CN109230142A - A kind of scheduling method for optimizing route of intensive warehousing system multiple working - Google Patents
A kind of scheduling method for optimizing route of intensive warehousing system multiple working Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
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Abstract
The invention discloses a kind of scheduling method for optimizing route of intensive warehousing system multiple working, belong to intensive warehousing system configuration optimization technical field.The following steps are included: 1) the warehousing and storage activities mode completed elevator in multiple working and the cooperation of primary and secondary shuttle is as object, building multiple working process corresponding mathematical model most short for target with multiple working total time；2) dispatching sequence wait be put in storage and to outbound cargo in multiple working is optimized using the mathematical model that step 1) is established using Hybrid Particle Swarm, determines the optimal scheduling scheme for completing scheduling in multiple working.Rationally, effective solution multiple working of the multitask under improves warehousing system and goes out to be put in storage operating efficiency, reduces logistics cost for this method design.
Description
Technical field
The invention belongs to intensive warehousing system configuration optimization technical fields, and in particular to a kind of intensive compound work of warehousing system
The scheduling method for optimizing route of industry.
Background technique
With the rise of intelligence manufacture, automation, intelligentized intensive memory technology are becoming China's shelf industry
Main trend, to further increase its operating efficiency, the intensive warehousing system (Shuttle-Carrier of primary and secondary shuttle vehicle type
Warehousing System, SCWS) it comes into being.The system operational speed is fast, positioning accuracy is high, storage capacity is big, expansible
Property it is strong, the industries such as tobacco, medicine and Cold Chain Logistics tool have been widely used.
Due to the coordinating operation feature of elevator in the intensive warehousing system of primary and secondary shuttle vehicle type and female vehicle, sub- vehicle increase it is whole
Therefore the complexity of a storage scheduling is the effective means for further increasing system throughput performance using reasonable scheduling strategy,
And whether this path for depending greatly on system execution schedule job is reasonable.When system receives kinds of goods, storage is ordered out
How Dan Hou goes out warehousing orders to kinds of goods and carries out reasonable arrangement, cooperate elevator and primary and secondary shuttle efficiently and complete order times
Business, which becomes, further increases the critical issue that warehousing system goes out to be put in storage operating efficiency, reduction logistics cost.
Summary of the invention
To solve the above-mentioned problems, the purpose of the present invention is to provide a kind of scheduling roads of intensive warehousing system multiple working
Diameter optimization method.
The present invention is to be achieved through the following technical solutions:
A kind of scheduling method for optimizing route of intensive warehousing system multiple working, comprising the following steps:
1) the warehousing and storage activities mode for completing elevator in multiple working and the cooperation of primary and secondary shuttle is as object, with compound
It is target that operation total time is most short, constructs the corresponding mathematical model of multiple working process；
2) mathematical model established using step 1) in multiple working wait be put in storage and scheduling to outbound cargo is suitable
Sequence is optimized using Hybrid Particle Swarm, determines that multiple working completes the optimal scheduling scheme of scheduling.
Preferably, multiple working is made of double command cycle operation several times, and double command cycle operation includes primary
The Delivery of the input work of putaway stock i and an outbound cargo j, wherein putaway stock i is located at (xi, yi, zi), out
Library cargo j is located at (xj, yj, zj), wherein (i, j=1,2,3 ..., n)；When input work in certain double command cycle operation or
When Delivery lacks, supplement coordinate is the cargo of (0,0,0), as virtual outbound or input work, is formed complete double
Times command cycle operation.
It is further preferred that the target of optimization is to keep multiple working total time most short, mathematical linguistics description are as follows:
Wherein, TFThe storage multiple working total time out of the order taking responsibility, T are executed for intensive warehousing systemukFor kth (k
=1,2 ..., n) it is a access pair operation journey time.
It is further preferred that the corresponding mathematical model of multiple working process indicates are as follows:
Wherein, if being located on the same floor to putaway stock with to outbound cargo:
If being located at different layers to putaway stock and to outbound cargo:
Wherein, TFThe storage multiple working total time out of the order taking responsibility, T are executed for intensive warehousing systemukFor kth (k
=1,2 ..., n) a access pair operation journey time, shelf column where x indicates cargo, y indicates that layer where cargo, z indicate goods
Shelf row where object, i and j indicate two cargos of once-combined operation,It runs for elevator to the time of layer where cargo,It runs for female vehicle to the i cargo column mouth time,The goods yard time where running for sub- vehicle to i cargo,Indicate female vehicle from i cargo
Column mouth brings into operation until sub- vehicle takes j cargo and carries it to layer I/O point the time it takes,Indicate elevator
The time used in the layer of j cargo place is reached from layer where i cargo,It is run from j cargo column mouth to this layer of I/O point for female vehicle
Time,It runs for sub- vehicle to the time in j cargo goods yard,For elevator from layer where j cargo run to the position I/O when
Between, tsfFor elevator positioning time, tsrFor the interaction time of elevator and primary and secondary shuttle, tcfFor female vehicle positioning time, tcrFor
The time of female sub- vehicle of vehicle loading/unloading, tzfFor the time of sub- vehicle positioning, tzrFor the time of sub- vehicle loading/unloading cargo.
It is further preferred that Hybrid Particle Swarm combination particle swarm algorithm, ant group algorithm and genetic algorithm obtain.
It is further preferred that the step of being optimized using Hybrid Particle Swarm are as follows:
1) it initializes
It is first randomly generated NPA particle, and by NAAnt is placed at random in N number of point, and sets Studying factors c1、c2、
Inertia weight ω, initial information element concentration τ0, track relative importance α, visibility relative importance β, crossover probability Pc, variation
Probability Pm, then initialize particle group parameters and ant colony parameter；
2) fitness value is calculated
For fitness function, the fitness of particle in population is calculated according to fitness function
f1；
3) current individual optimal value and global optimum are recorded
According to fitness f1Individual optimal value P is selected from populationp=(pp1,pp2,,ppD) and global optimum Pg, and
Record its individual optimal value and the corresponding position P of global optimumpcAnd Pcg；
4) by entering iterative cycles after step 3), when iterative cycles are less than number Tmax, it performs the following operations:
A) allow ant that there is " particle " characteristic, by NAAnt is placed at random in N number of point, i.e., using individual extreme value information with
And global extremum information guides transfer, transition probability to the position of ant next iteration are as follows:
In formula, τpq(t) --- t momentpqPheromone concentration on side；ηpq(t) --- t moment ant is transferred to from point p
The heuristic greedy method of point q；α --- track relative importance；β --- visibility relative importance； ak--- kth ant can
To select the point set travelled, ak=0,1 ..., and n-1 }-tak, takFor taboo list, travelled for recording ant k
Point；
B) passing through t moment, whole ants is all passed by each point, the path length that every ant is passed by is calculated, and
Shortest path length is saved, while updating the pheromone concentration on each path side, pheromone concentration updates as follows:
τpq(t+n)=ρ τpq(t)+Δτpq
In formula, ρ --- pheromones volatility coefficient, and 0 ρ≤1 <；Δτpq--- in each ergodic process of ant on the side pq
Pheromone concentration incrementss, and:
C) intersect
By q-th of particle position x1qWith global optimum position PcgIntersect with doing, crossing formula are as follows: x '1q=x1q+ω*
Pcg, then by x '1qWith individual optimal value position PiIntersection obtains x "1q=x '1q+ω*Pcg；ω is inertia weight；Q=1,2 ..., D,
D is optimizing Spatial Dimension, crossover probability Pc；
D) it makes a variation
To x "1qIt carries out mutation operation and obtains x2q, variation formula is x2q=[0.5+rand ()] * x "1q, rand () be (0,
1) random number between, mutation probability Pm；
E) after step d), the new fitness value f of each particle is calculated2；
F) each particle is calculated in two particle x2qAnd x1qFitness value changes delta E, Δ E=f1-f2If Δ E < e,
Particle position is then updated to x2q, otherwise the not position of more new particle, is still x1q, e is the range for allowing objective function to be deteriorated；
G) after step f), individual optimal value is updated with personal best particle；
H) after step g), global optimum and global optimum position are searched out；
I) after step h), speed and position to all particles are updated according to following formula, obtain the next generation
Particle goes to step a), is recycled next time:
In formula, Xp=(xp1,xp2,,xpD)、Vp=(vp1,vp2,,vpD) be p-th of particle Position And Velocity, n is iteration
Number, p=1,2 ..., N, q=1,2 ..., D；ω is inertia weight；c1、c2For Studying factors；r1、 r2Between (0,1)
Random number；
5) when the number of iterations is equal to TmaxWhen, iteration stopping, and export final global optimum and global optimum position
It sets.
It is further preferred that NPValue be 100, TmaxValue be 50, Studying factors c1、c2Equal value is taking for 2, e
Value is 3, and the value of inertia weight ω is 0.9, and initial information element concentration takes τ0Value be 100, track relative importance α's takes
Value is 1.5, and the value of visibility relative importance β is 2, crossover probability PcValue be 0.8, mutation probability PmValue be
0.2。
Compared with prior art, the invention has the following beneficial technical effects:
The scheduling method for optimizing route of intensive warehousing system multiple working disclosed by the invention, by elevator in multiple working
The warehousing and storage activities mode completed with the cooperation of primary and secondary shuttle is most short for target with multiple working total time as object, using knot
The Hybrid Particle Swarm for closing particle swarm algorithm, ant group algorithm and cross and variation operator, which optimizes scheduling path model, to be asked
Solution, seeks optimal scheduling path planning scheme, goes out to be put in storage operating efficiency to reach and improve warehousing system, reduces logistics cost
Purpose.
Detailed description of the invention
Fig. 1 is the flow chart of the invention using Hybrid Particle Swarm；
Fig. 2 is the model schematic of intensive warehousing system of the invention under OXYZ coordinate system；
Fig. 3 is that intensive warehousing system of the invention is made when putaway stock and the storage that goes out when outbound cargo is located on the same floor
Industry schematic diagram；
Fig. 4 is that intensive warehousing system of the invention is made when putaway stock and the storage that goes out when outbound cargo is located at different layers
Industry schematic diagram；
Fig. 5 is three kinds of Hybrid Particle Swarm, ant group algorithm, particle swarm algorithm algorithms in the case where 25 tasks are to multiple working
Restrain comparison diagram；
Fig. 6 is three kinds of Hybrid Particle Swarm, ant group algorithm, particle swarm algorithm algorithms in the case where 50 tasks are to multiple working
Restrain comparison diagram；
Wherein, 1 is vertical conveyor, and 2 be female vehicle, and 3 be sub- vehicle, and 4 be cargo, and 5 arrange for shelf, and 6 be cross track, and 7 are
Lifting platform, 8 be layer I/O point, and 9 be I/O platform.
Specific embodiment
The present invention is described in further detail below, and the explanation of the invention is not limited.
Referring to fig. 2, the model under OXYZ coordinate system, primary and secondary shuttle are converted by the intensive warehousing system of primary and secondary shuttle vehicle type
The multiple working of the intensive warehousing system of formula is storage integration of operation, and regulation preferentially completes an inbound task in the process, so
After complete another outbound task, constitute a complete double command cycle operation.If putaway stock i's and outbound cargo j
Goods yard coordinate is respectively (xi, yi, zi) and (xj, yj, zj), when input work in certain double command cycle operation or outbound are made
When industry lacks, supplement coordinate is the cargo of (0,0,0), (enters) library operation out as virtual, forms complete double order week
Phase operation.According to wait go out, whether goods yard is in same layer where putaway stock, a point situation discussion is carried out.
1) (y is located on the same floor to putaway stock and to outbound cargoi=yj)
Referring to Fig. 3, cargo 4 to be put in storage is loaded into I/O platform 9 by vertical conveyor 1 first and is transported to yiLayer I/O
Then cargo 4 is transported to 5 mouthfuls of shelf column of storage by plugging into for straton mother's shuttle by point 8, most stepmother's vehicle 2 discharges sub- vehicle
3, cargo 4 is stored to final storage goods yard (x by sub- vehicle 3i, yi, zi) point, complete input work.Next, sub- vehicle 3 returns
The female vehicle 2 for returning and carrying goods column mouth, by cross track 6, female vehicle 2 continues traveling to outbound goods column mouth and discharges sub- vehicle 3, by son
Vehicle 3 is travelled to outbound goods yard (xj, yj, zj) point progress picking, goods column mouth is returned after the completion and carries female vehicle 2, finally by primary and secondary
The interactive process of shuttle and vertical conveyor 1 will be transferred to lifting platform 7 to outbound cargo, be transported cargo by vertical conveyor 1
The Delivery that cargo is completed to I/O platform 9 is sent, so far, the double command cycle operation of single terminates.
According to above-mentioned operation process, can be obtained in the intensive warehousing system of primary and secondary shuttle vehicle type to putaway stock and to outbound goods
Multiple working journey time when object is located on the same floor
In formula,It runs for elevator to the layer time where cargo,It runs for female vehicle to the i cargo column mouth time,For son
Vehicle runs the goods yard time where to i cargo,Indicate that female vehicle brings into operation from i cargo column mouth until sub- vehicle takes j cargo and will
It is transported to a layer I/O point the time it takes, tsfFor elevator positioning time, tsrFor the interaction of elevator and primary and secondary shuttle
Time, tcfFor female vehicle positioning time, tcrThe time of (unloading) sub- vehicle, t are filled for female vehiclezfFor sub- vehicle positioning time, tzrFor sub- vehicle dress
The time of (unloading) cargo.
2) it is located at different layers (y to putaway stock and to outbound cargoi≠yj)
Referring to fig. 4, cargo 4 to be put in storage is loaded into I/O platform 9 by vertical conveyor 1 first and is transported to yiLayer I/O
Point 8, at the same time, yjThe primary and secondary shuttle of layer, which also brings into operation, carries out picking to outbound goods yard；Then, vertical conveyor 1
Up (down) to yiLayer carries out the interaction of cargo with straton mother's shuttle, after straton mother's shuttle plugs into cargo, lifting
Machine continues up (down) to yjLayer, interacts with straton mother's shuttle, I/O platform 9 is back to after cargo is taken out.This its
In, yiWhen straton mother's shuttle carries out stock, female vehicle 2 discharges sub- vehicle 3, by sub- vehicle 3 by cargo storage to final storage goods yard
(xi, yi, zi) point, after completing input work, sub- vehicle 3 returns and carries female vehicle 2 of goods column mouth, by cross track 6, female 2 row of vehicle
It sails to yiLayer I/O point 8 simultaneously stops operating, and waits next assignment instructions；yjWhen straton mother's shuttle carries out picking, female vehicle 2 discharges son
Vehicle 3, by sub- vehicle 3 traveling to outbound goods yard (xj, yj, zj) point progress picking, goods column mouth is returned after the completion and carries female vehicle 2, most
The interactive process for passing through primary and secondary shuttle and vertical conveyor 1 afterwards, will be transferred to lifting platform 7 to outbound cargo.Work as vertical lift
When machine 1 is by goods handling to I/O platform, the double command cycle operation of single terminates.
According to above-mentioned operation process, can be obtained in the intensive warehousing system of primary and secondary shuttle vehicle type to putaway stock and to outbound goods
Multiple working journey time of level when different layers
In formula in addition to having explained symbol meaning,It runs for elevator to the layer time where cargo,Indicate elevator from
Time used in layer where layer reaches j cargo where i cargo,For female vehicle from j cargo column mouth run to this layer of I/O point when
Between, tj wIt runs for sub- vehicle to the time in j cargo goods yard,It runs for layer where elevator from j cargo to the time of the position I/O.
Therefore, if in order taking responsibility it is a total of n access pair, kth (k=1,2 ..., n) it is a access pair operation stroke when
Between be Tuk, then the intensive warehousing system of primary and secondary shuttle vehicle type executes the storage multiple working total time T out of the order taking responsibilityFAre as follows:
Emulation is optimized to the scheduling path optimization model of building, show that multiple working waits being put in storage and to outbound cargo
Optimal scheduling sequence.
When the intensive warehousing system executive job scheduling instruction of primary and secondary shuttle formula, which is similar to city
Distance matrix traveling salesman problem (Traveling Salesman Problem, TSP) of dynamic change with access order difference,
Belong to NP-hard problem.Thus, there is no effective polynomial time algorithms can accurately solve the intensive storehouse of primary and secondary shuttle vehicle type
The operation journey time model of storage system.At this stage, researcher is partial to for the method for solving of TSP problem using artificial intelligence
Algorithm, such as genetic algorithm, ant group algorithm, particle swarm algorithm, although outstanding solution ability can be embodied in solution,
Also all have the shortcomings that respective.Therefore which kind of intelligent algorithm no matter is selected to solve optimization problem, it is more preferable in order to obtain
Solution effect, be both needed to carry out corresponding algorithm improvement to it.
1.1 design Hybrid Particle Swarms carry out problem solving
In conjunction with the respective advantage of particle swarm algorithm, ant group algorithm and genetic algorithm, Hybrid Particle Swarm is designed
(Hybrid Particle Swarm Optimization, HPSO) is to the scheduling path of the intensive warehousing system of primary and secondary shuttle formula
Optimization problem is solved.The key technology of the intensive warehousing system working path of primary and secondary shuttle vehicle type is solved to HPSO algorithm below
It is illustrated:
1) it initializes
It is first randomly generated NPA particle, and by NAAnt is placed at random in N number of point, and sets Studying factors c1、c2、
Inertia weight ω, initial information element concentration τ0, track relative importance α, visibility relative importance β, crossover probability Pc, variation
Probability Pm, then initialize particle group parameters and ant colony parameter；
2) fitness value is calculated
WithFor fitness function, the fitness of particle in population is calculated according to fitness function
f1；
3) current individual optimal value and global optimum are recorded
According to fitness f1Individual optimal value P is selected from populationp=(pp1,pp2,…,ppD) and global optimum Pg,
And record its individual optimal value and the corresponding position P of global optimumpcAnd Pcg；
4) by entering iterative cycles after step 3), when iterative cycles are less than number Tmax, it performs the following operations:
A) allow ant that there is " particle " characteristic, by NAAnt is placed at random in N number of point, i.e., using individual extreme value information with
And global extremum information guides transfer, transition probability to the position of ant next iteration are as follows:
In formula, τpq(t) --- t momentpqPheromone concentration on side；ηpq(t) --- t moment ant is transferred to from point p
The heuristic greedy method of point q；α --- track relative importance；β --- visibility relative importance； ak--- kth ant can
To select the point set travelled, ak=0,1 ..., and n-1 }-tak, takFor taboo list, travelled for recording ant k
Point；
B) passing through t moment, whole ants is all passed by each point, the path length that every ant is passed by is calculated, and
Shortest path length is saved, while updating the pheromone concentration on each path side, pheromone concentration updates as follows:
τpq(t+n)=ρ τpq(t)+Δτpq
In formula, ρ --- pheromones volatility coefficient, and 0 ρ≤1 <；Δτpq--- in each ergodic process of ant on the side pq
Pheromone concentration incrementss, and:
C) intersect
By q-th of particle position x1qWith global optimum position PcgIntersect with doing, crossing formula are as follows: x '1q=x1q+ω*
Pcg, then by x '1qWith individual optimal value position PiIntersection obtains x "1q=x '1q+ω*Pcg；ω is inertia weight；Q=1,2 ..., D,
D is optimizing Spatial Dimension, crossover probability Pc；
D) it makes a variation
To x "1qIt carries out mutation operation and obtains x2q, variation formula is x2q=[0.5+rand ()] * x "1q, rand () be (0,
1) random number between, mutation probability Pm；
E) after step d), the new fitness value f of each particle is calculated2；
F) each particle is calculated in two particle x2qAnd x1qFitness value changes delta E, Δ E=f1-f2If Δ E < e,
Particle position is then updated to x2q, otherwise the not position of more new particle, is still x1q, e is the range for allowing objective function to be deteriorated；
G) after step f), individual optimal value is updated with personal best particle；
H) after step g), global optimum and global optimum position are searched out；
I) after step h), speed and position to all particles are updated according to following formula, obtain the next generation
Particle goes to step a), is recycled next time:
In formula, Xp=(xp1,xp2,…,xpD)、Vp=(vp1,vp2,…,vpD) be p-th of particle Position And Velocity, n is
The number of iterations, p=1,2 ..., N, q=1,2 ..., D；ω is inertia weight；c1、c2For Studying factors；r1、r2Between (0,1)
Random number；
5) when the number of iterations is equal to TmaxWhen, iteration stopping, and export final global optimum and global optimum position
It sets.
1.2 experiment simulations and analysis
1) parameter setting
In order to which the scheduling routing problem to the intensive warehousing system of primary and secondary shuttle formula carries out under different operation assignments
Example analysis, and verify the superiority of HPSO algorithm, below according to obtained warehousing system basic configuration and stock's allocation scheme,
The different batches task setting relevant parameter provided according to certain Medical Logistics Distribution Center carries out numerical experiment and emulation, and to reality
Result is tested to be analyzed and evaluated.It is 100 that Population Size is arranged in HPSO algorithm, maximum number of iterations 500, other parameter settings
It is shown in Table 1.
2) simulation result
Under multiple working mode, respectively with small lot operation (25 tasks to) and high-volume operation (50 tasks to)
Carry out numerical experiment emulation.
Multiple working task list result is shown in Table 2, table 3 respectively, according to finally obtained 25 task of HPSO algorithm optimization
To multiple working path order are as follows: (1-17) → (2-23) → (3-5) → (4-4) → (5-15) → (6-1) → (7-22) →
(8-7)→(9-2)→(10-18)→( 11-16)→(12-11)→(13-8)→(14-9)→(15-25)→(16-24)→
(17-19)→(18-13)→(19-6) →(20-12)→(21-20)→(22-14)→(23-21)→(24-3)→(25-
10), total operation time TF=1228.3s.
50 tasks are to multiple working path order are as follows: (1-1) → (2-9) → (3-30) → (4-20) → (5-7) → (6-
38)→(7-40)→(8-48)→(9-14)→(10-25) →(11-4)→(12-33)→(13-10)→(14-24)→
(15-44)→(16-6)→(17-19)→(18-36)→(19- 23)→(20-13)→(21-8)→(21-11)→(23-3)
→(24-49)→(25-47)→(26-41)→(27-21)→( 28-29)→(29-22)→(30-27)→(31-5)→
(32-42)→(33-31)→(34-18)→(35-37)→(36-4 3)→(37-15)→(38-26)→(39-39)→(40-
28)→(41-2)→(42-2)→(43-32)→(44-35)→(4 5-34)→(46-12)→(47-46)→(48-16)→
(49-50) → (50-45), total operation time TF=2507.6s.
3) simulation analysis
In order to verify the superiority of HPSO algorithm, under equal conditions small lot operation and high-volume work pattern are distinguished
It is optimized using ACO (ant group algorithm), PSO (particle swarm algorithm), it is suitable to acquire multiple working using MATLAB R2016b programming
Sequence and total operation journey time, algorithmic statement comparison diagram are as shown in Figure 5, Figure 6.Then, each algorithm routine is run 100 times, meter
It calculates the operation journey time mean value under its distinct methods, average deviation, average optimization efficiency and calculates the time, as shown in table 4.
Data and convergence curve can be seen that under different work scale in analysis chart, and ACO algorithm the convergence speed is most slow,
And solving precision is poor；PSO algorithm the convergence speed is very fast, but falls into local optimum quickly；HPSO algorithm the convergence speed is fast, and
It is higher in iteration initial stage solution efficiency, new explanation can be constantly explored, meanwhile, under cross and variation strategy, algorithm constantly jumps out office
Portion is optimal, and solving precision is higher.Also, under different work scale, although the calculating time of HPSO algorithm ratio ACO, PSO algorithm
It is longer, but the robustness of algorithm and optimization efficiency have apparent advantage compared with ACO, PSO algorithm, meanwhile, algorithm optimization efficiency with
Increasing for cultivation scale and be improved.
Table is arranged in 1 HPSO algorithm parameter of table
2 multiple working task list of table (25 tasks to)
3 multiple working task list of table (50 tasks to)
Multiple working experimental result of the 4 three kinds of algorithms of table under different scales problem
。
Claims (7)
1. a kind of scheduling method for optimizing route of intensive warehousing system multiple working, which comprises the following steps:
1) the warehousing and storage activities mode for completing elevator in multiple working and the cooperation of primary and secondary shuttle is as object, with multiple working
It is target that total time is most short, constructs the corresponding mathematical model of multiple working process；
2) dispatching sequence wait be put in storage and to outbound cargo in multiple working is adopted using the mathematical model that step 1) is established
It is optimized with Hybrid Particle Swarm, determines that multiple working completes the optimal scheduling scheme of scheduling.
2. the scheduling method for optimizing route of intensive warehousing system multiple working as described in claim 1, which is characterized in that compound
Operation is made of double command cycle operation several times, and double command cycle operation includes the input work of a putaway stock i
With the Delivery of an outbound cargo j, wherein putaway stock i is located at (xi, yi, zi), outbound cargo j is located at (xj, yj, zj),
Wherein (i, j=1,2,3 ..., n)；When input work in certain double command cycle operation or Delivery lack, supplement is sat
It is designated as the cargo of (0,0,0), as virtual outbound or input work, forms complete double command cycle operation.
3. a kind of scheduling method for optimizing route of intensive warehousing system multiple working as claimed in claim 2, which is characterized in that
The target of optimization is to keep multiple working total time most short, mathematical linguistics description are as follows:
Wherein, TFThe storage multiple working total time out of the order taking responsibility, T are executed for intensive warehousing systemukFor kth (k=1,
2 ..., n) it is a access pair operation journey time.
4. a kind of scheduling method for optimizing route of intensive warehousing system multiple working as claimed in claim 2, which is characterized in that
The corresponding mathematical model of multiple working process indicates are as follows:
Wherein, if being located on the same floor to putaway stock with to outbound cargo:
If being located at different layers to putaway stock and to outbound cargo:
Wherein, TFThe storage multiple working total time out of the order taking responsibility, T are executed for intensive warehousing systemukFor kth (k=1,
2 ..., n) it is a access pair operation journey time, x indicate cargo where shelf column, y indicate cargo where layer, z indicate cargo institute
In shelf row, i and j indicate two cargos of once-combined operation,It runs for elevator to the time of layer where cargo,For
Female vehicle was run to the i cargo column mouth time,The goods yard time where running for sub- vehicle to i cargo,Indicate female vehicle from i cargo column mouth
It brings into operation until sub- vehicle takes j cargo and carries it to layer I/O point the time it takes,Indicate elevator from i goods
Time used in layer where layer reaches j cargo where object,It is run from j cargo column mouth to the time of this layer of I/O point for female vehicle,
tj wIt runs for sub- vehicle to the time in j cargo goods yard,It runs for layer where elevator from j cargo to the time of the position I/O, tsf
For elevator positioning time, tsrFor the interaction time of elevator and primary and secondary shuttle, tcfFor female vehicle positioning time, tcrFor female vehicle
The time of the sub- vehicle of loading/unloading, tzfFor the time of sub- vehicle positioning, tzrFor the time of sub- vehicle loading/unloading cargo.
5. a kind of scheduling method for optimizing route of intensive warehousing system multiple working as claimed in claim 2, which is characterized in that
Hybrid Particle Swarm combination particle swarm algorithm, ant group algorithm and genetic algorithm obtain.
6. a kind of scheduling method for optimizing route of intensive warehousing system multiple working as claimed in claim 5, which is characterized in that
The step of being optimized using Hybrid Particle Swarm are as follows:
1) it initializes
It is first randomly generated NPA particle, and by NAAnt is placed at random in N number of point, and sets Studying factors c1、c2, inertia power
Weight ω, initial information element concentration τ0, track relative importance α, visibility relative importance β, crossover probability Pc, mutation probability Pm,
Then initialization particle group parameters and ant colony parameter；
2) fitness value is calculated
WithFor fitness function, the fitness f of particle in population is calculated according to fitness function1；
3) current individual optimal value and global optimum are recorded
According to fitness f1Individual optimal value P is selected from populationp=(pp1,pp2,…,ppD) and global optimum Pg, and remember
Record its individual optimal value and the corresponding position P of global optimumpcAnd Pcg；
4) by entering iterative cycles after step 3), when iterative cycles are less than number Tmax, it performs the following operations:
A) allow ant that there is " particle " characteristic, by NAAnt is placed at random in N number of point, that is, utilizes individual extreme value information and complete
Office's extreme value information guides transfer, transition probability to the position of ant next iteration are as follows:
In formula, τpq(t) --- t momentpqPheromone concentration on side；ηpq(t) --- t moment ant is transferred to point q's from point p
Heuristic greedy method；α --- track relative importance；β --- visibility relative importance；ak--- kth ant can choose
The point set travelled, ak=0,1 ..., and n-1 }-tak, takFor taboo list, for recording the point that ant k had been travelled；
B) pass through t moment, whole ants is all passed by each point, calculates the path length that every ant is passed by, and save
Shortest path length, while the pheromone concentration on each path side is updated, pheromone concentration updates as follows:
τpq(t+n)=ρ τpq(t)+Δτpq
In formula, ρ --- pheromones volatility coefficient, and 0 ρ≤1 <；Δτpq--- pheromones on the side pq in each ergodic process of ant
Concentration incrementss, and:
C) intersect
By q-th of particle position x1qWith global optimum position PcgIntersect with doing, crossing formula are as follows: x '1q=x1q+ω*Pcg, then
By x '1qWith individual optimal value position PiIntersection obtains x "1q=x '1q+ω*Pcg；ω is inertia weight；Q=1,2 ..., D, D are to seek
Excellent Spatial Dimension, crossover probability Pc；
D) it makes a variation
To x "1qIt carries out mutation operation and obtains x2q, variation formula is x2q=[0.5+rand ()] * x "1q, rand () be (0,1) it
Between random number, mutation probability Pm；
E) after step d), the new fitness value f of each particle is calculated2；
F) each particle is calculated in two particle x2qAnd x1qFitness value changes delta E, Δ E=f1-f2It, will if Δ E < e
Particle position is updated to x2q, otherwise the not position of more new particle, is still x1q, e is the range for allowing objective function to be deteriorated；
G) after step f), individual optimal value is updated with personal best particle；
H) after step g), global optimum and global optimum position are searched out；
I) after step h), speed and position to all particles are updated according to following formula, obtain next-generation particle,
Step a) is gone to, is recycled next time:
In formula, Xp=(xp1,xp2,…,xpD)、Vp=(vp1,vp2,…,vpD) be p-th of particle Position And Velocity, n is iteration
Number, p=1,2 ..., N, q=1,2 ..., D；ω is inertia weight；c1、c2For Studying factors；r1、r2Between (0,1) with
Machine number；
5) when the number of iterations is equal to TmaxWhen, iteration stopping, and export final global optimum and global optimum position.
7. a kind of scheduling method for optimizing route of intensive warehousing system multiple working as claimed in claim 6, which is characterized in that
NPValue be 100, TmaxValue be 50, Studying factors c1、c2The value that equal value is 2, e is 3, the value of inertia weight ω
It is 0.9, initial information element concentration takes τ0Value be 100, the value of track relative importance α is 1.5, and visibility is relatively important
Property β value be 2, crossover probability PcValue be 0.8, mutation probability PmValue be 0.2.
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