CN111191304A - Construction site layout method based on stochastic strategy and multi-objective optimization algorithm - Google Patents
Construction site layout method based on stochastic strategy and multi-objective optimization algorithm Download PDFInfo
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Abstract
本发明提供一种基于随机策略与多目标优化算法的施工场地平面布置方法，以待布置设施对应的矩形的左上顶点的位置坐标为决策变量，进行随机策略布置，并对随机生成的布置方案进行防重叠约束，形成多目标优化算法的初始种群；以实数编码的形式将随机生成的设施的布置位置坐标作为染色体基因座上的基因参与多目标优化算法的迭代运算，并在交叉、变异操作上进行自适应算法改进，最终得到最优的施工场地布置方案。本发明的有益效果：解决了现有施工场地布置策略存在的易丢解和局部最优解弊端，充分发挥了智能算法的全局寻优特点；从多个工程实际需求出发，最终的场地布置方案不再单一满足某一方面的需求，从整体上提高了场地布置方案的各项功能需求。
The invention provides a construction site layout method based on a random strategy and a multi-objective optimization algorithm. The position coordinates of the upper left vertex of the rectangle corresponding to the facilities to be arranged are used as decision variables, and the random strategy layout is carried out, and the randomly generated layout plan is carried out. Anti-overlapping constraints, forming the initial population of the multi-objective optimization algorithm; in the form of real number coding, the randomly generated facility layout coordinates are used as genes on the chromosome locus to participate in the iterative operation of the multi-objective optimization algorithm, and in the crossover and mutation operations. The adaptive algorithm is improved, and the optimal construction site layout scheme is finally obtained. The beneficial effects of the invention are as follows: the disadvantages of easy lost solution and local optimal solution existing in the existing construction site layout strategy are solved, and the global optimization characteristics of the intelligent algorithm are fully exerted; starting from the actual needs of multiple projects, the final site layout scheme It no longer meets the needs of a single aspect, and improves the functional requirements of the site layout scheme as a whole.
Description
Technical Field
The invention relates to a construction site plane arrangement method based on a random strategy and a multi-objective optimization algorithm.
Background
At present, an intelligent construction site plane arrangement method mainly comprises the steps of establishing a target model to be achieved by site arrangement, and finally obtaining an optimal site arrangement scheme meeting a target by means of intelligent optimization characteristics of an existing intelligent algorithm, such as a genetic algorithm, an ant colony algorithm, a particle swarm algorithm and the like; before the algorithm is applied, the method needs to determine the arrangement mode of the facilities in the site, and then the algorithm intelligently plans the facilities to be arranged according to the arrangement mode. The arrangement mode of facilities in the existing site mainly adopts an automatic line-changing arrangement strategy, namely, the facilities to be arranged are numbered firstly, and then the facilities are arranged line by line in a mode of randomly disordering the numbering sequence; if the sum of the facility lengths in the same row and the sum of the actual spacing between facilities in that row exceed the maximum lateral space length limit, the last facility in the row automatically enters the next row.
The problem that optimal solutions are lost easily occurs in the process of performing optimal arrangement by adopting an intelligent algorithm, because after all facilities are arranged according to an automatic line-changing mode, the possibility of performing facility arrangement on the rest arrangement space of an arrangement area is 0, and a possible more optimal target value obtained by a layout scheme for performing arrangement on the rest arrangement space is indirectly lost, that is, the diversity of an initial population is weakened in the initial stage of the algorithm, the intelligent algorithm performs intelligent optimization in the diversity-defective initial population, and an obtained final solution may be only a local optimal solution but not a global optimal solution. Therefore, although the site layout by using the layout strategy can optimize the site layout scheme to a certain extent, the global optimization characteristics of the algorithm and the optimal optimization target of the site layout scheme are not fully exerted.
Disclosure of Invention
Aiming at the defects that the existing arrangement strategy is easy to lose and solve and the global optimality is weak, the invention provides a construction site plane arrangement method based on a random strategy and a multi-objective optimization algorithm.
The invention provides a construction site plane arrangement method based on a random strategy and a multi-objective optimization algorithm, which comprises the initial stage of site arrangement by adopting the random strategy:
101. gridding a plane area of the whole construction site to form a plurality of square areas with the same area size, and establishing a two-dimensional coordinate system of the construction site based on the square areas;
102. modeling facilities to be arranged into regular rectangles according to respective actual sizes, taking the position coordinate of any vertex of each rectangle as a decision variable of a random strategy, and randomly generating facility position coordinates meeting the construction site space limitation on the basis of the two-dimensional coordinate system established in the step 101;
103. for any facility to be arranged, judging whether a randomly generated position coordinate meets an anti-overlapping constraint, if so, reserving the position coordinate as an arrangement scheme of the facility to be arranged, and if not, abandoning the position coordinate and randomly generating a new position coordinate again until the anti-overlapping constraint is met;
104. executing the steps 103 and 104 on all facilities to be distributed to obtain a site distribution scheme, wherein the site distribution scheme is used as a sample in an initial population of the multi-objective optimization algorithm;
105. and (5) repeatedly executing the step 102 and the step 104 according to the sample number of the initial population to generate the initial population of the multi-objective optimization algorithm.
Further, the method also comprises an optimization stage which adopts a multi-objective optimization algorithm for improvement:
201. giving a cross probability, and determining a locus interval to be crossed between two chromosomes in a multipoint cross mode;
202. performing gene crossing operation on the loci one by one, judging whether the crossed genes meet anti-overlapping constraint or not, if so, retaining the gene crossing operation, otherwise, canceling the gene crossing operation; until all chromosomes in the population complete the cross operation;
203. giving variation probability, and determining a locus interval to be varied between two chromosomes in a uniform variation mode;
204. dividing an arrangement area in a construction site into a plurality of areas, and determining space coordinate ranges among different areas;
205. determining the area where each facility is located in the site arrangement scheme corresponding to the chromosome to be mutated according to the areas divided in the step 204;
206. sequentially carrying out random variation on genes on the loci to other regions except the region where the genes are located according to the locus interval to be varied, judging whether the varied genes meet anti-overlapping constraint, if so, retaining the gene variation operation, otherwise, canceling the gene variation operation;
207. repeating the steps 203 to 206 until the mutation operation of all chromosomes in the population is completed; then, performing non-dominated sorting and congestion degree calculation to obtain new filial generations;
208. and (3) repeating the step 201 and 207 by taking the obtained new filial generation as a parent, wherein after finite iterations, the obtained filial generation tends to be stable on three targets, and the chromosome corresponding to the finally obtained filial generation is the optimal plane layout scheme set of the construction site.
Further, the determination process of the anti-overlapping constraint is as follows: and sequentially judging whether facilities to be arranged are overlapped with facilities with determined position coordinates in the site, judging that the facilities to be arranged meet anti-overlapping constraint when the facilities to be arranged are not overlapped with the facilities with the determined position coordinates in the site, and otherwise judging that the facilities to be arranged do not meet the anti-overlapping constraint.
Further, the specific process of judging whether to overlap is as follows: respectively obtaining the centroid coordinate (x) of the facilities to be arranged for judgment according to the facility position coordinate and the actual size1，y1) Length of L1Width of W1The centroid coordinate of the facility for which the position coordinates have been determined is (x)2，y2) Length of L2Width of W2(ii) a When | x1-x2|≥(L1+L2) 2 and y1-y2|≥(W1+W2) When/2, the facilities to be arranged are judged not to overlap with the facilities with the determined position coordinates, otherwise, the facilities to be arranged are judged to overlap with the facilities with the determined position coordinates.
Further, in step 101, the area size of the square region is 1 × 1 m.
Further, in the step 102, the top left vertex of the rectangle is used as a decision variable.
Further, in the step 105, the initial population is composed of a plurality of chromosomes, the number of chromosomes is the number of samples of the initial population, wherein one chromosome represents one site layout scheme obtained in the step 104; and for each facility to be arranged in the site arrangement scheme, coding the decision variables in a real number coding mode to form genes on the chromosome.
Further, the interleaving operation is: loci { x on the first chromosome to be crossed1，…，xnN genes in the sequence are individually crossed with the locus { X ] on the second chromosome to be crossed1，…，XnAnd (4) crossing n genes, and exchanging the facility arrangement positions corresponding to the crossed genes on the chromosome with the first chromosome after crossing.
Further, the three targets are a flow distance, a risk interaction value, and an affinity, respectively.
The technical scheme provided by the invention has the beneficial effects that:
(1) the random arrangement strategy provided by the invention overcomes the defects of easy lost solution and local optimal solution existing in the existing construction site arrangement strategy, fully exerts the global optimization characteristics of an intelligent algorithm, and obtains a site arrangement scheme closer to the target requirement;
(2) the invention provides a multi-objective optimization algorithm as a carrier of the intelligent planning of the random layout strategy, starting from a plurality of actual engineering requirements, so that the final site layout scheme does not singly meet the requirements of a certain aspect any more but simultaneously meets the requirements of a plurality of aspects, and the functional requirements of the site layout scheme are integrally improved;
(3) the random arrangement strategy based on meshing provided by the invention can be used for further development of an arrangement strategy aiming at irregular facilities, has strong expandability, and enables an optimization result to be further close to the actual engineering, so that a site arrangement scheme has higher construction operability.
Drawings
FIG. 1 is a flow chart of a construction site plane arrangement method based on a random strategy and a multi-objective optimization algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a randomly generated construction site layout scheme provided by an embodiment of the present invention;
FIG. 3 is a schematic illustration of flow distances provided by an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a multi-objective optimization algorithm provided by an embodiment of the present invention;
fig. 5 is a Pareto solution set schematic diagram of the multi-objective optimization algorithm provided by the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
Referring to fig. 1, an embodiment of the present invention provides a method for arranging a plane of a construction site based on a random strategy and a multi-objective optimization algorithm, including an initial stage of performing site arrangement by using the random strategy and an optimization stage of performing improvement by using the multi-objective optimization algorithm, wherein:
the specific process of the initial stage is as follows:
101. gridding a plane area of the whole construction site to form a plurality of square areas with the same area size, and establishing a two-dimensional coordinate system of the construction site based on the square areas; preferably, the size of the square area in this embodiment is 1 × 1 m.
102. Modeling facilities to be arranged into regular rectangles { F) according to respective actual sizes1，…，Fn}，F1，…，FnA number indicating facilities to be arranged, n indicating the number of facilities to be arranged; taking the position coordinate of any vertex of the rectangle as a decision variable of a random strategy, and randomly generating a facility position coordinate meeting the construction site space limitation on the basis of the two-dimensional coordinate system established in the step 101;
103. for any facility F to be arrangediAnd judging whether the randomly generated position coordinates meet the anti-overlapping constraint, if so, reserving the position coordinates as the arrangement scheme of the facilities to be arranged, and if not, giving up the position coordinates and randomly generating new position coordinates again until the anti-overlapping constraint is met.
The specific process of step 103 is as follows:
facility F to be arrangediFacility F sequentially corresponding to the first i-1 determined position coordinates1，…，Fi-1Judging whether the facilities F to be arranged are overlapped or notiWith the first i-1 facilities { F1，…，Fi-1All the facilities F are not overlapped, the facilities F to be arranged are judgediSatisfying the anti-overlapping constraint, otherwise, judging the facility F to be arrangediThe anti-overlap constraint is not satisfied; wherein the judging process comprises: respectively obtaining facilities F according to the position coordinates and the actual sizes of the facilitiesiHas a centroid coordinate of (x)1，y1) Length of L1Width of W1And the centroid coordinate of the facility for which the position coordinates have been determined is (x)2，y2) Length of L2Width of W2(ii) a When | x1-x2|≥(L1+L2) 2 and y1-y2|≥(W1+W2) At time/2, the facility F is judgediDoes not overlap with the facility of the determined position coordinates, otherwise, judges the facility FiOverlapping with the facility of the determined position coordinates.
Specifically, referring to fig. 2, in the present embodiment, the top left vertex of the rectangle is taken as the decision variable, and the horizontal and vertical coordinates a1(1, 3) are randomly generated to obtain the rectangle a, a1(1, 3) as the facility F to be arranged1The facility location coordinates of (a); then, the abscissa B1(2, 4) is randomly generated, resulting in a rectangle B, and the abscissa C1(5, 6) is randomly generated again since the rectangle B overlaps the rectangle a, resulting in a rectangle C which satisfies the overlap prevention constraint with the rectangle a, and thus C1(5, 6) is taken as the facility F to be arranged2The facility location coordinates of.
104. And (5) executing the steps 103 and 104 on all facilities to be distributed to obtain a site distribution scheme, wherein the site distribution scheme is used as a sample in the initial population of the multi-objective optimization algorithm.
105. And (5) repeatedly executing the step 102 and the step 104 according to the sample number of the initial population to generate the initial population of the multi-objective optimization algorithm. Specifically, the initial population consists of a plurality of chromosomes, and the number of chromosomes is the sample number of the initial population, wherein one chromosome represents one site arrangement scheme obtained in step 104; for each facility to be arranged in the site arrangement scheme, the decision variable (namely the vertex coordinate of the upper left corner of the rectangle) is coded in a real number coding mode to form a gene on the chromosome.
It should be noted that the plurality of site layout schemes generated in the initial stage cannot meet the actual construction requirements, so the embodiment adopts a multi-objective optimization algorithm for improvement. In this embodiment, the optimization objective includes a flow distance, a risk interaction value, and an affinity; wherein:
the flow distance takes into account the number of flows between facilities for evaluating the actual use cost of the site placement solution. For any site arrangement scheme, the distance between facilities can be obtained according to the arrangement position of each facility, please refer to fig. 3, the distance is the sum of the transverse distance and the longitudinal distance between centroids, in fig. 3, fijRepresents the distance between facility i and facility j, dijIndicating workers, managers in a day by statistical meansAnd the number of times the construction material member flows between the facility i and the facility j, and the distance between the facility i and the facility k and the distance between the facility j and the facility k and the number of times the construction material member flows are obtained, thereby determining the flow distance between the facility i, the facility j, and the facility k to be f3＝fijdij+fikdik+fkjdkj. When the flow distance is too large, unnecessary material transportation cost can be increased, and the management cost can be indirectly consumed when the personnel walk too large, so that the multi-objective optimization algorithm of the embodiment is superior and inferior by comparing the flow distances of different field arrangement schemes.
The risk interaction value is used for evaluating the safety degree of a site arrangement scheme, two types of facilities to be arranged are considered in the risk interaction value, one type is a risk source facility and is a dangerous temporary facility, and accidents such as object striking, mechanical injury, lifting injury, fire and the like often occur to the facilities on a construction site, such as a reinforcing steel bar processing field, a component storage yard and the like; the other type is a vulnerability facility, which refers to a temporary facility with weak self-defense capacity against external risks and often influenced by risk source facilities, such as an office, a staff dormitory and the like; when the damage existing in the risk source facility is transmitted to the vulnerability facility in the site, the process that the vulnerability facility feeds back the degree of damage of the vulnerability facility is regarded as a risk interaction process, the risk interaction value evaluates the interaction process, and the smaller the risk interaction value, the safer the site arrangement scheme is.
The intimacy is used for evaluating the efficiency of a site arrangement scheme, and in engineering construction, site arrangement conforms to the principle of functional zoning, and temporary facilities with similar construction processes and frequent personnel flow need to be arranged nearby, so that the work communication between workers and managers is more convenient, and the construction activities are more efficient and smooth; the intimacy is determined by quantitatively analyzing the traffic of materials and personnel among different facilities and qualitatively analyzing the convenience in management and supervision, and particularly, the arrangement of partial facilities needs to be specified in advance, for example, a reinforcement cage processing area and a reinforcement stacking area are required to be arranged together in principle, so that the reinforcement cage processing and stacking are facilitated; when the site arrangement scheme is optimized, if facilities which should be arranged together are not arranged close to each other, the affinity of the site arrangement scheme is low.
In the optimization stage, a randomly generated site arrangement scheme is improved by considering three optimization targets of a flow distance, a risk interaction value and intimacy, and the specific process of the optimization stage is as follows:
201. given the crossover probability, and determining the locus interval to be crossed between two chromosomes in a multipoint crossover manner.
202. Performing gene crossing operation on the loci one by one, judging whether the crossed genes meet anti-overlapping constraint or not, if so, retaining the gene crossing operation, otherwise, canceling the gene crossing operation; until all chromosomes in the population have completed crossover operations. Specifically, referring to FIG. 1, loci { x ] on chromosome 1 are identified1，…，xnThe n genes in the gene map are individually linked to the loci { X ] on chromosome 21，…，XnN genes in the chromosome 1 and the chromosome 2 are crossed, the facility arrangement positions corresponding to the genes on the chromosome 1 and the chromosome 2 are exchanged after crossing, and then whether the crossed facility arrangement positions meet the anti-overlapping constraint or not is judged.
203. The mutation probability is given, and the locus interval to be mutated between two chromosomes is determined in a uniform mutation mode.
204. The method comprises the steps of dividing a layout area in a construction site into a plurality of areas, and determining the space coordinate range between different areas.
205. And determining the area where each facility is located in the site arrangement scheme corresponding to the chromosome to be mutated according to the areas divided in the step 204.
206. And sequentially carrying out random variation on the genes on the loci to the rest regions except the region where the genes are located according to the locus interval to be varied, judging whether the varied genes meet the anti-overlapping constraint, if so, keeping the gene variation operation, and otherwise, cancelling the gene variation operation.
207. Repeating the steps 203 to 206 until the mutation operation of all chromosomes in the population is completed; and then carrying out non-dominant sorting and congestion degree calculation to obtain new descendants.
208. Referring to fig. 4, the step 201 and 207 are repeated with the obtained new offspring as the parent, and after a limited number of iterations, the multi-objective optimization algorithm is completed, and the chromosome corresponding to the obtained offspring is the optimal plan layout scheme set of the construction site. Please refer to fig. 5, which is a Pareto solution set of the multi-objective optimization algorithm finally obtained in this embodiment, where a dot in the graph represents a construction site arrangement scheme, that is, a chromosome in a population, three axes corresponding to a three-dimensional coordinate system respectively represent three optimization objectives, that is, a flowing distance, a risk interaction value, and a intimacy, and a plurality of dots shown by arrows in the graph represent non-dominant solutions in the Pareto solution set of the multi-objective optimization algorithm, where the non-dominant solution set is an optimal site arrangement scheme set.
In this document, the terms front, back, upper and lower are used to define the components in the drawings and the positions of the components relative to each other, and are used for clarity and convenience of the technical solution. It is to be understood that the use of the directional terms should not be taken to limit the scope of the claims.
The features of the embodiments and embodiments described herein above may be combined with each other without conflict.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
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