CN108267954A - A kind of punctual Distribution path planning algorithm of the cutter with hard time window - Google Patents
A kind of punctual Distribution path planning algorithm of the cutter with hard time window Download PDFInfo
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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Abstract
The present invention provides a kind of punctual Distribution path planning algorithms of cutter with hard time window.First, Tools Requirement by considering station is detachable, for each procedure arrangement distribution time window of station, establish the majorized function using process average satisfaction and distribution vehicle number as target, the actual conditions in the punctual distribution vehicle path of workshop cutter are more accurately reflected, make the distribution project based on this formulation relatively reliable；Then, model solution is carried out using genetic algorithm, multiple solutions in search space are assessed, reduce the risk that traditional optimization algorithm is easily trapped into locally optimal solution, using the transition rule of probability come guidance search direction so that algorithm, which has, learns by oneself habit and adaptivity.
Description
Technical field
The invention belongs to logistics route planning control technical fields, and in particular to a kind of cutter with hard time window of workshop
Punctual Distribution path planning algorithm.
Background technology
With the rise and development of current home and abroad logistics distribution industry, the application of the logistics distribution the relevant technologies in workshop
Paid attention to more and more with development.As particularly important resources of production a kind of in NC lathing, dispense is also cutter
The important component of Workshop Production logistics system, it and production process are lived with the logistics of enterprises production process
Dynamic, punctual, the efficient dispatching of cutter is to produce stable, Effec-tive Function important leverage.
Current most of Discrete Manufacturing Enterprises, the cutter Distribution path Technical comparing of workshop is original, information-based journey
Degree is not also high, and the workshop of exhausted most enterprise is not also using advanced intelligent delivery system.Cutter in workshop, which is matched, to be passed through
Cheng Zhong typically dispenses personnel and is dispensed the required all cutters of workshop before station goes into operation according to production operation scheduling
To each station, which results in cutter overstocking by station.With the proposition of JIT production mode, enterprise is in order to reduce life
Waste caused by cutter is overstock by station during production, only when needed, amount on demand produces enterprise requirements process
Go out required product.Therefore the cutter dispatching in workshop should also be as being when process processing needs, and just need station
Cutter is dispensed into the station.
Vehicle Routing Problems (Vehicle Routing Problem, VRP) are in nineteen fifty-nine by Dantzig and Ramser
It is put forward for the first time, Vehicle Routing Problems (VRP's) of common logistics distribution is generally described as：A certain number of clients, respectively
There is the cargo demand of different number, home-delivery center provides cargo to client, is responsible for sending cargo by a fleet, it is appropriate to organize
Traffic route, target are so that the demand of client is met, and can reach that such as distance is most short, cost under certain constraint
The purpose of minimum, consuming time minimum.
But workshop cutter Distribution path problem and most of social logistics VRP problems are different, at present in workshop knife
In the research for having Distribution path problem, majority is all directly to apply mechanically the mathematical model for belonging to social logistics VRP, and only algorithm is carried out
It improves, and has ignored practical workshop cutter delivery process.
Invention content
For overcome the deficiencies in the prior art, the present invention provides a kind of cutter with hard time window punctual Distribution path planning
Algorithm.By being combed to workshop cutter delivery process, the difference of comparison workshop cutter dispatching problem and general VRP problems
Point considers that the Tools Requirement of station is detachable, is each process addition service hard time window of station, establishes with hard time window
The model of the punctual Distribution path planning of cutter, the model is with process average satisfaction highest and the minimum optimization of distribution vehicle quantity
Target, in constraints, it is contemplated that time window, each process can only be by a trolley service, each process in a subtask
Required tool type, quantity are known etc., and then the model of foundation is solved with genetic algorithm.
A kind of punctual Distribution path planning algorithm of the cutter with hard time window, it is characterised in that include the following steps：
Step 1：The on-stream time of each process of station and demand, the hard time of each process in known cutter warehouse center
Window, closed type vehicle route, and each process only has a vehicle dispatching, determines decision variable as follows：
Wherein, xijkTo represent the variable of traveling sequence and distribution trolley between process, yikFor represent process by which
The variable of vehicle dispatching, i, j represent process number, i=1,2 ..., N, j=1, and 2 ..., N, N are process sum, and k represents vehicle
Number, k=1,2 ..., K, K are vehicle fleet；
Step 2：Establish inequality constraints as follows respectively：
tid＜ ti＜ tiu (3)
Wherein, tidRepresent the time window lower limit of process i, tiuRepresent the time window upper limit of process i, tiRepresent that distribution vehicle arrives
Up to the time of station corresponding to process i；
Step 3：According toOptimization object function is established, wherein, μiFor work
The satisfaction of sequence i, according toIt calculates, tieRepresent the expectation trolley service time of process i；
Step 4：Popsize individual is randomly generated as initial population, nature of each individual for all process steps number
Number fully intermeshing, i.e. process number coding, popsize are genetic algorithm initiation parameter, and value range is 10~200；
Step 5：It is required according to the time window of each process, each individual is decoded respectively, i.e., process numbered and encoded
Station coding is converted into, specially：
Step a：The preceding 11min of the on-stream time for the process that the time of departure of trolley is initialized as going into operation earliest, for
An individual is inserted into 0 in first coding site of station coding first, and the process number for then from left to right traversing individual is compiled
Code, according to the running time between station corresponding to home-delivery center and process number, process can be reached in time window by finding out
Place station, if can find, by station number be put into station coding in, and by its process number from process number encode
In remove, at this point, the running time of trolley for trolley the time of departure plus home-delivery center reach process number corresponding to station
Running time, be transferred to step b；Otherwise, the time of departure for changing trolley is right for process number first coding site of coding
The on-stream time of process is answered to subtract 11min, then from left to right traversal process number encodes, and is numbered according to home-delivery center and process
Running time between corresponding station finds the place station that process can be reached in time window, and station number is put into
In station coding, and its process number is removed from process number coding, at this point, the running time of trolley dispatching a car for trolley
Time reaches the running time of station corresponding to process number plus home-delivery center, is transferred to step b；
Step b：With the running time basis of trolley, process number coding is from left to right traversed, institute is right according to process number
The running time between station is answered, finds the station where the process that can be reached in time window, station number is put into work
In the coding of position, and its process number is removed from process number coding, the running time of trolley is updated to trolley traveling
Time is plus the running time between station corresponding to process number.This step of repetition, the process until can not find the condition of satisfaction,
It is transferred to step c；
Step c：0 is added in the rearmost position of station coding, and judges whether the length that process number encodes at this time is 0,
If it is 0, decoding terminates；Otherwise, back to step a；
Step 6：According to fitness (i)=value (i), the suitable of each individual is calculated in i=1,2..., popsize
Angle value is answered, wherein value (i) is the sum of the process average satisfaction angle value of i-th of body and the inverse of vehicle number magnitude, and process is average
Satisfaction refers to the average value of the satisfaction of all non-zero corresponding processes of coding in station coding；Vehicle number magnitude refers to work
0 quantity subtracts 1 in the coding of position；
Step 7：The fitness value calculated according to step 6 selects initial population using Propertional model,
Obtain the high individual composition parent population of fitness；
Step 8：With certain crossover probability Pc, the individual that step 7 is selected is intersected using partial matching method, is obtained
To progeny population, PcValue range be 0.4~0.99；
Step 9：With certain mutation probability Pm, the progeny population that step 8 obtains is become using mutation algorithm is reversed
It is different, obtain next-generation interim population, PmValue range be 0.0001~0.1；
Step 10：The highest individual of fitness value in interim population is recorded, and judges whether to reach maximum iteration Nd,
If so, terminating, in the set that the individual maximum from every generation fitness of record is formed, maximum of fitness value is found out
Body, then the path of the maximum individual decoding gained of this fitness value is optimal cutter Distribution path；Otherwise, it is obtained with step 9
The interim population arrived is initial population, return to step 5, and iterations add 1, and the iterations initial value is 1, greatest iteration
Times NdValue range be 100~500.
The beneficial effects of the invention are as follows：Since the Tools Requirement for considering station is detachable, and it is each process of station
Distribution time window is arranged, establishes using process average satisfaction and distribution vehicle number as the majorized function of target, more accurately reflects
Actual conditions in workshop cutter punctual distribution vehicle path make the distribution project based on this formulation relatively reliable；With process
Average satisfaction maximum and distribution vehicle number at least for fitness function, can better meet for the suitable of hard time window
It should；Due to carrying out model solution using genetic algorithm, multiple solutions in search space are assessed, reduces tradition optimization and calculates
Method is easily trapped into the risk of locally optimal solution, using the transition rule of probability come guidance search direction so that algorithm, which has, to be learnt by oneself
Habit and adaptivity.
Description of the drawings
Fig. 1 is a kind of punctual Distribution path planning algorithm flow chart of cutter with hard time window of the present invention
Specific embodiment
The present invention is further described with reference to the accompanying drawings and examples, and the present invention includes but are not limited to following implementations
Example.
As shown in Figure 1, the present invention provides a kind of punctual Distribution path planning algorithm of cutter with hard time window, it is main to wrap
It includes and establishes path planning mathematical model and solution two large divisions is carried out to model using genetic algorithm.Detailed process is as follows：
1st, the mathematical model of the punctual Distribution path planning of cutter with hard time window constraint is established.
Cutter Distribution path planning problem can be described as：The cutter warehouse center of workshop possesses several small
Vehicle dispenses cutter on time according to enterprise's production operation scheduling to each process of each station.Distribution time window [the t of process iid,
tie,tiu], tidRepresent time window lower limit, tiuRepresent the time window upper limit, tieThe time for it is expected to reach is represented, and in fuzzy reservation
Time window [tid,tie,tiu] in consider the satisfaction μ of processi.Cutter vehicle is dispensed from cutter warehouse center, is station
Service returns to home-delivery center after completing dispatching task.It is required that reasonable arrangement vehicle and driving path, ensure the condition for meeting constraint
Under, it is desirable that the average satisfaction maximum of process and distribution vehicle minimum number.The present invention research be based on it is assumed hereinafter that：One cutter
Warehouse center, each process on-stream time of station is it is known that and known to each operation requirement；Closed type vehicle route；Each work
Sequence can only have a vehicle to dispense and ensure that each process can be satisfied；The time window of each process is it is known that and be hard time window.
(1) decision variable is determined as the following formula：
Wherein, xijkTo represent the variable of traveling sequence and distribution trolley between process, yikFor represent process by which
The variable of vehicle dispatching, i, j represent process number, i=1,2 ..., N, j=1, and 2 ..., N, N are process sum, and k represents vehicle
Number, k=1,2 ..., K, K are vehicle fleet；
(2) inequality constraints is established as follows：
tid＜ ti＜ tiu (11)
Wherein, tiRepresent that distribution vehicle reaches the time of station corresponding to process i.Formula (11) represents distribution vehicle to being opened
The beginning service time of the process of work must be within time window；Formula (12) represents that each process can only in primary dispatching task
By a distribution trolley service；Formula (13)-(14) represent that distribution trolley from home-delivery center, is finally returned to home-delivery center；
Formula (15)-(16) represent variable xijkWith yikRelation constraint.
(3) it determines optimization aim and establishes object function
The average satisfaction of process is maximum, i.e.,：
Distribution trolley quantity minimizes, i.e.,：
This is a multi-objective optimization question from the point of view of optimization angle, and the method that weighted array may be used asks multiple target
Topic transforms into single-objective problem, to realize the optimization to two object functions.Two above object function is converted to monocular
Scalar functions are：
Wherein, α, β are weight coefficient, and alpha+beta=1.Known according to practical experience, the average satisfaction and distribution trolley of process
Quantity is important compared to strong, is known according to order relation analytic approach, α ＞ β, if ω1=α, ω2=β,ByKnow, ω2=(1+ γ2)-1=0.38, then ω1=0.61, then round up, α=0.6, β=
0.4。
To sum up, optimization object function is：
2nd, the model of foundation is solved using genetic algorithm.
(1) construction individual generates initial population
Using natural number coding, i.e., a fully intermeshing of natural number of each individual for all process steps number, wherein, respectively
So number corresponds to the process number in delivery system.Popsize individual is randomly generated as initial population, in each individual from
So several sequences is the access order to process of algorithm.Popsize be genetic algorithm initiation parameter, value range 10
~200.
(2) each individual is decoded
It is required according to the time window of each process, each individual is decoded respectively, i.e., process numbered and encoded translated be
Station encodes, and is inserted into 0 (0 represents home-delivery center) into station coding, forms specific vehicle delivery route, haulage vehicle
It is home-delivery center to run beginning and end, i.e., returns to home-delivery center after completing dispatching task from home-delivery center every time.
Detailed process is as follows：
Step a：The preceding 11min of the on-stream time for the process that the time of departure of trolley is initialized as going into operation earliest, for
An individual is inserted into 0 in first coding site of station coding first, and the process number for then from left to right traversing individual is compiled
Code, according to the running time between station corresponding to home-delivery center and process number, process can be reached in time window by finding out
Place station, if can find, by station number be put into station coding in, and by its process number from process number encode
In remove, at this point, the running time of trolley for trolley the time of departure plus home-delivery center reach process number corresponding to station
Running time, be transferred to step b；Otherwise, the time of departure for changing trolley is right for process number first coding site of coding
The on-stream time of process is answered to subtract 11min, then from left to right traversal process number encodes, and is numbered according to home-delivery center and process
Running time between corresponding station finds the place station that process can be reached in time window, and station number is put into
In station coding, and its process number is removed from process number coding, at this point, the running time of trolley dispatching a car for trolley
Time reaches the running time of station corresponding to process number plus home-delivery center, is transferred to step b；
Step b：With the running time basis of trolley, process number coding is from left to right traversed, institute is right according to process number
The running time between station is answered, finds the station where the process that can be reached in time window, station number is put into work
In the coding of position, and its process number is removed from process number coding, the running time of trolley is updated to trolley traveling
Time is plus the running time between station corresponding to process number.This step of repetition, the process until can not find the condition of satisfaction,
It is transferred to step c；
Step c：0 is added in the rearmost position of station coding, and judges whether the length that process number encodes at this time is 0,
If it is 0, decoding terminates；Otherwise, back to step a；
(3) fitness value is calculated
The present invention calculates the fitness value of each individual i, i=using fitness function fitness (i)=value (i)
1,2...,popsize。
Value (i) is the sum of the process average satisfaction angle value of i-th body and the inverse of vehicle number magnitude, calculating process
For：The first step by the gene of individual, from left to right, calculates the satisfaction that the vehicle in addition to home-delivery center reaches station, Ran Houzai
It is averaging satisfaction；Second step traverses the coding of individual, and the quantity of home-delivery center is obtained, and it is exactly distribution vehicle then to cut 1
Quantity；Third walks, and the inverse of first step desired value and second desired value is added as to the value of value (i).
(4) it selects
The fitness value calculated according to above step carries out selection opertor operation to initial population, selects fitness
It is worth high individual as parent, we use roulette selection method herein, and also known as Propertional model, basic thought is
The selected probability of each individual is directly proportional to fitness size.Concrete operations are as follows：
First, according to the fitness value of each individual, the probability that each individual is genetic in next-generation group is calculated：
Then, the accumulated probability of each individual is calculated：
Then, an equally distributed random number r is generated in [0,1] section, if r ＜ q (1), selects individual 1, it is no
Then, if q (k-1) ＜ r ＜ q (k), individual k is selected, repeats this process popsize times, completes selection.
(5) intersect
With certain crossover probability Pc, using partial matching method to it is selected above go out individual intersect, obtain filial generation
Population, PcValue range be 0.4~0.99.
First to the individual in population, it is combined with each other at random two-by-two, then the assembly be combineding with each other is randomly generated [0,
1] random number, then judge whether the random number of assembly is less than crossover probability Pc, if less than Pc, then crossover operation is carried out；With
For two parent individualities (1 234567 8) and (2 46853 1), the detailed process of partial matching method is：With
Machine selects the point of two intersections, and first point of addition is that position is 4, and second crossover location is 6, then the position between two points
Putting will intersect, and other positions are replicated or are replaced with the number to match.In the present embodiment, first father
456 is selected in generation individual, and in second parent individuality, 875 is selected.So 4 and 8,5 and 7,6 match with 5.It is first
First, corresponding position in filial generation 2 and filial generation 1 is added separately to 875 by 456, then, by the digital direct in other positions
It connects and copies in corresponding offspring, if the number has existed in the filial generation, carried out with corresponding matching rule
When replacing, such as 7 duplications are entered in filial generation 1, it is found that 7 are already present in filial generation, by searching for corresponding matching method
Then, it is found that 7 match with 5, then replicate 5, and find that 5 are also already present in the filial generation, search matching rule herein, find 5
It matches, 6 is replicated, 6 are not present in the filial generation with 6, it is possible to replicate by 6, so repeatedly, until station in filial generation
Number reach the length of definition, which, which creates, completes.
(6) it makes a variation
With certain mutation probability Pm, progeny population obtained above into row variation, is obtained using mutation algorithm is reversed
Next-generation interim population, PmValue range be 0.0001~0.1.
Reverse alternative method detailed process be：Each individual randomly generates the random number between [0,1], judges that random number is
It is no to be less than mutation probability Pm, if less than Pm, mutation operation is carried out to individual；It is encoded to assuming that any one in population is individual
" 1,2,3,4,5,6,7,8,9 ", two change points 2 and 6 are generated at random, i.e., " 1,2,3,4,5,6,7,8,9 " carry out variation section
Reverse to obtain new individual " 1,6,3,4,5,2,7,8,9 ".
(7) judgement that evolution conditions stop
The highest individual of fitness value in interim population is recorded, and judges whether to reach maximum iteration Nd, if so,
Terminate, in the individual set formed maximum from every generation fitness of record, find out the individual of fitness value maximum, then this
The path of the maximum individual decoding gained of a fitness value is optimal cutter Distribution path；Otherwise, it is obtained with step 9 interim
Population is initial population, return to step 5, and iterations add 1, and the iterations initial value is 1, maximum iteration Nd's
Value range is 100~500.
The specific emulation data and result of the present embodiment are as follows：
Table 1 is the information of each station, wherein the practical on-stream time produced is earliest from 8:00 starts, and is convenience of calculation, will
It becomes earliest since 0min, and other process on-stream time is with this toward pusher.Running time of the table 2 between each station.Work
200 iteration that sequence sorts Jing Guo genetic algorithm export optimal solution.The number of initial population is 50, and crossover probability 0.6 becomes
Different probability is 0.001.Table 3 is the result being calculated using inventive algorithm.As can be seen that compared to the knife to each station
Tool demand disposably all dispenses, and this each process to station of the present invention adds hard time window, then to each process by
It is a to be dispensed, it can more embody under JIT production and " only when process processing needs, just be dispensed into the cutter that station needs
The station " improves the punctuality of dispatching.
Table 1
Table 2
Table 3
Claims (1)
1. a kind of punctual Distribution path planning algorithm of the cutter with hard time window, it is characterised in that include the following steps：
Step 1：The on-stream time of each process of station and demand in known cutter warehouse center, each process hard time window,
Closed type vehicle route, and each process only has a vehicle dispatching, determines decision variable as follows：
Wherein, xijkTo represent the variable of traveling sequence and distribution trolley between process, yikTo represent process is matched by which vehicle
The variable sent, i, j represent process number, i=1,2 ..., N, j=1, and 2 ..., N, N are process sum, and k represents the number of vehicle,
K=1,2 ..., K, K are vehicle fleet；
Step 2：Establish inequality constraints as follows respectively：
tid＜ ti＜ tiu (3)
Wherein, tidRepresent the time window lower limit of process i, tiuRepresent the time window upper limit of process i, tiRepresent that distribution vehicle reaches work
The time of station corresponding to sequence i；
Step 3：According toOptimization object function is established, wherein, μiFor process i
Satisfaction, according toIt calculates, tieRepresent the expectation trolley service time of process i；
Step 4：Popsize individual is randomly generated as initial population, each individual is complete for the natural number of all process steps number
Arrangement, i.e. process number coding, popsize are genetic algorithm initiation parameter, and value range is 10~200；
Step 5：It is required according to the time window of each process, each individual is decoded respectively, i.e., is numbered process encoded translated
It is encoded for station, specially：
Step a：The preceding 11min of the on-stream time for the process that the time of departure of trolley is initialized as going into operation earliest, for one
Individual is inserted into 0 in first coding site of station coding first, then from left to right traverses the process number coding of individual,
According to the running time between station corresponding to home-delivery center and process number, the institute that process can be reached in time window is found out
In station, if can find, station number is put into station coding, and by its process number from process number coding
Fall, at this point, the running time of trolley adds the row of station corresponding to home-delivery center's arrival process number for the time of departure of trolley
The time is sailed, is transferred to step b；Otherwise, the time of departure for changing trolley is work corresponding to process number first coding site of coding
The on-stream time of sequence subtracts 11min, and then from left to right traversal process number encodes, right according to home-delivery center and process number institute
The running time between station is answered, finds the place station that process can be reached in time window, station number is put into station
In coding, and its process number is removed from process number coding, at this point, the running time of trolley is the time of departure of trolley
In addition home-delivery center reaches the running time of station corresponding to process number, it is transferred to step b；
Step b：Based on the running time of trolley, process number coding is from left to right traversed, according to corresponding to numbering process
Running time between station finds the station where the process that can be reached in time window, and station number is put into station
In coding, and its process number is removed from process number coding, the running time of trolley is updated to a trolley when driving
Between plus the running time corresponding to process number between station.This step is repeated, the process until can not find the condition of satisfaction turns
Enter step c；
Step c：0 is added in the rearmost position of station coding, and judges whether the length that process number encodes at this time is 0, if
It is 0, then decoding terminates；Otherwise, back to step a；
Step 6：According to fitness (i)=value (i), the fitness of each individual is calculated in i=1,2..., popsize
Value, wherein value (i) are the sum of the process average satisfaction angle value of i-th body and the inverse of vehicle number magnitude, process average satisfaction
Degree refers to the average value of the satisfaction of all non-zero corresponding processes of coding in station coding；Vehicle number magnitude refers to that station is compiled
0 quantity subtracts 1 in code；
Step 7：The fitness value calculated according to step 6 selects initial population using Propertional model, obtains
The high individual composition parent population of fitness；
Step 8：With certain crossover probability Pc, the individual that step 7 is selected is intersected using partial matching method, obtains son
For population, PcValue range be 0.4~0.99；
Step 9：With certain mutation probability Pm, the progeny population that step 8 obtains into row variation, is obtained using mutation algorithm is reversed
To next-generation interim population, PmValue range be 0.0001~0.1；
Step 10：The highest individual of fitness value in interim population is recorded, and judges whether to reach maximum iteration Nd, if so,
Then terminate, in the set formed from the highest individual of every generation fitness value of record, find out the individual of fitness value maximum,
Then the path of the maximum individual decoding gained of this fitness value is optimal cutter Distribution path；Otherwise, it is obtained with step 9
Interim population is initial population, return to step 5, and iterations add 1, and the iterations initial value is 1, maximum iteration
NdValue range be 100~500.
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| CN109934364A (en) * | 2019-03-10 | 2019-06-25 | 西北工业大学 | A kind of cutter Panel management allocator based on Global Genetic Simulated Annealing Algorithm |
| CN111291906A (en) * | 2020-02-18 | 2020-06-16 | 中车贵阳车辆有限公司 | Railway freight car wisdom maintenance system |
| CN112508478A (en) * | 2020-12-02 | 2021-03-16 | 北京理工大学 | Flexible logistics distribution task allocation method based on self-organizing automated guided vehicle |
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