A method, device, client and storage medium for determining an engineering construction plan

Abstract

The present invention discloses a method, device, client and storage medium for determining a construction plan of an engineering project, the method comprising: obtaining construction task information of the engineering project; obtaining constraints and goals of each of the N construction parties; based on the construction task information of the engineering project, using a genetic algorithm, according to the constraints and goals of the N construction parties, searching for a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range as the construction plan of the engineering project. The plan can optimize the construction plan and improve construction efficiency by using a genetic algorithm to realize a construction plan with collaborative work of multiple participants.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent construction technology, and specifically relates to a method, device, client and storage medium for determining an engineering construction plan, and especially relates to a method, device, client and storage medium for formulating an optimal rush work plan. Background Art

[0002] During the construction process, various factors often cause certain tasks to be completed late, resulting in delays in the total construction period. In order to avoid schedule breaches, the construction unit needs to adjust the construction plan for unfinished work to ensure the total construction period. The method used in the relevant plan is to increase labor and other resources to work overtime to catch up. However, as the labor resources of the project become increasingly scarce and the deployment of professional equipment becomes increasingly difficult, how to formulate the plan with the lowest risk and the greatest feasibility becomes an important task.

[0003] However, the relevant plans did not differentiate the construction tasks according to the responsible units; but the actual construction process is undertaken by different professional construction units, and different units cannot replace each other, and the resources of different units are difficult to share; therefore, the rush work optimization methods in the relevant plans cannot evenly distribute the risks among the multiple parties involved in the construction, which may easily lead to the rush work plans not being accepted by various professional units, and difficult to apply to rush work involving multiple construction units such as architecture, structure, and electromechanical; and the methods proposed in the relevant plans all require a large amount of data on schedule adjustments and cost changes, which are actually difficult to collect and prepare, making them difficult to apply.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device, client and storage medium for determining a construction plan of an engineering project, so as to solve the problem that it is difficult to optimize the construction plan when multiple parties participate in the construction of the construction project, so as to achieve the effect of optimizing the construction plan and improving construction efficiency by realizing a construction plan with collaborative work of multiple parties through the use of a genetic algorithm.

[0006] The present invention provides a method for determining a construction plan for a project, wherein the project has N construction parties, where N is a positive integer; when all the N construction parties participate in the construction of the project, the method for determining the construction plan for the project comprises: obtaining construction task information of the project; for each of the N construction parties, obtaining constraints and objectives of each of the N construction parties; based on the construction task information of the project, using a genetic algorithm, and according to the constraints and objectives of the N construction parties, searching for a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range, as the construction plan for the project.

[0007] In some embodiments, the construction task information of the project includes: a set of construction tasks of the project, recorded as a construction task set; the construction task information of the project also includes: a matrix of resources required for the construction tasks of the project, recorded as a resource requirement matrix; and / or, the goals of the N construction parties, including: the target construction period of the project, a set of compressed construction period tasks in the construction task set of the project that can compress the construction period, the maximum compression time of each task in the compressed construction period task set taking into account construction period risks, construction costs and construction resources, and the maximum total compression time shared by the N construction parties; the constraints of the N construction parties include: the compression time of each task in the compressed construction period task set is less than or equal to the maximum compression time of the task; the total compression time of the construction period of each construction party among the N construction parties for its own tasks is less than or equal to the maximum total compression time; the construction period of the construction plan of the project is less than or equal to the target construction period of the project.

[0008] In some embodiments, based on the construction task information of the project, a genetic algorithm is used to find a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range according to the constraints and goals of the N construction parties, as the construction plan of the project, including: according to the goals of the N construction parties, using the Monte Carlo random method to construct an initial chromosome library of rush work plans for the project; in the initial chromosome library of rush work plans for the project, each rush work plan includes: the compressed time of the corresponding task in the compressed construction period task set; traversing each rush work plan in the initial chromosome library of the rush work plan for the project, using the critical path calculation method to calculate the construction period of each rush work plan in the initial chromosome library of the rush work plan for the project, and deleting the rush work plans whose construction period exceeds the target construction period of the project from the initial chromosome library of the rush work plan for the project, to obtain a new initial chromosome library of the rush work plan for the project; for the For each rush work plan in the initial chromosome library of the new rush work plan for the project, a genetic gene optimization algorithm is used to calculate the optimal resource-balanced chromosome segment of each task in the compressed construction period task set to form a new rush work plan resource optimization chromosome library for the project; and the new rush work plan resource optimization chromosome library for the project is added to the new rush work plan for the project; a random competitive selection algorithm is used to screen the chromosome libraries in the resource optimization chromosome library of the new rush work plan for the project according to the resource balance index of the resource optimization chromosome library of the new rush work plan for the project to form a new resource optimization chromosome library; the chromosomes in the new resource optimization chromosome library are crossed and mutated to form a new generation of chromosome library; according to the constraints of the N construction parties, the new generation of chromosome library is repaired to obtain a repaired chromosome library; and a rush work plan corresponding to a group of chromosomes with the smallest resource balance index is selected from the repaired chromosome library as the construction plan for the project.

[0009] In some embodiments, the new generation chromosome library is repaired according to the constraints of N construction parties to obtain a repaired chromosome library, including: traversing each rush work plan corresponding to the new generation chromosome library, using a critical path calculation method to calculate the construction period of each rush work plan corresponding to the new generation chromosome library, and the total time difference of each construction task corresponding to the new generation chromosome library; if the total time difference of each construction task corresponding to the new generation chromosome library is 0, and the compressed total duration of the construction period of the task belonging to the corresponding construction party meets the constraints of the construction party, then calculating the resource balance index caused by compressing the construction period by 1 day; from the construction tasks corresponding to the new generation chromosome library, selecting the construction task with the smallest resource balance index, compressing the construction period of the construction task by one day, and modifying the chromosome corresponding to the construction task in the new generation chromosome library to complete the repair of the new generation chromosome library.

[0010] Matching the above method, the present invention, on the other hand, provides a device for determining a construction plan of an engineering project, wherein the project has N construction parties, where N is a positive integer; when all N construction parties participate in the construction of the project, the device for determining the construction plan of the project comprises: an acquisition unit configured to acquire construction task information of the project; the acquisition unit is further configured to acquire constraints and objectives of each of the N construction parties; a determination unit is configured to use a genetic algorithm based on the construction task information of the project, and to find a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range according to the constraints and objectives of the N construction parties, as the construction plan of the project.

[0011] In some embodiments, the construction task information of the project includes: a set of construction tasks of the project, recorded as a construction task set; the construction task information of the project also includes: a matrix of resources required for the construction tasks of the project, recorded as a resource requirement matrix; and / or, the goals of the N construction parties, including: the target construction period of the project, a set of compressed construction period tasks in the construction task set of the project that can compress the construction period, the maximum compression time of each task in the compressed construction period task set taking into account construction period risks, construction costs and construction resources, and the maximum total compression time shared by the N construction parties; the constraints of the N construction parties include: the compression time of each task in the compressed construction period task set is less than or equal to the maximum compression time of the task; the total compression time of the construction period of each construction party among the N construction parties for its own tasks is less than or equal to the maximum total compression time; the construction period of the construction plan of the project is less than or equal to the target construction period of the project.

[0012] In some embodiments, the determination unit, based on the construction task information of the project, uses a genetic algorithm, and according to the constraints and goals of the N construction parties, searches for a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range as the construction plan of the project, including: constructing an initial chromosome library of rush work plans for the project according to the goals of the N construction parties using a Monte Carlo random device; in the initial chromosome library of the rush work plans for the project, each rush work plan includes: the compressed time of the corresponding task in the compressed construction period task set; traversing each rush work plan in the initial chromosome library of the rush work plans for the project, using a critical path calculation device to calculate the construction period of each rush work plan in the initial chromosome library of the rush work plans for the project, and deleting the rush work plans whose construction period exceeds the target construction period of the project from the initial chromosome library of the rush work plans for the project, so as to obtain a new initial chromosome library of the rush work plans for the project; For each rush work plan in the initial chromosome library of the new rush work plan for the project, a genetic gene optimization algorithm is used to calculate the optimal resource-balanced chromosome segment of each task in the compressed construction period task set to form a new rush work plan resource optimization chromosome library for the project; and the new rush work plan resource optimization chromosome library for the project is added to the new rush work plan for the project; a random competitive selection algorithm is used to screen the chromosome libraries in the resource optimization chromosome library of the new rush work plan for the project according to the resource balance index of the resource optimization chromosome library of the new rush work plan for the project to form a new resource optimization chromosome library; the chromosomes in the new resource optimization chromosome library are crossed and mutated to form a new generation of chromosome library; according to the constraints of the N construction parties, the new generation of chromosome library is repaired to obtain a repaired chromosome library; and a rush work plan corresponding to a group of chromosomes with the smallest resource balance index is selected from the repaired chromosome library as the construction plan for the project.

[0013] In some embodiments, the determination unit repairs the new generation chromosome library according to the constraints of the N construction parties to obtain a repaired chromosome library, including: traversing each rush work plan corresponding to the new generation chromosome library, using a critical path calculation device to calculate the construction period of each rush work plan corresponding to the new generation chromosome library, and the total time difference of each construction task corresponding to the new generation chromosome library; if the total time difference of each construction task corresponding to the new generation chromosome library is 0, and the compressed total duration of the construction period of the task belonging to the corresponding construction party meets the constraints of the construction party, then calculating the resource balance index caused by compressing the construction period by 1 day; from the construction tasks corresponding to the new generation chromosome library, selecting the construction task with the smallest resource balance index, compressing the construction period of the construction task by one day, and modifying the chromosome corresponding to the construction task in the new generation chromosome library to complete the repair of the new generation chromosome library.

[0014] Matching the above-mentioned device, the present invention further provides a client, including: the device for determining the engineering construction plan mentioned above.

[0015] In accordance with the above method, the present invention provides a storage medium on the other hand, wherein the storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the method for determining the engineering construction plan described above.

[0016] Therefore, the scheme of the present invention, through the adoption of a genetic algorithm, determines a construction plan for collaborative work among multiple construction parties based on the resource input of the multiple construction parties, the construction scope and the task compression time, while ensuring that the compression amount of each task is within the set range and that each construction party shares the task and risk; thereby, by using a genetic algorithm to implement a construction plan for collaborative work among multiple construction parties, the construction plan can be optimized and construction efficiency can be improved.

[0017] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for determining an engineering construction plan of the present invention;

[0020] Figure 2 A flow chart of an embodiment of the method of the present invention for finding a construction plan whose resource balance is within a set balance range and whose construction period risk is within a set risk range;

[0021] Figure 3 This is a schematic diagram of a process of repairing the new generation chromosome library in an embodiment of the method of the present invention;

[0022] Figure 4 It is a structural schematic diagram of an embodiment of a device for determining an engineering construction plan of the present invention;

[0023] Figure 5 A schematic diagram of the overall process of an embodiment of a method for formulating an optimal rush work plan;

[0024] Figure 6 This is a schematic diagram of chromosome coding;

[0025] Figure 7 Schematic diagram of the chromosome crossover process for resource optimization;

[0026] Figure 8 Schematic diagram of the chromosome crossing process optimized for rush work;

[0027] Fig. 9 Schematic diagram of resource demand curves for different compression schemes;

[0028] Fig.10 This is a schematic diagram of the basic information table of each task;

[0029] Fig.11 A schematic diagram of the compressed total working time table for each unit;

[0030] Fig.12 Schematic diagram of the output result table for selecting resource optimization index from chromosome library GA.

[0031] In conjunction with the accompanying drawings, the reference numerals in the embodiments of the present invention are as follows:

[0032] 102-acquisition unit; 104-determination unit. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] According to an embodiment of the present invention, a method for determining a construction plan is provided, such as Figure 1The flowchart of an embodiment of the method of the present invention is shown. The project has N construction parties, where N is a positive integer. When the N construction parties are involved in the construction of the project, the method for determining the construction plan of the project includes: steps S110 to S130.

[0035] In step S110, the construction task information of the project is obtained.

[0036] In some implementations, the construction task information of the project includes: a set of construction tasks of the project, recorded as a construction task set. The construction task information of the project also includes: a matrix of resources required by the construction tasks of the project, recorded as a resource requirement matrix.

[0037] Figure 5 The overall flow diagram of an embodiment of a method for formulating an optimal rush work plan is shown in FIG. Figure 5 As shown, the solution of the present invention provides a method for formulating an optimal rush work plan, comprising the following steps:

[0038] Step 1: Input construction task information. The construction task information includes construction task tk i The set TM of construction tasks TM = {tk i}, i = 1-m, m is the number of tasks, i is a positive number. Construction task tk i =(id i , the earliest start time es i , the earliest completion time ee i , free time difference ff i , duration d i ). Each construction task tk i For various task resources i The demand matrix R = {each task resource r i}, r i = {Total amount of various resource requirements r ik}, i = 1-m. The weighting coefficients of various resources W = {w k}, k = 1-n, k is the resource type serial number, and n is the total number of resource types.

[0039] Fig.10 The following is a schematic diagram of the basic information table of each task. Taking the progress optimization of a check-in island at an airport as an example, it involves five units: civil engineering, electromechanical, weak current, decoration and baggage. The total plan includes 67 processes with a total construction period of 86 days. Some of the construction task information is as follows: Fig.10 As shown in the basic information table of each task, assuming that only the balance of labor resources is considered, the total labor demand of each task is as follows Fig.10The basic information table of each task is shown in the table. For the task of "Main Steel Structure of Check-in Island", the earliest start is the first day, the earliest completion is the 15th day, the construction period is 15 days, and the maximum compression is 5 days. The construction unit is civil engineering, and the total labor demand is 60 man-days.

[0040] In step S120, for each of the N construction parties, the constraints and goals of each construction party are obtained.

[0041] In some embodiments, the goals of the N construction parties include: the target construction period of the project, a compressed construction period task set in the construction task set of the project that can compress the construction period, the maximum compressed time of each task in the compressed construction period task set taking into account construction period risks, construction costs and construction resources, and the maximum total compressed time shared by the N construction parties.

[0042] The constraints of the N construction parties include: the compression time of each task in the compressed construction period task set is less than or equal to the maximum compression time of the task; the total compression time of the construction period of each construction party among the N construction parties for its own task is less than or equal to the maximum total compression time; the construction period of the construction plan of the project is less than or equal to the target construction period of the project.

[0043] like Figure 5 As shown, the solution of the present invention provides a method for formulating an optimal rush work plan, which also includes the following steps:

[0044] Step 2: Each participant inputs constraints and goals. The goal is mainly the total target duration Tq, and the goals of each participant can be said to be constraints.

[0045] The goals entered by each participant include: target duration Tq. The constraints entered by each participant include: the set of construction tasks that can compress the target duration Tq, the maximum compressed time TK under comprehensive consideration of risk, cost and resource conditions g ={Construction task tk gj , the maximum compression time c of each task gj,max}, and the maximum total compression time C shared by each construction unit g g . Where gj = 1-kg, gj is a positive number, kg is the number of compressible tasks per unit g. The maximum compression time allocated to construction unit g exceeds the maximum total compression time C g After that, the construction progress risk of the construction unit g is too great, that is, the compression time c of each construction task gj Exceed the maximum compression amount c of each task gj,max It is not feasible. Please refer to formula (1) to formula (3) for details:

[0046] The compressed time of each construction task cgj ≤ Maximum compression amount of each task c gj,max (1).

[0047] Total compression time for each unit g

[0048] The construction period T of the construction plan ≤ the target construction period Tq (3).

[0049] Fig.11 The following is a schematic diagram of the total compressed time table for each unit. Taking the above check-in island progress optimization as an example, the target duration Tq = 72 days, which needs to be compressed by 14 days. The maximum compressed time cmax of each task is as follows: Fig.10 As shown in the basic information table of each task, the maximum compression amount of each unit is as follows Fig.11 The total compressed working time of each unit is shown in the table.

[0050] In step S130, based on the construction task information of the project and using a genetic algorithm, according to the constraints and goals of the N construction parties, a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range is found as the construction plan for the project.

[0051] The solution of the present invention proposes a schedule optimization algorithm that considers the balance of compression contributions of each construction unit, and particularly relates to a method for using a genetic algorithm to implement the formulation of a collaborative work plan for multiple participants, supporting each construction unit to find a rush work plan with the most balanced resources and the lowest risk according to various constraints, and is particularly suitable for multi-professional cross-construction optimization under limited working space, and can assist construction managers in finding a construction plan with the most balanced resources and the lowest construction period risk. In addition, less data needs to be input, reducing user input and improving the practicality of the algorithm.

[0052] Among them, the genetic algorithm, or genetic algorithm, is a method of searching for the optimal solution by simulating the natural evolution process. The algorithm uses computer simulation operations to convert the problem-solving process into a process similar to the crossover and mutation of chromosome genes in biological evolution. When solving more complex combinatorial optimization problems, it can usually obtain better optimization results faster than some conventional optimization algorithms.

[0053] In some embodiments, in step S130, based on the construction task information of the project, a genetic algorithm is used to find a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range according to the constraints and goals of the N construction parties. For the specific process of the construction plan of the project, please refer to the following exemplary description.

[0054] Combine the following Figure 2The flowchart of an embodiment of the method of the present invention for finding a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range is shown, further illustrating the specific process of finding a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range in step S130, including: steps S210 to S260.

[0055] Step S210, based on the goals of the N construction parties, the Monte Carlo random method is used to construct an initial chromosome library of the rush work plan for the project. In the initial chromosome library of the rush work plan for the project, each rush work plan includes: the compressed time of the corresponding task in the compressed construction period task set. The Monte Carlo random method, that is, the statistical simulation method, is a type of numerical calculation method guided by probability statistics theory. In essence, it is to estimate the whole with the part. The more samples are taken, the closer it is to the optimal solution.

[0056] like Figure 5 As shown, the solution of the present invention provides a method for formulating an optimal rush work plan, which also includes the following steps:

[0057] Step 3: Based on the constraints and goals input by each participant, the Monte Carlo random method is creatively used to construct the initial chromosome library of the rush work plan GA = {ga j}, where each rush plan ga j =(compression time c of each compressible task gj ), (c g1 ,c g2 ,…,c gk1 ) is the compression duration of each compressible task in unit g, and the number of cyclic evolutions z=0.

[0058] Figure 6 The chromosome encoding diagram is shown in Figure 2. Figure 6 As shown. Each rush work plan j =(c 11 ,c 12 ,…,c 1k1 ,c 21 ,c 22 ,…,c 1k2 ,…,c g1 ,c g2 ,…,c gkg ,). For example, the first rush plan ga1 = (0,0,…,0,0,0,…,0,…,0,0,…,0), the second rush plan ga2 = (1,2,…,2,1,1,…,2,…,2,1,…,2), and the third rush plan ga3 = (2,2,…,1,1,2,…,2,…,3,1,…,1).

[0059] Step S220, traverse each rush work plan in the initial chromosome library of rush work plans for the project, use the critical path calculation method to calculate the construction period of each rush work plan in the initial chromosome library of rush work plans for the project, and delete the rush work plans whose construction period exceeds the target construction period of the project from the initial chromosome library of rush work plans for the project, so as to obtain a new initial chromosome library of rush work plans for the project.

[0060] like Figure 5 As shown, the solution of the present invention provides a method for formulating an optimal rush work plan, which also includes the following steps:

[0061] Step 4: Traverse each rush work plan ga in the initial chromosome library GA j , using the critical path method (CPM) to calculate the time required for each rush work plan j The duration T j . j The duration T j >Scheme ga for target construction period Tq j Delete the initial chromosome pool GA from the rush program.

[0062] For example, the first rush plan ga1 has a duration T1 = 86 days, which does not meet the demand and is deleted. The second rush plan ga2 and the third rush plan ga3 have a duration of 72 days, which meets the demand.

[0063] Step S230: For each rush work plan in the initial chromosome library of the new rush work plan of the project, a genetic optimization algorithm is used to calculate the optimal resource balancing chromosome segment of each task in the compressed construction period task set to form a new rush work plan resource optimization chromosome library of the project. The new rush work plan resource optimization chromosome library of the project is added to the new rush work plan of the project.

[0064] like Figure 5 As shown, the solution of the present invention provides a method for formulating an optimal rush work plan, which also includes the following steps:

[0065] Step 5: For each rushing scheme ga in the initial chromosome library GA of the rushing scheme j Without affecting the total construction period, the genetic optimization algorithm is used to calculate the optimal resource balancing chromosome segment s for each task j ={es1,es2,…,es j}, j = 1-m. Add to ga i , where j The earliest start time for each task. Add the earliest start time parameter to the construction plan to achieve resource balancing without changing the construction period.

[0066] The optimization in step 5 is only performed within the free time difference range for each construction task, so it does not affect the start and end time of subsequent tasks, and therefore does not affect the total construction period.

[0067] Step 5.1: Traverse each rush work scheme ga in the initial chromosome library GA j , take the second rush work plan ga2 as an example.

[0068] Step 5.2: Use the critical path calculation method (CPM) to calculate the ga of each rush plan j Various construction tasks in tk ji The free time difference ff ji .

[0069] For example: Construction task tk ji The free time difference ff ji =(0,0,3,2,0,3,2,0,0,0,3,0).

[0070] Step 5.3: Use Monte Carlo random method to construct resource optimization chromosome library GS = {gs i}: Each chromosome model gs i =(s1,s2,…,s m ), where gs i ≤ff ji , set the number of iterations z1 = 0.

[0071] Each chromosome model gs i like Figure 6 For example, the first chromosome model gs1 = (0,0,0,0,0,0,0,0,0,0,0), and the second chromosome model gs2 = (0,0,1,1,0,2,1,0,0,0,1,0).

[0072] Step 5.4: Model gs for each chromosome i , calculate its resource balance index as the fitness evaluation index F i , see the examples shown in formula (4), formula (5), formula (6) and formula (7) for details:

[0073]

[0074] Among them, all construction tasks per day are tk ik The resource demand weighted value R(t) is:

[0075]

[0076] This method assumes that the construction task tkik The total resource demand remains unchanged, the daily resource demand is the same during working hours, and the construction task tk ik The weighted values ​​of various resources at any time R ik (t) is:

[0077]

[0078] All construction tasks per day tk ik The mean R of the weighted resource demand R(t) m for:

[0079]

[0080] Among them, F i For each chromosome model gs i Calculate its resource balance index as the fitness evaluation index, T j For construction plan j The construction period of j represents the jth rush work plan ga j The serial number of each construction task is tk. ik The resource demand weighted value, R m For all construction tasks every day tk ik The mean of the resource demand weighted values ​​R(t).

[0081] For example: Fig. 9 Schematic diagram of resource demand curves for different compression schemes. For the second chromosome model gs2, the resource optimal fitness evaluation index F i =0.51. For the third chromosome model gs3, the resource optimal fitness evaluation index F i =0.94. The resource demand curves of the second chromosome model gs2 and the third chromosome model gs3 are as follows: Fig. 9 shown.

[0082] Step 5.5: Use the random competition selection algorithm to select the fitness value F i , the chromosome library in the resource optimization chromosome library GS is screened to form a new chromosome library GS', that is, the fitness value F i High element gs i Entering the new chromosome pool GS' at a higher frequency.

[0083] For example, add two third chromosome models gs3 to the new chromosome library GS', and add one second chromosome model gs2 to the new chromosome library GS'. The adaptability of the elements in the new chromosome library GS' is greater than that in the resource-optimized chromosome library GS. However, the number of elements in the resource-optimized chromosome library GS is the same as the number of elements in the new chromosome library GS'.

[0084] Step 5.6: Form a new generation of chromosome library GS through crossover and mutation, which specifically includes the following steps:

[0085] Step 5.6.1: Use the roulette wheel selection algorithm to select each chromosome model gs i =(s1,s2,…,s m ) randomly selects a point p for crossover.

[0086] For example, p=4.

[0087] Step 5.6.2: Change the parent chromosome gs j and gs j+1 Perform chromosome crossover at point p and use the sub-chromosome gs generated after crossover j '、gs j+1 '. z1=z1+1.

[0088] for example: Figure 7 The schematic diagram of the chromosome crossover process for resource optimization is shown in Figure 2. The first chromosome model gs1 and the second chromosome model gs2 are crossed to generate gs2'=(0,0,1,1,0,0,0,0,0,0,0,0), gs1'=(0,0,0,0,0,2,1,0,0,0,1,0), as shown in Figure 2. Figure 7 shown.

[0089] Step 5.6.3: Use the sub-chromosome gs generated after crossover j '、gs j+1 'Replace gs j The parent chromosome gs j and gs j+1 The two daughter chromosomes are new chromosomes generated by the crossover of the two parent chromosomes, see Figure 7 The sub-chromosome is a new generation of chromosomes after iterative optimization, and generally has a higher fitness index.

[0090] Step 5.6.4: Randomly select part of the chromosome gs according to the mutation rate pm j Perform mutation processing, that is, select part of the chromosome gs j Any element s i , in the case of satisfying some chromosomes gs j Any element s i s i ≤FF ji In this case, a new value is set randomly.

[0091] For example, if we select the second chromosome model gs2 and set c3=2, then gs2=(0,0,2,1,0,0,0,0,0,0,0,0,0). c3 refers to the value of the point of the chromosome of gs2.

[0092] Step 5.7: When the number of iterations z1 is equal to the number of evolution requirements Z1, go to step 5.7. Otherwise, return to step 5.4.

[0093] Step 5.8: Find the chromosome gs with the largest fitness value in the resource optimization chromosome library GS j , add the resource-optimized chromosome library GS to ga j In, that is, ga j = {ga j , gs j}=(c 11 ,c 12 ,…,c 1k1 ,c 21 ,c 22 ,…,c 1k2 ,…,c g1 ,c g2 ,…,c gkg ,s1,s2,…,s m ).

[0094] Rush work plan j =(compression time c of each compressible task gj ), (c g1 ,c g2 ,…,c gk1 ) is the compression duration of each compressible task in unit g, and the number of cyclic evolutions z=0.

[0095] For example: ga2 = (1,2,…,2,1,1,…,2,…,2,1,…,2,0,0,1,1,0,2,1,0,0,0,1,0).

[0096] Step 6: When the number of iterations z reaches the maximum number of evolutions Z, go to step 10. Otherwise go to step 8. For example: the number of iterations z = 1000. Z is the number of iterations in the first stage; Z1 is the number of iterations in the second stage.

[0097] Step S240, using a random competitive selection algorithm, based on the resource balance index of the resource optimization chromosome library of the new rush work plan for the project, the chromosome library in the resource optimization chromosome library of the new rush work plan for the project is screened to form a new resource optimization chromosome library.

[0098] like Figure 5 As shown, the solution of the present invention provides a method for formulating an optimal rush work plan, which also includes the following steps:

[0099] Step 7: Use the random competition selection algorithm to select the fitness value F i (i.e., the fitness evaluation index F i), the chromosome library in the resource optimization chromosome library GS is screened to form a new chromosome library GA', that is, the fitness value F i High element ga i Entering the new chromosome pool GA' at a higher frequency.

[0100] For example: for ga2, its fitness evaluation index F i =0.562. For ga3, its optimal fitness evaluation index F i =0.95. Then select 2 ga3 and 1 ga2 and add them to the new chromosome library GA'. Make the average fitness value of the elements in the new chromosome library GA' higher than that of the elements in the initial chromosome library GA of the rush scheme.

[0101] Step S250, performing crossover and mutation on the chromosomes in the new resource-optimized chromosome library to form a new generation chromosome library.

[0102] like Figure 5 As shown, the solution of the present invention provides a method for formulating an optimal rush work plan, which also includes the following steps:

[0103] Step 8: Crossover and mutation of the compression scheme chromosomes to form a new generation chromosome library GA" = {ga j}, set the number of iterations z = z + 1.

[0104] Step 8.1: Traverse each scheme ga in the new generation chromosome library GA j .

[0105] For example: gs2.

[0106] Step 8.2: Use a random algorithm to j = {ga j , gs j}=(c 11 ,c 12 ,…,c 1k1 ,c 21 ,c 22 ,…,c 1k2 ,…,c g1 ,c g2 ,…,c gkg ) randomly selects a point p for crossover.

[0107] For example: p=3.

[0108] Step 8.3: Add the parent chromosome ga j and ga j+1 Perform chromosome crossover at point p and use the daughter chromosome ga generated after crossover j ', ga j+1 '.

[0109] for example: Figure 8 Schematic diagram of chromosome crossover process for speedy work optimization. After the second chromosome model gs2 and the third chromosome model gs3 cross at p=3, ga2'=(2,2,1,1,1,…,2,…,2,1,…,2,0,0,1,1,0,2,1,0,0,0,1,0). ga3'=(1,2,2,1,2,…,2,…,3,1,…,1,0,0,2,1,0,1,2,0,0,0,2,0), as shown in Figure 8 shown.

[0110] Step 8.4: Calculate the sub-chromosome ga j 'and ga j+1 'Whether the constraint formulas (1) and (2) are satisfied, if so, proceed to step 8.6. If not, proceed to step 8.5.

[0111] Step 8.5: Pair of chromosomes ga j ', ga j+1 'Perform mutation processing, that is, for elements c that do not meet the constraint formulas (1) and (2) gj Randomly select a value under the constraints of formulas (1) and (2) and proceed to step 8.6.

[0112] Step 8.6: Use the subchromosome ga j ',ga j+1 'Replace ga j The parent chromosome ga j and ga j+1 .

[0113] Step S260: repair the new generation chromosome library according to the constraints of the N construction parties to obtain a repaired chromosome library. Select a rush work plan corresponding to a group of chromosomes with the smallest resource balance index from the repaired chromosome library as the construction plan for the project.

[0114] like Figure 5 As shown, the solution of the present invention provides a method for formulating an optimal rush work plan, which also includes the following steps:

[0115] Step 9: Considering the impact of compressed task duration on resource balance and various constraints, repair each chromosome in the new generation chromosome library GA, and return to step 4 after the repair is completed.

[0116] Step 10: Select resource optimization index F from chromosome library GA i The smallest group is output as the result.

[0117] Fig.12Schematic diagram of the output result table of selecting resource optimization index from chromosome library GA. Fig.12 As shown, the optimal solution is ga = (3, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1).

[0118] The solution of the present invention supports multiple construction participants to input the rush work range of each key path and the total compression period of each task according to complex construction conditions such as resource input, ensuring that the compression amount of each task is within a controllable range and that each construction party shares the rush work tasks and risks. The genetic algorithm is used to automatically calculate the most resource-balanced and feasible rush work plan, reduce risks, and ensure the construction period.

[0119] In some embodiments, in step S260, the new generation chromosome library is repaired according to the constraints of the N construction parties to obtain a specific process of repairing the chromosome library, as shown in the following exemplary description.

[0120] Combine the following Figure 3 The flowchart of an embodiment of repairing the new generation chromosome library in the method of the present invention further illustrates the specific process of repairing the new generation chromosome library in step S260, including: step S310 to step S330.

[0121] Step S310, traverse each rush work plan corresponding to the new generation chromosome library, and use the critical path calculation method to calculate the construction period of each rush work plan corresponding to the new generation chromosome library and the total time difference of each construction task corresponding to the new generation chromosome library.

[0122] Step S320: If the total time difference of each construction task corresponding to the new generation chromosome library is 0, and the total compressed duration of the task of the corresponding construction party meets the constraints of the construction party, then calculate the resource balance index caused by compressing the construction period by 1 day.

[0123] Step S330, selecting the construction task with the smallest resource balance index from the construction tasks corresponding to the new generation chromosome library, compressing the construction period of the construction task by one day, and modifying the chromosome corresponding to the construction task in the new generation chromosome library to complete the repair of the new generation chromosome library.

[0124] See also Figure 5 In the example shown, step 9 includes the following steps:

[0125] Step 9.1: Traverse the new generation chromosome library GA" each scheme ga j If the traversal is complete, go to step 9.7. Otherwise, go to step 9.2.

[0126] For example: ga3.

[0127] Step 9.2: Calculate ga using the critical path calculation method (CPM) j 'The construction period T j And the total time difference TF of each task i If T j If it is not greater than the target construction period Tq, return to step 9.1. Otherwise, proceed to step 9.3.

[0128] For example: The construction period corresponding to ga3 is T=73 days, which is 1 day longer than the target construction period Tq.

[0129] Step 9.3: Traverse the solution ga j All construction tasks in tk ji .

[0130] Step 9.4: If the construction task tk ji Total time difference TF i = 0, and the total compression time of the construction unit g meets the following conditions, then go to step 9.5. Otherwise, return to step 9.3:

[0131]

[0132] For example: tk1, tk2, tk3, tk in ga3 12 The total time difference of tasks is 0, but only tk 12 The total compression time of the corresponding construction unit is spare.

[0133] Step 9.5: Calculate the resource increase index caused by compressing the construction period by 1 day:

[0134]

[0135] Among them, ω ji The resource increase index or resource balance index, R ji For the task tk ji The total weighted resources required, t s For construction tasks tk ji The start time, d ji (d i ) is the construction task tk ji The current construction period. Therefore, the problem that the solution does not meet the construction period threshold of each unit can quickly form a feasible plan and improve resource balance.

[0136] Step 9.6: Find the resource balance index ω ji Minimal Task tk ji , shortening the construction period by 1 day, i.e. modifying chromosome ga j Element C ji =c ji +1. Return to step 9.2.

[0137] For example: ga3, c 12 =0+1=1. ga3=(1,2,2,1,2,…,2,…,3,1,…,2,0,0,2,1,0,1,2,0,0,0,2,0).

[0138] Step 9.7: Repair completed.

[0139] The solution of the present invention supports multiple parties to input their respective speed-up limits and share risks. It supports each unit to input the maximum compression amount of compressible tasks to avoid exponential increase of resources and costs. Through the two-stage genetic optimization algorithm, the speed-up plan with the most balanced resources and the lowest risk is found, which is particularly suitable for the optimization of speed-up plans for multi-professional cross-construction under the condition of limited working surface. The cross-mutated plan is quickly repaired through the repair method to improve the optimization calculation speed.

[0140] The technical solution of this embodiment is adopted, and the genetic algorithm is used to determine the construction plan for the collaborative work of multiple participants according to the resource input of the multiple construction parties, the construction scope and the task compression time, while ensuring that the compression amount of each task is within the set range and that each construction party shares the tasks and risks. Thus, by using the genetic algorithm to implement the construction plan for the collaborative work of multiple participants, the construction plan can be optimized and the construction efficiency can be improved.

[0141] According to an embodiment of the present invention, a device for determining a construction plan corresponding to the method for determining a construction plan is also provided. Figure 4 The schematic diagram of the structure of an embodiment of the device of the present invention is shown. The project has N construction parties, where N is a positive integer. When the N construction parties are involved in the construction of the project, the device for determining the construction plan of the project includes: an acquisition unit 102 and a determination unit 104.

[0142] The acquisition unit 102 is configured to acquire the construction task information of the project. The specific functions and processing of the acquisition unit 102 refer to step S110.

[0143] In some implementations, the construction task information of the project includes: a set of construction tasks of the project, recorded as a construction task set. The construction task information of the project also includes: a matrix of resources required by the construction tasks of the project, recorded as a resource requirement matrix.

[0144] Figure 5 The overall process diagram of an embodiment of an optimal rush work plan formulation device is shown in FIG. Figure 5 As shown, the solution of the present invention provides an optimal rush work plan formulation device, comprising the following steps:

[0145] Step 1: Input construction task information. The construction task information includes construction task tk i The set TM of construction tasks TM = {tk i}, i = 1-m, m is the number of tasks. Construction task tk i =(id i , the earliest start time es i , the earliest completion time ee i , free time difference ff i , duration d i ). Each construction task tk i For various task resources i The demand matrix R = {each task resource r i}, r i = {Total amount of various resource requirements r ik}, i = 1-m. The weighting coefficients of various resources W = {w k}, k=1-n.

[0146] Fig.10 The following is a schematic diagram of the basic information table of each task. Taking the progress optimization of a check-in island at an airport as an example, it involves five units: civil engineering, electromechanical, weak current, decoration and baggage. The total plan includes 67 processes with a total construction period of 86 days. Some of the construction task information is as follows: Fig.10 As shown in the basic information table of each task, assuming that only the balance of labor resources is considered, the total labor demand of each task is as follows Fig.10 The basic information table of each task is shown in the table. For the task of "Main Steel Structure of Check-in Island", the earliest start is the first day, the earliest completion is the 15th day, the construction period is 15 days, and the maximum compression is 5 days. The construction unit is civil engineering, and the total labor demand is 60 man-days.

[0147] The acquisition unit 102 is further configured to acquire the constraint conditions and goals of each of the N construction parties. The specific functions and processing of the acquisition unit 102 are also shown in step S120.

[0148] In some embodiments, the goals of the N construction parties include: the target construction period of the project, a compressed construction period task set in the construction task set of the project that can compress the construction period, the maximum compressed time of each task in the compressed construction period task set taking into account construction period risks, construction costs and construction resources, and the maximum total compressed time shared by the N construction parties.

[0149] The constraints of the N construction parties include: the compression time of each task in the compressed construction period task set is less than or equal to the maximum compression time of the task; the total compression time of the construction period of each construction party among the N construction parties for its own task is less than or equal to the maximum total compression time; the construction period of the construction plan of the project is less than or equal to the target construction period of the project.

[0150] like Figure 5 As shown, the solution of the present invention provides an optimal rush work plan formulation device, which also includes the following steps:

[0151] Step 2: Each participant enters constraints and goals.

[0152] The goals entered by each participant include: target duration Tq. The constraints entered by each participant include: the set of construction tasks that can compress the target duration Tq, the maximum compressed time TK under comprehensive consideration of risk, cost and resource conditions g ={Construction task tk gj , the maximum compression time c of each task gj,max}, and the maximum total compression time C shared by each construction unit g g . Where gj = 1-kg, kg is the number of compressible tasks per unit g. The maximum compression time allocated to construction unit g exceeds the maximum total compression time C g After that, the construction progress risk of the construction unit g is too great, that is, the compression time c of each construction task gj Exceed the maximum compression amount c of each task gj,max It is not feasible. Please refer to formula (1) to formula (3) for details:

[0153] The compressed time of each construction task c gj ≤ Maximum compression amount of each task c gj,max (1).

[0154] Total compression time for each unit g

[0155] The construction period T of the construction plan ≤ the target construction period Tq (3).

[0156] Fig.11 The following is a schematic diagram of the total compressed time table for each unit. Taking the above check-in island progress optimization as an example, the target duration Tq = 72 days, which needs to be compressed by 14 days. The maximum compressed time cmax of each task is as follows: Fig.10 As shown in the basic information table of each task, the maximum compression amount of each unit is as follows Fig.11 The total compressed working time of each unit is shown in the table.

[0157] The determination unit 104 is configured to use a genetic algorithm based on the construction task information of the project and according to the constraints and goals of the N construction parties, to find a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range as the construction plan of the project. The specific functions and processing of the determination unit 104 refer to step S130.

[0158] The scheme of the present invention proposes a schedule optimization algorithm that considers the balance of compression contributions of each construction unit, and particularly relates to a device that uses a genetic algorithm to implement the formulation of a collaborative work plan for multiple participants, supporting each construction unit to find a rush work plan with the most balanced resources and the lowest risk according to various constraints, and is particularly suitable for multi-professional cross-construction optimization under limited working space, and can assist construction managers in finding a construction plan with the most balanced resources and the lowest construction period risk. In addition, less data needs to be input, reducing user input and improving the practicality of the algorithm.

[0159] In some implementations, the determining unit 104 uses a genetic algorithm based on the construction task information of the project and according to the constraints and goals of the N construction parties to find a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range as the construction plan of the project, including:

[0160] The determination unit 104 is also specifically configured to construct an initial chromosome library of the rush work plan for the project based on the goals of the N construction parties using a Monte Carlo random device. In the initial chromosome library of the rush work plan for the project, each rush work plan includes: the compressed time of the corresponding task in the compressed construction period task set. The specific functions and processing of the determination unit 104 can also be found in step S210. The Monte Carlo random device, that is, the statistical simulation device, is a type of numerical calculation device guided by probability statistics theory. In essence, it is to estimate the whole with the part. The more samples are taken, the closer it is to the optimal solution.

[0161] like Figure 5 As shown, the solution of the present invention provides an optimal rush work plan formulation device, which also includes the following steps:

[0162] Step 3: Based on the constraints and goals input by each participant, the Monte Carlo random device is creatively used to construct the initial chromosome library of the rush work plan GA = {ga j}, where each rush plan ga j =(compression time c of each compressible task gj ), (c g1 ,c g2 ,…,c gk1 ) is the compression duration of each compressible task in unit g, and the number of cyclic evolutions z=0.

[0163] Figure 6 The chromosome encoding diagram is shown in Figure 2. Figure 6 As shown. Each rush work plan j =(c 11 ,c 12 ,…,c 1k1 ,c 21 ,c 22 ,…,c 1k2 ,…,c g1 ,c g2 ,…,c gkg ,). For example, the first rush plan ga1 = (0,0,…,0,0,0,…,0,…,0,0,…,0), the second rush plan ga2 = (1,2,…,2,1,1,…,2,…,2,1,…,2), and the third rush plan ga3 = (2,2,…,1,1,2,…,2,…,3,1,…,1).

[0164] The determination unit 104 is further configured to traverse each rush work plan in the initial chromosome library of the rush work plan of the project, calculate the duration of each rush work plan in the initial chromosome library of the rush work plan of the project using a critical path calculation device, and delete the rush work plan whose duration exceeds the target duration of the project from the initial chromosome library of the rush work plan of the project, thereby obtaining a new rush work plan initial chromosome library of the project. The specific functions and processing of the determination unit 104 are also shown in step S220.

[0165] like Figure 5 As shown, the solution of the present invention provides an optimal rush work plan formulation device, which also includes the following steps:

[0166] Step 4: Traverse each rush work plan ga in the initial chromosome library GA j , using the critical path method (CPM) to calculate the time required for each rush work plan j The duration T j . j The duration T j >Scheme ga for target construction period Tq j Delete the initial chromosome pool GA from the rush program.

[0167] For example, the first rush plan ga1 has a duration T1 = 86 days, which does not meet the demand and is deleted. The second rush plan ga2 and the third rush plan ga3 have a duration of 72 days, which meets the demand.

[0168] The determination unit 104 is further configured to use a genetic optimization algorithm to calculate the optimal resource balancing chromosome segment of each task in the compressed duration task set for each rush work plan in the initial chromosome library of the new rush work plan for the project, and form a new rush work plan resource optimization chromosome library for the project. The new rush work plan resource optimization chromosome library for the project is added to the new rush work plan for the project. The specific functions and processing of the determination unit 104 are also shown in step S230.

[0169] like Figure 5 As shown, the solution of the present invention provides an optimal rush work plan formulation device, which also includes the following steps:

[0170] Step 5: For each rushing scheme ga in the initial chromosome library GA of the rushing scheme j Without affecting the total construction period, the genetic optimization algorithm is used to calculate the optimal resource balancing chromosome segment s for each task j ={es1,es2,…,es j}, j = 1-m. Add to ga i , where j The earliest start time for each task. Add the earliest start time parameter to the construction plan to achieve resource balancing without changing the construction period.

[0171] Step 5.1: Traverse each rush work scheme ga in the initial chromosome library GA j , take the second rush work plan ga2 as an example.

[0172] Step 5.2: Use the critical path calculator (CPM) to calculate the ga of each rush plan j Various construction tasks in tk ji The free time difference ff ji .

[0173] For example: Construction task tk ji The free time difference ff ji =(0,0,3,2,0,3,2,0,0,0,3,0).

[0174] Step 5.3: Use Monte Carlo randomization to construct a resource-optimized chromosome library GS = {gs i}: Each chromosome model gs i =(s1,s2,…,s m ), where gs i ≤ff ji , set the number of iterations z1 = 0.

[0175] Each chromosome model gs i like Figure 6For example, the first chromosome model gs1 = (0,0,0,0,0,0,0,0,0,0,0), and the second chromosome model gs2 = (0,0,1,1,0,2,1,0,0,0,1,0).

[0176] Step 5.4: Model gs for each chromosome i , calculate its resource balance index as the fitness evaluation index F i , see the examples shown in formula (4), formula (5), formula (6) and formula (7) for details:

[0177]

[0178] Among them, all construction tasks per day are tk ik The resource demand weighted value R(t) is:

[0179]

[0180] The device assumes that the construction task tk ik The total resource demand remains unchanged, the daily resource demand is the same during working hours, and the construction task tk ik The weighted values ​​of various resources at any time R ik (t) is:

[0181]

[0182] All construction tasks per day tk ik The mean R of the weighted resource demand R(t) m for:

[0183]

[0184] Among them, F i For each chromosome model gs i Calculate its resource balance index as the fitness evaluation index, T j For construction plan j The construction period of j represents the jth rush work plan ga j The serial number of each construction task is tk. ik The resource demand weighted value, R m For all construction tasks every day tk ik The mean of the resource demand weighted values ​​R(t).

[0185] For example: Fig. 9 Schematic diagram of resource demand curves for different compression schemes. For the second chromosome model gs2, the resource optimal fitness evaluation index F i=0.51. For the third chromosome model gs3, the resource optimal fitness evaluation index F i =0.94. The resource demand curves of the second chromosome model gs2 and the third chromosome model gs3 are as follows: Fig. 9 shown.

[0186] Step 5.5: Use the random competition selection algorithm to select the fitness value F i , the chromosome library in the resource optimization chromosome library GS is screened to form a new chromosome library GS', that is, the fitness value F i High element gs i Entering the new chromosome pool GS' at a higher frequency.

[0187] For example, add two third chromosome models gs3 to the new chromosome library GS', and add one second chromosome model gs2 to the new chromosome library GS'. The adaptability of the elements in the new chromosome library GS' is greater than that in the resource-optimized chromosome library GS. However, the number of elements in the resource-optimized chromosome library GS is the same as the number of elements in the new chromosome library GS'.

[0188] Step 5.6: Form a new generation of chromosome library GS through crossover and mutation, which specifically includes the following steps:

[0189] Step 5.6.1: Use the roulette wheel selection algorithm to select each chromosome model gs i =(s1,s2,…,s m ) randomly selects a point p for crossover.

[0190] For example, p=4.

[0191] Step 5.6.2: Change the parent chromosome gs j and gs j+1 Perform chromosome crossover at point p and use the sub-chromosome gs generated after crossover j '、gs j+1 '. z1=z1+1.

[0192] for example: Figure 7 The schematic diagram of the chromosome crossover process for resource optimization is shown in Figure 2. The first chromosome model gs1 and the second chromosome model gs2 are crossed to generate gs2'=(0,0,1,1,0,0,0,0,0,0,0,0), gs1'=(0,0,0,0,0,2,1,0,0,0,1,0), as shown in Figure 2. Figure 7 shown.

[0193] Step 5.6.3: Use the sub-chromosome gs generated after crossover j '、gs j+1 'Replace gs jThe parent chromosome gs j and gs j+1 .

[0194] Step 5.6.4: Randomly select part of the chromosome gs according to the mutation rate pm j Perform mutation processing, that is, select part of the chromosome gs j Any element s i , in the case of satisfying some chromosomes gs j Any element s i s i ≤FF ji In this case, a new value is set randomly.

[0195] For example, if we select the second chromosome model gs2 and set c3=2, then gs2=(0,0,2,1,0,0,0,0,0,0,0,0,0). c3 refers to the value of the point of the chromosome of gs2.

[0196] Step 5.7: When the number of iterations z1 is equal to the number of evolution requirements Z1, go to step 5.7. Otherwise, return to step 5.4.

[0197] Step 5.8: Find the chromosome gs with the largest fitness value in the resource optimization chromosome library GS j , add the resource-optimized chromosome library GS to ga j In, that is, ga j = {ga j , gs j}=(c 11 ,c 12 ,…,c 1k1 ,c 21 ,c 22 ,…,c 1k2 ,…,c g1 ,c g2 ,…,c gkg ,s1,s2,…,s m ).

[0198] Rush work plan j =(compression time c of each compressible task gj ), (c g1 ,c g2 ,…,c gk1 ) is the compression duration of each compressible task in unit g, and the number of cyclic evolutions z=0.

[0199] For example: ga2 = (1,2,…,2,1,1,…,2,…,2,1,…,2,0,0,1,1,0,2,1,0,0,0,1,0).

[0200] Step 6: When the number of iterations z reaches the maximum number of evolutions Z, go to step 10. Otherwise go to step 8. For example: the number of iterations z = 1000. Z is the number of iterations in the first stage; Z1 is the number of iterations in the second stage.

[0201] The determination unit 104 is further configured to adopt a random competition selection algorithm to screen the chromosome library in the new resource optimization chromosome library of the project according to the resource balance index of the new resource optimization chromosome library of the project to form a new resource optimization chromosome library. The specific function and processing of the determination unit 104 are also shown in step S240.

[0202] like Figure 5 As shown, the solution of the present invention provides an optimal rush work plan formulation device, which also includes the following steps:

[0203] Step 7: Use the random competition selection algorithm to select the fitness value F i (i.e., the fitness evaluation index F i ), the chromosome library in the resource optimization chromosome library GS is screened to form a new chromosome library GA', that is, the fitness value F i High element ga i Entering the new chromosome pool GA' at a higher frequency.

[0204] For example: for ga2, its fitness evaluation index F i =0.562. For ga3, its optimal fitness evaluation index F i =0.95. Then select 2 ga3 and 1 ga2 and add them to the new chromosome library GA'. Make the average fitness value of the elements in the new chromosome library GA' higher than that of the elements in the initial chromosome library GA of the rush scheme.

[0205] The determination unit 104 is further configured to perform crossover and mutation on the chromosomes in the new resource-optimized chromosome library to form a new generation chromosome library. The specific functions and processing of the determination unit 104 are also shown in step S250.

[0206] like Figure 5 As shown, the solution of the present invention provides an optimal rush work plan formulation device, which also includes the following steps:

[0207] Step 8: Crossover and mutation of the compression scheme chromosomes to form a new generation chromosome library GA" = {ga j}, set the number of iterations z = z + 1.

[0208] Step 8.1: Traverse each scheme ga in the new generation chromosome library GA j .

[0209] For example: gs2.

[0210] Step 8.2: Use a random algorithm to j = {ga j , gs j}=(c 11 ,c 12 ,…,c 1k1 ,c 21 ,c 22 ,…,c 1k2 ,…,c g1 ,c g2 ,…,c gkg ) randomly selects a point p for crossover.

[0211] For example: p=3.

[0212] Step 8.3: Add the parent chromosome ga j and ga j+1 Perform chromosome crossover at point p and use the daughter chromosome ga generated after crossover j ', ga j+1 '.

[0213] for example: Figure 8 Schematic diagram of chromosome crossover process for speedy work optimization. After the second chromosome model gs2 and the third chromosome model gs3 cross at p=3, ga2'=(2,2,1,1,1,…,2,…,2,1,…,2,0,0,1,1,0,2,1,0,0,0,1,0). ga3'=(1,2,2,1,2,…,2,…,3,1,…,1,0,0,2,1,0,1,2,0,0,0,2,0), as shown in Figure 8 shown.

[0214] Step 8.4: Calculate the sub-chromosome ga j 'and ga j+1 'Whether the constraint formulas (1) and (2) are satisfied, if so, proceed to step 8.6. If not, proceed to step 8.5.

[0215] Step 8.5: Pair of chromosomes ga j ', ga j+1 'Perform mutation processing, that is, for elements c that do not meet the constraint formulas (1) and (2) gj Randomly select a value under the constraints of formulas (1) and (2) and proceed to step 8.6.

[0216] Step 8.6: Use the subchromosome ga j ',ga j+1 'Replace ga j The parent chromosome ga j and gaj+1 .

[0217] The determination unit 104 is further configured to repair the new generation chromosome library according to the constraints of the N construction parties to obtain a repaired chromosome library. A rush work plan corresponding to a group of chromosomes with the smallest resource balance index is selected from the repaired chromosome library as the construction plan for the project. The specific functions and processing of the determination unit 104 are also shown in step S260.

[0218] like Figure 5 As shown, the solution of the present invention provides an optimal rush work plan formulation device, which also includes the following steps:

[0219] Step 9: Considering the impact of compressed task duration on resource balance and various constraints, repair each chromosome in the new generation chromosome library GA, and return to step 4 after the repair is completed.

[0220] Step 10: Select resource optimization index F from chromosome library GA i The smallest group is output as the result.

[0221] Fig.12 Schematic diagram of the output result table of selecting resource optimization index from chromosome library GA. Fig.12 As shown, the optimal solution is ga = (3, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1).

[0222] The solution of the present invention supports multiple construction participants to input the rush work range of each key path and the total compression period of each task according to complex construction conditions such as resource input, ensuring that the compression amount of each task is within a controllable range and that each construction party shares the rush work tasks and risks. The genetic algorithm is used to automatically calculate the most resource-balanced and feasible rush work plan, reduce risks, and ensure the construction period.

[0223] In some implementations, the determining unit 104 repairs the new generation chromosome library according to the constraints of the N construction parties to obtain a repaired chromosome library, including:

[0224] The determination unit 104 is further configured to traverse each rush work scheme corresponding to the new generation chromosome library, and use a critical path calculation device to calculate the construction period of each rush work scheme corresponding to the new generation chromosome library and the total time difference of each construction task corresponding to the new generation chromosome library. The specific function and processing of the determination unit 104 are also shown in step S310.

[0225] The determination unit 104 is further configured to calculate the resource balance index caused by compressing the construction period by 1 day if the total time difference of each construction task corresponding to the new generation chromosome library is 0 and the total compressed duration of the construction period of the corresponding construction party's own task meets the constraints of the construction party. The specific functions and processing of the determination unit 104 are also shown in step S320.

[0226] The determination unit 104 is further configured to select the construction task with the smallest resource balance index from the construction tasks corresponding to the new generation chromosome library, compress the construction period of the construction task by one day, and modify the chromosome corresponding to the construction task in the new generation chromosome library to complete the repair of the new generation chromosome library. The specific functions and processing of the determination unit 104 are also shown in step S330.

[0227] See also Figure 5 In the example shown, step 9 includes the following steps:

[0228] Step 9.1: Traverse the new generation chromosome library GA" each scheme ga j If the traversal is complete, go to step 9.7. Otherwise, go to step 9.2.

[0229] For example: ga3.

[0230] Step 9.2: Use the critical path calculator (CPM) to calculate ga j 'The construction period T j And the total time difference TF of each task i If T j If it is not greater than the target construction period Tq, return to step 9.1. Otherwise, proceed to step 9.3.

[0231] For example: The construction period corresponding to ga3 is T=73 days, which is 1 day longer than the target construction period Tq.

[0232] Step 9.3: Traverse the solution ga j All construction tasks in tk ji .

[0233] Step 9.4: If the construction task tk ji Total time difference TF i = 0, and the total compression time of the construction unit g meets the following conditions, then go to step 9.5. Otherwise, return to step 9.3:

[0234]

[0235] For example: tk1, tk2, tk3, tk in ga3 12 The total time difference of tasks is 0, but only tk 12The total compression time of the corresponding construction unit is spare.

[0236] Step 9.5: Calculate the resource increase index caused by compressing the construction period by 1 day:

[0237]

[0238] Among them, ω ji For resource increase index or resource balance index, R ji For the task tk ji The total weighted resources required, t s For construction tasks tk ji The start time, d ji (d i ) is the construction task tk ji The current construction period. Therefore, the problem that the solution does not meet the construction period threshold of each unit can quickly form a feasible plan and improve resource balance.

[0239] Step 9.6: Find the resource balance index ω ji Minimal Task tk ji , shortening the construction period by 1 day, i.e. modifying chromosome ga j Element C ji =c ji +1. Return to step 9.2.

[0240] For example: ga3, c 12 =0+1=1. ga3=(1,2,2,1,2,…,2,…,3,1,…,2,0,0,2,1,0,1,2,0,0,0,2,0).

[0241] Step 9.7: Repair completed.

[0242] The scheme of the present invention supports multiple participants to input their respective rush limits and share risks. It supports each unit to input the maximum compression amount of compressible tasks to avoid exponential increase of resources and costs. Through the two-stage genetic optimization algorithm, the rush plan with the most balanced resources and the lowest risk is found, which is particularly suitable for the optimization of rush plans for multi-professional cross-construction under the condition of limited working surface. The cross-mutated plan is quickly repaired by the repair device to improve the optimization calculation speed.

[0243] Since the processing and functions implemented by the device of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned method, for the details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments, and no further elaboration will be made here.

[0244] By adopting the technical solution of the present invention, a genetic algorithm is used to determine the construction plan for collaborative work among multiple parties based on the resource input of multiple construction parties, the construction scope and the task compression time, while ensuring that the compression amount of each task is within the set range and that each construction party shares the tasks and risks. This can assist construction managers in finding the construction plan with the most balanced resources and the lowest construction period risk.

[0245] According to an embodiment of the present invention, a client corresponding to the device for determining a construction plan is also provided. The client may include: the device for determining a construction plan described above.

[0246] Since the processing and functions implemented by the client of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned device, for the details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments and will not be repeated here.

[0247] By adopting the technical solution of the present invention, a genetic algorithm is used to determine the construction plan for collaborative work among multiple parties involved in the construction based on the resource input of the multiple parties involved in the construction, the construction scope and the task compression time, while ensuring that the compression amount of each task is within the set range and that each construction party shares the task and risk. This can obtain the most balanced and feasible rush work plan.

[0248] According to an embodiment of the present invention, a storage medium corresponding to the method for determining the engineering construction plan is also provided, wherein the storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the method for determining the engineering construction plan described above.

[0249] Since the processing and functions implemented by the storage medium of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned method, for the details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments, and no further elaboration will be made here.

[0250] By adopting the technical solution of the present invention, a genetic algorithm is used to determine a construction plan for collaborative work among multiple parties based on the resource input of multiple construction parties, the construction scope and the task compression time, while ensuring that the compression amount of each task is within the set range and that each construction party shares the tasks and risks. This can reduce risks and ensure the construction period.

[0251] In summary, it is easy for those skilled in the art to understand that, under the premise of no conflict, the above-mentioned advantageous methods can be freely combined and superimposed.

[0252] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of the claims of the present invention.

Claims

1. A method for determining a construction plan. It is characterized in that It is applicable to multi-professional cross-construction optimization under limited working space, and can assist construction managers in finding the construction plan with the most balanced resources and the lowest construction period risk; the project has N construction parties, where N is a positive integer; In the case where N construction parties all participate in the construction of the project, the method for determining the project construction plan includes: Obtaining construction task information of the project; For each of the N construction parties, the constraints and goals of each construction party are obtained; the goals of the N construction parties include: the target construction period of the project, the compressed construction period task set in the construction task set of the project that can compress the construction period, the maximum compressed time of each task in the compressed construction period task set under comprehensive consideration of construction period risk, construction cost and construction resources, and the maximum compressed total time shared by the N construction parties; the constraints of the N construction parties include: the compressed time of each task in the compressed construction period task set is less than or equal to the maximum compressed time of the task; the total compressed duration of the construction period of each construction party of the N construction parties for its own tasks is less than or equal to the maximum compressed total time; the construction period of the construction plan of the project is less than or equal to the target construction period of the project; Based on the construction task information of the project, a genetic algorithm is used to find a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range according to the constraints and goals of the N construction parties, as the construction plan of the project; by adopting a genetic algorithm, according to the resource input of the multiple construction parties, the construction scope and the task compression time, under the condition that the compression amount of each task is within the set range and each construction party shares the task and risk, a construction plan for the collaborative work of multiple participants is determined; Wherein, based on the construction task information of the project, using a genetic algorithm, according to the constraints and goals of the N construction parties, a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range is found as the construction plan of the project, including: According to the goals of the N construction parties, an initial chromosome library of the rush work plan of the project is constructed by using the Monte Carlo random method; in the initial chromosome library of the rush work plan of the project, each rush work plan includes: the compressed time of the corresponding task in the compressed construction period task set; Traversing each rush work plan in the initial chromosome library of rush work plans for the project, using a critical path calculation method to calculate the duration of each rush work plan in the initial chromosome library of rush work plans for the project, and deleting the rush work plans whose duration exceeds the target duration of the project from the initial chromosome library of rush work plans for the project, to obtain a new initial chromosome library of rush work plans for the project; For each rush work plan in the initial chromosome library of the new rush work plan of the project, a genetic gene optimization algorithm is used to calculate the optimal resource balancing chromosome segment of each task in the compressed construction period task set to form a new rush work plan resource optimization chromosome library of the project; and the new rush work plan resource optimization chromosome library of the project is added to the new rush work plan of the project; Using a random competitive selection algorithm, according to the resource balance index of the resource optimization chromosome library of the new rush-work solution of the project, the chromosome library in the resource optimization chromosome library of the new rush-work solution of the project is screened to form a new resource optimization chromosome library; Crossing and mutating the chromosomes in the new resource-optimized chromosome library to form a new generation chromosome library; According to the constraints of the N construction parties, the new generation chromosome library is repaired to obtain a repaired chromosome library; and a rush work plan corresponding to a group of chromosomes with the smallest resource balance index is selected from the repaired chromosome library as the construction plan for the project.

2. The method for determining the engineering construction plan according to claim 1, It is characterized in that in, The construction task information of the project includes: a set of construction tasks of the project, recorded as a construction task set; the construction task information of the project also includes: a matrix of resources required for the construction tasks of the project, recorded as a resource requirement matrix.

3. The method for determining the engineering construction plan according to claim 1, It is characterized in that According to the constraints of the N construction parties, the new generation chromosome library is repaired to obtain a repaired chromosome library, including: Traversing each rush work scheme corresponding to the new generation chromosome library, and using a critical path calculation method to calculate the construction period of each rush work scheme corresponding to the new generation chromosome library and the total time difference of each construction task corresponding to the new generation chromosome library; If the total time difference of each construction task corresponding to the new generation chromosome library is 0, and the total compressed duration of the task of the corresponding construction party meets the constraints of the construction party, then calculate the resource balance index caused by compressing the construction period by 1 day; From the construction tasks corresponding to the new generation chromosome library, select the construction task with the smallest resource balance index, compress the construction period of the construction task by one day, and modify the chromosome corresponding to the construction task in the new generation chromosome library to complete the repair of the new generation chromosome library.

4. A device for determining a construction plan corresponding to the method for determining a construction plan according to any one of claims 1 to 3, It is characterized in that It is applicable to multi-professional cross-construction optimization under limited working space, and can assist construction managers in finding the construction plan with the most balanced resources and the lowest construction period risk; the project has N construction parties, where N is a positive integer; In the case where N construction parties all participate in the construction of the project, the device for determining the project construction plan includes: An acquisition unit, configured to acquire construction task information of the project; The acquisition unit is further configured to acquire the constraints and goals of each of the N construction parties for each of the N construction parties; the goals of the N construction parties include: the target construction period of the project, a compressed construction period task set in the construction task set of the project that can compress the construction period, the maximum compressed time of each task in the compressed construction period task set under comprehensive consideration of construction period risk, construction cost and construction resources, and the maximum compressed total time shared by the N construction parties; the constraints of the N construction parties include: the compressed time of each task in the compressed construction period task set is less than or equal to the maximum compressed time of the task; the total compressed duration of the construction period of each construction party of the N construction parties for its own tasks is less than or equal to the maximum compressed total time; the construction period of the construction plan of the project is less than or equal to the target construction period of the project; The determination unit is configured to use a genetic algorithm based on the construction task information of the project, and to find a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range according to the constraints and goals of the N construction parties, as the construction plan of the project; by adopting a genetic algorithm, according to the resource input of the multiple construction parties, the construction scope and the task compression time, while ensuring that the compression amount of each task is within the set range and that each construction party shares the task and risk. , Determine a construction plan for collaborative work among multiple parties.

5. The device for determining the engineering construction plan according to claim 4, It is characterized in that in, The construction task information of the project includes: a set of construction tasks of the project, recorded as a construction task set; the construction task information of the project also includes: a matrix of resources required for the construction tasks of the project, recorded as a resource demand matrix; and / or, The objectives of the N construction parties include: the target construction period of the project, a set of compressed construction period tasks in the construction task set of the project that can compress the construction period, the maximum compressed time of each task in the compressed construction period task set in consideration of construction period risk, construction cost and construction resources, and the maximum total compressed time shared by the N construction parties; The N constraints of the construction party include: The compression time of each task in the compressed duration task set is less than or equal to the maximum compression time of the task; The total compressed duration of the construction period of each of the N construction parties for its own task is less than or equal to the maximum compressed total time; The construction period of the construction plan for the project is less than or equal to the target construction period of the project.

6. The device for determining the engineering construction plan according to claim 5, It is characterized in that The determining unit, based on the construction task information of the project and using a genetic algorithm, searches for a construction plan with a resource balance within a set balance range and a construction period risk within a set risk range according to the constraints and goals of the N construction parties, as the construction plan of the project, including: According to the goals of the N construction parties, an initial chromosome library of the rush work plan of the project is constructed by using a Monte Carlo random device; in the initial chromosome library of the rush work plan of the project, each rush work plan includes: the compressed time of the corresponding task in the compressed construction period task set; Traversing each rush work plan in the initial chromosome library of rush work plans for the project, using a critical path calculation device to calculate the duration of each rush work plan in the initial chromosome library of rush work plans for the project, and deleting the rush work plans whose duration exceeds the target duration of the project from the initial chromosome library of rush work plans for the project, thereby obtaining a new initial chromosome library of rush work plans for the project; For each rush work plan in the initial chromosome library of the new rush work plan of the project, a genetic gene optimization algorithm is used to calculate the optimal resource balancing chromosome segment of each task in the compressed construction period task set to form a new rush work plan resource optimization chromosome library of the project; and the new rush work plan resource optimization chromosome library of the project is added to the new rush work plan of the project; Using a random competitive selection algorithm, according to the resource balance index of the resource optimization chromosome library of the new rush-work solution of the project, the chromosome library in the resource optimization chromosome library of the new rush-work solution of the project is screened to form a new resource optimization chromosome library; Crossing and mutating the chromosomes in the new resource-optimized chromosome library to form a new generation chromosome library; According to the constraints of the N construction parties, the new generation chromosome library is repaired to obtain a repaired chromosome library; and a rush work plan corresponding to a group of chromosomes with the smallest resource balance index is selected from the repaired chromosome library as the construction plan for the project.

7. The device for determining a construction plan according to claim 6, It is characterized in that The determining unit repairs the new generation chromosome library according to the constraints of the N construction parties to obtain a repaired chromosome library, including: Traversing each rush work scheme corresponding to the new generation chromosome library, using a critical path calculation device to calculate the construction period of each rush work scheme corresponding to the new generation chromosome library and the total time difference of each construction task corresponding to the new generation chromosome library; If the total time difference of each construction task corresponding to the new generation chromosome library is 0, and the total compressed duration of the task of the corresponding construction party meets the constraints of the construction party, then calculate the resource balance index caused by compressing the construction period by 1 day; From the construction tasks corresponding to the new generation chromosome library, select the construction task with the smallest resource balance index, compress the construction period of the construction task by one day, and modify the chromosome corresponding to the construction task in the new generation chromosome library to complete the repair of the new generation chromosome library.

8. A client, It is characterized in that include: A device for determining an engineering construction plan as described in any one of claims 4 to 7.

9. A storage medium, It is characterized in that The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the method for determining the engineering construction plan according to any one of claims 1 to 3.

Images