CN114429266A - Intelligent optimization method for building construction scheduling scheme - Google Patents

Intelligent optimization method for building construction scheduling scheme Download PDF

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CN114429266A
CN114429266A CN202111484766.8A CN202111484766A CN114429266A CN 114429266 A CN114429266 A CN 114429266A CN 202111484766 A CN202111484766 A CN 202111484766A CN 114429266 A CN114429266 A CN 114429266A
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姚刚
王明溥
邓睿
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Abstract

The invention discloses an intelligent optimization method for a building construction scheduling scheme, and belongs to the technical field of construction scheduling optimization. The method comprises the following steps: establishing a multi-target particle swarm construction scheduling optimization model with the shortest construction period, the lowest construction cost and the highest construction quality as targets; selecting an individual historical optimal scheme and a global optimal scheme by adopting a pareto principle, an external storage library and an adaptive grid; a construction scheduling scheme to be optimized is established, the problem of priority constraint is solved, and the operability and universality of the intelligent optimization problem of the construction scheduling scheme of the building construction by the model are improved. The method has the advantages of high feasibility, strong capability of searching the optimal scheme and the like, and can provide a reasonable building construction scheduling optimal scheme for a decision maker so as to reduce the construction period and the construction cost to the maximum extent and ensure the construction quality at the same time.

Description

Intelligent optimization method for building construction scheduling scheme
Technical Field
The invention relates to the field of construction scheduling of constructional engineering, in particular to an intelligent optimization method for a building construction scheduling scheme.
Background
The construction scheduling optimization is always a very important research field in the building world, and the importance of the construction scheduling optimization is not only reflected in the level of a model method, but also reflected in the level of practical application. When a construction scheduling scheme is formulated, how to shorten the construction period, reduce the cost, reduce the resource consumption, improve the construction quality and the like are important challenges faced by relevant practitioners all the time.
At present, the existing optimization method is applied to construction scheduling of house buildings, differences among different building projects cannot be fully considered, and the method is low in practicability and operability. Therefore, an intelligent optimization method for building construction scheduling, which is easy to implement and high in precision, is urgently needed.
Disclosure of Invention
The invention aims to provide an intelligent optimization method for a building construction scheduling scheme, which aims to solve the problems in the prior art.
The technical scheme adopted for achieving the purpose of the invention is that the intelligent optimization method of the building construction scheduling scheme comprises the following steps:
1) establishing a multi-target particle swarm building construction scheduling optimization model by taking the shortest construction period, the lowest construction cost and the highest construction quality as optimization targets, and determining the total number of building construction scheduling schemes in the model, namely determining the particle swarm size;
2) building a to-be-optimized scheme for construction scheduling of the building, determining parameter variables and constraint regulations required by the model, and improving the universality of the model in solving the problem of optimization of construction scheduling of the building;
3) setting values of learning factors, inertial weights and mutation operators in the model, and setting the upper limit of the quantity of the scheduling schemes to be constructed and the maximum iteration number of the model, wherein the upper limit of the quantity of the scheduling schemes to be constructed comprises the maximum allowable value of an external storage library;
4) setting the value ranges of the resource options and the selection trends of each construction activity in each scheme, namely determining the value ranges of the positions and the speeds of the particles, and randomly initializing the values of the resource options and the selection trends of the construction activities in the value ranges, namely initializing the positions and the speeds of the particles;
5) calculating the construction period, construction cost and construction quality of each scheme according to the objective function, namely calculating a fitness value;
6) evaluating each scheme independently, and selecting an optimal construction activity resource option combination as an individual historical optimal scheme according to a pareto governing principle, namely updating the individual historical optimal scheme;
7) evaluating all schemes, screening out non-inferior solutions in all schemes according to a pareto domination principle, storing the non-inferior solutions in an external storage library as an archiving scheme, performing second round of screening in the external storage library, and removing the inferior solutions in the archiving scheme;
8) judging whether the number of the archived schemes exceeds the maximum allowable value of an external storage library, if so, screening according to the adaptive grid density to remove redundant schemes, and if not, executing the next step;
9) judging whether the loop execution reaches the maximum iteration times or the solution set is converged, if so, outputting an archiving scheme, namely outputting a non-inferior solution set in the external storage library scheme, and if not, adding 1 to the iteration times and then executing the step 10);
10) selecting an optimal construction activity resource option combination in the archiving scheme by adopting a roulette selection method to serve as a global optimal scheme, namely updating the global optimal scheme;
11) randomly initializing the schemes in the variation range, namely performing particle variation operation;
12) and (5) calculating resource options and selection trends of each construction activity in each scheme according to an updating formula of the multi-objective particle swarm optimization algorithm, namely updating the speed and the position of the particles, and returning to execute the step 5).
Further, in the step 1), the construction period, the construction cost and the construction quality are simultaneously used as optimization targets, and the optimization target function is as follows:
minimized construction period PTmin：
Figure BDA0003397083790000021
Wherein: i denotes the serial number of the construction activity, n denotes the resourceNumber of options, Ti，nRepresenting the construction period that the construction activity i on the key path selects the resource option n to continue;
minimized construction cost PCmin：
Figure BDA0003397083790000022
Wherein: ci，nRepresenting the construction cost consumed when the construction activity i selects the resource option n; maximum construction quality PQmax：
Figure BDA0003397083790000023
Wherein: w is ai，nRepresents the weight of the construction activity i in the overall quality representation of the project, Qi，nAnd representing the construction quality performance when the construction activity i selects the resource option n.
Further, the construction scheduling scheme to be optimized in the step 2) is a built-in table of the model, and differences among different building projects are reflected through the following three input parameters: building area A ten thousand m2B pieces of equipment lines and R yuan/m of house building cost index of area where project is located2(ii) a After determining the key construction activities of the building and the priority constraints among the key construction activities, quantifying the construction period, construction cost and construction quality of each construction activity to form a construction scheduling scheme to be optimized.
Further, step 10) determining a global optimum by introducing an external repository, the external repository including an archive controller and a grid;
further, the archive controller is configured to determine whether to add the solution NS to the grid of the external repository, and includes the following steps:
if the external repository is empty, storing the scheme NS; if the external repository already exists in the scheme S1 and S1 dominates NS, then the scheme S1 is still stored; if the external repository already exists in the scheme S1 and NS dominates S1, storing the scheme NS and removing the scheme S1; if the external repository already exists in the scheme S1 and the NS and S1 do not have a dominating relationship, then the schemes NS and S1 are stored.
Further, the grid is an objective function space formed by a plurality of hypercubes, and when a scheme set with uniform distribution is generated, the method comprises the following steps:
10-1) determining the boundaries of the grid: at the t-th iteration, the grid boundary is (minPT)t，maxPTt)、(minPCt，maxPCt) And (minPQ)t，maxPQt) Minimum and maximum fitness values calculated by the particles stored in the external repository according to an objective function of construction period PT, construction cost PC and construction quality PQ;
10-2) calculating the modular length of each grid:
Figure BDA0003397083790000031
Figure BDA0003397083790000032
Figure BDA0003397083790000033
wherein: Δ PTtIs the modular length of the grid in the direction of the target function PT during t iterations; delta PCtThe length of the grid in the direction of the target function PC is the length of the grid in t iterations; Δ PQtIs the modulus length of the grid in the direction of the objective function PQ at t iterations; d is a divisor used for determining the number of hypercubes in the grid;
10-3) calculating the grid number of all the particles determined to be stored in the external memory bank,
Figure BDA0003397083790000034
wherein,
Figure BDA0003397083790000041
the fitness value is calculated by a PT target function of a particle u in the grid during t iterations, and Int is a function for taking an integer downwards;
10-4) determining the particle number, i.e. the particle density, of each grid;
10-5) selecting a global optimum using roulette.
Further, in step 12), the particle updates the velocity according to the individual historical optimal solution and the global optimal solution, and then updates the position according to the current velocity, wherein the velocity updating formula is as follows:
vk(t+1)＝ω·vk(t)+c1·r1(Pk(t)-xk(t))+c2·r2(Rg(t)-xk(t)) (7)
displacement update formula:
xk(t+1)＝xk(t)+vk(t+1) (8)
wherein: k is 1, 2.. K, K being the total number of particles in the population; e, E is the limit number of iterations; v. ofk(t) the velocity of the kth particle at the tth iteration; x is a radical of a fluorine atomk(t) is the position of the kth particle at the tth iteration; p isk(t) an individual historical optimal solution for the particles selected using the dominating relationship; rg(t) is a global optimal solution for the particle selected from the external repository at t iterations; omega is an inertia weight, a non-negative number, and is used for controlling the influence of the previous speed on the current speed; r is1And r2Is a random number and takes a value range of [0,1]]；c1，c2Is an acceleration constant, i.e., a learning factor;
after the initialization and updating of the particles, the position and velocity values of the particles that exceed the value range are adjusted to the limit values.
Further, in step 11), the whole population is divided into three parts with equal size, and different mutation operators are adopted for the three parts; the particles of the first sub-fraction will be completely invariant; the particles of the second sub-part adopt uniform variation, namely the particles meeting the variation requirement in the range are randomly initialized, and the variation range does not change along with the iteration times; the particles of the third sub-portion are randomly initialized with non-uniform variation, i.e., the particles satisfying the variation requirement in the range, and the variation range is reduced with the number of iterations.
The invention has the beneficial effects that:
1. the construction scheduling optimization model constructed by the invention is suitable for solving the multi-objective construction scheduling optimization problem;
2. the building construction scheduling scheme to be optimized effectively improves the universality and operability of the model in solving the problem of building construction scheduling optimization;
3. the method adopts an external storage library, a self-adaptive grid method and a roulette method, and improves the convergence of a building construction scheduling optimization model;
4. the invention adopts three kinds of particle variation operation, effectively prevents the building construction scheduling optimization model from falling into local convergence;
5. the invention can provide a construction scheduling scheme meeting the optimization goal for a decision maker, so as to reduce the construction period and the construction cost to the maximum extent and improve the construction quality to the maximum extent.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a construction scheduling optimization objective;
FIG. 3 is a schematic view of an archive controller;
fig. 4 is a schematic diagram of an optimization iteration result.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and modifications can be made without departing from the technical idea of the invention and the scope of the invention according to the common technical knowledge and the conventional means in the field.
Example 1:
referring to fig. 1, the embodiment discloses an intelligent optimization method for a building construction scheduling scheme, which includes the following steps:
1) establishing a multi-objective particle swarm building construction scheduling optimization model by taking the shortest construction period, the lowest construction cost and the highest construction quality as optimization targets, determining the total number of building construction scheduling schemes in the model, namely determining the particle swarm size, wherein the particle swarm size is set to be 200; meanwhile, the construction period, the construction cost and the construction quality are taken as optimization targets, and the optimization objective function is as follows:
minimized construction period PTmin：
Figure BDA0003397083790000051
Wherein: i denotes the serial number of the construction activity, n denotes the serial number of the resource option, Ti，nRepresenting the construction period that the construction activity i on the key path selects the resource option n to continue;
minimized construction cost PCmin：
Figure BDA0003397083790000052
Wherein: ci，nRepresenting the construction cost consumed when the construction activity i selects the resource option n; maximizing construction quality PQmax：
Figure BDA0003397083790000053
Wherein: w is ai，nRepresents the weight of the construction activity i in the overall quality representation of the project, Qi，nRepresenting the construction quality performance when the construction activity i selects the resource option n; referring to fig. 2, a schematic diagram of the construction scheduling optimization objective of this embodiment is shown;
2) building a to-be-optimized scheme for construction scheduling of the building, determining parameter variables and constraint regulations required by the model, and improving the universality of the model in solving the problem of optimization of construction scheduling of the building; wherein the construction scheduleThe scheme to be optimized is a built-in table of the model, and the difference among different building projects is reflected by the following three input parameters: building area A ten thousand m2B pieces of equipment lines and R yuan/m of house building cost index of area where project is located2(ii) a After determining the key construction activities of the building and the priority constraints among the construction activities, quantifying the construction period, construction cost and construction quality of each construction activity to form a construction scheduling to-be-optimized scheme, wherein the table 1 shows the construction scheduling to-be-optimized scheme provided by the embodiment of the invention:
TABLE 1
Figure BDA0003397083790000061
Figure BDA0003397083790000071
The data sources in the table 1 are illustrated, the construction period is determined according to the national unified construction installation project period quota (TY01-89-2016) in combination with actual project data, the construction cost is determined according to the Chongqing city housing construction and decoration project pricing quota (CQJZDE-2018) and the Chongqing city construction project cost quota (CQFYDE-2018), the housing construction cost index is converted, so that the housing construction cost is suitable for various housing constructions, and the score and the weight of the construction quality performance are quantified by adopting an expert scoring method and an analytic hierarchy process.
3) Setting values of a learning factor, an inertia weight and a mutation operator in the model, wherein in the embodiment, a linear descending inertia weight is adopted, the inertia weight range is 0.4-0.95, the learning factor is 2, and the mutation operator randomly takes a value in a [0,1] range; setting the upper limit of the quantity of the required construction scheduling schemes and the maximum iteration number of the model, wherein the upper limit of the quantity of the required construction scheduling schemes comprises the maximum allowable value of an external storage library, the maximum iteration number is set to be 200 in the embodiment, and the maximum iteration number is set to be 120;
4) setting the value ranges of the resource options and the selection trends of each construction activity in each scheme, namely determining the value ranges of the positions and the speeds of the particles, and randomly initializing the values of the resource options and the selection trends of the construction activities in the value ranges, namely initializing the positions and the speeds of the particles; in this embodiment, the resource option combination of each construction activity in the construction scheduling scheme is used as each particle position, the numerical value is an integer and ranges from 1 to Mi (i is 1.. multidot.n), where Mi is the number of resource options of the construction activity i in table 1, and therefore the particle position range is limited to [1, Mi ], and the integer is rounded. The selection trend of construction activities, namely the particle speed range is limited to [1-Mi, Mi-1 ];
5) calculating the construction period, construction cost and construction quality of each scheme according to the objective function, namely calculating a fitness value;
6) evaluating each scheme independently, and selecting an optimal construction activity resource option combination as an individual historical optimal scheme according to a pareto governing principle, namely updating the individual historical optimal scheme;
7) evaluating all schemes, screening out non-inferior solutions in all schemes according to a pareto domination principle, storing the non-inferior solutions in an external storage library as an archiving scheme, performing second-round screening in the external storage library, and removing inferior solutions in the archiving scheme;
8) judging whether the number of the archived schemes exceeds the maximum allowable value of an external storage library, if so, screening according to the adaptive grid density to remove redundant schemes, and if not, executing the next step;
9) judging whether the loop execution reaches the maximum iteration times or the solution set is converged, if so, outputting an archiving scheme, namely outputting a non-inferior solution set in the external storage library scheme, and if not, adding 1 to the iteration times and then executing the step 10);
10) selecting an optimal construction activity resource option combination in the archiving scheme by adopting a roulette selection method to serve as a global optimal scheme, namely updating the global optimal scheme; determining a global optimal scheme by adopting a method of introducing an external storage library, wherein the external storage library comprises an archive controller and a grid; fig. 3 is a schematic diagram of an archive controller according to an embodiment of the present invention, and includes the following sub-steps:
if the external storage library is empty, storing the scheme NS; if the external repository already exists in the scheme S1 and S1 dominates NS, then the scheme S1 is still stored; if the external repository already exists in the scheme S1 and NS dominates S1, storing the scheme NS and removing the scheme S1; if the external repository already exists in the scheme S1 and the NS and S1 have no dominance relationship, storing the scheme NS and S1;
the grid is an objective function space formed by a plurality of hypercubes, and when a scheme set with uniform distribution is generated, the method comprises the following steps:
10-1) determining the boundaries of the grid: at the t-th iteration, the grid boundary is (minPT)t，maxPTt)、(minPCt，maxPCt) And (minPQ)t，maxPQt) Minimum and maximum fitness values calculated by the particles stored in the external repository according to an objective function of construction period PT, construction cost PC and construction quality PQ;
10-2) calculating the modular length of each grid:
Figure BDA0003397083790000081
Figure BDA0003397083790000082
Figure BDA0003397083790000083
wherein: Δ PTtIs the modular length of the grid in the direction of the target function PT during t iterations; delta PCtThe length of the grid in the direction of the target function PC is the length of the grid in t iterations; Δ PQtIs the modulus length of the grid in the direction of the objective function PQ at t iterations; d is a divisor used for determining the number of hypercubes in the grid;
10-3) calculating the grid number of all the particles determined to be stored in the external memory bank,
Figure BDA0003397083790000091
wherein,
Figure BDA0003397083790000092
the fitness value is calculated by a PT target function of a particle u in the grid during t iterations, and Int is a function for taking an integer downwards;
10-4) determining the particle number, i.e. the particle density, of each grid;
10-5) selecting a global optimum using roulette;
in addition, the roulette method selects a global optimal solution, and in order to ensure diversity of a non-inferior solution set and explore more space, the lower the particle density in the grid, the greater the probability of selection; using 10 divided by the number of particles in the unit grid zjFitness f (z) as the jth unit gridj) (J ═ 1, 2.., J), J being the number of unit cells in which the particles are present. Calculating the fitness proportion, i.e. the ratio of the fitness of the jth unit grid to the sum of all the fitness, e.g.
Figure BDA0003397083790000093
Wherein p (z)j) Is a unit grid zjFitness ratio of f (z)j) Is a unit grid zjThe fitness of (2). Calculating cumulative probability qjI.e. the sum of the fitness ratios of the 1 st to the j' th unit cell, e.g.
Figure BDA0003397083790000094
Wherein q isjIs a unit grid zjThe cumulative probability of (c). Randomly generating o e [0,1]From q1Start to judge if q is1If the number is more than o, the unit grid 1 is selected; if q is1If less than o, j +1, judging q2Up to qj> o, the unit grid j is selected. Then, o e [0,1] is randomly generated again]From q1The judgment was started and J selections were made in total. The unit grid that is selected the most times (the number may be greater than 1) is determined,randomly selecting a particle in the determined grid as a global optimal solution.
11) Randomly initializing the schemes in the variation range, namely performing particle variation operation; dividing the whole population into three parts with equal size, and adopting different mutation operators for the three parts; the particles of the first sub-fraction will be completely invariant; the particles of the second sub-portion will adopt uniform variation, i.e. the particles meeting the variation requirement within the range are randomly initialized, and the variation range does not change along with the iteration number; the particles of the third sub-part adopt non-uniform variation, namely the particles meeting the variation requirement in the range are randomly initialized, and the variation range is reduced along with the iteration times;
12) calculating resource options and selection trends of each construction activity in each scheme according to an updating formula of the multi-target particle swarm optimization algorithm, namely updating the speed and the position of the particles, and returning to execute the step 5);
in step 12), the particle updates the velocity according to the individual historical optimal scheme and the global optimal scheme, and then updates the position according to the current velocity, wherein the velocity updating formula is as follows:
vk(t+1)＝ω·vk(t)+c1·r1(Pk(t)-xk(t))+c2·r2(Rg(t)-xk(t)) (7)
the displacement update formula:
xk(t+1)＝xk(t)+vk(t+1) (8)
wherein: k is 1, 2.. K, K being the total number of particles in the population; e, E is the limit number of iterations; v. ofk(t) the velocity of the kth particle at the tth iteration; x is the number ofk(t) is the position of the kth particle at the tth iteration; pk(t) an individual historical optimal solution for the particles selected using the dominating relationship; rg(t) is a global optimal solution for the particle selected from the external repository at t iterations; omega is inertia weight, is a non-negative number, is 0.4-0.95 and is used for controlling the influence of the previous speed on the current speed; r is1And r2Is a random number and takes a value range of [0,1]]；c1，c2Taking 2 as an acceleration constant, namely a learning factor;
after the initialization and updating of the particles, the position and velocity values of the particles that exceed the value range are adjusted to the limit values.
In this embodiment, the building area a is 2.45 ten thousand meters2The number of the equipment lines B is 4, and the house building cost index is 1216 yuan/m2And, the construction scheduling optimization result output in the step 9) is shown in fig. 4, and fig. 4 is a schematic diagram of an optimization iteration result provided by the embodiment of the present invention, and 163 optimized construction scheduling optimization schemes, all of which are pareto optimal, have a shortest construction period of 148 days, a lowest cost of 38457722 yuan, and a highest quality score of 7.64, are obtained.
The decision maker can further screen the scheme according to the requirements of the project to be optimized on the construction period, the construction cost and the construction quality. Assuming that a decision maker needs to simultaneously meet a construction scheduling scheme with a construction period of less than 150 days, a construction cost of less than 4 million yuan and a construction quality of more than 6.5 minutes, the obtained construction scheduling optimization scheme is shown in table 2, and the numbers under pareto optimal solution in table 2 are resource options of each construction activity in table 1
TABLE 2
Figure BDA0003397083790000101
Figure BDA0003397083790000111
Example 2:
the embodiment discloses an intelligent optimization method for a house building construction scheduling scheme, which comprises the following steps:
1) establishing a multi-target particle swarm building construction scheduling optimization model by taking the shortest construction period, the lowest construction cost and the highest construction quality as optimization targets, and determining the total number of building construction scheduling schemes in the model, namely determining the particle swarm size;
2) building a to-be-optimized scheme for construction scheduling of the building, determining parameter variables and constraint regulations required by the model, and improving the universality of the model in solving the problem of optimization of construction scheduling of the building;
3) setting values of learning factors, inertial weights and mutation operators in the model, and setting the upper limit of the quantity of the scheduling schemes to be constructed and the maximum iteration number of the model, wherein the upper limit of the quantity of the scheduling schemes to be constructed comprises the maximum allowable value of an external storage library;
4) setting the value ranges of the resource options and the selection trends of each construction activity in each scheme, namely determining the value ranges of the positions and the speeds of the particles, and randomly initializing the values of the resource options and the selection trends of the construction activities in the value ranges, namely initializing the positions and the speeds of the particles;
5) calculating the construction period, construction cost and construction quality of each scheme according to the objective function, namely calculating a fitness value;
6) evaluating each scheme independently, and selecting an optimal construction activity resource option combination as an individual historical optimal scheme according to a pareto governing principle, namely updating the individual historical optimal scheme;
7) evaluating all schemes, screening out non-inferior solutions in all schemes according to a pareto domination principle, storing the non-inferior solutions in an external storage library as an archiving scheme, performing second-round screening in the external storage library, and removing inferior solutions in the archiving scheme;
8) judging whether the number of the archived schemes exceeds the maximum allowable value of an external storage library, if so, screening according to the adaptive grid density to remove redundant schemes, and if not, executing the next step;
9) judging whether the loop execution reaches the maximum iteration times or the solution set is converged, if so, outputting an archiving scheme, namely outputting a non-inferior solution set in the external storage library scheme, and if not, adding 1 to the iteration times and then executing the step 10);
10) selecting an optimal construction activity resource option combination in the archiving scheme by adopting a roulette selection method to serve as a global optimal scheme, namely updating the global optimal scheme;
11) randomly initializing the schemes in the variation range, namely performing particle variation operation;
12) and (5) calculating resource options and selection trends of each construction activity in each scheme according to an updating formula of the multi-objective particle swarm optimization algorithm, namely updating the speed and the position of the particles, and returning to execute the step 5).
Example 3:
the main steps of this embodiment are the same as those of embodiment 2, and further, in step 1), the construction period, the construction cost and the construction quality are simultaneously taken as optimization targets, and the optimization objective function is as follows:
minimized construction period PTmin：
Figure BDA0003397083790000121
Wherein: i denotes the serial number of the construction activity, n denotes the serial number of the resource option, Ti，nRepresenting the construction period that the construction activity i on the key path selects the resource option n to continue;
minimized construction cost PCmin：
Figure BDA0003397083790000122
Wherein: ci，nRepresenting the construction cost consumed when the construction activity i selects the resource option n; maximum construction quality PQmax：
Figure BDA0003397083790000123
Wherein: w is ai，nRepresents the weight of the construction activity i in the overall quality representation of the project, Qi，nRepresenting the construction quality performance when the construction activity i selects the resource option n;
example 4:
the main steps of this embodiment are the same as embodiment 2, and further, the construction scheduling scheme to be optimized in step 2) is a built-in table of a model, and the following three input parameters are used for embodying different building projectsThe difference of (a): building area A ten thousand m2B pieces of equipment lines and R yuan/m of building construction cost index of area where project is located2(ii) a After determining the key construction activities of the building and the priority constraints among the key construction activities, quantifying the construction period, construction cost and construction quality of each construction activity to form a construction scheduling scheme to be optimized.
Example 5:
the main steps of this embodiment are the same as embodiment 2, and further, step 10) determines a global optimal solution by using a method of introducing an external repository, where the external repository includes an archive controller and a grid;
example 6:
the main steps of this embodiment are the same as those of embodiment 5, and further, the archive controller is configured to determine whether to add the solution NS to the grid of the external repository, and includes the following steps:
if the external repository is empty, storing the scheme NS; if the external repository already exists in the scheme S1 and S1 dominates NS, then the scheme S1 is still stored; if the external repository already exists in the scheme S1 and NS dominates S1, storing the scheme NS and removing the scheme S1; if the external repository already exists in the scheme S1 and the NS and S1 do not have a dominating relationship, then the schemes NS and S1 are stored.
Example 7:
the main steps of this embodiment are the same as those of embodiment 5, and further, when the grid is an objective function space formed by a plurality of hypercubes and a uniformly distributed scheme set is generated, the method includes the following steps:
10-1) determining the boundaries of the grid: at the t-th iteration, the grid boundary is (minPT)t，maxPTt)、(minPCt，maxPCt) And (minPQ)t，maxPQt) Minimum and maximum fitness values calculated by the particles stored in the external repository according to an objective function of construction period PT, construction cost PC and construction quality PQ;
10-2) calculating the modular length of each grid:
Figure BDA0003397083790000131
Figure BDA0003397083790000132
Figure BDA0003397083790000133
wherein: Δ PTtIs the modular length of the grid in the direction of the target function PT during t iterations; delta PCtThe length of the grid in the direction of the objective function PC is t times of iteration; Δ PQtIs the modulus length of the grid in the direction of the objective function PQ at t iterations; d is a divisor used for determining the number of hypercubes in the grid;
10-3) calculating the grid number of all the particles determined to be stored in the external memory repository,
Figure BDA0003397083790000134
wherein,
Figure BDA0003397083790000141
the fitness value is calculated by a PT target function of a particle u in the grid during t iterations, and Int is a function for taking an integer downwards;
10-4) determining the particle number, i.e. the particle density, of each grid;
10-5) selecting a global optimum using roulette.
Example 8:
the main steps of this embodiment are the same as embodiment 5, and further, in step 12), the particle updates the velocity according to the individual historical optimal solution and the global optimal solution, and then updates the position according to the current velocity, where the velocity update formula is:
vk(t+1)＝ω·vk(t)+c1·r1(Pk(t)-xk(t))+c2·r2(Rg(t)-xk(t)) (7)
displacement update formula:
xk(t+1)＝xk(t)+vk(t+1) (8)
wherein: k is 1, 2.. K, K being the total number of particles in the population; e, E is the limit number of iterations; v. ofk(t) the velocity of the kth particle at the tth iteration; x is the number ofk(t) is the position of the kth particle at the tth iteration; pk(t) an individual historical optimal solution for the particles selected using the dominating relationship; rg(t) is a global optimal solution for the particle selected from the external repository at t iterations; omega is an inertia weight, a non-negative number, and is used for controlling the influence of the previous speed on the current speed; r is a radical of hydrogen1And r2Is a random number and has a value range of [0,1]]；c1，c2Is an acceleration constant, i.e., a learning factor;
after the initialization and updating of the particles, the position and velocity values of the particles that exceed the value range are adjusted to the limit values.
Example 9:
the main steps of this embodiment are the same as embodiment 2, and further, in step 11), the whole population is divided into three parts with equal size, and different mutation operators are adopted for the three parts; the particles of the first sub-fraction will be completely invariant; the particles of the second sub-portion will adopt uniform variation, i.e. the particles meeting the variation requirement within the range are randomly initialized, and the variation range does not change along with the iteration number; the particles of the third sub-portion are randomly initialized with non-uniform variation, i.e. particles satisfying the variation requirement within the range, and the variation range is reduced with the iteration number.

Claims (8)

1. An intelligent optimization method for a building construction scheduling scheme is characterized by comprising the following steps: the method comprises the following steps:
1) establishing a multi-target particle swarm building construction scheduling optimization model by taking the shortest construction period, the lowest construction cost and the highest construction quality as optimization targets, and determining the total number of building construction scheduling schemes in the model as the determined particle swarm size;
2) building a to-be-optimized scheme for construction scheduling of the building, determining parameter variables and constraint regulations required by the model, and improving the universality of the model in solving the problem of optimization of construction scheduling of the building;
3) setting values of learning factors, inertial weights and mutation operators in the model, and setting the upper limit of the quantity of the scheduling schemes to be constructed and the maximum iteration number of the model, wherein the upper limit of the quantity of the scheduling schemes to be constructed comprises the maximum allowable value of an external storage library;
4) setting the value ranges of the resource options and the selection trends of each construction activity in each scheme as the value ranges for determining the positions and the speeds of the particles, and randomly initializing the value of the resource options and the selection trends of the construction activities in the value ranges as the positions and the speeds of the initialized particles;
5) calculating the construction period, construction cost and construction quality of each scheme according to the objective function as a calculated fitness value;
6) evaluating each scheme independently, and selecting an optimal construction activity resource option combination as an individual historical optimal scheme as an updated individual historical optimal scheme according to a pareto governing principle;
7) evaluating all schemes, screening out non-inferior solutions in all schemes according to a pareto domination principle, storing the non-inferior solutions in an external storage library as an archiving scheme, performing second-round screening in the external storage library, and removing inferior solutions in the archiving scheme;
8) judging whether the number of the archived schemes exceeds the maximum allowable value of an external storage library, if so, screening according to the adaptive grid density to remove redundant schemes, and if not, executing the next step;
9) judging whether the loop execution reaches the maximum iteration times or the solution set is converged, if so, outputting an archiving scheme as a non-inferior solution set in the scheme of outputting the external storage library, and if not, adding 1 to the iteration times and then executing the step 10);
10) selecting an optimal construction activity resource option combination in the archiving scheme by adopting a roulette selection method as a global optimal scheme and updating the global optimal scheme;
11) randomly initializing the scheme within the variation range as the operation of particle variation;
12) and (5) calculating resource options and selection trends of each construction activity in each scheme according to an updating formula of the multi-objective particle swarm optimization algorithm, taking the resource options and the selection trends as the speed and the position of the updating particles, and returning to execute the step 5).
2. The intelligent optimization method for the house building construction scheduling scheme according to claim 1, wherein the method comprises the following steps: in the step 1), the construction period, the construction cost and the construction quality are simultaneously taken as optimization targets, and the optimization target function is as follows:
minimized construction period PTmin：
Figure FDA0003397083780000021
Wherein: i denotes the serial number of the construction activity, n denotes the serial number of the resource option, Ti，nRepresenting the construction period of the construction activity i on the key path, wherein the resource option n is selected continuously;
minimized construction cost PCmin：
Figure FDA0003397083780000022
Wherein: ci，nRepresenting the construction cost consumed when the construction activity i selects the resource option n;
maximum construction quality PQmax：
Figure FDA0003397083780000023
Wherein: w is ai，nRepresents the weight of the construction activity i in the overall quality representation of the project, Qi，nAnd representing the construction quality performance when the construction activity i selects the resource option n.
3. The intelligent optimization method for the house building construction scheduling scheme according to claim 1 or 2, wherein the method comprises the following steps: the construction scheduling scheme to be optimized in the step 2) is a built-in table of the model, and differences among different building projects are reflected through the following three input parameters: building area A ten thousand m2B pieces of equipment lines and R yuan/m of house building cost index of area where project is located2(ii) a After determining the key construction activities of the building and the priority constraints among the key construction activities, quantifying the construction period, construction cost and construction quality of each construction activity to form a construction scheduling scheme to be optimized.
4. The intelligent optimization method for the building construction scheduling scheme according to claim 1 or 2, characterized in that: step 10) the global optimum is determined using a method that introduces an external repository, the external repository comprising an archive controller and a grid.
5. The intelligent optimization method for the house building construction scheduling scheme according to claim 4, wherein the method comprises the following steps: the archive controller is used for judging whether to add the scheme NS to the grid of the external storage library, and comprises the following steps:
if the external repository is empty, storing the scheme NS; if the external repository already exists in the scheme S1 and S1 dominates NS, then the scheme S1 is still stored; if the external repository already exists in the scheme S1 and NS dominates S1, storing the scheme NS and removing the scheme S1; if the external repository already exists in the scheme S1 and the NS and S1 do not have a dominating relationship, then the schemes NS and S1 are stored.
6. The intelligent optimization method for the house building construction scheduling scheme according to claim 4, wherein the method comprises the following steps: the grid is an objective function space formed by a plurality of hypercubes, and when a scheme set with uniform distribution is generated, the method comprises the following steps:
10-1) determining the boundaries of the grid: at the t-th iteration, the grid boundary is (minPT)t，maxPTt)、(minPCt，maxPCt) And (minPQ)t，maxPQt) The minimum and maximum fitness values of the particles stored in the external repository are calculated according to an objective function of construction period PT, construction cost PC and construction quality PQ;
10-2) calculating the modular length of each grid:
Figure FDA0003397083780000031
Figure FDA0003397083780000032
Figure FDA0003397083780000033
wherein: Δ PTtIs the modular length of the grid in the direction of the target function PT during t iterations; delta PCtThe length of the grid in the direction of the objective function PC is t times of iteration; Δ PQtIs the modulus length of the grid in the direction of the objective function PQ at t iterations; d is a divisor used for determining the number of hypercubes in the grid;
10-3) calculating the grid number of all the particles determined to be stored in the external memory bank,
Figure FDA0003397083780000034
wherein,
Figure FDA0003397083780000035
the fitness value is calculated by a PT target function of a particle u in the grid during t iterations, and Int is a function for taking an integer downwards;
10-4) determining the particle number of each grid as the particle density;
10-5) selecting a global optimum using roulette.
7. The intelligent optimization method for the house building construction scheduling scheme according to claim 4, wherein the method comprises the following steps: in step 12), the particle updates the velocity according to the individual historical optimal scheme and the global optimal scheme, and then updates the position according to the current velocity, wherein the velocity updating formula is as follows:
vk(t+1)＝ω·vk(t)+c1·r1(Pk(t)-xk(t))+c2·r2(Rg(t)-xk(t)) (7)
displacement update formula:
xk(t+1)＝xk(t)+vk(t+1) (8)
wherein: k is 1, 2.. K, K being the total number of particles in the population; e, E is the limit number of iterations; v. ofk(t) the velocity of the kth particle at the tth iteration; x is the number ofk(t) is the position of the kth particle at the tth iteration; pk(t) an individual historical optimal solution for the particles selected using the dominating relationship; rg(t) is a global optimal solution for the particle selected from the external repository at t iterations; omega is an inertia weight, a non-negative number, and is used for controlling the influence of the previous speed on the current speed; r is1And r2Is a random number and takes a value range of [0,1]]；c1，c2Acceleration constant as learning factor;
after the initialization and updating of the particles, the position and velocity values of the particles that exceed the value range are adjusted to the limit values.
8. The intelligent optimization method for the house building construction scheduling scheme according to claim 1, wherein the method comprises the following steps: in step 11), dividing the whole population into three parts with equal size, and adopting different mutation operators for the three parts; the particles of the first sub-fraction will be completely invariant; the particles of the second sub-part adopt uniform variation, the particles meeting the variation requirement in the range are randomly initialized, and the variation range does not change along with the iteration times; the particles of the third sub-portion are subjected to non-uniform variation, particles meeting variation requirements within the range are randomly initialized, and the variation range is reduced along with the iteration number.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114862523A (en) * 2022-05-30 2022-08-05 重庆大学 Bidding method and system based on carbon emission
WO2024022566A1 (en) * 2022-07-26 2024-02-01 Exigo Holding Aps System and method for location-based construction scheduling
CN117726054A (en) * 2024-02-18 2024-03-19 深圳大学 Building scheme optimization method, terminal and readable storage medium

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114862523A (en) * 2022-05-30 2022-08-05 重庆大学 Bidding method and system based on carbon emission
CN114862523B (en) * 2022-05-30 2024-04-02 重庆大学 Bidding method and system based on carbon emission
WO2024022566A1 (en) * 2022-07-26 2024-02-01 Exigo Holding Aps System and method for location-based construction scheduling
CN117726054A (en) * 2024-02-18 2024-03-19 深圳大学 Building scheme optimization method, terminal and readable storage medium

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