CN108090633A - Pipe gallery route selection planing method - Google Patents
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- 238000004422 calculation algorithm Methods 0.000 claims abstract description 14
- 238000010276 construction Methods 0.000 claims abstract description 14
- 210000000349 chromosome Anatomy 0.000 claims description 27
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Abstract
The present invention provides a kind of pipe gallery route selection planing method.The described method includes：Obtain the target and influence factor of the pipe gallery route selection planning on multi-level；The target planned according to pipe gallery route selection and each layer influence factor structure Bi-level Programming Models；The optimal route selection placement scheme of the Bi-level Programming Models is solved using genetic algorithm.The present invention can be effectively improved the reasonability of pipe gallery route selection layout, reducing the construction costs, improve service ability by building the Bi-level Programming Models of pipe gallery.
Description
Technical field
The present invention relates to Municipal facilities planning technical field more particularly to a kind of pipe gallery route selection planing methods.
Background technology
Urban Underground pipe gallery (hereinafter referred to as pipe gallery) is also known as " common trench ", is the one kind for being laid in subsurface
Structures, it can accommodate a variety of common lines such as telecommunications, electric power, water supply, heating power, and possess complete draining, illumination, communication,
The facilities such as monitoring.Piping lane is to ensure that function path gives full play to, and efficiently uses urban underground space, improves urban environment, is increased
Strong urban earthquake hazard protection anti-disaster ability, it is ensured that the civilian infrastructure that urban safety is operated and generated.
The route selection of China's pipe gallery plans that also in the preliminary trial stage piping lane route selection quantification of planning model is relatively
Few, the route selection quantification of planning scale-model investigation of the linear infrastructure such as domestic and international railway, highway is more, this is the route selection model of piping lane
Research provides reference.But the route selection project study over-borrowing of existing linear infrastructure helps single level decision rule mould
Type, and the route selection of pipe gallery planning is related to many influence factors, is a multi-level decision problem.Using single level
Decision rule model may make the route selection of pipe gallery plan layout confusion occur, fund energy waste, and service range is small etc.
Problems.
The content of the invention
Pipe gallery route selection planing method provided by the invention can be effectively improved the reasonable of pipe gallery route selection layout
Property, it reducings the construction costs, improves service ability.
In a first aspect, the present invention provides a kind of pipe gallery route selection planing method, the described method includes：
Obtain the target and influence factor of the pipe gallery route selection planning on multi-level；
The target planned according to pipe gallery route selection and each layer influence factor structure Bi-level Programming Models；
The optimal route selection placement scheme of the Bi-level Programming Models is solved using genetic algorithm.
Optionally, the target planned according to pipe gallery route selection and each layer influence factor build Bi-level Programming Models bag
It includes：
Determine upper strata plan model target and influence factor and the target of definite lower floor's plan model and influence because
Element；
It establishes using upper strata influence factor as the upper strata object function of constraints and establishes using lower floor's influence factor as about
Lower floor's object function of beam condition.
Optionally, upper strata object function of the foundation using upper strata influence factor as constraints includes：
Establish the upper strata plan model that object function is minimised as with construction cost, upper strata bound for objective function bag
Include piping lane construction cost, originally water transport energy consumption cost, sewage transport energy consumption cost and electricity transport energy consumption cost；
Lower floor object function of the foundation using lower floor's influence factor as constraints includes：
Establish lower floor's plan model that object function is turned to service ability maximum, lower floor's bound for objective function bag
It includes：Originally water transport energy consumption cost, sewage transport energy consumption cost and electricity transport energy consumption cost.
Optionally, the object function of the upper strata plan model is：
Constraints is：
Wherein, h represents the cost needed for laying unit length piping lane；LaRepresent the length of section a；Mould is planned for upper strata
The decision variable of type, indicate whether section a lay piping lane, whenRepresent laying, whenExpression is not laid；
WithThe energy consumed when transport tap water, sewage and electricity in the unit interval is represented respectively；
WithThe flow of tap water, sewage and electricity in pipe gallery is represented respectively；
WithThe time of transport tap water, sewage and electricity in pipe gallery is represented respectively；
B is the overall budget of laying piping lane；
PmaxRepresent the maximum energy consumption for allowing consumption.
Optionally, the object function of lower floor's plan model is：
Constraints is：
Wherein, it is describedFor the decision variable of upper strata plan model,WithIt represents respectively in pipe gallery certainly
The flow of water, sewage and electricity,WithService weight after each section laying piping lane.
Optionally, the optimal route selection placement scheme that the Bi-level Programming Models are solved using genetic algorithm is included：
Using the decision variable of the upper strata plan model as the gene of chromosome, determine chromosome coding mode and generate
Initial population, every chromosome corresponds to a kind of pipe gallery route selection placement scheme in the initial population；
The feasible solution of initial population is solved according to the constraints of upper strata plan model, obtains feasible pipe gallery route selection
Placement scheme；
Compare the service range of each feasible route selection placement scheme according to lower floor's object function, obtain service range
The flow of each energy when maximum；
According to the flow rate calculation upper strata object function of obtained each energy, the fitness of every chromosome is obtained；
According to the fitness of every chromosome corresponding genetic operator is selected to be iterated optimization, the something lost to feasible population
Passing operator includes：Selection opertor, crossover operator and mutation operator；
When reaching maximum iteration, the highest chromosome of fitness is exported, obtains optimal pipe gallery route selection cloth
Office's scheme.
Pipe gallery route selection planing method provided in an embodiment of the present invention is summarized pipe gallery by multi-level simulation tool and is selected
The construction cost of pipe gallery is minimized the object function as upper strata decision-making, by integrated pipe by the target and influence factor of line
The service ability of corridor maximizes the object function as lower floor's decision-making, by road environment, the economic condition of coverage, government's wealth
Constraints of the influence factors such as political affairs ability to bear as each layer builds Bi-level Programming Models, and genetic algorithm is recycled to solve
Optimal solution obtains the optimal route selection placement scheme of pipe gallery.Compared with prior art, the present invention is by building dual layer resist mould
Type can be effectively improved the reasonability of pipe gallery route selection layout, reducing the construction costs, and improve service ability.
Description of the drawings
Fig. 1 is the flow chart of one embodiment of the invention pipe gallery route selection planing method；
Fig. 2 is the flow chart that one embodiment of the invention solves Bi-level Programming Models using genetic algorithm.
Specific embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, the technical solution in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is only
Only it is part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill
Personnel's all other embodiments obtained without making creative work, belong to the scope of protection of the invention.
The present invention provides a kind of pipe gallery route selection planing method, as shown in Figure 1, the described method includes：
S11, target and influence factor that the pipe gallery route selection on multi-level is planned are obtained；
The route selection planning of pipe gallery is related to road environment, the economic condition of coverage, government finance ability to bear etc.
Many and diverse influences factor is related to the specific interests of department, entity and individual, is a multi-level decision problem.The present invention
The influence factor of different level is summarized using multi-level simulation tool method, with investigation, expert interviewing and analysis of cases on the spot
Method summarizes the target and key factor of different level pipe gallery route selection, can be effectively improved using traditional single level
Route selection layout is unreasonable caused by decision-making solves the problems, such as infrastructure line planning.
S12, the target planned according to pipe gallery route selection and each layer influence factor build Bi-level Programming Models；
Optionally it is determined that the target and influence factor of upper strata plan model and the target of definite lower floor's plan model and
Influence factor；
It establishes using upper strata influence factor as the upper strata object function of constraints and establishes using lower floor's influence factor as about
Lower floor's object function of beam condition.
The present invention uses Bi-level Programming Models by pipe gallery route selection PROBLEM DECOMPOSITION for upper strata decision problem and lower floor's decision-making
Problem, upper strata decision problem and lower floor's decision problem all summarize respective target and influence factor, using each layer influence factor as
Constraints establishes upper strata object function and lower floor's object function respectively.
The object function and constraints of upper strata decision problem are not only related with upper strata decision variable, but also depend on down
The optimal solution of layer decision problem, and the optimal solution of lower layer problem is influenced by upper strata decision variable, so that pipe gallery
Route selection planning problem becomes the system optimization problem with double-deck hierarchical structure.
The construction cost of pipe gallery is summarized as upper strata decision problem by the present embodiment, and the service ability of pipe gallery is returned
It receives as lower floor's decision problem.Optionally, upper strata object function of the foundation using upper strata influence factor as constraints includes：
Establish the upper strata plan model that object function is minimised as with construction cost, upper strata bound for objective function bag
Include piping lane construction cost, originally water transport energy consumption cost, sewage transport energy consumption cost and electricity transport energy consumption cost.
The optimal set that piping lane route selection layout how is determined from system perspective has been described in detail in upper strata plan model, so as to reach
Cost to entire piping lane line network system is minimum.The present embodiment has chosen corresponding index in terms of the energy and construction cost, wraps
Include piping lane construction cost, originally water transport energy consumption cost, sewage transport energy consumption cost, electricity transport energy consumption cost composition object function
Characterize the generalized cost of piping lane line network system operation, and to provided funds, the constraints in terms of energy consumption, establish upper strata
Plan model：
The constraints of upper strata plan model is：
Wherein, h represents the cost needed for laying unit length piping lane；LaRepresent the length of section a；Mould is planned for upper strata
The decision variable of type, indicate whether section a lay piping lane, whenRepresent laying, whenExpression is not laid；
WithThe energy consumed when transport tap water, sewage and electricity in the unit interval is represented respectively；
WithThe flow of tap water, sewage and electricity in pipe gallery is represented respectively；
WithThe time of transport tap water, sewage and electricity in pipe gallery is represented respectively；
B is the overall budget of laying piping lane；
PmaxRepresent the maximum energy consumption for allowing consumption.
Optionally, lower floor object function of the foundation using lower floor's influence factor as constraints includes：
Establish lower floor's plan model that object function is turned to service ability maximum, lower floor's bound for objective function bag
It includes：Originally water transport energy consumption cost, sewage transport energy consumption cost and electricity transport energy consumption cost.
Lower floor's plan model be piping lane user under conditions of definite piping lane line network, carry out route choosing come reach clothes
It is engaged in widest in area.Disperse in view of the position of waterworks, sewage plant and electric power supply plant in piping lane line network, cause piping lane user
The rule and principle of (pipeline unit) Path selection are different, and the present embodiment considers the route choosing of each pipeline unit,
Most wide to reach pipe gallery service range, service group is up to target, establishes lower floor's plan model：
The constraints of lower floor's plan model is：
Wherein, it is describedFor the decision variable of upper strata plan model,WithIt represents respectively in pipe gallery certainly
The flow of water, sewage and electricity,WithService weight after each section laying piping lane.
S13, the optimal route selection placement scheme that the Bi-level Programming Models are solved using genetic algorithm.
Optionally, Bi-level Programming Models are solved using genetic algorithm to be as follows：
Step 1, using the decision variable of the upper strata plan model as the gene of chromosome, determine chromosome coding mode
And initial population is generated, every chromosome corresponds to a kind of pipe gallery route selection placement scheme in the initial population；
Specifically, step 1 includes following sub-step：
Step 1.1：Determine chromosome coding scheme, each gene pairs answers the decision variable in each section on chromosome
I-th (i >=0 and i ∈ N) a chromosome SiUsing binary coding mode, the vector combination of one group of decision variable, length are generated
For | A |, every chromosome represents a kind of pipe gallery route selection placement scheme.
Step 1.2：The relevant parameter of genetic algorithm is set, mainly includes population scale Ρsize, crossover probability pc, variation it is general
Rate pm, maximum iteration Gmax。
Step 1.3：Initial population is generated, generates Ρ at randomsizeIt is a | A | the 0-1 vectors of dimension.
Step 2, the feasible solution that initial population is solved according to the constraints of upper strata plan model, obtain feasible integrated pipe
Corridor route selection placement scheme；
Specifically, step 2 includes following sub-step：
Step 2.1：Make i=0.
Step 2.2：Make i=i+1.
Step 2.3：Examine whether the i chromosomes meet constraints formula (1)-(4) (i.e. pact of upper strata object function
Beam condition), carry out step 2.5 if meeting；Otherwise step 2.4 is carried out.
Step 2.4：The gene position that chromosome intermediate value is 1 is randomly choosed, its value is become into 0, and return to step 2.3.
Step 2.5：If i=Ρsize, algorithm terminates, and obtains feasible population；Otherwise, return to step 2.2.
Step 3, the service range for comparing each feasible route selection placement scheme according to lower floor's object function, are taken
The flow of each energy during scope maximum of being engaged in includes the flow of tap water, sewage and electricity；
Step 4, the flow rate calculation upper strata object function according to obtained each energy, obtain the fitness of every chromosome,
The fitness f of every chromosomefit(Si)=1/fit (Si)；
Step 5 selects corresponding genetic operator to be iterated optimization to feasible population according to the fitness of every chromosome,
The genetic operator includes：Selection opertor, crossover operator and mutation operator；
Specifically, according to the fitness of every obtained chromosome, the action condition for meeting which kind of genetic manipulation, choosing are judged
It selects corresponding genetic operator and acts on current population.By the effect of genetic operator, the population optimized.Below to each heredity
The action condition of operation (selection operation, crossover operation and mutation operation) is specifically described, and genetic algorithm solves optimal route selection
The flow of placement scheme is as shown in Figure 2.
A), selection operation.Using roulette selection：1. calculate the fitness summation of all populations
2. calculate each selected probability3. generate a random number rrand∈ [0,1], ifThen select SiPerform following operation.
B), crossover operation.Intersected using single-point：1. the chromosome for selecting to obtain is matched to obtain NSA pairing, 2.
Make i=0；I=i+1；3. generate a random number rrand∈ [0,1], if r≤pc, then a random site parameter P is generatedpos∈
(0, | A |), and perform single-point in this position and intersect；If 4. i=Ns, then crossover operation terminate, otherwise return 2..
C), mutation operation.Using uniform variation：1. make i=0；2. make i=i+1；3. generate a random number rrand∈
[0,1], if r≤pm, then corresponding gene morph, i.e., the value of i-th of gene is become 0 (or becoming 1 from 0) from 1, otherwise
It returns 2.；4. repetitive operation, until i=| A | Psize。
Step 5：When reaching maximum iteration, the highest chromosome of fitness is exported, obtains optimal pipe gallery
Route selection placement scheme.
Specifically, judge whether to reach maximum iteration.If having reached maximum iteration, algorithm terminates, output
The highest chromosome of fitness is optimal solution in population, obtains optimal route selection placement scheme；Otherwise return to step 2.
Pipe gallery route selection planing method provided in an embodiment of the present invention is summarized pipe gallery by multi-level simulation tool and is selected
The construction cost of pipe gallery is minimized the object function as upper strata decision-making, by integrated pipe by the target and influence factor of line
The service ability of corridor maximizes the object function as lower floor's decision-making, by road environment, the economic condition of coverage, government's wealth
Constraints of the influence factors such as political affairs ability to bear as each layer builds Bi-level Programming Models, and genetic algorithm is recycled to solve
Optimal solution obtains the optimal route selection placement scheme of pipe gallery.
One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the program can be stored in a computer read/write memory medium
In, the program is upon execution, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access
Memory, RAM) etc..
The above description is merely a specific embodiment, but protection scope of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, all should by the change or replacement that can be readily occurred in
It is included within the scope of the present invention.Therefore, protection scope of the present invention should be subject to the protection scope in claims.
Claims (6)
1. a kind of pipe gallery route selection planing method, which is characterized in that the described method includes：
Obtain the target and influence factor of the pipe gallery route selection planning on multi-level；
The target planned according to pipe gallery route selection and each layer influence factor structure Bi-level Programming Models；
The optimal route selection placement scheme of the Bi-level Programming Models is solved using genetic algorithm.
2. according to the method described in claim 1, it is characterized in that, the target planned according to pipe gallery route selection and each layer
Influence factor structure Bi-level Programming Models include：
Determine the target of upper strata plan model and influence factor and the target and influence factor of definite lower floor's plan model；
It establishes using upper strata influence factor as the upper strata object function of constraints and establishes using lower floor's influence factor as constraint item
Lower floor's object function of part.
3. according to the method described in claim 2, it is characterized in that, the foundation is using upper strata influence factor as the upper of constraints
Layer object function includes：
The upper strata plan model that object function is minimised as with construction cost is established, upper strata bound for objective function includes pipe
Corridor construction cost, originally water transport energy consumption cost, sewage transport energy consumption cost and electricity transport energy consumption cost；
Lower floor object function of the foundation using lower floor's influence factor as constraints includes：
Lower floor's plan model that object function is turned to service ability maximum is established, lower floor's bound for objective function includes：
Originally water transport energy consumption cost, sewage transport energy consumption cost and electricity transport energy consumption cost.
4. according to the method described in claim 3, it is characterized in that, the object function of the upper strata plan model is：
Constraints is：
<mrow>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>a</mi>
<mo>&Element;</mo>
<mi>A</mi>
</mrow>
</munder>
<msubsup>
<mi>&lambda;</mi>
<mn>1</mn>
<mi>c</mi>
</msubsup>
<msubsup>
<mi>t</mi>
<mi>a</mi>
<mi>c</mi>
</msubsup>
<msubsup>
<mi>x</mi>
<mi>a</mi>
<mi>c</mi>
</msubsup>
<mo>+</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>a</mi>
<mo>&Element;</mo>
<mi>A</mi>
</mrow>
</munder>
<msubsup>
<mi>&lambda;</mi>
<mn>2</mn>
<mi>c</mi>
</msubsup>
<msubsup>
<mi>t</mi>
<mi>b</mi>
<mi>c</mi>
</msubsup>
<msubsup>
<mi>x</mi>
<mi>b</mi>
<mi>c</mi>
</msubsup>
<mo>+</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>a</mi>
<mo>&Element;</mo>
<mi>A</mi>
</mrow>
</munder>
<msubsup>
<mi>&lambda;</mi>
<mn>3</mn>
<mi>c</mi>
</msubsup>
<msubsup>
<mi>t</mi>
<mi>c</mi>
<mi>c</mi>
</msubsup>
<msubsup>
<mi>x</mi>
<mi>c</mi>
<mi>c</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
<mrow>
<msubsup>
<mi>x</mi>
<mi>a</mi>
<mi>c</mi>
</msubsup>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
<mo>;</mo>
<msubsup>
<mi>x</mi>
<mi>b</mi>
<mi>c</mi>
</msubsup>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
<mo>;</mo>
<msubsup>
<mi>x</mi>
<mi>c</mi>
<mi>c</mi>
</msubsup>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
</mrow>
Wherein, h represents the cost needed for laying unit length piping lane；LaRepresent the length of section a；For upper strata plan model
Decision variable, indicate whether section a lay piping lane, whenRepresent laying, whenExpression is not laid；
WithThe energy consumed when transport tap water, sewage and electricity in the unit interval is represented respectively；
WithThe flow of tap water, sewage and electricity in pipe gallery is represented respectively；
WithThe time of transport tap water, sewage and electricity in pipe gallery is represented respectively；
B is the overall budget of laying piping lane；
PmaxRepresent the maximum energy consumption for allowing consumption.
5. according to the method described in claim 4, it is characterized in that, the object function of lower floor's plan model is：
Constraints is：
<mrow>
<msubsup>
<mi>x</mi>
<mi>a</mi>
<mi>c</mi>
</msubsup>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
<mo>;</mo>
<msubsup>
<mi>x</mi>
<mi>b</mi>
<mi>c</mi>
</msubsup>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
<mo>;</mo>
<msubsup>
<mi>x</mi>
<mi>c</mi>
<mi>c</mi>
</msubsup>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
</mrow>
Wherein, it is describedFor the decision variable of upper strata plan model,WithRespectively represent pipe gallery in tap water,
The flow of sewage and electricity,WithService weight after each section laying piping lane.
6. according to the method described in claim 5, it is characterized in that, described solve the dual layer resist mould using genetic algorithm
The optimal route selection placement scheme of type includes：
Using the decision variable of the upper strata plan model as the gene of chromosome, determine chromosome coding mode and generate initial
Population, every chromosome corresponds to a kind of pipe gallery route selection placement scheme in the initial population；
The feasible solution of initial population is solved according to the constraints of upper strata plan model, obtains feasible pipe gallery route selection layout
Scheme；
Compare the service range of each feasible route selection placement scheme according to lower floor's object function, obtain service range maximum
When each energy flow；
According to the flow rate calculation upper strata object function of obtained each energy, the fitness of every chromosome is obtained；
According to the fitness of every chromosome corresponding genetic operator is selected to be iterated optimization to feasible population, the heredity is calculated
Attached bag includes：Selection opertor, crossover operator and mutation operator；
When reaching maximum iteration, the highest chromosome of fitness is exported, obtains optimal pipe gallery route selection layout side
Case.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108959801A (en) * | 2018-07-20 | 2018-12-07 | 国通广达（北京）技术有限公司 | A kind of pipe gallery section optimization method and system |
| CN115358034A (en) * | 2022-08-31 | 2022-11-18 | 扬州工业职业技术学院 | Intelligent arrangement method and system based on BIM (building information modeling) comprehensive optimization design |
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