in step V4, optimal solution is that update temperature institute is right in the SP of current state pond
The logistics route answered.
According to one embodiment of present invention, in step s 5, it is obtained under the more different business constraint rules more
A optimal solution, to obtain optimal logistics route.
The present invention also provides a kind of logistics route device for planning, comprising:
Data processing module is suitable for initializing order data, including cleans to the order data；
Packetization module is suitable for being packaged the order data according to business constraint rule；
Adaptive simulated annealing module is adapted for carrying out visual evoked potential estimation to obtain the optimal of logistics route planning
Solution；
It unpacks module, suitable for judging whether the order data can continue to split, and the order data can be carried out
It unpacks；
Object module is obtained, is suitable for obtaining optimal logistics route.
According to one embodiment of present invention, the Adaptive simulated annealing module includes:
State pool unit is established, definition status pond SP and its capacity n is suitable for, initializes n annealed initial state S0And its
Corresponding initialization temperature T0, the initialization temperature is determined according to the variation of objective function in sampling several times；
Interior circulation execution unit, including N number of computing resource, each computing resource be suitable for from the state pool SP with
Machine selects a state S0, state S is obtained after executing inner cyclic processn, according to shape at each temperature in the inner cyclic process
State standard deviation carries out temperature update；
Judgement terminates unit, suitable for judging whether the inner cyclic process meets termination condition；
Optimal solution unit is obtained, suitable for obtaining the optimal solution in the state pool SP.
According to one embodiment of present invention, the initialization temperature T0According to the following formula:
Wherein, Δ C is the mean value of objective function decreasing value in the sampling several times of init state progress, m1And m2Respectively
Objective function increases in sampling several times for this and reduced number, α receive generally for initialization time difference solution in control simulated annealing
The custom parameter of rate.
According to one embodiment of present invention, the state SnUnder temperature TnAccording to the following formula:
Wherein, δ is one customized a small amount of, σ (Tn-1) it is temperature Tn-1When status criteria it is poor, by SnIt is updated according to setting quasi-
Then the state in the state pool SP is updated.
According to one embodiment of present invention, in the packetization module, the business constraint rule includes the order
Data include same provider and same facility.
According to one embodiment of present invention, it is established in state pool unit described, according to the capacity of the state pool SP
N annealed initial state S of random initializtion0And its corresponding temperature T0, or initialize n according to certain prior information and anneal just
Beginning state S0And its corresponding temperature T0。
According to one embodiment of present invention, in the Adaptive simulated annealing module, using resource lock to update
State state pool SP.
According to one embodiment of present invention, in the module of unpacking, if still there is the business constraint rule to be not carried out,
Then the order data can continue to split.
According to one embodiment of present invention, the optimal solution is updated corresponding to temperature in presently described state pool SP
Logistics route.
According to one embodiment of present invention, in the acquisition object module, the more different business constraint rules
Multiple optimal solutions of lower acquisition, to obtain optimal logistics route.
The present invention also provides a kind of computer-readable mediums, are stored thereon with computer instruction, the computer instruction
The step of any one of aforementioned logistics route planing method is held when operation.
The present invention also provides a kind of logistics system, including memory and processor, being stored on the memory can
The computer instruction run on the processor, which is characterized in that the processor executes when running the computer instruction
The step of any one of aforementioned logistics route planing method.
The present invention is the logistics route planing method based on simulated annealing, and annealing ginseng is adjusted using adaptive model
Number is not necessarily to manual intervention, guarantees the effective use of computing resource, and is greatly lowered by the packing of order and strategy of unpacking
Optimize the time, promote search speed, successfully solves the logistics route planning problem of large-scale order form.
Detailed description of the invention
For the above objects, features and advantages of the present invention can be clearer and more comprehensible, below in conjunction with attached drawing to tool of the invention
Body embodiment elaborates, in which:
Fig. 1 is the flow chart of the logistics route planing method based on simulated annealing of one embodiment of the invention；
Fig. 2 is the state pool of one embodiment of the invention and the flow diagram of inner cyclic process；
Fig. 3 is the structural schematic diagram of the logistics route device for planning based on simulated annealing of one embodiment of the invention.
Specific embodiment
For the above objects, features and advantages of the present invention can be clearer and more comprehensible, below in conjunction with attached drawing to tool of the invention
Body embodiment elaborates.
In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, but the present invention can be with
It is different from other way described herein using other and implements, therefore the present invention is by the limit of following public specific embodiment
System.
As shown in the application and claims, unless context clearly prompts exceptional situation, " one ", "one", " one
The words such as kind " and/or "the" not refer in particular to odd number, may also comprise plural number.It is, in general, that term " includes " only prompts to wrap with "comprising"
Include clearly identify the step of and element, and these steps and element do not constitute one it is exclusive enumerate, method or apparatus
The step of may also including other or element.
When the embodiment of the present application is described in detail, for purposes of illustration only, indicating that the sectional view of device architecture can disobey general proportion work
Partial enlargement, and the schematic diagram is example, should not limit the range of the application protection herein.In addition, in practical system
It should include the three-dimensional space of length, width and depth in work.
For the convenience of description, herein may use such as " under ", " lower section ", " being lower than ", " following ", " top ", "upper"
Etc. spatial relationship word the relationships of an elements or features shown in the drawings and other elements or feature described.It will reason
Solve, these spatial relationship words be intended to encompass in use or device in operation, other than the direction described in attached drawing
Other directions.For example, being described as be in other elements or feature " below " or " under " if overturning the device in attached drawing
Or the direction of the element of " following " will be changed to " top " in the other elements or feature.Thus, illustrative word " under
Side " and " following " can include upper and lower both direction.Device may also have other directions (to be rotated by 90 ° or in its other party
To), therefore spatial relation description word used herein should be interpreted accordingly.In addition, it will also be understood that being referred to as when one layer at two layers
" between " when, it can be only layer between described two layers, or there may also be one or more intervenient layers.
In the context of this application, structure of the described fisrt feature in the "upper" of second feature may include first
Be formed as the embodiment directly contacted with second feature, also may include that other feature is formed between the first and second features
Embodiment, such first and second feature may not be direct contact.
By the prior art it is found that the thought source of simulated annealing in statistical thermodynamics solid matter it is annealed
Journey.In entire optimization process, with the reduction of temperature, new state is received with the probability being gradually reduced.Work as annealing process
When very long enough, it can prove that optimum results must position globally optimal solution.Simulated annealing is generally by interior circulation and outer circulation
Two layers of composition, wherein interior circulation is the repeated sampling process under Current Temperatures, outer circulation controls the decline of optimization process temperature.One
As for, since the optimization process of simulated annealing must smoothly make annealing temperature slow as far as possible enough, thus optimized
The reduction strategy of temperature and optimization efficiency are two big difficult points of algorithm in journey.
Fig. 1 is the flow chart of the logistics route planing method based on simulated annealing of one embodiment of the invention.Fig. 2 is
The state pool of one embodiment of the invention and the flow diagram of inner cyclic process.As shown, the present invention provides one kind to be based on
The logistics route planing method of simulated annealing, comprising:
Step S1, order data initialization.The initialization procedure includes cleaning to the order data.
Step S2, pre-packaged are packaged the order data according to business constraint rule.In one embodiment, business is about
Beam rule includes that order data includes same provider and same facility, i.e., by the order with same provider, same facility
It is packaged, to reduce the search space in annealing algorithm to logistics route.It is readily comprehensible, it is right according to different business constraint rules
The packing result of order data is different, is packaged, unpacks every time, is to be packaged to order data with different business constraint rule
With processing of unpacking, can illustrate hereinafter.
Step S3 obtains the optimal solution of logistics route planning based on visual evoked potential estimation.
Step S4, judges whether order data can continue to split, if can split, unpacks to order data, returns
Step S2.The step is equivalent to traversal business constraint rule, if there are also the business constraint not used rules, just to being packaged
Order data is unpacked, return step S2, is implemented to be packaged with the business constraint rule not used, is continued adaptively to anneal
Algorithm.In fact, order data packing and strategy of unpacking are increased, at the beginning of the optimizing phase when order data scale amount is very big
Phase reduces volumes of searches, and the optimization time is greatly reduced, and expands search range in optimization latter stage, simulated annealing is avoided to fall into part
It is optimal.
Step S5 obtains optimal logistics route.According under different business constraint rule, packaged data are carried out distributed
Visual evoked potential estimation, multiple optimal solutions obtained are compared, to obtain optimum state, though and it is somebody's turn to do stateful
Corresponding optimal logistics route.
Further, step S3 includes following 4 steps:
Step V1, definition status pond SP and its capacity n initialize n annealed initial state S0And its corresponding initialization
Temperature T0, the initialization temperature is determined according to the variation of objective function in sampling several times.
Step V2, under Distributed Architecture, it is assumed that available computational resources (thread/process) are N, are sent out each computing resource
One calculating task of cloth.In this way, each computing resource randomly chooses a state S0 from state pool SP, and then execute certain
State S is obtained after the inner cyclic process of lengthn, carried out in the inner cyclic process according to status criteria difference at each temperature
Temperature updates.
Step V3, judges whether inner cyclic process meets termination condition, if not meeting, return step V2.Assuming that certain is for the moment
It is carved with a computing resource of N ' (N ' < N) and is calculating circulation in annealing, if a certain interior circulation reaches termination condition, no longer distribute
New calculating task is completed circulation and state pool SP temperature in respectively to a computing resource of current N ' and is updated.With reference to Fig. 2,
State in state pool SP has been updated to Sn-1, corresponding temperature Tn-1And state Sn, corresponding temperature Tn。
Step V4 chooses the optimal solution in state pool SP if meeting termination condition.The optimal solution is adaptive as this time
The last solution of simulated annealing.Optimal solution can be understood as update temperature in the SP of current state pond and corresponding
Logistics route.
Preferably, initializing temperature T in step V10According to the following formula:
Wherein, Δ C is the mean value of objective function decreasing value in the sampling several times of init state progress, m1And m2Respectively
Objective function increases in sampling several times for this and reduced number, α receive generally for initialization time difference solution in control simulated annealing
The custom parameter of rate.
Preferably, in state SnUnder temperature TnAccording to the following formula:
Wherein, δ is one customized a small amount of, σ (Tn-1) it is temperature Tn-1When status criteria it is poor, by SnIt is updated according to setting quasi-
Then the state in the state pool SP is updated.
Logistics route planing method provided by the invention is using the basic subrack based on distributed self-adaption simulated annealing
Frame, on the basis of meeting all business constraints, phase and latter stage increase order according to certain business constraint rule before optimization
Be packaged and unpack strategy.Volumes of searches is reduced at initial stage optimizing phase, the optimization time can be greatly reduced, expands in optimization latter stage and searches
Rope range, avoids simulated annealing from falling into local optimum.The distributed simulation annealing algorithm ensure that the effective of computing resource
Utilize, adaptive strategy avoids the artificial adjustment of annealing parameter, optimize early period and latter stage heuritic approach accelerate it is whole
The solution of body simulated annealing successfully solves the logistics route planning problem of large-scale order form.
Preferably, in step V1, according to n annealed initial state S of capacity random initializtion of state pool SP0And its it is right
The temperature T answered0, or according to n annealed initial state S of certain prior information initialization0And its corresponding temperature T0。
Preferably, state pool SP is updated using resource lock in step V3, it is accurate to ensure to update result.
Preferably, optimal solution in step s3 is to update logistics route corresponding to temperature in the SP of current state pond.
Fig. 3 is the structural schematic diagram of the logistics route device for planning based on simulated annealing of one embodiment of the invention.
As shown, the present invention also provides a kind of logistics route device for planning 300 based on simulated annealing.The generating means
300 include:
Data processing module 310 is suitable for initializing order data, including cleans to the order data.
Packetization module 320 is suitable for being packaged order data according to business constraint rule.
Adaptive simulated annealing module 330 is adapted for carrying out visual evoked potential estimation to obtain logistics route planning
Optimal solution
Module of unpacking 340 suitable for judging whether order data can continue to split, and can unpack to order data.
Object module 350 is obtained, is suitable for obtaining optimal logistics route.
Preferably, Adaptive simulated annealing module 330 further comprises:
State pool unit 331 is established, definition status pond SP and its capacity n is suitable for, initializes n annealed initial state S0And
Its corresponding initialization temperature T0, the initialization temperature is determined according to the variation of objective function in sampling several times.
Interior circulation execution unit 332 is adapted for carrying out interior circulation, and interior circulation execution unit includes N number of computing resource, Mei Geji
It calculates resource and randomly chooses a state S0 from state pool SP, obtain state S after executing inner cyclic processn, in the interior circulation
Temperature update is carried out according to status criteria difference at each temperature in the process.
Judgement terminates unit 333, suitable for judging whether inner cyclic process meets termination condition.
Optimal solution unit 334 is obtained, suitable for obtaining the optimal solution in state pool SP.
Preferably, initialization temperature T0According to the following formula:
Wherein, Δ C is the mean value of objective function decreasing value in the sampling several times of init state progress, m1And m2Respectively
Objective function increases in sampling several times for this and reduced number, α receive generally for initialization time difference solution in control simulated annealing
The custom parameter of rate.
Preferably, state SnUnder temperature TnAccording to the following formula:
Wherein, δ is one customized a small amount of, σ (Tn-1) it is temperature Tn-1When status criteria it is poor, by SnIt is updated according to setting quasi-
Then the state in the state pool SP is updated.
Preferably, in the packetization module, it includes same provider and identical that business constraint rule, which includes order data,
Factory, it can the order data of same provider and same facility is implemented to be packaged.
Preferably, in establishing state pool unit, according to n annealed initial state of capacity random initializtion of state pool SP
S0And its corresponding temperature T0, or according to n annealed initial state S of certain prior information initialization0And its corresponding temperature T0。
Preferably, updating state pool SP using resource lock in Adaptive simulated annealing module.
Preferably, if still there is business constraint rule to be not carried out, order data can continue to split in module of unpacking.
Preferably, optimal solution is to update logistics route corresponding to temperature in the SP of current state pond.
Preferably, comparing the multiple optimal solutions obtained under different business constraint rule in obtaining object module, to obtain
Optimal logistics route.
The present invention also provides a kind of computer readable storage medium, computer readable storage medium is non-volatile memories
Medium or non-transitory storage media, are stored thereon with computer instruction, and computer instruction executes any of the above-described kind when running and is based on
Step corresponding to the logistics route planing method of simulated annealing, details are not described herein again.
The present invention also provides a kind of logistics system, including memory and processor, being stored on memory can locate
The computer instruction run on reason device, processor execute any of the above-described kind based on simulated annealing when running computer instruction
The step of logistics route planing method.
The application has used particular words to describe embodiments herein.As " one embodiment ", " embodiment ",
And/or " some embodiments " means a certain feature relevant at least one embodiment of the application, structure or feature.Therefore, it answers
Emphasize and it is noted that " embodiment " or " one embodiment " that is referred to twice or repeatedly in this specification in different location or
" alternate embodiment " is not necessarily meant to refer to the same embodiment.In addition, certain in one or more embodiments of the application
Feature, structure or feature can carry out combination appropriate.
The some aspects of order forecast method of the invention can completely by hardware execute, can completely by software (including
Firmware, resident software, microcode etc.) it executes, can also be executed by combination of hardware.Hardware above or software are referred to alternatively as
" data block ", " module ", " engine ", " unit ", " component " or " system ".Processor can be one or more dedicated integrated electricity
Road (ASIC), digital signal processor (DSP), digital signal processing device (DAPD), programmable logic device (PLD), scene
Programmable gate array (FPGA), processor, controller, microcontroller, microprocessor or a combination thereof.In addition, the application's is each
Aspect may show as the computer product being located in one or more computer-readable mediums, which includes computer-readable
Program coding.For example, computer-readable medium may include, but be not limited to, magnetic storage device is (for example, hard disk, floppy disk, magnetic
Band ...), CD (for example, compact disk CD, digital versatile disc DVD ...), smart card and flash memory device (for example, card,
Stick, key drive ...).
Computer-readable medium may include the propagation data signal containing computer program code in one, such as in base
Take or as carrier wave a part.There are many transmitting signal possibility form of expression, including electromagnetic form, light form etc.,
Or suitable combining form.Computer-readable medium can be any computer-readable in addition to computer readable storage medium
Medium, the medium can realize communication, propagation or transmission for making by being connected to an instruction execution system, device or equipment
Program.Program coding on computer-readable medium can be propagated by any suitable medium, including nothing
The combination of line electricity, cable, fiber optic cables, radiofrequency signal or similar mediums or any of above medium.
In addition, except clearly stating in non-claimed, the sequence of herein described processing element and sequence, digital alphabet
Using or other titles use, be not intended to limit the sequence of the application process and method.Although by each in above-mentioned disclosure
Kind of example discuss it is some it is now recognized that useful inventive embodiments, but it is to be understood that, such details only plays explanation
Purpose, appended claims are not limited in the embodiment disclosed, on the contrary, claim is intended to cover and all meets the application
The amendment and equivalent combinations of embodiment spirit and scope.For example, although system component described above can be set by hardware
It is standby to realize, but can also be only achieved by the solution of software, such as pacify on existing server or mobile device
Fill described system.
Similarly, it is noted that in order to simplify herein disclosed statement, to help real to one or more application
Apply the understanding of example, above in the description of the embodiment of the present application, sometimes by various features merger to one embodiment, attached drawing or
In descriptions thereof.But this disclosure method is not meant to mention in aspect ratio claim required for the application object
And feature it is more.In fact, the feature of embodiment will be less than whole features of the single embodiment of above-mentioned disclosure.
The number of description ingredient, number of attributes is used in some embodiments, it should be appreciated that such to be used for embodiment
The number of description has used qualifier " about ", " approximation " or " generally " to modify in some instances.Unless in addition saying
It is bright, " about ", " approximation " or " generally " show the variation that the number allows to have ± 20%.Correspondingly, in some embodiments
In, numerical parameter used in description and claims is approximation, approximation feature according to needed for separate embodiment
It can change.In some embodiments, numerical parameter is considered as defined significant digit and using the reservation of general digit
Method.Although the Numerical Range and parameter in some embodiments of the application for confirming its range range are approximation, specific real
It applies in example, being set in for such numerical value is reported as precisely as possible in feasible region.
Although the present invention is described with reference to current specific embodiment, those of ordinary skill in the art
It should be appreciated that above embodiment is intended merely to illustrate the present invention, can also make in the case where no disengaging spirit of that invention
Various equivalent change or replacement out, therefore, as long as to the variation of above-described embodiment, change in spirit of the invention
Type will all be fallen in the range of following claims.
Claims (10)
1. a kind of logistics route planing method, comprising:
Step S1, order data initialization, cleans the order data；
Step S2, pre-packaged are packaged the order data according to business constraint rule；
Step S3 obtains the optimal solution of logistics route planning based on visual evoked potential estimation；
Step S4, judges whether the order data can continue to split, if can split, unpacks to the order data,
Return step S2；
Step S5 obtains optimal logistics route.
2. logistics route planing method as described in claim 1, which is characterized in that step S3 includes:
Step V1, definition status pond SP and its capacity n initialize n annealed initial state S0And its corresponding initialization temperature
T0, the initialization temperature is determined according to the variation of objective function in sampling several times；
Step V2, it is assumed that available computational resources N, each computing resource randomly choose a shape from the state pool SP
State S0, and state S is obtained after then executing the inner cyclic process of certain lengthn, according at each temperature in the inner cyclic process
Status criteria difference carry out temperature update；
Step V3, judges whether inner cyclic process meets termination condition, if not meeting, return step V2；
Step V4 chooses the optimal solution in the state pool SP.
3. logistics route planing method as claimed in claim 2, which is characterized in that the initialization temperature T0According to the following formula:
Wherein, Δ C is the mean value of objective function decreasing value in the sampling several times of init state progress, m1And m2Respectively this
Objective function increases in sampling several times and reduced number, α are initialization time difference solution acceptance probability in control simulated annealing
Custom parameter.
4. logistics route planing method as claimed in claim 3, which is characterized in that in the state SnUnder temperature TnAccording to
Following formula:
Wherein, δ is one customized a small amount of, σ (Tn-1) it is temperature Tn-1When status criteria it is poor, by SnAccording to setting replacement criteria pair
State in the state pool SP is updated.
5. logistics route planing method as described in claim 1, which is characterized in that the business constraint rule includes described orders
Single packet contains same provider and same facility.
6. logistics route planing method as claimed in claim 2, which is characterized in that in step V1, according to the state pool
N annealed initial state S of capacity random initializtion of SP0And its corresponding temperature T0, or according to certain prior information initialization n
A annealed initial state S0And its corresponding temperature T0。
7. logistics route planing method as claimed in claim 2, which is characterized in that in step V3, come more using resource lock
The new state pool SP.
8. a kind of logistics route device for planning characterized by comprising
Data processing module is suitable for initializing order data, including cleans to the order data；
Packetization module is suitable for being packaged the order data according to business constraint rule；
Adaptive simulated annealing module is adapted for carrying out visual evoked potential estimation to obtain the optimal solution of logistics route planning；
It unpacks module, suitable for judging whether the order data can continue to split, and can unpack to the order data；
Object module is obtained, is suitable for obtaining optimal logistics route.
9. a kind of computer-readable medium, is stored thereon with computer instruction, which is characterized in that when the computer instruction is run
Perform claim requires the step of any one of 1 to 7 logistics route planing method.
10. a kind of logistics system, including memory and processor, it is stored with and can transports on the processor on the memory
Capable computer instruction, which is characterized in that perform claim requirement 1 to 7 is any when the processor runs the computer instruction
The step of item logistics route planing method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910382917.5A CN110309946A (en) | 2019-05-09 | 2019-05-09 | Logistics route method and device for planning, computer-readable medium and logistics system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910382917.5A CN110309946A (en) | 2019-05-09 | 2019-05-09 | Logistics route method and device for planning, computer-readable medium and logistics system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110309946A true CN110309946A (en) | 2019-10-08 |
Family
ID=68075509
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910382917.5A Pending CN110309946A (en) | 2019-05-09 | 2019-05-09 | Logistics route method and device for planning, computer-readable medium and logistics system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110309946A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111539674A (en) * | 2020-04-29 | 2020-08-14 | 众能联合数字技术有限公司 | Order combining method for logistics transportation of engineering machinery rental scene |
| CN112906973A (en) * | 2021-03-10 | 2021-06-04 | 浙江银江云计算技术有限公司 | Family doctor follow-up visit path recommendation method and system |
| CN115099755A (en) * | 2022-07-25 | 2022-09-23 | 深圳市中农网有限公司 | Method and device for splitting instant logistics order |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107491836A (en) * | 2017-08-10 | 2017-12-19 | 上海汽车集团股份有限公司 | A kind of distribution method of order, device and electronic equipment |
| CN108647810A (en) * | 2018-04-19 | 2018-10-12 | 安吉汽车物流股份有限公司 | The distribution method and device of order shipment, computer-readable medium |
| CN108764777A (en) * | 2018-04-26 | 2018-11-06 | 浙江工商大学 | Electronic logistic car dispatching method with time window and system |
| CN109583650A (en) * | 2018-11-30 | 2019-04-05 | 浙江工商大学 | A kind of method of the addressing of electric vehicle electrical changing station and logistics distribution combined dispatching |
-
2019
- 2019-05-09 CN CN201910382917.5A patent/CN110309946A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107491836A (en) * | 2017-08-10 | 2017-12-19 | 上海汽车集团股份有限公司 | A kind of distribution method of order, device and electronic equipment |
| CN108647810A (en) * | 2018-04-19 | 2018-10-12 | 安吉汽车物流股份有限公司 | The distribution method and device of order shipment, computer-readable medium |
| CN108764777A (en) * | 2018-04-26 | 2018-11-06 | 浙江工商大学 | Electronic logistic car dispatching method with time window and system |
| CN109583650A (en) * | 2018-11-30 | 2019-04-05 | 浙江工商大学 | A kind of method of the addressing of electric vehicle electrical changing station and logistics distribution combined dispatching |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111539674A (en) * | 2020-04-29 | 2020-08-14 | 众能联合数字技术有限公司 | Order combining method for logistics transportation of engineering machinery rental scene |
| CN112906973A (en) * | 2021-03-10 | 2021-06-04 | 浙江银江云计算技术有限公司 | Family doctor follow-up visit path recommendation method and system |
| CN112906973B (en) * | 2021-03-10 | 2023-10-24 | 浙江银江云计算技术有限公司 | Family doctor follow-up path recommendation method and system |
| CN115099755A (en) * | 2022-07-25 | 2022-09-23 | 深圳市中农网有限公司 | Method and device for splitting instant logistics order |
| CN115099755B (en) * | 2022-07-25 | 2022-11-11 | 深圳市中农网有限公司 | Method and device for splitting instant logistics order |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gutierrez-Rodríguez et al. | Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning | |
| AU2024200810A1 (en) | Training tree-based machine-learning modeling algorithms for predicting outputs and generating explanatory data | |
| US10949746B2 (en) | Efficient parallel training of a network model on multiple graphics processing units | |
| CN109146084B (en) | Machine learning method and device based on cloud computing | |
| CN104335232B (en) | Continuous time, spike neutral net was based on the simulation of event | |
| CN113033811B (en) | Processing method and device for two-quantum bit logic gate | |
| CN110309946A (en) | Logistics route method and device for planning, computer-readable medium and logistics system | |
| US11775721B2 (en) | Quantum circuit decomposition by integer programming | |
| CN109669452A (en) | A kind of cloud robot task dispatching method and system based on parallel intensified learning | |
| CN114915630B (en) | Task allocation method, network training method and device based on Internet of Things equipment | |
| US11847546B2 (en) | Automatic data preprocessing | |
| CN109839927B (en) | Method and device for robot path planning | |
| Thevenin et al. | Makespan minimisation for a parallel machine scheduling problem with preemption and job incompatibility | |
| Habib | Hands-on Q-learning with python: Practical Q-learning with openai gym, Keras, and tensorflow | |
| WO2020143848A2 (en) | Determining action selection policies of an execution device | |
| Lim et al. | Simulated-annealing-based hyper-heuristic for flexible job-shop scheduling | |
| KR101522306B1 (en) | A system and control method for a meta-heuristic algorithm utilizing similarity for performance enhancement | |
| US11922315B2 (en) | Neural adapter for classical machine learning (ML) models | |
| CN117201308A (en) | Network resource allocation method, system, storage medium and electronic equipment | |
| WO2020200774A1 (en) | Method for flexible, fast all-reduce on arbitrary tree topology | |
| US11823039B2 (en) | Safe and fast exploration for reinforcement learning using constrained action manifolds | |
| US11630703B2 (en) | Cluster update accelerator circuit | |
| US20200243165A1 (en) | Prediction model for determining whether feature vector of data in each of multiple input sequences should be added to that of the other data in the sequence | |
| JPWO2020054402A1 (en) | Neural network processing device, computer program, neural network manufacturing method, neural network data manufacturing method, neural network utilization device, and neural network miniaturization method | |
| Matis et al. | Reinforcement learning for weighted p-median problem |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191008 |