CN109081030A - A kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type - Google Patents
A kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type 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
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Abstract
The invention discloses a kind of method for optimizing configuration of intensive warehousing system of primary and secondary shuttle vehicle type, it is most short with warehousing system throughput time, system energy consumption is minimum and warehouse cost is at least target, multiple-objection optimization is carried out using the non-dominated sorted genetic algorithm with elitism strategy, seek optimal warehousing system allocation plan, initial stage policy decision foundation is provided when planning warehouse for enterprise personnel, guarantee that every operation in warehousing activity is efficient, while progress in phase, also reduce the intensive warehousing system of primary and secondary shuttle vehicle type because planning and designing it is improper caused by risk, with biggish practical application meaning.
Description
Technical field
The invention belongs to warehousing system configuration optimization fields, and in particular to a kind of intensive warehousing system of primary and secondary shuttle vehicle type is matched
The optimization method set.
Background technique
Continuous growth with current enterprise to intensive and information-based warehouse logistics dispatching demand, automatic stereowarehouse
Using more and more extensive.And automate, intelligentized intensive memory technology is becoming the main trend of shelf industry, will wear
Shuttle car and intensive commodity shelf system carry out coordinating operation, it has also become a kind of emerging efficient logistics solution, and in enterprise
Extensive use is arrived.In order to further increase its operating efficiency, the intensive warehousing system (Shuttle- of primary and secondary shuttle vehicle type
Carrier Warehousing System, SCWS) it comes into being.The system operational speed is fast, positioning accuracy is high, storage capacity
Greatly, scalability is strong, has been widely used in the industries such as tobacco, medicine and Cold Chain Logistics tool.It is intensive based on primary and secondary shuttle vehicle type
The plurality of advantages of warehousing system, how research carries out effective configuring to it, to reduce company investment cost and equipment energy consumption,
So as to improve storage economic benefit and social environment benefit, there is important practical significance and application value.
Correlative study person is only unilaterally related to storage facilities layout, goods when carrying out early stage planning to warehousing system mostly
Frame configuration or number of devices optimization.Since warehousing system physical layout and apparatus lack flexibility relatively, meanwhile, storage system
Operational efficiency of uniting is higher, and equipment loss rate is also corresponding higher, therefore, it is necessary to be that equilibrium considers effect at warehousing system planning initial stage
The factors such as rate, cost and energy consumption carry out comprehensive reasonable configuration to storage rack and specification of equipment, but the research phase of this respect at present
To scarcity.
Summary of the invention
The purpose of the present invention is to provide a kind of method for optimizing configuration of intensive warehousing system of primary and secondary shuttle vehicle type.
In order to achieve the above objectives, the invention adopts the following technical scheme:
1) determine that multiple optimization aims and decision variable involved in the configuration optimization of warehousing system, the decision variable are
Parameter relevant to warehousing system configuration；
2) according to warehousing and storage activities mode and decision variable, establishment step 1) determine each optimization aim mathematical model；
3) mathematical model established according to step 2), solves decision variable using Multipurpose Optimal Method, according to
Solving result optimizes the configuration of warehousing system.
Preferably, the optimization aim is selected from throughput time, energy consumption and the cost of warehousing system.
Preferably, the parameter that the decision variable is selected from the storage capacity of warehousing system and primary and secondary shuttle is run is (specific
It include: the acceleration a of the number of plies M of shelf, columns C, number of rows A, shelf number N, elevatory, elevator maximum speed vy, it is female
The acceleration a of vehiclex, female vehicle maximum speed vxAnd the maximum speed v of sub- vehiclez)。
Preferably, the step 2) is specifically includes the following steps: by the warehousing system for being related to the multiple optimization aim
Configuration optimization problem is converted into the multi-objective optimization question based on Pareto；The multi-objective optimization question table based on Pareto
It is shown as:
Minf (X)=min { f1(X),f2(X),f3(X)}
Wherein, f1(X)、f2(X) and f3It (X) is respectively warehousing system average throughput time, warehousing system energy consumption and storehouse
Store up the objective function of cost, QminFor the smallest storage capacity requirement, Q (X) is storage capacity, Xl、XuRespectively decision variable X's
Lower bound, the upper bound.
Preferably, the f1(X)、f2(X)、f3(X) it respectively indicates are as follows:
f2(X)=PNTshift·nwd·nweeks·ηSCWS
f3(X)=ISL+ISR+TP·(ISA+IEC)
Wherein, E (DCC)SCWSIndicate the intended travel time of unit commodity shelf system, k indicates that transaction count, N indicate shelf
Number；P is elevator and the general power of primary and secondary shuttle operation, T in unit commodity shelf systemshiftDaily for unit commodity shelf system
Operating time, nwdIt works weekly number of days for warehousing system, nweeksFor the annual work week number of warehousing system, ηSCWSFor warehousing system
Efficiency；ISLFor access arrangement cost of investment, ISRFor shelf cost of investment, ISATake up an area rent cost, I for shelfECFor energy consumption at
This, TPFor the expected service life of warehousing system.
Preferably, the intended travel time E (DCC) of the unit commodity shelf systemSCWSFor the intended travel time of elevator
With the maximum value of shelf single layer primary and secondary shuttle intended travel time, the intended travel time E (DCC) of elevatorliftAccording to I/O
Platform is to storage layer I/O point, outbound layer I/O point to I/O platform and enters, running time and elevator between outbound layer I/O point
It is calculated with primary and secondary shuttle interaction time and elevator positioning time；Shelf single layer primary and secondary shuttle intended travel time root
According to the intended travel time E (DCC) of primary and secondary shuttleshuttleAnd shelf number of plies M is calculated, the intended travel of primary and secondary shuttle
Time E (DCC)shuttleAccording to storage goods yard respective column mouth to layer I/O point running time, outbound goods yard respective column mouth to layer I/O
Point running time, the entering of storage goods yard respective column mouth to outbound goods yard respective column mouth running time and sub- vehicle, outbound goods yard are extremely
When time, sub- vehicle law days, female vehicle positioning time and the sub- vehicle that respective column mouth two-way time, female vehicle load and unload sub- vehicle position
Between be calculated.
Preferably, the step 3) is specifically includes the following steps: using the non-dominated sorted genetic algorithm with elitism strategy
Multi-objective optimization question based on Pareto is solved, Pareto optimal solution set is obtained.
Preferably, in the non-dominated sorted genetic algorithm with elitism strategy, crossover probability is 0.7~0.9, and variation is general
Rate is 0.1~0.2.
The beneficial effects of the present invention are embodied in:
The present invention establishes multiple-objection optimization mathematical model for the configuration optimization of the intensive warehousing system of primary and secondary shuttle vehicle type, leads to
It crosses solution and seeks optimal warehousing system allocation plan collection, provide initial stage policy decision foundation when planning warehousing system for enterprise personnel,
Guarantee every operation in warehousing activity efficiently, while carry out in phase, also reduce primary and secondary shuttle vehicle type and intensively store in a warehouse and be
System because planning and designing it is improper caused by risk, have biggish practical application meaning.
Further, the present invention with practical problem be guiding, finally establish with the handling capacity of warehousing system, energy consumption and
Cost three corresponding targets and that establishes objective function embody form, in conjunction with the decision variable of selection, obtain effectively
Mathematical model, so as to by the solution of the multi-objective optimization question based on Pareto, obtain the excellent of warehousing system configuration
Change result.
Further, the present invention carries out multiple-objection optimization, algorithm using the non-dominated sorted genetic algorithm with elitism strategy
Pareto optimal is evenly distributed, and allows that there are multiple and different equivalent solutions, while reducing the complexity of solution, in turn ensures
The superiority of solution.
Further, the present invention is by the optimization (crossover probability, mutation probability) to algorithm parameter, so that Pareto disaggregation
Distribution is extensive, is evenly distributed.
Detailed description of the invention
Fig. 1 is the intensive warehousing system illustraton of model of primary and secondary shuttle vehicle type (unit shelf)；
In Fig. 1: 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.
Fig. 2 is the forward position the Pareto figure of configuration optimization problem.
Specific embodiment
The present invention is described in detail with reference to the accompanying drawings and examples.
The present invention provides a kind of method for optimizing configuration of intensive warehousing system of primary and secondary shuttle vehicle type, the specific steps are as follows:
Step 1, the building intensive warehousing system configuration optimization model of primary and secondary shuttle vehicle type
1.1 pairs of intensive warehousing systems of primary and secondary shuttle formula are analyzed and researched, and determine decision variable and optimization aim.
Referring to Fig. 1, 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 intensive warehousing system of formula includes multiple unit shelf that can carry out the operation of in/out library simultaneously, the unit constituted based on unit load frame
Commodity shelf system includes several layers shelf (every layer of shelf can be divided into several shelf column 5), vertical conveyor 1, primary and secondary shuttle
(including female vehicle 2 and sub- vehicle 3), cargo 4, cross track 6, lifting platform 7, layer I/O point 8 and I/O platform 9.Take I/O platform 9 for original
Point (O), elevator drives lifting platform 7 are in vertical motion in Y direction and (can reach the different layer of shelf), and female vehicle 2 will
Transversely track 6 carries out X-direction transport (shelf that can reach different arrange) for sub- vehicle 3 and cargo 4, sub- vehicle 3 by cargo 4 into
Row Z-direction transports (can reach the different row of shelf).
Specific input work, Delivery and picking can be described as:
(1) input work: vertical conveyor 1 will be transported to the position equivalent layer I/O (i.e. layer I/O point 8), the layer to stock object
Female vehicle 2 carries after stock object through cargo 4 is transferred to the column mouth that shelf correspond to shelf column 5 by cross track 6, then female vehicle 2
Sub- vehicle 3 is discharged, the corresponding goods yard (goods at i.e. described shelf column row's cross-point locations corresponding with shelf will be transported to stock object
Lattice) after, female vehicle 2 carries sub- vehicle 3 again and returns to this layer of I/O point.
(2) Delivery: the column mouth of shelf column 5 where the primary and secondary shuttle of layer moves to cargo where outbound cargo, to son
After vehicle 3 completes picking, the sub- vehicle 3 for being loaded with cargo 4 is transported to a layer I/O point 8 by female vehicle 2, it will by plugging into for vertical conveyor 1
Goods handling finally carries out the processing such as entrucking of cargo to I/O platform 9.
In addition, double command cycle (Double Command can be used for the Efficient Operation for guaranteeing warehousing system
Cycle, abbreviation DCC) mode carries out warehousing and storage activities, i.e. and vertical conveyor 1 and primary and secondary shuttle can cooperate in once-through operation
Execute the Delivery of the input work and another cargo of completing a cargo.
The present invention is most short with the warehousing system average throughput time, system energy consumption is minimum and warehouse cost is at least storage system
The target of system configuration optimization, with the number of plies M of unit shelf, columns C, number of rows A, unit shelf number N, elevator in warehousing system
Acceleration ay, elevator maximum speed vy, female vehicle acceleration ax, female vehicle maximum speed vxAnd the maximum speed of sub- vehicle
Spend vzFor the decision variable of warehousing system configuration optimization.
1.2 establish warehousing system average throughput time model
Running time that the warehousing system average throughput time includes vertical conveyor and the operation of primary and secondary shuttle are when driving
Between.Under DCC mode, for the vertical conveyor 1 in unit shelf after the inbound task for completing a cargo, lifting platform can nothing
Need to return to I/O platform 9 just go to it is next to outbound goods yard layer carry out Delivery.Similarly, primary and secondary shuttle completes cargo
Input work after, without coming back to a layer I/O point, can continue to arrange to outbound cargo and carry out Delivery.
Definition: E (DCC)liftFor the intended travel time of elevator, E (DCC)shuttleFor the intended travel of primary and secondary shuttle
Time, E (DCC)SCWSFor the intended travel time of unit commodity shelf system.
(1) the intended travel time model of elevator
Elevator is when executing the in/out library operation under DCC mode, two one way running times and one comprising elevator
Running time between a two target, i.e. I/O platform are to storage layer I/O point, outbound layer I/O point to I/O platform and enter, outbound layer I/O
Running time between point, while can also undergo interaction time and 3 positioning times of 4 elevators loadings or release cargo.Root
The upper time obtains accordingly:
Wherein, H is pallet height (along the y axis, H=h × M, h are goods lattice height), tsrIt is loaded into or discharges for elevator
The interaction time of cargo, tsfFor elevator positioning time.
(2) the intended travel time model of primary and secondary shuttle
Primary and secondary shuttle is when executing the in/out library operation under DCC mode, two one way running times comprising female vehicle
(storage goods yard respective column mouth to layer I/O point running time and outbound goods yard respective column mouth to layer I/O point running time), one
Four lists of running time (storage goods yard respective column mouth to outbound goods yard respective column mouth running time) and sub- vehicle between two targets
Journey running time (enters, outbound goods yard to respective column mouth two-way time), at the same can also undergo 4 female vehicles dress (unloading) sub- vehicles when
Between, 2 sub- vehicles dress (unloading) cargo times, 3 female vehicle positioning times and 4 sub- vehicle positioning times.It is obtained according to the above time:
Wherein, tcrThe time of (unloading) sub- vehicle, t are filled for female vehiclezr(unloading) cargo time, t are filled for sub- vehiclecfWhen being positioned for female vehicle
Between, tzfFor sub- vehicle positioning time, L is shelf length (along the x axis, L=l × C, l are goods lattice length), and W is shelf width (edge
Z-direction, W=w × A, w are goods lattice width).
Since the intensive warehousing system of primary and secondary shuttle vehicle type is when carrying out the operation of in/out library, usually worn by elevator and primary and secondary
Shuttle car cooperation fulfils assignment, therefore, the intended travel time E (DCC) of entire unit commodity shelf systemSCWSFor elevator and single layer
The maximum value of female shuttle intended travel time, due to including that an elevator and M cover primary and secondary shuttle, shelf in unit shelf
Every layer of primary and secondary shuttle can operation simultaneously, therefore the intended travel time of single layer primary and secondary shuttle need to be averaged.That is:
Wherein, M indicates the number of plies of unit shelf.
In conclusion entire warehousing system average throughput time TRSCWS(s) as follows:
Wherein, k indicates that transaction count, k=2 under double command cycle, N indicate the number of unit shelf.
1.3 establish warehousing system energy consumption model
In the intensive warehousing system of primary and secondary shuttle vehicle type, warehousing system energy consumption mainly includes elevator, female vehicle and sub- vehicle
Sports energy consumption.The year power consumption EC of the intensive warehousing system of entire primary and secondary shuttle vehicle typeSCWS(kwh/) is as follows:
ECSCWS=PNTshift·nwd·nweeks·ηSCWS
Wherein, P is elevator and the general power of primary and secondary shuttle operation, T in unit commodity shelf systemshiftFor unit shelf system
It unites daily operating time (h), nwdIt works weekly number of days for warehousing system, nweeksFor the annual work week number (week) of warehousing system,
ηSCWSFor the efficiency of warehousing system.
Due to elevator Y direction operational process and female vehicle the operational process of X-direction include accelerator,
At the uniform velocity process and it is decelerated to stopped process, and sub- vehicle travels at the uniform speed always in Z-direction, therefore elevator and female vehicle need to consider
Accelerate, slow down and at the uniform velocity when power, and power when sub- vehicle need to only consider uniform motion.So the calculation method of P is as follows:
P=(Px+Pz)·M+Py
Wherein, M indicates the number of plies of unit shelf, Px、Py、PzRespectively represent movement X-axis, Y-axis, Z-direction female vehicle,
The power of elevator, sub- vehicle, calculation method are as follows:
Wherein, PxTa、PyTaIndicate maximum power when female vehicle, elevator accelerated motion, txa、tyaIndicate female vehicle, elevator
Accelerate to the time of maximum speed, PxTv、PyTvIndicate power when female vehicle, elevator uniform motion, txv、tyvIt indicates female vehicle, rise
The time of drop machine uniform motion, PxTb、PyTbIndicate female vehicle, maximum power when elevator makees retarded motion, txb、tybIndicate female
Vehicle, elevator are decelerated to the time of stop motion, FzTvFor sub- vehicle tractive force, vzIndicate the sub- vehicle speed of service (i.e. maximum speed),
ηz_shuttleIndicate the efficiency of sub- vehicle.
1.4 establish warehouse cost model
The warehouse cost of the intensive warehousing system of primary and secondary shuttle vehicle type mainly include access arrangement cost of investment, shelf investment at
Originally, four aspects of shelf land occupation rent cost and energy consumption cost.
The warehouse cost TC of the intensive warehousing system of entire primary and secondary shuttle vehicle typeSCWSIt is as follows:
TCSCWS=ISL+ISR+TP·(ISA+IEC)
In formula, ISLIndicate access arrangement cost of investment, ISRIndicate shelf cost of investment, ISAIndicate shelf land occupation rent at
This, IECIndicate energy consumption cost, TPIndicate the expected service life of the intensive warehousing system of primary and secondary shuttle vehicle type.And each cost calculation side
Method is as follows:
ISL=(Clift+M·Cshuttle)·N
ISR=CSR·M·C·A·N
ISA=CSA·L·W·N
IEC=CEC·ECSCWS
Wherein, CliftIndicate the cost of an elevator, CshuttleIndicate the cost of a set of primary and secondary shuttle, CSRIndicate single
The cost in a goods yard, CSAIndicate the land lease cost of every square metre of area, CECIndicate the cost of the every degree electricity of commercial power,
ECSCWSIndicate the year power consumption of the entire intensive warehousing system of primary and secondary shuttle vehicle type.
1.5 by configuration optimization model conversation be the multi-objective optimization question based on Pareto
The configuration optimization of the intensive warehousing system of primary and secondary shuttle vehicle type is related to time-optimized average throughput, energy optimization and storehouse
Therefore storage cost optimization converts the multi-objective optimization question based on Pareto for the configuration optimization problem of the warehousing system.It asks
The purpose for solving multi-objective optimization question is to find out Pareto optimal solution set, and be supplied to decision-maker, by decision-maker according to need
It wants and requires to determine a final solution.The multi-objective optimization question can be described with mathematical linguistics are as follows:
Minf (X)=min { f1(X),f2(X),f3(X)}
Wherein, f1(X)、f2(X)、f3It (X) is respectively warehousing system average throughput time, warehousing system energy consumption and storage
The objective function of cost.Introduce the smallest storage capacity QminEnsure reasonably store in a warehouse capacity Q (X), Q (X) is according to decision variable meter
It calculates (M, C, A are minimized), and its design space boundary value, X is not to be exceeded in each decision variable Xu、XlRespectively upper,
Lower bound.M,C,A,N,vx,vy,ax,ay,vzThe decision variable represented in warehousing system configuration optimization (is related to the hardware of warehousing system
Configuration and equipment operating parameter situation), it is respectively as follows: number of plies M, columns C, number of rows A, the unit shelf number N, lifting of unit shelf
The acceleration a of machiney, elevator maximum speed vy, female vehicle acceleration ax, female vehicle maximum speed vxAnd the maximum of sub- vehicle
Speed vz。
Step 2 solves Optimized model, determines the allocation optimum of warehousing system
The present invention is using the non-dominated sorted genetic algorithm (NSGA- II) with elitism strategy to above-mentioned based on the more of Pareto
Objective optimisation problems are solved.Specific step is as follows, chooses maximum number of iterations Gen=200:
(1) it encodes
It is encoded to configuring scheme using MIXED INTEGER coding mode.By a complete configuring scheme view
For item chromosome, individual is configured to (M, C, A, N, vx,vy,ax,ay,vz), all genes on every chromosome correspond to
One feasible allocation plan.
(2) initialization population
Initial parent population P is randomly generated in MATLAB0(population number n=100).And non-dominant row is carried out to population
Sequence makes each individual be endowed order, i.e., each solution (individual) is assigned to one and non-dominant level (1 is optimal level)
Corresponding fitness value.
(3) progeny population is generated
Initial population is optimized using the selection in basic genetic algorithmic, intersection, mutation operation, crossover probability Pc=
0.9, mutation probability Pm=0.1 obtain the progeny population Q that new Population Size is n0。
(4) non-dominated ranking
Parent and filial generation are merged into new group Rt=Pt∪Qt(t=0,1 ...), to RtNon-dominated ranking is carried out, according to
It is secondary to obtain non-dominant disaggregation Z1,Z2,…,Zi,…。
(5) crowding distance sorts
By non-dominant disaggregation ZiIt is ranked up according to crowding distance, selects the highest preceding n individual composition of crowding distance new
Parent population Pt+1, which is elite retention strategy.It is selected according to crowding distance sequence under individual entrance winning in population
One operation.
(6) swarm optimization
To new godfather population Pt+1Execute selection, intersection, mutation operation (the identical parameter setting of step 3) carry out population it is excellent
Change, obtains new progeny population Qt+1。
(7) termination condition
If the number of iterations is equal to maximum number of iterations Gen, output is as a result, terminate；Otherwise, return step (4).
Simulation example
The example of different configuration assembled schemes is carried out for the configuration optimization problem to the intensive warehousing system of primary and secondary shuttle formula
Analysis, the intensive warehousing system device parameter of primary and secondary shuttle vehicle type and certain medical object that the present invention is provided according to certain storage facilities company
The corresponding scene of actual job design data that home-delivery center provides is flowed, carries out numerical experiment and emulation with MATLAB.Wherein
The minimum storage correlated conditions such as capacity requirement (i.e. l × w × h) and access arrangement kinematic parameter in the intensive warehouse of primary and secondary shuttle vehicle type
Constraint, parameter value be shown in Table 1, table 2.
According to II algorithm steps of NSGA-, using MATLAB R2016b software to the intensive storehouse of primary and secondary shuttle vehicle type established
Library configuration optimization model is solved.After 200 iteration, the forward position Pareto of configuration optimization problem has been obtained.Referring to figure
2, Pareto forward positions are approximately a smooth curve, and disaggregation distribution is extensive, is evenly distributed, therefore, the forward position Pareto
On each disaggregation be Pareto optimal solution set.
By the available 100 groups of Pareto optimal solution sets of calculated result, each disaggregation is one alternative
Configuring scheme, referring to table 3.Enterprise can refer to calculated result, according to their needs (for handling capacity, energy consumption and at
The difference of this three optimization aims stresses) and select different allocation plans.
The storage capacity of table 1. and service requirement
The storage specification of table 2. and equipment run relevant parameter
In PxTa、PxTv、PxTbIn will use tractive force F's and efficiency eta
(such as), calculate F=Gk, G=(m+ load weight) g, Py、PzIt is similar.K is when calculating elevator
kir, calculate sub- vehicle and mother Che Shiwei kr。
3. configuring scheme optimal solution set of table
Claims (8)
1. a kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type, it is characterised in that: the following steps are included:
1) multiple optimization aims and decision variable involved in the configuration optimization of warehousing system are determined, the decision variable is and storehouse
Storage system configures relevant parameter；
2) according to warehousing and storage activities mode and decision variable, establishment step 1) determine each optimization aim mathematical model；
3) mathematical model established according to step 2), solves decision variable using Multipurpose Optimal Method, according to solution
As a result the configuration of warehousing system is optimized.
2. a kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type according to claim 1, it is characterised in that:
The optimization aim is selected from throughput time, energy consumption and the cost of warehousing system.
3. a kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type according to claim 1, it is characterised in that:
The decision variable is selected from the storage capacity of warehousing system and the parameter of primary and secondary shuttle operation.
4. a kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type according to claim 1, it is characterised in that:
The step 2) is specifically includes the following steps: the configuration optimization problem for the warehousing system for being related to the multiple optimization aim is converted
For the multi-objective optimization question based on Pareto；The multi-objective optimization question based on Pareto indicates are as follows:
Minf (X)=min { f1(X),f2(X),f3(X)}
Wherein, f1(X)、f2(X) and f3(X) be respectively the warehousing system average throughput time, warehousing system energy consumption and storage at
This objective function, QminFor the smallest storage capacity requirement, Q (X) is storage capacity, Xl、XuUnder respectively decision variable X
Boundary, the upper bound, M are the number of plies of shelf, and C is the columns of shelf, and A is the number of rows of shelf, and N is shelf number, ayFor adding for elevator
Speed, vyFor the maximum speed of elevator, axFor the acceleration of female vehicle, vxFor the maximum speed of female vehicle, vzFor the maximum speed of sub- vehicle
Degree.
5. a kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type according to claim 4, it is characterised in that:
The f1(X)、f2(X)、f3(X) it respectively indicates are as follows:
f2(X)=PNTshift·nwd·nweeks·ηSCWS
f3(X)=ISL+ISR+TP·(ISA+IEC)
Wherein, E (DCC)SCWSIndicate the intended travel time of unit commodity shelf system, k indicates that transaction count, N indicate shelf number；P
For the general power that elevator in unit commodity shelf system and primary and secondary shuttle are run, TshiftWhen for the daily work of unit commodity shelf system
It is long, nwdIt works weekly number of days for warehousing system, nweeksFor the annual work week number of warehousing system, ηSCWSFor the efficiency of warehousing system；
ISLFor access arrangement cost of investment, ISRFor shelf cost of investment, ISATake up an area rent cost, I for shelfECFor energy consumption cost, TPFor
The expected service life of warehousing system.
6. a kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type according to claim 5, it is characterised in that:
The intended travel time E (DCC) of the unit commodity shelf systemSCWSIt is worn for the intended travel time and shelf single layer primary and secondary of elevator
The maximum value of shuttle car intended travel time, the intended travel time E (DCC) of elevatorliftAccording to I/O platform to storage layer I/O
Point, outbound layer I/O point to I/O platform and enter, the running time between outbound layer I/O point and elevator and primary and secondary shuttle are handed over
Mutual time and elevator positioning time are calculated；The shelf single layer primary and secondary shuttle intended travel time is according to primary and secondary shuttle
Intended travel time E (DCC)shuttleAnd shelf number of plies M is calculated, the intended travel time E (DCC) of primary and secondary shuttleshuttle
According to storage goods yard respective column mouth to layer I/O point running time, outbound goods yard respective column mouth to layer I/O point running time, storage
Goods yard respective column mouth to the entering of outbound goods yard respective column mouth running time and sub- vehicle, outbound goods yard is round-trip to respective column mouth when
Between, female vehicle loads and unloads time, sub- vehicle law days, female vehicle positioning time and the sub- vehicle positioning time of sub- vehicle and is calculated.
7. a kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type according to claim 4, it is characterised in that:
The step 3) is specifically includes the following steps: using the non-dominated sorted genetic algorithm with elitism strategy to based on the more of Pareto
Objective optimisation problems are solved, and Pareto optimal solution set is obtained.
8. a kind of method for optimizing configuration of the intensive warehousing system of primary and secondary shuttle vehicle type according to claim 7, it is characterised in that:
In the non-dominated sorted genetic algorithm with elitism strategy, crossover probability is 0.7~0.9, and mutation probability is 0.1~0.2.
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