CN109063899A - Vehicle transport method and device for planning, electronic equipment and readable storage medium storing program for executing - Google Patents
Vehicle transport method and device for planning, electronic equipment and readable storage medium storing program for executing Download PDFInfo
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Abstract
The present embodiments relate to logistics management technical field, a kind of vehicle transport method and device for planning, electronic equipment and readable storage medium storing program for executing are disclosed.The vehicle transport planing method includes: to determine whole transport sections according to the position of the home-delivery center got, the energy recharge station location of the set and haulage vehicle that dispense place；Transportation Planning model is established according to the quantity of haulage vehicle and the operating mode of haulage vehicle in whole transport sections and acquisition, wherein, Transportation Planning model includes the objective function of electric power energy cost and fuel oil energy cost that haulage vehicle consumes on the transport section run over；The Transportation Planning model is solved based on preset algorithm, and determines the lowest energy cost for completing transport task consumption.It in the present invention, is solved by constructing model, the minimum solution of transportation power sources cost can be provided for the vehicle of different scales and dispatching place.
Description
Technical field
The present embodiments relate to logistics management technical field, in particular to a kind of vehicle transport method and device for planning,
Electronic equipment and readable storage medium storing program for executing.
Background technique
The case where based on current oil shortage of resources, finds the new energy and non-traditional under the requirement of sustainable development
Fuel has become international consensus.Under the overall situation for greatly developing low-carbon economy, environlnental logistics have become Development of Logistics Industry
One of important directions.With the development of environlnental logistics, loglstics enterprise gradually pays close attention to the application of hybrid vehicle.Hybrid power
Automobile have low energy consumption, pollute few advantage.As the transitional product between traditional product and electric car, hybrid vehicle
The hot spot of novel environment friendly development of automobile in the world is had become, they have good development prospect and market potential.
Hybrid vehicle consumption electric power and gasoline when driving.Hybrid vehicle takes in the fuel that each section consumes
Certainly in the driving mode of hybrid vehicle, including following situations: motor provide power, engine provide power or
Engine and motor provide power jointly.For example, battery-based mould can be used in hybrid vehicle if congestion in road
Formula, if the traffic conditions in next section are good, the mode based on gasoline is can be used to drive, in this way in hybrid vehicle
The consumption of the energy can be optimized.
At least there are the following problems in the prior art for inventor's discovery: although using hybrid vehicle in logistic pattern
Can reduce energy consumption and reduce pollution, but compared to traditional fuel vehicle, have the shortcomings that as follows: it is smaller to continuously drive mileage, and
The risk that fuel is used up during transportation is larger.Therefore, the use pattern to hybrid vehicle is needed in logistics distribution process
It is adjusted, and operating mode of the existing logistics route planning without the concern for haulage vehicle, existing solution path planning
Software only the minimum solution of transportation cost can be extrapolated to small-scale example within reasonable time, with solution
Scale is bigger, and e.g., user increases, and the business solution software used time is longer, cannot even provide solution party for large-scale scheme
Case.
Summary of the invention
Embodiment of the present invention is designed to provide a kind of vehicle transport method and device for planning, electronic equipment and readable
Storage medium is solved by constructing model, which considers operating mode of each car in transport section, Neng Gouzhen
The minimum solution of transportation power sources cost is provided to different vehicle operation modes and dispatching place.
In order to solve the above technical problems, embodiments of the present invention provide a kind of vehicle transport planing method, including with
Lower step:
Energy recharge station location according to the position of the home-delivery center got, the set in dispatching place and haulage vehicle is true
Fixed whole transport section, wherein energy recharge station includes electric power energy depot and fuel oil energy recharge station；
Fortune is established according to the operating mode of the quantity and haulage vehicle of whole transport sections and the haulage vehicle of acquisition
Defeated plan model, wherein Transportation Planning model include the electric power energy that is consumed on the transport section run over of haulage vehicle at
The objective function of this and fuel oil energy cost；
Transportation Planning model is solved based on preset algorithm, and determines the lowest energy cost for completing transport task consumption.
Embodiments of the present invention additionally provide a kind of vehicle transport device for planning, comprising: determining module, model generate mould
Block and solution module；
Determining module, for according to the position of the home-delivery center got, the set in dispatching place and the energy of haulage vehicle
Source feeds station location and determines whole transport sections, wherein energy recharge station includes that electric power energy depot and the fuel oil energy are mended
To station；
Model generation module, for the quantity and transport vehicle according to whole transport sections and the haulage vehicle of acquisition
Operating mode establish Transportation Planning model, wherein Transportation Planning model includes haulage vehicle in the transport section run over
The electric power energy cost of upper consumption and the objective function of fuel oil energy cost；
Module is solved, for determining the optimal solution of Transportation Planning model based on preset algorithm.
Embodiments of the present invention additionally provide a kind of electronic equipment, comprising:
At least one processor；And the memory being connect at least one processor communication；Wherein, memory stores
There is the instruction that can be executed by least one processor, instruction is executed by least one processor, so that at least one processor energy
Enough execute above-mentioned vehicle transport planing method.
Embodiments of the present invention additionally provide a kind of computer readable storage medium, are stored with computer program, calculate
Machine program realizes above-mentioned vehicle transport planing method when being executed by processor.
Embodiment of the present invention in terms of existing technologies, determines transport rule according to the relevant information of transportation scale first
Model is drawn, includes the operating mode of haulage vehicle and the energy for completing the consumption of transport task haulage vehicle in the Transportation Planning model
The objective function of cost, the different corresponding energy cost of operating mode in same section of transport section is different, then according to objective function
The cost of total energy of the consumption in haulage vehicle driving process is capable of determining that, because regardless of the scale transported
To be indicated by Transportation Planning model, in addition, the process of solution is directly simplified to model solution, when effectively shortening solution
Between, the Transportation Planning model determined for different scales can solve, and improve the versatility of the vehicle transport planing method.
In addition, Transportation Planning model further includes constraint condition and decision variable；
Constraint condition includes: that haulage vehicle works in one mode of operation, haulage vehicle consumes on a transport section
Fuel oil energy cost is minimum, a dispatching place by a haulage vehicle carries out delivery service, a haulage vehicle by one
Primary, all haulage vehicle in a energy recharge station from home-delivery center's line and finally returns that home-delivery center, arrival one
It is negative when the vehicle fleet size of section node is identical as the vehicle fleet size for leaving section node, each haulage vehicle leaves home-delivery center
It carries one or more of equal with the dispatching place demand that it is serviced no more than the cargo dead-weight of preset value, each haulage vehicle
Combination；
Decision variable includes: that remaining capacity, the haulage vehicle in one dispatching place of haulage vehicle arrival leave a dispatching
Remaining capacity, the haulage vehicle in place reach the Fuel Remained amount in a dispatching place, haulage vehicle leaves a dispatching place
Fuel Remained amount, haulage vehicle reaches the dispatching cargo dead-weight in place, haulage vehicle leaves the loading in a dispatching place
Whether amount, haulage vehicle reach dispatching place, whether haulage vehicle arrives under determining operating mode by a transport section
Up to the combination in one or more of dispatching place.
In the embodiment, the Transportation Planning model established is set more to meet reality by the setting of bound variable and decision variable
Border requires and user demand, but also the solving result by the Transportation Planning model is more reliable.
In addition, the operating mode of haulage vehicle include motor be sustainer supplemented by the first Working mould of power is provided
Formula, engine are that the second operating mode, motor and the engine supplemented by main motor coordinate quite to provide the of power jointly
Three operating modes and with only by engine provide power the 4th operating mode.
In the embodiment, the cost of the energy of the consumption of haulage vehicle is also different under different operating modes, so that
The different transport settable different operating modes in section so that it is determined that haulage vehicle consumption energy cost it is more acurrate.
In addition, based on preset algorithm solve Transportation Planning model, and determine complete transport task consumption lowest energy at
This, specifically includes:
Based on Transportation Planning model, primary group is generated, wherein a particle includes that haulage vehicle goes out from home-delivery center
Hair is eventually returned to the sum of the transport section of home-delivery center's process；Determine that haulage vehicle is being transported in particle by preset pattern algorithm
Operating mode in defeated section, and calculate the adaptive value of particle；Determine the neighborhood of particle；By variable neighborhood search algorithm to particle
It is iterated；The population after iteration is updated after being iterated to particle；If it is determined that the number of iterations of particle is greater than preset
Maximum number of iterations, alternatively, determining that the number remained unchanged after particle iteration is greater than the default greatest iteration remained unchanged time
Number；The corresponding traffic program of population after determining iteration is the optimal solution of Transportation Planning model；It is determined and is completed according to optimal solution
The lowest energy cost of transport task consumption.
In the embodiment, population is generated based on Transportation Planning model, Transportation Planning model is solved based on particle algorithm,
It shortens and solves the time.
In addition, operating mode of the haulage vehicle in transport section in particle is determined by preset pattern algorithm, to every
A particle calculates adaptive value, specifically includes: determining all dispatching places for including in particle；Be arranged it is each dispatching place at
This label, wherein different operating modes corresponds to different cost tags；Each dispatching ground is determined under the constraint of constraint condition
The operating mode of point；The adaptive value of particle is calculated according to the corresponding cost tag of operating mode.
In addition, being iterated by variable neighborhood search algorithm to particle, specifically include: two dispatchings ground in selection particle
Point exchange position, alternatively, the dispatching place in particle is moved to another position, alternatively, by two dispatchings ground in particle
Position between point swaps.
In addition, updating iteration after being iterated to particle after being iterated by variable neighborhood search algorithm to particle
Before population afterwards, vehicle transport planing method further include: obtain the sequence that place is dispensed in particle；More by Boolean variable
The contiguous range of new particle；According to the sequence in the dispatching place of the contiguous range of particle more new particle.
Detailed description of the invention
One or more embodiments are illustrated by the picture in corresponding attached drawing, these exemplary theorys
The bright restriction not constituted to embodiment, the element in attached drawing with same reference numbers label are expressed as similar element, remove
Non- to have special statement, composition does not limit the figure in attached drawing.
Fig. 1 is the flow chart of vehicle transport planing method in first embodiment of the invention；
Fig. 2 a is haulage vehicle fuel consumption and speed in the first operating mode in first embodiment of the invention
Relationship；
Fig. 2 b is haulage vehicle electric quantity consumption amount and speed in the first operating mode in first embodiment of the invention
Relationship；
Fig. 3 a is haulage vehicle fuel consumption and speed in the second operating mode in first embodiment of the invention
Relationship；
Fig. 3 b is haulage vehicle electric quantity consumption amount and speed in the second operating mode in first embodiment of the invention
Relationship；
Fig. 4 a is haulage vehicle fuel consumption and speed in third operating mode in first embodiment of the invention
Relationship；
Fig. 4 b is haulage vehicle electric quantity consumption amount and speed in third operating mode in first embodiment of the invention
Relationship；
Fig. 5 is the pass of haulage vehicle fuel consumption and speed in four operating modes in first embodiment of the invention
System；
Fig. 6 is the flow chart of another vehicle transport planing method in first embodiment of the invention；
Fig. 7 is the flow chart of vehicle transport planing method in second embodiment of the invention；
Fig. 8 is the structure chart of vehicle transport device for planning in third embodiment in the present invention；
Fig. 9 is the structure chart of electronic equipment in four embodiment of the invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with attached drawing to the present invention
Each embodiment be explained in detail.However, it will be understood by those skilled in the art that in each embodiment party of the present invention
In formula, in order to make the reader understand this application better, many technical details are proposed.But even if without these technical details
And various changes and modifications based on the following respective embodiments, the application technical solution claimed also may be implemented.
The first embodiment of the present invention is related to a kind of vehicle transport planing methods.Detailed process is as shown in Figure 1.Including such as
Under implementation steps:
Step 101: according to the position of the home-delivery center got, dispensing the set in place and the energy recharge of haulage vehicle
Station location determines whole transport sections.
Wherein, energy recharge station includes electric power energy depot and fuel oil energy recharge station.
Specifically, the collection in dispatching place is combined into the ground point set for needing cargo, it is referred to as client set.It gets
Position, the set in dispatching place and the energy recharge station location of haulage vehicle of home-delivery center, can also specify distribution vehicle
Departure place and destination, can determine whole transport sections that distribution vehicle may pass through.The transport section of the whole
Including dispensing the transport section in place, from a dispatching place to the transport road in another dispatching place from home-delivery center to one
Section or from one dispense place to energy recharge station transport section.
In one concrete implementation, a transport section is defined in an oriented network, wherein if the oriented network is
G=(V, E), wherein V includes home-delivery center position, dispenses the set in place and the position at energy recharge station.E=(i, j) |
I, j ∈ V }, it is the set in all transport sections, wherein i and j respectively indicates starting and the terminal in a transport section, (i, j)
Indicate a transport section from starting point i to terminal j.
It should be noted that each haulage vehicle, when setting out, identical cargo dead-weight is all Q, and haulage vehicle is all mixed
Close power vehicle, battery capacity Qe, tankage Qg, each hybrid vehicle battery and fuel tank before setting out all are full
, when hybrid electric vehicle sails to charging station, hybrid vehicle is disposably fully charged, similarly when reaching gas station
Also oil is filled it up with.
It is noted that can determine each transport according to certain algorithm after determining whole transport sections
The transport section that the possibility of vehicle is passed through is not limited merely to the set by home-delivery center position and dispatching place in practice
The mode for determining transport section, can also be directly obtained whole transport sections, and e.g., input dispatching map simultaneously marks whole
Transport section.Herein with no restrictions to the determining mode for transporting section.
Step 102: according to the Working mould of the quantity and haulage vehicle of whole transport sections and the haulage vehicle of acquisition
Formula establishes Transportation Planning model.
Wherein, Transportation Planning model include the electric power energy cost that is consumed on the transport section run over of haulage vehicle and
The objective function of fuel oil energy cost.
Specifically, objective function is indicated by formula are as follows:
(formula 1),
Wherein, Z indicates target value, and i, j indicate the starting in the transport section that haulage vehicle passes through and the label of terminal, and K indicates fortune
The set of defeated vehicle, k indicate vehicle set in a haulage vehicle, R indicate vehicle operation mode set, r indicate from
The operating mode of the transport section haulage vehicle of starting point i to terminal j, peIndicate unit price of power, pgIndicate unit oil price,
Indicate that the haulage vehicle use pattern r that number is k has travelled the electricity that the transport section from i to j consumes,Indicate that number is k
Haulage vehicle use pattern r travelled from i to j transport section consumption oil mass, xijkrIf indicating, haulage vehicle k uses mould
By the running section from i to j, then value is 1 to formula r；Otherwise, value 0.
Specifically, the operating mode of haulage vehicle include motor be sustainer supplemented by provide power first work
The second operating mode, motor and the engine coordination that power is provided based on mode, engine and supplemented by motor are quite common
The third operating mode of power is provided and only engine provides the 4th operating mode of power.
It should be noted that the haulage vehicle is hybrid vehicle, due to the cost of the energy consumed in vehicle travel process
It is related to haulage vehicle drive speed, then it is indicated not by the consumption of the consumption of unit distance fuel oil and unit distance electricity
Same operating mode, selection carry out the energy-output ratio under the transport section from i to j, the first work under four kinds of operating modes
It is that main motor provides the mode of power that provides supplemented by power engine in operation mode, the relationship of fuel consumption and speed is such as
Shown in Fig. 2 a, the relationship of electric quantity consumption and speed is as shown in Figure 2 b；It is mainly that engine provides power electric in second operating mode
The relationship of the mode of offer power supplemented by motivation, fuel consumption and speed is as shown in Figure 3a, the relationship of electric quantity consumption and speed
As shown in Figure 3b；Third operating mode is that motor and engine are coordinated quite to provide power, that is, engine and electricity jointly
Motivation provides power jointly, is the mode of motor and engine balance work, relationship such as Fig. 4 a of fuel consumption and speed
Shown, the relationship of electric quantity consumption and speed is as shown in Figure 4 b；4th operating mode is that only engine provides the mode of power, combustion
The relationship of oilconsumption and speed is as shown in figure 5, provide power without motor, then the consumption of electricity is 0.
By the solution to Transportation Planning model, the minimum value in objective function is determined.By taking logistics transportation as an example, in transport
The heart needs to complete 10 clients dispatching task, and the quantity of goods that each client needs is different from, by a fleet such as 3 vehicles
It is responsible for completing the dispatching task of cargo, meets the cargo demand of each client and the energy cost that the fleet is consumed is minimum.
It should be noted that decision of the haulage vehicle also by the constraint of physical condition and route or travel by vehicle in actual transport,
The length in the transport section that the Constraint of such as haulage vehicle, the constraint of amount of gasoline, vehicle select and the traffic condition of road.
Specifically, establishing the Transportation Planning model further includes constraint condition and decision variable.
One in the specific implementation, constraint condition includes: haulage vehicle work in one mode of operation on a transport section
Make, the fuel oil energy cost of haulage vehicle consumption is minimum, a dispatching place is by haulage vehicle progress a delivery service, one
Haulage vehicle from home-delivery center's line and finally returns that dispatching by primary, all haulage vehicle in an energy recharge station
Center, the vehicle fleet size for reaching a section node are identical as the vehicle fleet size for leaving section node, each haulage vehicle leaves
It is equal with the dispatching place demand that it is serviced that load when home-delivery center is no more than preset value, the cargo dead-weight of each haulage vehicle
One or more of combination.
Decision variable includes: that remaining capacity, the haulage vehicle in one dispatching place of haulage vehicle arrival leave a dispatching
Remaining capacity, the haulage vehicle in place reach the Fuel Remained amount in a dispatching place, haulage vehicle leaves a dispatching place
Fuel Remained amount, haulage vehicle reaches the dispatching cargo dead-weight in place, haulage vehicle leaves the loading in a dispatching place
Whether amount, haulage vehicle reach dispatching place, whether haulage vehicle arrives under determining operating mode by a transport section
Up to the combination in one or more of dispatching place.
Constraint condition is indicated by publicity are as follows:
Formula 2: ∑k∈Kyik=1；Wherein, i ∈ N, N indicate the set in dispatching place, and i indicates any dispatching place.
Formula 3: ∑k∈Kyik≤1；Wherein, i ∈ Gg∪Ge, indicate that haulage vehicle can only pass through the same fuel oil energy recharge
It stands or electric power energy depot is primary, QeIndicate the battery capacity of haulage vehicle, QgIndicate the tankage of haulage vehicle.
Publicity 4: ∑i∈v∑r∈Rxilkr=∑j∈v∑r∈Rxlikr=ylk；Wherein, l ∈ V, k ∈ k, r ∈ R indicate to reach one
The vehicle fleet size of section node is identical as the vehicle fleet size for leaving the section node, and V indicates the transport in whole transport sections
The set of node, 1 indicates a section node.
Formula 5:Wherein, i ∈ Ge∪0k∈K；Indicate that haulage vehicle k leaves section
Remaining electricity when node i, M indicate the consumption of the energy under r operating mode.
Formula 6:Wherein, i ∈ Ge∪ 0k ∈ K formula 5 and formula 6 indicate haulage vehicle from
Ensure that the battery of the haulage vehicle is fully charged after opening dispatching place or energy recharge station.
Formula 7:Wherein, i ∈ Gg∪0k∈K；Indicate that haulage vehicle k leaves section
Remaining amount of fuel when node i.
Formula 8:Wherein, i ∈ Gg∪0k∈K；Formula 7 and formula 8 indicate haulage vehicle from
Ensure that the fuel tank of the haulage vehicle fills it up with fuel oil after opening dispatching place or energy recharge station.
Formula 9:Wherein, i ∈ N ∪ Ggk∈K；Indicate remaining when haulage vehicle k reaches section node i
Electricity, it is ensured that the remaining power for reaching the hybrid vehicle of client or gas station is equal to the mixing for leaving client or gas station
The remaining power of power vehicle.
Formula 10:Wherein, i ∈ N ∪ Gek∈K；Indicate remaining when haulage vehicle k reaches section node i
Amount of fuel, it is ensured that the Fuel Remained amount that hybrid vehicle reaches client or charging station, which is equal to, leaves the mixed of client or charging station
Close power vehicle Fuel Remained amount.
Formula 11:Wherein, i, j ∈ V, k ∈ K, r ∈ R.
Formula 12:Wherein, i, j ∈ V, k ∈ K, r ∈ R；Formula 11 and formula 12
Indicate haulage vehicle from section node i to the battery the constraint relationship of section node j.
Formula 13:Wherein, i, j ∈ V, k ∈ K, r ∈ R.
Formula 14:Wherein, i, j ∈ V, k ∈ K, r ∈ R；Formula 13 and formula
14 indicate haulage vehicle from section node i to the fuel consumption the constraint relationship of section node j.
Formula 15:Expression ensures that as haulage vehicle point of arrival j, cargo dead-weight is equal to starting point i, and Q indicates to carry
Goods amount, q indicate the current cargo dead-weight of haulage vehicle q.
Specifically, decision variable includes: yikIndicate that value is 1, otherwise, value if haulage vehicle k reaches transit node i
It is 0；xijkrIndicate that value is 1, otherwise, value 0 if vehicle use pattern r is from i to j；Indicate that haulage vehicle arrives
Up to the value of section node i electricity；Indicate that haulage vehicle leaves the value of the i remaining capacity of section node；
Indicate that haulage vehicle reaches the value of section node i amount of fuel；Indicate that haulage vehicle leaves the i residue combustion of section node
The value of oil mass；Indicate that haulage vehicle reaches the cargo dead-weight value of section node i；Indicate that haulage vehicle leaves
The value of the cargo dead-weight of section node.
It is noted that the quantity of constraint condition can carry out increasing or decreasing for adaptability according to the actual situation, herein
With no restrictions, above-mentioned is only constraint condition and decision variable for example, being not particularly limited.
Step 103: Transportation Planning model being solved based on preset algorithm, and determines the lowest energy for completing transport task consumption
Cost.
Specifically, can be indicated in step 103 by PSO Algorithm Transportation Planning model by generating particle
Possible solution, and optimal solution is finally determined after updating, so that the energy cost for completing transport task consumption is minimum.
The implementation steps specifically comprise the following steps 1031 to step 1037, as shown in Figure 6:
Step 1031: being based on Transportation Planning model, generate primary group, wherein a particle include haulage vehicle from
Home-delivery center, which sets out, is eventually returned to the sum of the transport section of home-delivery center's process.
Step 1032: operating mode of the haulage vehicle in transport section in particle is determined by preset pattern algorithm,
And calculate the adaptive value of particle.
Step 1033: determining the neighborhood of particle.
Step 1034: particle being iterated by variable neighborhood search algorithm.
In one concrete implementation, particle is iterated by variable neighborhood search algorithm, including but not limited to, selects grain
Two dispatching places in son exchange position, alternatively, the dispatching place in particle is moved to another position, alternatively, by grain
The position between two dispatching places in son swaps.
Specifically, the information for including in a particle is the transport road that a haulage vehicle completes that transport task is passed through
Section a, that is to say, that particle is exactly the traveling scheme an of haulage vehicle, is changed by variable neighborhood search algorithm to particle
After instead of, then the solution in particle can generate variation, then obtain the sequence that place is dispensed in particle；More by Boolean variable
The contiguous range of new particle；According to the sequence in the dispatching place of the contiguous range of particle more new particle.
It should be noted that Boolean variable is the variable defined when carrying out change neighborhood search, preset Boolean variable is
Very, when Boolean variable be it is false, then the neighborhood of the particle is updated, the new neighborhood of the particle is searched for, to the label of the particle
It is determined and determines the optimal solution of particle.
Step 1035: the population after iteration is updated after being iterated to particle.
Step 1036: if it is determined that the number of iterations of particle is greater than preset maximum number of iterations, alternatively, determining particle iteration
The number remained unchanged later is greater than the default maximum number of iterations remained unchanged.
Step 1037: the corresponding traffic program of population after determining iteration is the optimal solution of Transportation Planning model；According to
Optimal solution determines the lowest energy cost for completing transport task consumption.
Specifically, each particle is a possible solution in population, determine the adaptive value of particle and to particle into
Row iteration updates, and finally determines the minimum optimal solution of energy cost, and this improved particle algorithm can effectively shorten model
The solution time, improve user experience.
In terms of existing technologies, Transportation Planning model is determined according to the relevant information of transportation scale first, the transport
The objective function of the energy cost of operating mode and completion transport task haulage vehicle consumption in model including haulage vehicle, together
The different corresponding energy cost of operating mode in one section of transport section is different, then is capable of determining that haulage vehicle according to objective function
The cost of total energy of consumption in driving process, because regardless of how the scale of transport can pass through Transportation Planning model
It indicates, in addition, the process of the solution directly simplified to model solution, effectively shortens and solve the time, different scales is determined
Transportation Planning model can solve, improve the versatility of the vehicle transport planing method.
Second embodiment of the present invention is related to a kind of vehicle transport planing method.Second embodiment and the first embodiment party
Formula is roughly the same, is in place of the main distinction: in second embodiment of the invention, specifically illustrating and solves Transportation Planning model
The method for obtaining optimal solution, specifically, the process of the vehicle transport planing method is as shown in Figure 7.
It should be noted that step 1031 is identical as step 201, step 1033 to step 1037 respectively with step 206 to
Step 210 is identical, and details are not described herein again.
Step 202: determining all dispatching places for including in particle.
Step 203: the cost tag in each dispatching place of setting, wherein different operating modes corresponds to different costs
Label.
Step 204: determining that haulage vehicle reaches the operating mode in each dispatching place under the constraint of constraint condition.
Step 205: the adaptive value of particle is calculated according to the corresponding cost tag of operating mode.
Specifically, each particle is the transit route that the haulage vehicle completes that transport task is passed through, and including passing through
Transport section, wherein each haulage vehicle generates a series of label, each label packet on corresponding each section node
Containing seven parts.First part indicates the mode used from node i to node j.Second part display reaches the residue electricity of node j
Amount.Part III represents the remaining amount of gasoline of node j.The cost of Part IV display label.Whether Part V records label
It is eliminated.Part VI records feasibility.Part VII has recorded the mode that last section uses.
Specifically, being evaluated for each label by a cost function, which is indicated
Are as follows:
Formula 16:C (ρ)=Z (ρ)+(θqMax (0, q (ρ)-Q)+θeMax (0, e (ρ)-Qe)+θgMax (0, g (ρ)-
Qg)；Wherein, ρ indicates the corresponding sequence node of the haulage vehicle.Q (ρ) is that the load of haulage vehicle k is measured in violation of rules and regulations, and e (ρ) is transport
The electricity of vehicle k is measured in violation of rules and regulations, and g (ρ) is that the oil mass of haulage vehicle k is measured in violation of rules and regulations.θq, θ e, θgBe known quantity respectively be load punishment because
Son, battery penalty factor and gasoline penalty factor.
Specifically, because operating mode difference can there are many labels to generate in a section node, the corresponding section node
Subsequent section node can also generate more labels, in order to avoid the number of labels increase of section node generation below is too fast,
Such case is avoided by the way that a governance rule is arranged, ifWithIndicate two labels under same node.WithIt is this
Haulage vehicle reaches the remaining capacity of node i using different operating modes, whereinWithIt respectively indicates are as follows:
Formula 17:Wherein,Indicate that the haulage vehicle reaches section with a kind of operating mode
Remaining electricity when node.
Formula 18:Wherein,Indicate that the haulage vehicle reaches section with another operating mode
Remaining electricity when node.
WithIt is the haulage vehicle using the remaining gasoline at different operating mode arrival node is, whereinWith
It respectively indicates are as follows:
Formula 19:Wherein,Indicate that the haulage vehicle reaches section with a kind of operating mode
Remaining amount of fuel when node.
Formula 20:Wherein,Indicate that the haulage vehicle reaches section with another operating mode
Remaining amount of fuel when node.
It rulesGovernance relationship set up when the constraint relationship that formula 21 to formula 24 indicates is set up:
Formula 21:
Formula 22:
Formula 23:
Formula 24:
Wherein, α and β is the scale parameter of battery and gasoline respectively；The parameter of γ >=1 γ will be according to the mark of each section node
Number dynamics adjustment is signed, adjustment formula formula 25 indicates:
Formula 25:WhereinIt is the number of labels below node i,It is
One threshold parameter, for indicating number of labels related with each section node, if above-mentioned formula γ value is enabled less than 1
γ=max { 1, γ }.Finally, the feasible and cost for determining that Present solutions are not yet eliminated in the last one node is minimum
Label is final label, which is solution.
It should be understood thatIt is a very important variable, it can guarantee to solve by determining the process of label
The certainly quality and solving speed of scheme.It is suitable in order to findSize is needed by being constantly changingSize compare
Better value is determined compared with the quality of solution and solving speed.The maximum value of setting is 500, in this case,
Modified particle swarm optiziation needs long time that could run, because governance rule at this time is equivalent to one strong rule and advises
Then, a large amount of label is had not to be eliminated, withReduction, modified particle swarm optiziation solving speed becomes faster.When
When equal to 1, modified particle swarm optiziation can solve in the shortest time, but cost disparities at this time are too big.In contrast,
It is final to determineValue be equal to 25.It is merely exemplary explanation herein, specifically with no restrictions.
Specifically, particle can find optimal solution by flight, and specifically include: particle can root after determining population
It is updated according to flying speed and flight position, particle flight position and flying speed more new formula are as follows:
Formula 26:
Formula 27:
Wherein,It is the flying speed of particle i in kth time iteration；It is the flight position of particle i in kth time iteration；It is the individual extreme value of particle i；gBkIt is global extremum；Rand () is a random number on section [0,1]；W is inertia power
Weight；c1It is cognition coefficient；c2It is coefficient of association.During iteration, the speed of particle and position are limited in a specific range
It is interior.MeanwhileAnd gBkIt constantly updates, updates last output gB after the number of iterations is completedkAs globally optimal solution.
Particle swarm algorithm and mixed integer linear programming in small-scale, middle scale and extensive example is shown below
The comparison of solver (such as: CPLEX) solving model, table 1 are the solution in small-scale example, and haulage vehicle is 2, dispatching
Place has 10.
Table 1, application of the particle swarm algorithm in small-scale example in the present invention
When the customer quantity in example reaches 10, the model that CPLEX is solved can become intractable.In table 2 is shown
Etc. the performance of modified particle swarm optiziation under scales.As the validity of comparison algorithm, we are found under model using CPLEX
Boundary (LB) then compares LB gap=(Zp-Lp)/Lp.As can be seen from the results, the particle algorithm LB that is averaged in the present invention is poor
Away from being equal to 3.71%, this is similar to small scale experiments LB gap.The time is averagely solved equal to 141.5s (less than 3 minutes), this hair
Particle swarm algorithm in bright can obtain preferable solution in a relatively short period of time.So particle swarm algorithm is medium-scale
In the case where show it is good.
Table 2, application of the particle swarm algorithm in middle scale example in the present invention
In large-scale experiment, test when including the case where 30 to 100 client sets.Have in all cases
Four kinds of modes.In order to illustrate the practical application meaning of the algorithm proposed, We conducted more intuitive decision rules and this
The comparative experiments of algorithm in invention.Decision rule refers to vehicle always using i.e. mainly electric power provides based on the second operating mode
Power (the least gasoline consumption mode of every road occupation).The decision rule is very intuitive, and frequent someone makes in reality
With.Table 3 is that particle swarm algorithm is improved in extensive example compared with decision rule, and mean gap 28.32% illustrates
The practical application meaning of the algorithm.As a result it is also shown that the average time that modified particle swarm optiziation can solve 100 clients is
1063.1s。
Table 3, application of the particle swarm algorithm in extensive example in the present invention
It should be noted that the algorithm comparison based on above-mentioned different scales, the particle swarm algorithm in the present invention is being based on
The solution of Transportation Planning model, the solution time is shorter, and the value solved is more reliable.
The step of various methods divide above, be intended merely to describe it is clear, when realization can be merged into a step or
Certain steps are split, multiple steps are decomposed into, as long as including identical logical relation, all in the protection scope of this patent
It is interior；To adding inessential modification in algorithm or in process or introducing inessential design, but its algorithm is not changed
Core design with process is all in the protection scope of the patent.
Third embodiment of the invention is related to a kind of vehicle transport device for planning, and structure is as shown in figure 8, comprise determining that
Module 401, model generation module 402 and solution module 403.
Determining module 401, for according to the position of the home-delivery center got, the set in dispatching place and haulage vehicle
Energy recharge station location determines whole transport sections, wherein energy recharge station includes electric power energy depot and the fuel oil energy
Depot；
Model generation module 402, for the quantity and transport according to whole transport sections and the haulage vehicle of acquisition
The operating mode of vehicle establishes Transportation Planning model, wherein Transportation Planning model includes haulage vehicle on the transport road run over
The objective function of the electric power energy cost and fuel oil energy cost that are consumed in section；
Module 430 is solved, for determining the optimal solution of Transportation Planning model based on preset algorithm.
It is not difficult to find that present embodiment is system embodiment corresponding with first embodiment, present embodiment can be with
First embodiment is worked in coordination implementation.The relevant technical details mentioned in first embodiment still have in the present embodiment
Effect, in order to reduce repetition, which is not described herein again.Correspondingly, the relevant technical details mentioned in present embodiment are also applicable in
In first embodiment.
It is noted that each module involved in present embodiment is logic module, and in practical applications, one
A logic unit can be a physical unit, be also possible to a part of a physical unit, can also be with multiple physics lists
The combination of member is realized.In addition, in order to protrude innovative part of the invention, it will not be with solution institute of the present invention in present embodiment
The technical issues of proposition, the less close unit of relationship introduced, but this does not indicate that there is no other single in present embodiment
Member.
Four embodiment of the invention is related to a kind of electronic equipment, as shown in figure 9, including at least one processor 501；With
And the memory 502 being connect at least one processor communication；Wherein, be stored with can be by least one processor for memory 502
502 instructions executed, instruction is executed by least one processor 501, so that at least one processor 501 is able to carry out first
Or the vehicle transport planing method in second embodiment.
Specifically, processor 501 is used for, according to the set and transport of the position of the home-delivery center got, dispatching place
The energy recharge station location of vehicle determines whole transport sections, wherein energy recharge station includes electric power energy depot and combustion
Oily energy recharge station；According to the operating mode of the quantity and haulage vehicle of whole transport sections and the haulage vehicle of acquisition
Establish Transportation Planning model, wherein Transportation Planning model includes the electric power that haulage vehicle consumes on the transport section run over
The objective function of energy cost and fuel oil energy cost；Transportation Planning model is solved based on preset algorithm, and determines and completes transport
The lowest energy cost of task consumption.
Wherein, memory is connected with processor using bus mode, and bus may include the bus of any number of interconnection
And bridge, bus link together the various circuits of one or more processors and memory.Bus can also will be such as peripheral
Various other circuits of equipment, voltage-stablizer and management circuit or the like link together, these are all well known in the art
, therefore, it will not be further described herein.Bus interface provides interface between bus and transceiver.Transceiver
Can be an element, be also possible to multiple element, such as multiple receivers and transmitter, provide for over a transmission medium with
The unit of various other device communications.The data handled through processor are transmitted on the radio medium by antenna, further,
Antenna also receives data and transfers data to processor.
Processor is responsible for managing bus and common processing, can also provide various functions, including periodically, peripheral interface,
Voltage adjusting, power management and other control functions.And memory can be used for storage processor and execute operation when institute
The data used.
5th embodiment of the invention is related to a kind of computer readable storage medium, is stored with computer program,
It will be appreciated by those skilled in the art that implementing the method for the above embodiments is that can pass through
Program is completed to instruct relevant hardware, which is stored in a storage medium, including some instructions are used so that one
A equipment (can be single-chip microcontroller, chip etc.) or processor (processor) execute each embodiment the method for the application
All or part of the steps.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can store journey
The medium of sequence code.
It will be understood by those skilled in the art that the respective embodiments described above are to realize specific embodiments of the present invention,
And in practical applications, can to it, various changes can be made in the form and details, without departing from the spirit and scope of the present invention.
Claims (10)
1. a kind of vehicle transport planing method characterized by comprising
It is determined according to the energy recharge station location of the position of the home-delivery center got, the set in dispatching place and haulage vehicle complete
The transport section in portion, wherein the energy recharge station includes electric power energy depot and fuel oil energy recharge station；
According to the quantity of the haulage vehicle in whole the transport section and acquisition and the Working mould of the haulage vehicle
Formula establishes Transportation Planning model, wherein the Transportation Planning model includes the haulage vehicle on the transport road run over
The objective function of the electric power energy cost and fuel oil energy cost that are consumed in section；
The Transportation Planning model is solved based on preset algorithm, and determines the lowest energy cost for completing transport task consumption.
2. vehicle transport planing method according to claim 1, which is characterized in that the Transportation Planning model further includes about
Beam condition and decision variable；
The constraint condition include: on the transport section haulage vehicle work in one mode of operation, be described
The fuel oil energy cost of haulage vehicle consumption is minimum, a dispatching place match by a haulage vehicle and be taken
Business, a haulage vehicle are by primary, all haulage vehicle in an energy recharge station from the dispatching
Center line sets out and finally returns that the home-delivery center, reaches the vehicle fleet size of a section node and leave the section node
Vehicle fleet size is identical, load when each haulage vehicle leaves home-delivery center is no more than preset value, each transport
The combination one or more of equal with the dispatching place demand that it is serviced of the cargo dead-weight of vehicle；
The decision variable includes: remaining capacity, the haulage vehicle that the haulage vehicle reaches a dispatching place
Leave the dispatching remaining capacity in place, the haulage vehicle reach a dispatching place Fuel Remained amount,
The haulage vehicle leaves the Fuel Remained amount in a dispatching place, the haulage vehicle reaches a dispatching place
Cargo dead-weight, the haulage vehicle leave whether the dispatching cargo dead-weight in place, the haulage vehicle reach described match
Send whether place, the haulage vehicle reach in the dispatching place under determining operating mode by a transport section
One or more combinations.
3. vehicle transport planing method according to claim 2, which is characterized in that the operating mode packet of the haulage vehicle
Include motor be the first operating mode of power is provided supplemented by sustainer, based on the engine supplemented by the motor the
Two operating modes, the motor and the engine are coordinated quite to provide the third operating mode of power jointly and only by institute
It states engine and the 4th operating mode of power is provided.
4. vehicle transport planing method according to claim 3, which is characterized in that described based on described in preset algorithm solution
Transportation Planning model, and determine the lowest energy cost for completing transport task consumption, it specifically includes:
Based on the Transportation Planning model, primary group is generated, wherein a particle includes haulage vehicle from the dispatching
The heart, which sets out, is eventually returned to the sum of the transport section of home-delivery center's process；
The operating mode of the haulage vehicle in the transport section in the particle is determined by preset pattern algorithm, and
Calculate the adaptive value of the particle；
Determine the neighborhood of the particle；
The particle is iterated by variable neighborhood search algorithm；
The population after iteration is updated after being iterated to the particle；
If it is determined that the number of iterations of the particle is greater than preset maximum number of iterations, alternatively, after determining the particle iteration
The number remained unchanged is greater than the default maximum number of iterations remained unchanged；
The corresponding traffic program of population after determining the iteration is the optimal solution of the Transportation Planning model；
The lowest energy cost for completing transport task consumption is determined according to the optimal solution.
5. vehicle transport planing method according to claim 4, which is characterized in that described true by preset pattern algorithm
The operating mode of the haulage vehicle in the transport section, calculates adaptive value to each particle, specifically in the fixed particle
Include:
Determine all dispatching places for including in the particle；
The cost tag in each dispatching place is set, wherein different operating modes corresponds to different cost tags；
Determine that the haulage vehicle reaches the operating mode in each dispatching place under the constraint of the constraint condition；
The corresponding cost tag calculates the adaptive value of the particle according to the operation mode.
6. vehicle transport planing method according to claim 4 or 5, which is characterized in that described to be calculated by becoming neighborhood search
Method is iterated the particle, specifically includes:
Two in particle dispatching place exchange positions are selected, alternatively, by the dispatching place in the particle
It is moved to another position, alternatively, the position between the dispatching place of two in the particle is swapped.
7. vehicle transport planing method according to claim 4 or 5, which is characterized in that described to be calculated by becoming neighborhood search
It is described the particle to be iterated before the population after updating iteration later after method is iterated the particle, institute
State vehicle transport planing method further include:
Obtain the sequence that place is dispensed described in the particle；
The contiguous range of the particle is updated by Boolean variable；
The sequence in the dispatching place of the particle is updated according to the contiguous range of the particle.
8. a kind of vehicle transport device for planning characterized by comprising determining module, model generation module and solution module；
The determining module, for according to the position of the home-delivery center got, the set in dispatching place and the energy of haulage vehicle
Source feeds station location and determines whole transport sections, wherein the energy recharge station includes electric power energy depot and fuel oil energy
Source depot；
The model generation module, for according to the quantity of whole transport sections and the haulage vehicle of acquisition, with
And the operating mode of the haulage vehicle establishes Transportation Planning model, wherein the Transportation Planning model includes the transport vehicle
The objective function of the electric power energy cost and fuel oil energy cost that are consumed on the transport section run over；
The solution module, for determining the optimal solution of the Transportation Planning model based on preset algorithm.
9. a kind of electronic equipment characterized by comprising
At least one processor；And the memory being connect at least one described processor communication；Wherein, the memory
It is stored with the instruction that can be executed by least one described processor, described instruction is executed by least one described processor, so that
At least one described processor is able to carry out vehicle transport planing method as claimed in claim 1.
10. a kind of computer readable storage medium, is stored with computer program, which is characterized in that the computer program is located
It manages when device executes and realizes the described in any item vehicle transport planing methods of claim 1-7.
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