A production scheduling optimization method for prefabricated buildings considering resource constraints

Abstract

The present invention provides a method for optimizing production scheduling of prefabricated buildings that takes resource constraints into consideration. First, based on the premise of balancing the production load of each production line, several initialization production plans are obtained based on a heuristic algorithm. Then, based on the premise of limited production resources and component production constraints, different production plans are analyzed to obtain a detailed production process diagram. Then, the production process diagrams of different production plans are evaluated using the optimization indicators of different production requirements as evaluation indicators of the process diagram. Finally, based on a genetic algorithm model in an evolutionary environment, the initialized production plans are iteratively screened and modified to obtain the optimal production scheduling plan under different production requirements. The component scheduling optimization method of the present invention can effectively reduce the impact of an enterprise's response to emergencies on production, ensure the continuity, efficiency, and low cost of production, and solve the problems of resource conflicts, high production costs, and untimely order delivery during component scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of resource scheduling and component production scheduling for prefabricated building components, and in particular to a method for optimizing production scheduling of prefabricated buildings taking resource constraints into consideration. Background Art

[0002] Prefabricated buildings are prefabricated buildings made by factory-processing various types of building components needed by the construction industry. The components are then transported to the construction site and then connected and assembled for use. Compared with traditional cast-in-place structural buildings on construction sites, prefabricated buildings have the advantages of industrialization, scale, speed and low cost, and are an important direction for the country to develop green building industrialization.

[0003] Prefabricated component production is the core of prefabricated construction. The continuous improvement of its intelligent production level is an urgent need for the development of prefabricated buildings. With the country's vigorous development of prefabricated buildings, the variety and quantity of components have increased dramatically, and demand is growing. However, the production of prefabricated components for prefabricated buildings still faces many bottlenecks: the low degree of standardization in component production, the low level of digital informationization in factory production, and low resource utilization in factories make it difficult to achieve the ideal level of large-scale and intelligent production of components.

[0004] Currently, there are many production scheduling software programs on the market. However, most of these software programs focus on a global perspective, analyzing production processes and monitoring workshop production status. They are targeted at specific companies and business processes and are not fully adapted to their actual conditions and production needs. Furthermore, the software is highly versatile, and different software programs have different technical architectures, resulting in cumbersome operations and complex indicator systems. These algorithms are difficult to adapt without addressing specific problems. With the promotion of prefabricated buildings, prefabricated components are large in size, diverse in types, and complex in structure. This has brought great difficulties to the scheduling of prefabricated component resources, intelligent production scheduling, and the design of intelligent technical solutions. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing production scheduling of prefabricated buildings taking into account resource constraints, so as to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for optimizing production scheduling of prefabricated buildings considering resource constraints specifically includes the following steps:

[0008] S1. Based on the premise of balancing the production load of each production line, several initialization production plans are obtained based on the heuristic algorithm;

[0009] S2. Based on the premise of limited production resources and component production constraints, different production plans are analyzed to obtain a detailed production process diagram;

[0010] S3. Using the optimization index of different production requirements as the evaluation index of the process diagram, evaluate the production process diagram of different production plans;

[0011] S4. Based on the genetic algorithm model of the evolutionary environment, the initialized production plan is iteratively screened and modified to obtain the optimal production scheduling plan under different production demands.

[0012] Furthermore, the initialization production plan is specifically obtained by the following method:

[0013] S101. Analyze the set of components to be scheduled in the order, including order attributes, component attributes, mold attributes, process attributes, process time attributes, production line attributes, and resource attributes, to provide specific information for generating a scheduling plan for the initial components;

[0014] S102, generating a production plan including a random production order of the components based on the random number according to the component numbers and quantity;

[0015] S103, assigning a production line to each component based on a heuristic algorithm according to the production sequence of the components;

[0016] S104, combining steps S102 and S103 to obtain an initialization production plan;

[0017] S105. Repeat step S104 to obtain a set containing a large number of initialization production plans.

[0018] Furthermore, the detailed production process diagram is obtained specifically by the following method:

[0019] S201. Select an initial production plan, traverse each component in turn according to the production order of the components, and determine the component type and the corresponding production line;

[0020] S202. Based on the premise of limited production resources and component production constraints, determine a production process diagram for the component, where the production process diagram for the component includes all process flows of the component and the start and end times of each process of the component production;

[0021] S203, repeat step S202 for each component of the initialized production plan to obtain a complete production process diagram of the plan;

[0022] S204. Repeat steps S201-S203 to obtain a production process diagram for each initialized production plan.

[0023] Furthermore, the expression of the component production constraint is:

[0024]

[0025] Where: S represents the start time of the process; P represents the duration of the process; A l,j represents the set of all the immediate predecessor components of component j on production line l; L and l represent the production line set and a specific production line respectively; i and j are component indexes, representing a component in the component set; k is the component process index, representing a process of a component; MT and mt represent the mold type set and a specific mold respectively; R and r represent the resource set and a specific resource respectively; PC and pc represent the component set and a specific component respectively; T represents the time within a production cycle of a batch component;

[0026] Specifically, formula (1) is the production machine constraint of the workstation on the production line; formula (2) is the logical relationship constraint of the process before; formula (3) is the mold quantity constraint; formula (4) is the resource limitation constraint; formula (5) represents the non-negative and non-empty constraints of construction period, resources, index, time, collection, etc.

[0027] Furthermore, the production process diagram includes all component information and all resource conditions within a production cycle. The component information includes the production line to which the component is allocated, all processes and categories of the component, various resources required for each process, and the start and end time of each process.

[0028] Furthermore, for different production plans, the evaluation function of the production process diagram is:

[0029]

[0030] Where: MinFinishTime represents the shortest completion time; MinCriticalLineLoad represents the minimum critical line load; MinLineAllLoad represents the minimum total line load; MinMoldCost represents the minimum mold cost.

[0031] Furthermore, the iterative screening and modification of the initialization production plan is specifically achieved through the following steps:

[0032] S401, using the initialized production plan as the initialized parent generation of the genetic algorithm;

[0033] S402, performing algorithmically designed selection, crossover, and mutation operations on the component production sequence of the parent generation production plan and the production line corresponding to the component production, thereby obtaining a child generation;

[0034] S403: Repeat steps S2-S3 for the obtained offspring, and use it as the parent of step S401;

[0035] S404: Repeat step S403 until the optimization goal of the evolutionary generation or production plan of the algorithm is achieved, and then end the algorithm process;

[0036] S405. Repeat step S404 to output the production process diagram of the component, that is, the optimal production scheduling plan of the component method.

[0037] Furthermore, the algorithm is designed to select some individuals from the parent population and construct an individual selection pool, and the offspring individuals are directly obtained from the individuals in the selection pool.

[0038] Furthermore, the crossover designed by the algorithm is to select some individuals from the parent population and construct an individual mating pool, and the offspring individuals are generated by pairwise crossover from the individuals in the mating pool.

[0039] Furthermore, the variation designed by the algorithm is to select some individuals from the parent population and construct an individual variation pool, and the offspring individuals are generated by mutation of the individuals in the variation pool.

[0040] It can be seen from the above technical solutions that the present invention is based on a genetic algorithm model in an evolutionary environment, with limited production resources and component process constraints as the premise. Based on the genetic algorithm model in an evolutionary environment, the optimal detailed production scheduling plan for batch components is obtained under different production requirements. It can effectively reduce the impact of enterprises on production when responding to emergencies, ensure the continuity, high efficiency and low cost of production, and solve the problems of resource conflicts, high production costs, and untimely order delivery during component scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flowchart of the steps of the construction resource-constrained production scheduling optimization method of the present invention;

[0042] Figure 2 Schematic diagram of the flow of the construction resource-constrained production scheduling optimization method according to the present invention;

[0043] Figure 3 A schematic diagram of the double-layer gene encoding of the individual initialization production scheme of the present invention;

[0044] Figure 4 This is a schematic diagram of component order production parameters within the production cycle of the present invention;

[0045] Figure 5 This is a schematic diagram of the mold and worker constraint relationship of the present invention;

[0046] Figure 6 This is a schematic diagram of the production scheduling optimization target of the present invention;

[0047] Figure 7 Schematic diagram of pseudo code of genetic algorithm of the present invention;

[0048] Figure 8 It is a schematic diagram of the genetic algorithm flow of the present invention;

[0049] Figure 9 A Gantt chart for scheduling component orders within the production cycle of the present invention;

[0050] Figure 10 This is the component order scheduling schedule within the production cycle of the present invention. DETAILED DESCRIPTION

[0051] A preferred embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0052] like Figure 1 The method for optimizing production scheduling of prefabricated buildings considering resource constraints shown in the figure specifically includes the following steps:

[0053] S1. Based on the premise of balancing the production load of each production line, several initialization production plans are obtained based on the heuristic algorithm;

[0054] S2. Based on the premise of limited production resources and component production constraints, different production plans are analyzed to obtain a detailed production process diagram;

[0055] S3. Using the optimization index of different production requirements as the evaluation index of the process diagram, evaluate the production process diagram of different production plans;

[0056] S4. Based on the genetic algorithm model of the evolutionary environment, the initialized production plan is iteratively screened and modified to obtain the optimal production scheduling plan under different production demands.

[0057] Specifically, such as Figure 2 As shown in the figure, the specific workflow is as follows: according to the order requirements, the population is initialized, the fitness function is calculated for each individual in the population to obtain a complete individual, and then selection, crossover, and mutation operations are performed on the population, the population is updated, and it is determined whether the end condition is reached, that is, the maximum number of iterations of population evolution; finally, the optimal production scheduling plan for batch components within a production cycle is output. The optimal production scheduling plan can be evaluated from the aspects of shortest completion time, minimum mold cost, minimum critical production line load, and minimum total production line load.

[0058] In this preferred embodiment, for the above step S1, the initialization production plan is specifically obtained by the following method:

[0059] S101. Analyze the set of components to be scheduled in the order, including order attributes, component attributes, mold attributes, process attributes, process time attributes, production line attributes, and resource attributes, to provide specific information for generating a scheduling plan for the initial components;

[0060] S102, generating a production plan including a random production order of the components based on the random number according to the component numbers and quantity;

[0061] S103, assigning a production line to each component based on a heuristic algorithm according to the production sequence of the components;

[0062] S104, combining steps S102 and S103 to obtain an initialization production plan;

[0063] S105. Repeat step S104 to obtain a set containing a large number of initialization production plans.

[0064] In this preferred embodiment, the above-mentioned set of initialized production plans contains a large number of individuals, and each individual is expressed as an individual double-layer gene coding map. This is because the production scheduling process of prefabricated components mainly involves two aspects of information: one is the different production order of prefabricated components, that is, the different component numbers, and the other is the different production lines where the prefabricated components are located. Therefore, in this algorithm, a double-layer coding method based on the different corresponding production lines of components and the different production orders of components is proposed to perform coding operations on the production scheduling of prefabricated components. Figure 3 As shown, the genetic sequence of an individual consists of two layers: the first layer represents the production order code, and the second layer represents the assembly line code. The first layer, PC_0_01_0 > PC_0_02_0 > PC_0_03_0 > PC_0_02_1, represents the priority order of component production. The second layer, 1#-2#-1#-2#, represents the production line sequence of the upper-layer components. This double-layer genetic code can be used to determine the production order and production line information of each component. The production order of components on production line 1 is PC_0_01_0 > PC_0_03_0, and the production order on production line 2 is PC_0_02_0 > PC_0_02_1.

[0065] In specific use, it is necessary to calculate the fitness function for each individual. Before calculating the individual fitness function, each individual needs to be decoded to obtain a detailed production process diagram. The individual fitness function is calculated from the process diagram. For the obtained initialization scheme, its detailed production process diagram is obtained by the following method:

[0066] S201. Select an initial production plan, traverse each component in turn according to the production order of the components, and determine the component type and the corresponding production line;

[0067] S202. Based on the premise of limited production resources and component production constraints, determine a production process diagram for the component, where the production process diagram for the component includes all process flows of the component and the start and end times of each process of the component production;

[0068] S203, repeat step S202 for each component of the initialized production plan to obtain a complete production process diagram of the plan;

[0069] S204, repeating steps S201-S203 to obtain a production process diagram for each initialized production plan;

[0070] The decoding process needs to consider the component process constraints and factory resource constraints, such as Figure 4 As shown in the figure, based on the component order production parameter table within the production cycle, the set of components to be produced is determined to be: PC_0_01_0, PC_0_02_0, PC_0_03_0, and PC_0_02_1. Detailed information about these components includes the number of process steps and their duration. Regarding the required resources, taking component PC_0_01_0 as an example, component PC_0_01_0 is produced on production line 1 and requires mold A. There are six processes in total, including mold cleaning, mold installation, material vibration, plate leveling, maintenance, and quality inspection and repair. It is assumed that the production time for each process is 1 unit time. Regarding worker resources, the first two processes require worker A, the middle two processes require worker B, and the last two processes require worker C. The six process steps of this component's process constraints are produced on two production lines.

[0071] like Figure 5 As shown in Figure 2, the resource constraints for component production consider two types of resources, molds and workers. There are two types of molds, mold A and mold B, with one each. There are three types of workers, worker A, worker B, and worker C, with one, two, and one each.

[0072] Specifically, the expression of the component production constraint is:

[0073]

[0074] Where: S represents the start time of the process; P represents the duration of the process; A l,j represents the set of all the immediate predecessor components of component j on production line l; l and l represent the production line set and a specific production line respectively; i and j are component indexes, representing a component in the component set; k is the component process index, representing a process of a component; MT and mt represent the mold type set and a specific mold respectively; R and r represent the resource set and a specific resource respectively; PC and pc represent the component set and a specific component respectively; T represents the time within a production cycle of a batch component;

[0075] Specifically, formula (1) is the constraint of the production machine at the workstation on the production line, which represents the set of all the immediate predecessors of component j. Only when all the immediate predecessors have completed process step k, process step k of component j can be carried out. It means that a production machine at a certain workstation on any production line can only process one process at a time, and can only proceed to the next process after the previous component has been processed.

[0076] Formula (2) is the logical relationship constraint of the process before and after, which indicates the constraint relationship between different processes of the same component. The prerequisite for the start of the current process of the component is that its previous process has ended, that is, the start time of process k+1 of any component is greater than or equal to the end time of process k.

[0077] Formula (3) is the mold quantity constraint, which means that the number of molds on different production lines is limited. When all molds are occupied, the next component can only proceed to the mold installation process after the component that first undergoes the demolding process releases the mold.

[0078] Formula (4) is a resource-limited constraint. The resources shared by the production lines include labor, and different processes require different labor resources, including steel workers, concrete workers, mold workers, and other types of workers. The labor resources are limited, and the amount of a certain resource occupied by each production line at the same time cannot exceed the upper limit of the resource.

[0079] Formula (5) represents the non-negative and non-empty constraints of duration, resources, index, time, collection, etc.

[0080] The production process diagram described in this preferred embodiment includes all component information and all resource conditions within a production cycle. The component information includes the production line to which the component is assigned, all processes and categories of the component, various resources required for each process, and the start and end time of each process.

[0081] for Figure 4The production process of this component is simplified to: mold cleaning, mold installation, material distribution and vibration, smoothing and pressing, maintenance, quality inspection and repair; components 1 and 2 perform various process activities on production lines 1# and 2#, respectively; the production processes and required processes of components 1 and 2 are the same. In terms of molds, the two components share mold A from the process "mold installation" to "maintenance". In terms of workers, the two components require worker A from "mold cleaning" to "mold installation", worker B from "material distribution and vibration" to "maintenance", and worker C from "maintenance" to "quality inspection and repair". In terms of resource constraints, there is one mold A, one worker A, one worker B, and one worker C; components 1 and 2 use the same mold and workers on different production lines to produce different components, so there is a constraint relationship between them; the production process of component 1 starts on production line 1#. At this time, the supply of molds and workers is sufficient, there is no interruption in the process, and production is normal. In the production process of component 2, component 2 can only start the "mold table cleaning" process when worker A is released after component 1 completes the "mold installation" process. In addition, component 2 can only start the "mold installation" process when mold A is released after component 1 completes the "maintenance" process. Therefore, the production of component 2 requires two process interruptions, waiting for the release of worker A and the release of mold A respectively.

[0082] like Figure 6 The production scheduling optimization target diagram shown in the figure uses the batch components and process steps within the production cycle and the production resource constraints as the algorithm input conditions. Based on the genetic algorithm model in the evolutionary environment, the optimal batch component detailed production scheduling plan is obtained under different production requirements. The evaluation values ​​of the plan can be: shortest completion time, minimum critical line load, minimum total line load, and minimum mold cost. Specifically, the evaluation function of the production process diagram is:

[0083]

[0084] Where: MinFinishTime represents the minimum processing time; in the specific set of used production lines, for the set of all components produced by a particular production line, the line with the longest production time (line release time). The line release time is determined by the completion time of the last process of the last component produced by a particular production line; MinCriticalLineLoad represents the minimum critical line load; in the specific set of used production lines, for the set of all components produced by a particular production line, for the set with the shortest total processing time for all components; MinLineAllLoad represents the minimum total line load; in the specific set of used production lines, for the set of all components produced by all production lines, for the set with the shortest total processing time for all components; MinMoldCost represents the minimum mold cost, which is the minimum cost of the mold used for the set of all components produced by all production lines in a production cycle; for each type of mold, the mold price is determined, and the maximum number of molds that can be used simultaneously in a production cycle is determined.

[0085] Through the steps described above, a large number of initialized individuals of the algorithm are obtained, which constitute the initial population of the algorithm, that is, a large number of production scheduling plans and production indicators of the plans. According to the initialized population, the genetic algorithm operation based on the evolutionary environment is started to obtain the optimal production plan.

[0086] Genetic algorithm pseudo code is as follows Figure 7 The specific genetic algorithm process is as shown in Figure 8 As shown, the initial population step initializes the population, including encoding, decoding, and fitness function calculation. Then, within the While loop, the end condition gen <= G is used to determine whether the algorithm has reached the end condition. The select, crossover, and mutate operations are performed within the loop. A new while loop is used to determine whether the number of offspring populations meets the condition. If it does, the while loop ends. If not, the offspring population is constructed until a complete offspring population is constructed. After completing a While loop, the number of iterations gen + 1 is increased until gen <= G no longer meets the condition. At this point, the loop is exited, resulting in the optimal production scheduling plan.

[0087] Specifically, the iterative screening and modification of the initialization production plan described in this preferred embodiment is specifically implemented by the following steps:

[0088] S401, using the initialized production plan as the initialized parent generation of the genetic algorithm;

[0089] S402, performing algorithmically designed selection, crossover, and mutation operations on the component production sequence of the parent generation production plan and the production line corresponding to the component production, thereby obtaining a child generation;

[0090] S403: Repeat steps S2-S3 for the obtained offspring, and use it as the parent of step S401;

[0091] S404: Repeat step S403 until the optimization goal of the evolutionary generation or production plan of the algorithm is achieved, and then end the algorithm process;

[0092] S405. Repeat step S404 to output the production process diagram of the component, that is, the optimal production scheduling plan of the component method.

[0093] In specific use, the detailed description and operation of the selection, crossover and mutation operations in the genetic process are as follows:

[0094] The algorithm is designed to select some individuals from the parent population and construct an individual selection pool, and the offspring individuals are directly obtained from the individuals in the selection pool. The rule for the individuals to enter the selection pool is as follows: the fitness values ​​of the individuals in the parent population are added together to obtain the total fitness value; the fitness value of a single individual is divided by the total fitness value to obtain the probability of the individual being selected, and the sum of the probabilities of the individual being selected is 1; a roulette wheel based on probability distribution is constructed based on the cumulative probability of the individuals; for the selection of the roulette wheel, a random number in the interval [0–1] is generated, and if the random number is less than or equal to the cumulative probability of the individual and greater than the cumulative probability of individual 1, the individual is selected to enter the selection pool;

[0095] The crossover algorithm is designed to select some individuals from the parent population and construct an individual mating pool. The offspring individuals are generated by pairwise crossover from the individuals in the mating pool. The crossover rule is: randomly exchange the component production sequence segments and the production line segments corresponding to the component production of two individuals. Whether an individual of the parent population can enter the mating pool is determined by the mating probability Pc.

[0096] The mutation designed by the algorithm is to select some individuals from the parent population and construct an individual mutation pool, and the offspring individuals are generated by mutation of the individuals in the mutation pool; the mutation will be: for the component production sequence of the individual and the production line corresponding to the component production, two components in the individual component production sequence and their corresponding production lines are randomly exchanged; whether the individual of the parent population can enter the mating pool is determined by the mutation probability Pm.

[0097] The genetic algorithm terminates when a certain number of evolutionary generations has been reached, which is manually set. Once this number is reached, the final offspring population is obtained, which is also the algorithm's ultimate optimal population, containing a large number of optimal component production scheduling plans. Based on different production requirements, the individual fitness functions are used as an evaluation basis to determine the required production plan, which is then displayed in the form of a Gantt chart and a production schedule, followed by a detailed analysis and explanation of the scheduling results.

[0098] like Figure 9 As shown in the figure, the first component PC_0_01_0 is produced on the 1# production line. The process is continuously produced without interruption and the execution of the six process activities is completed in sequence. PC_0_02_0 is produced on another production line 2# following the component PC_0_01_0. Figure 9 As shown in the figure: Process 2-1 is interrupted because of the restriction of worker A. Process 2-1 needs to wait for worker A to be released in process 1-2 before it can be produced. Process 2-2 is interrupted because of the restriction of mold A. Process 2-2 needs to wait for mold A to be released in process 1-5 before it can be produced. The following processes 2-3 to 2-6 are not interrupted because they have sufficient resources. PC_0_03_0 is produced on another production line 1# following component PC_0_02_0. Figure 9 As shown: Process 3-1 is interrupted because of the restriction of worker A. Process 3-1 needs to wait until worker A is released from process 2-2 before it can proceed to process 3-1; Process 3-3 is interrupted because of the restriction of worker B. Process 3-3 needs to wait until worker B is released from process 2-4 before it can proceed to process 3-3; Process 3-6 is interrupted because of the restriction of worker C. Process 3-6 needs to wait until worker B is released from process 2-6 before it can proceed to process 3-6; It should be noted that because component PC_0_03_0 requires mold B, it does not conflict with mold A required by PC_0_01_0 on the same production line 1#, and the start time of process 3-2 is less than the end time of process 1-2 of PC_0_01_0 on the same production line 1#, process 3-2 is not interrupted. PC_0_02_1 is produced on another production line 2# following component PC_0_03_0. Figure 9 As shown in the figure: Process 4-1 is interrupted because it is restricted by worker A. Process 4-1 can only be produced after worker A is released in process 3-1; Process 4-2 is interrupted because it is restricted by mold A. Process 4-2 can only be produced after mold A is released in process 2-5. The subsequent processes 4-3 to 4-6 are not interrupted because they have sufficient resources. Figure 10The production scheduling results shown in the production schedule include detailed arrangements for each component, including the start and end time of each process.

[0099] The above-described embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for optimizing production scheduling of prefabricated buildings considering resource constraints, characterized in that: The specific steps include: S1. Based on the premise of balancing the production load of each production line, several initialization production plans are obtained based on the heuristic algorithm; S2. Based on the premise of limited production resources and component production constraints, different production plans are analyzed to obtain a detailed production process diagram; The detailed production process diagram is specifically obtained by the following method: S201. Select an initial production plan, traverse each component in turn according to the production order of the components, and determine the component type and the corresponding production line; S202. Based on the premise of limited production resources and component production constraints, determine a production process diagram for the component, where the production process diagram for the component includes all process flows of the component and the start and end times of each process of the component production; S203, repeat step S202 for each component of the initialized production plan to obtain a complete production process diagram of the plan; S204, repeating steps S201-S203 to obtain a production process diagram for each initialized production plan; The expression of the component production constraint is: Formula (1) is the constraint of the production machine at the workstation on the production line; Formula (2) is the constraint of the logical relationship of the process before; Formula (3) is the constraint of the number of molds; Formula (4) is the resource limitation constraint; Formula (5) represents the non-negative and non-empty constraints of duration, resource, index, time, and set; where: S represents the start time of the process; P represents the duration of the process; A l,j represents the set of all the immediate predecessor components of component j on production line l; L and l represent the production line set and a specific production line respectively; i and j are component indexes, representing a component in the component set; k is the component process index, representing a process of a component; MT and mt represent the mold type set and a specific mold respectively; R and r represent the resource set and a specific resource respectively; PC and pc represent the component set and a specific component respectively; T represents the time within a production cycle of a batch component; S3. Using the optimization index of different production requirements as the evaluation index of the process diagram, evaluate the production process diagram of different production plans; S4. Based on the genetic algorithm model of the evolutionary environment, the initialized production plan is iteratively screened and modified to obtain the optimal production scheduling plan under different production demands.

2. The method for optimizing production scheduling of prefabricated buildings considering resource constraints according to claim 1, characterized in that: The initialization production plan is specifically obtained by the following method: S101. Analyze the set of components to be scheduled in the order, including order attributes, component attributes, mold attributes, process attributes, process time attributes, production line attributes, and resource attributes, to provide specific information for generating a scheduling plan for the initial components; S102, generating a production plan including a random production order of the components based on the random number according to the component numbers and quantity; S103, assigning a production line to each component based on a heuristic algorithm according to the production sequence of the components; S104, combining steps S102 and S103 to obtain an initialization production plan; S105. Repeat step S104 to obtain a set containing a large number of initialization production plans.

3. The method for optimizing production scheduling of prefabricated buildings considering resource constraints according to claim 1, characterized in that: The production process diagram includes all component information and all resource conditions within a production cycle. The component information includes the production line to which the component is allocated, all processes and categories of the component, various resources required for each process, and the start and end time of each process.

4. The method for optimizing production scheduling of prefabricated buildings considering resource constraints according to claim 3 is characterized in that: For different production plans, the evaluation function of the production process diagram is: Where: MinFinishTime represents the shortest completion time; MinCriticalLineLoad represents the minimum critical line load; MinLineAllLoad represents the minimum total line load; MinMoldCost represents the minimum mold cost.

5. The method for optimizing production scheduling of prefabricated buildings considering resource constraints according to claim 1, characterized in that: The iterative screening and modification of the initial production plan is specifically achieved by the following steps: S401, using the initialized production plan as the initialized parent generation of the genetic algorithm; S402, performing algorithmically designed selection, crossover, and mutation operations on the component production sequence of the parent generation production plan and the production line corresponding to the component production, thereby obtaining a child generation; S403: Repeat steps S2-S3 for the obtained offspring, and use it as the parent of step S401; S404: Repeat step S403 until the optimization goal of the evolutionary generation or production plan of the algorithm is achieved, and then end the algorithm process; S405. Repeat step S404 to output the production process diagram of the component, that is, the optimal production scheduling plan of the component method.

6. The method for optimizing production scheduling of prefabricated buildings considering resource constraints according to claim 5, characterized in that: The algorithm is designed to select some individuals from the parent population and construct an individual selection pool, and the offspring individuals are directly obtained from the individuals in the selection pool.

7. The method for optimizing production scheduling of prefabricated buildings considering resource constraints according to claim 5, characterized in that: The crossover designed by the algorithm is to select some individuals from the parent population and construct an individual mating pool, and the offspring individuals are generated by pairwise crossover from the individuals in the mating pool.

8. The method for optimizing production scheduling of prefabricated buildings considering resource constraints according to claim 5, characterized in that: The variation designed by the algorithm is to select some individuals from the parent population and construct an individual variation pool, and the offspring individuals are generated by mutation of the individuals in the variation pool.

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