Chemical production scheduling method, system, equipment and medium

Abstract

The invention discloses a chemical production scheduling method, system, equipment and medium, and relates to the technical field of production scheduling, and the method comprises the steps: converting a chemical production scheduling problem into a hybrid flow shop scheduling problem, and taking the minimization of a production cycle as a target, and constructing a mathematical model according to the definition of the hybrid flow shop scheduling problem and a chemical production flow; and generating an initial solution and a file set of the chemical production task based on the mathematical model, updating the file set by using the initial solution to obtain a current optimal solution, and performing variable neighborhood search on the initial solution and updating the optimal solution when the current optimal solution does not meet a termination condition. And taking the updated optimal solution as a chemical production scheduling scheme. According to the invention, intelligent chemical production scheduling can be realized in a complex and changeable production environment, the production efficiency is improved, and the method has important practical significance for promoting intelligent and automatic development of the chemical industry.

Description

Technical Field

[0001] This invention relates to the field of production scheduling technology, and in particular to a chemical production scheduling method, system, equipment and medium. Background Technology

[0002] Chemical production scheduling is a crucial aspect of the chemical industry, involving multiple complex processes such as raw material pretreatment, reaction, separation and purification, and final product packaging. Each process requires operation on different equipment, and the production process is accompanied by multiple chemical production tasks, variables, and constraints, posing significant challenges to scheduling. Scheduling optimization not only ensures the efficient execution of chemical production tasks in a predetermined sequence but also requires the rational allocation of equipment resources to avoid bottlenecks and waste. With the expansion of chemical production scale and increasing complexity, existing scheduling methods are gradually becoming less efficient in addressing such problems and unable to meet the demands of modern production.

[0003] Against this backdrop, the Hybrid Flowshop Scheduling Problem (HFSP) provides an effective theoretical framework for chemical production scheduling. The HFSP is characterized by chemical production tasks requiring sequential processing through multiple stages, with each stage potentially handled concurrently by multiple machines. The goal of such problems is typically to minimize the production cycle; however, its characteristics—multiple chemical production tasks, multiple stages, multiple machines, and unequal processing times—make it an NP-hard (Nondeterministic Polynomial-hard) problem. As the problem size increases, traditional planning methods struggle to find the optimal solution to the HFSP within a reasonable timeframe. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by providing a chemical production scheduling method, system, equipment, and medium to solve the problems in the prior art.

[0005] The present invention specifically provides the following technical solution: A chemical production scheduling method includes the following steps: The chemical production scheduling problem is transformed into a mixed flow shop scheduling problem, and a mathematical model is constructed based on the definition of the mixed flow shop scheduling problem and the chemical production process with the goal of minimizing the production cycle. Based on the mathematical model, an initial solution and file set for chemical production tasks are generated. The file set is then updated using the initial solution to obtain the current optimal solution. If the current optimal solution does not meet the termination condition, a variable neighborhood search is performed on the initial solution to update the optimal solution. The updated optimal solution is then used as the chemical production scheduling scheme.

[0006] Preferably, the transformation of the chemical production scheduling problem into a mixed flow workshop scheduling problem specifically involves: A set of chemical production tasks in the chemical production scheduling problem J Transformed into sequential process Each process is carried out in a series of steps, and a system is set up at each step. Use the same equipment, and at least one process must meet the requirements. The scheduling problem of mixed assembly line workshops.

[0007] Preferably, the step of constructing a mathematical model based on the definition of the mixed flow shop scheduling problem and the chemical production process, with the goal of minimizing the production cycle, includes: Based on the processing sequence and equipment selection of each step in the chemical production process, the objective function for the mixed flow workshop scheduling problem and the chemical production process is obtained, and its specific expression is as follows: ; The objective function is to minimize the maximum completion time, i.e., the production cycle. For the maximum completion time; Based on the objective function, construct the constraints for the hybrid flow workshop scheduling problem and the chemical production process; A mathematical model is constructed based on the definition of the hybrid flow workshop scheduling problem and the objective function and constraints of the chemical production process; The specific constraints are as follows: The maximum completion time must be greater than or equal to the completion time of any chemical production task in its final stage. The specific expression is: ; Chemical production tasks in process k Only one machine can be selected for processing. The specific expression is: ; The start time of a chemical production task must be greater than or equal to 0, as expressed in the following expression: ; The completion time of a chemical production task at a certain process stage is the sum of the start time and the processing time, expressed as follows: ; In chemical production, if the start time of the subsequent process is greater than or equal to the completion time of the preceding process, the specific expression is as follows: ; On the same equipment, the start time of a subsequent chemical production task must be greater than or equal to the completion time of the previous task. The specific expression is as follows: ; Priority constraints require that two tasks processed on the same device must have a sequential relationship, specifically expressed as: ; ; ; in, Number of processes Chemical production task numbers Process The number of devices, Process Index , Chemical production task index , Device index, , :batch A single item in Processing time for each stage Maximum completion time Given a very large number, the decision variable is: Chemical production tasks In the process The start time, Chemical production tasks In the process The completion time, : Binary variable; if chemical production task In the process Assigned to equipment If processed as described above, the value will be 1. : Binary variable; assuming a chemical production task and In the process All allocated to equipment The above processing, and Compare If processed first, the value is 1.

[0008] Preferably, the step of encoding the solution into a one-dimensional vector, representing the processing sequence of the chemical production task in the first process using the one-dimensional vector, and obtaining the processing sequence and equipment selection of the chemical production task in subsequent processes through decoding rules, includes: Using a one-dimensional vector Indicate the solution, Index for chemical production tasks This represents the total number of chemical production tasks. In the first step, through encoding vectors The processing sequence of chemical production tasks is determined by the position of the chemical production task in the vector. In subsequent stages, the processing sequence of chemical production tasks is determined according to the first-come, first-served rule, and chemical production tasks are preferentially assigned to equipment that is in an idle state.

[0009] Preferably, the initial solution and archive set for generating chemical production tasks includes: Constructing vectors The chemical production tasks are arranged in ascending order according to their total processing time. Construct an empty vector and sequentially from vector Take a chemical production task and place it into an empty vector. The optimal position in the vector minimizes the completion time, until the vector... Empty; empty vector after completion This is the initial solution and the current main solution. ; Generate an archive set, wherein the archive set All are generated randomly and using the current main solution. Update the archive set, replace the worst solution in the archive set with the objective function, and select the current best solution.

[0010] Preferably, when the current optimal solution does not meet the termination condition, performing a variable neighborhood search on the initial solution to update the optimal solution includes: Using the current main solution Perform a variable neighborhood search within the nth neighborhood structure among multiple neighborhood structures. If a better solution is found, replace the current main solution with the better solution. Update archives and optimal solutions and the number of searches Set to 0 to use the new main solution. Restart the variable neighborhood search; A dynamic acceptance criterion is used to determine whether to accept a new master solution, and when accepting, the new master solution is taken as the optimal solution.

[0011] Preferably, the step of using the updated optimal solution as the chemical production scheduling scheme includes: The optimal solution is encoded as a one-dimensional vector, which represents the processing sequence of the chemical production task in the first process. The processing sequence and equipment selection of the chemical production task in the subsequent processes are obtained through decoding rules.

[0012] This invention provides a chemical production scheduling system, comprising: The model building module is used to transform the chemical production scheduling problem into a mixed flow shop scheduling problem, and to build a mathematical model based on the definition of the mixed flow shop scheduling problem and the chemical production process with the goal of minimizing the production cycle. The update module is used to generate initial solutions and archive sets for chemical production tasks based on mathematical models, update the archive sets with the initial solutions, obtain the current optimal solution, and perform a variable neighborhood search on the initial solutions when the current optimal solution does not meet the termination conditions, update the optimal solution, and use the updated optimal solution as the chemical production scheduling scheme.

[0013] The present invention provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor performs the steps of the above-described chemical production scheduling method.

[0014] The present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described chemical production scheduling method.

[0015] Compared with the prior art, the present invention has the following significant advantages: This invention transforms the chemical production scheduling problem into a mixed flow shop scheduling problem, and constructs a mathematical model with the goal of minimizing the production cycle. This model accurately describes the relationship between the chemical production cycle, task sequence, and equipment selection, laying the foundation for intelligent chemical production scheduling. Based on this model, it generates initial solutions and a set of files for chemical production tasks. When the current optimal solution does not meet the termination condition, a variable neighborhood search is performed on the initial solution to update the optimal solution. The updated optimal solution is then used as the chemical production scheduling scheme. Based on the initialization strategy and combined with the variable neighborhood search algorithm, when facing complex multi-objective optimization chemical production tasks, the solution can be updated while ensuring the global optimal solution, effectively improving production efficiency and achieving efficient scheduling of multi-task chemical production. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the generation of new individuals through TPX crossover in this invention; Figure 3 The scheduling Gantt chart for the solution of the example; Figure 4 This is a flowchart of a chemical production scheduling method according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] like Figure 1 and Figure 4 As shown, this invention provides a chemical production scheduling method, which specifically includes the following steps: Step S1: Transform (abstract) the chemical production scheduling problem into a mixed flow shop scheduling problem, and construct a mathematical model based on the definition of the mixed flow shop scheduling problem and the chemical production process with the goal of minimizing the production cycle.

[0019] The chemical production scheduling problem is transformed into a mixed flow workshop scheduling problem, specifically: Chemical production involves multiple processes, and each process has multiple pieces of equipment to choose from. Multiple chemical production tasks can be abstracted into a mixed flow shop scheduling problem. This is defined as follows: a set of chemical production tasks in the chemical production scheduling problem... J Transformed into sequential process Each process is carried out in a series of steps, and a system is set up at each step. Use the same equipment, and at least one process must meet the requirements. This is a scheduling problem in a mixed flow workshop. Chemical production tasks can only be processed by one piece of equipment at each process stage; equipment cannot be changed. Equipment can only process one chemical production task at a time, and tasks can be immediately transported to the next stage after completion of the current stage. The main scheduling task is to determine the processing sequence of chemical production tasks at each stage and the equipment selection to minimize the production cycle. Furthermore, to simplify the problem, the following assumptions are made:

[0020] All chemical production tasks and equipment are available at time 0; equipment operation is not subject to interruption or preemption; the workshop has an infinite buffer zone; the processing time of chemical production tasks is known; equipment preparation and transportation operations for chemical production tasks are not considered.

[0021] Based on the problem definition and chemical production process, this invention constructs a mathematical model, wherein the symbols, decision variables, objective function, and constraints in the mathematical model are defined as follows: symbol: Number of processes; Chemical production task numbers; Process The number of devices; Process Index ; Chemical production task index ; Device index, ; :batch A single item in Processing time for each stage; Maximum completion time; : An extremely large number.

[0022] Decision variables: Chemical production tasks In the process Start time; Chemical production tasks In the process Completion time; : Binary variable; assuming a chemical production task In the process Assigned to equipment If the above processing is performed, the value is 1; otherwise, it is 0. : Binary variable; assuming a chemical production task and In the process All allocated to equipment The above processing, and Compare If it is processed first, the value is 1; otherwise, it is 0.

[0023] Based on the processing sequence and equipment selection of each step in the chemical production task, the objective function of the mixed flow workshop scheduling problem and the chemical production process is obtained, and its specific expression is as follows: (1); The objective function is to minimize the maximum completion time, i.e., the production cycle.

[0024] Based on the objective function, the constraints of the mixed flow workshop scheduling problem and the chemical production process are constructed, and a mathematical model is built using the definition of the mixed flow workshop scheduling problem and the objective function and constraints of the chemical production process.

[0025] The specific constraints are as follows: The maximum completion time must be greater than or equal to the completion time of any chemical production task in its final stage. The specific expression is: (2); Chemical production tasks in process k Only one machine can be selected for processing. The specific expression is: (3); The start time of a chemical production task must be greater than or equal to 0, as expressed in the following expression: (4); The completion time of a chemical production task at a certain process stage is the sum of the start time and the processing time, expressed as follows: (5); In chemical production, if the start time of the subsequent process is greater than or equal to the completion time of the preceding process, the specific expression is as follows: (6); On the same equipment, the start time of a subsequent chemical production task must be greater than or equal to the completion time of the previous task. The specific expression is as follows: (7); On the same equipment, the start time of the next chemical production task must be greater than or equal to the completion time of the previous task.

[0026] Priority constraints require that two chemical production tasks on the same equipment must have a sequential relationship, specifically expressed as: (8); (9); (10).

[0027] Step S2: Based on the mathematical model, generate an initial solution and file set for the chemical production task, and update the file set using the initial solution to obtain the current optimal solution. If the current optimal solution does not meet the termination condition, perform a variable neighborhood search on the initial solution to update the optimal solution, and use the updated optimal solution as the chemical production scheduling scheme.

[0028] An improved cooperative variable neighborhood search algorithm is proposed to achieve optimized control of chemical production scheduling. The proposed improved cooperative variable neighborhood search algorithm mainly consists of three parts: initialization, variable neighborhood search, and acceptance criteria. The detailed steps are as follows:

[0029] Step 2.1: Use the NEH-based heuristic algorithm to generate an initialization solution and build an archive set.

[0030] Step 2.1.1: Constructing Vectors The chemical production tasks are arranged in ascending order based on their total processing time.

[0031] Step 2.1.2: Construct an empty vector and sequentially from vector Take a chemical production task and place it into an empty vector. The optimal position in the vector minimizes the completion time, until the vector... Empty. The completed empty vector. This is the initial solution, also known as the current main solution. .

[0032] Step 2.1.3: Generate an archive set containing... Each solution in the archive is generated in a random manner (random chemical production task sequence).

[0033] Step 2.1.4: Use the current main solution Updating the archive set involves replacing the worst solution in the archive set with the best solution for the objective function and selecting the current optimal solution. .

[0034] Step 2.2: Determine if the algorithm meets the termination condition. If it does, the algorithm terminates and outputs the optimal solution. Otherwise, proceed to step 2.3.

[0035] Step 2.3: The main solution performs a variable neighborhood search, continuously exploring potential solutions and updating the optimal solution.

[0036] This invention employs three neighborhood structures: 1) Insertion operation: randomly selects a chemical production task from the vector and inserts it into another position in the vector. 2) Swap operation: randomly selects two chemical production tasks from the vector and swaps their positions in the vector. 3) Combination operation: first performs the insertion operation, then the swap operation. The maximum number of searches in each neighborhood structure is defined as follows: The detailed steps are as follows:

[0037] Step 2.3.1: Utilize the current main solution Perform a variable neighborhood search within the first neighborhood structure; if a better solution is found, replace the main solution with it. Update the file set and optimal solution according to step 2.1.4. and the number of searches Set to 0 to use the new main solution. Execute step 2.3.1. Otherwise, for each search performed, .if Then Set it to 0 and proceed to step 2.3.2.

[0038] Step 2.3.2: Main Solution Perform a variable neighborhood search within the second neighborhood structure; if a better solution is found, replace the main solution with it. Update the file set and optimal solution according to step 2.1.4. and the number of searches Set to 0 to use the new main solution. Execute step 2.3.1. Otherwise, for each search performed, .if Then Set it to 0 and proceed to step 2.3.3.

[0039] Step 2.3.3: Main Solution Perform a variable neighborhood search within the third neighborhood structure; if a better solution is found, replace the main solution with it. Update the file set and optimal solution according to step 2.1.4. and the number of searches Set to 0 to use the new main solution. Execute step 2.3.1. Otherwise, for each search performed, .if This concludes the variable neighborhood search process.

[0040] Step 2.4: Use dynamic acceptance criteria to determine whether to accept the current master solution or to regenerate the master solution collaboratively.

[0041] This invention is based on A dynamic reception criterion and threshold were designed. Set as .in, The running time of the algorithm, This represents the maximum running time of the algorithm. After the variable neighborhood search process is completed, a random decimal between 0 and 1 is generated. ,if If the current master solution is selected, it will be used as the master solution for the next iteration; otherwise, a new solution will be generated collaboratively. The collaboration process involves randomly selecting two solutions from the current archive set and performing TPX cross-validation (e.g., ...). Figure 2 (As shown), generate a new solution. Use the newly generated solution to replace the current master solution for the next iteration and attempt to update the archive and the optimal solution.

[0042] Step 2.5: Perform step 2.2.

[0043] The following solution addresses one example from the embodiments, using an intelligent chemical production scheduling method based on an improved cooperative variable neighborhood search algorithm to solve the example and obtain an efficient scheduling scheme.

[0044] The intelligent manpower scheduling method based on the improved cooperative variable neighborhood search algorithm proposed in this invention requires setting three parameters: file set size, etc. Maximum number of searches in each neighborhood structure and the termination condition of the algorithm After preliminary experiments and DOE parameter calibration, the present invention... Set to 15. Setting the value to 8 optimizes the method's performance. In actual production, the goal of scheduling is not to find the optimal solution, but to obtain an approximate optimal solution within a finite time. Therefore, this invention uses the maximum running time as the termination condition. Set as millisecond.

[0045] A chemical company has 10 chemical production tasks, each requiring 5 processes sequentially. The number of machines in each process is 3, 2, 3, 2, 3 respectively. The processing time for each process is shown in Table 1. The intelligent chemical production scheduling method based on an improved cooperative variable neighborhood search algorithm proposed in this paper is used to solve the problem. The time taken is 1.5 seconds, and the obtained scheduling scheme is {10, 3, 6, 2, 1, 4, 7, 8, 5, 9}. The detailed scheduling Gantt chart is shown below. Figure 3 As shown.

[0046] Table 1 Processing Time for Chemical Production Tasks The updated optimal solution as an intelligent chemical production scheduling scheme includes: encoding the optimal solution into a one-dimensional vector, using the one-dimensional vector to represent the processing sequence of chemical production tasks in the first process, and obtaining the processing sequence and equipment selection of chemical production tasks in subsequent processes through decoding rules.

[0047] Design encoding and decoding strategies to give practical meaning to the symbolic scheduling scheme.

[0048] Encoding is closely related to practical problems and serves as a symbolic representation of solutions. This invention employs a 1-dimensional vector. This indicates the solution (the processing sequence of a chemical production task in the first step). Index for chemical production tasks This represents the total number of chemical production tasks. It's important to note that each chemical production task can only appear once in a valid encoding vector, and the earlier a chemical production task's index appears in the vector, the higher its processing priority. The processing order of chemical production tasks in subsequent processes is determined by the decoding rules. For example, {5, 2, 1, 3, 4} is a valid solution representation, where the processing order of the chemical production tasks in the first stage is: Chemical Production Task 5, Chemical Production Task 2, Chemical Production Task 1, Chemical Production Task 3, and Chemical Production Task 4.

[0049] Decoding is used to translate the encoding into a feasible scheduling scheme. In this invention, there are two main scheduling problems: the processing sequence of chemical production tasks at each stage and equipment selection. In the first stage, the processing sequence of the chemical production tasks is determined by the encoded vector. The position of a chemical production task in the vector is determined as its processing order. In subsequent stages, the processing order of chemical production tasks is determined according to the "first-come, first-served" rule, meaning that the earlier a chemical production task arrives at a process step, the higher its processing priority. If two chemical production tasks arrive simultaneously, their priority relationship is randomly determined. For equipment selection, this invention determines the order based on the "first to be idle" rule, meaning that chemical production tasks are preferentially assigned to the equipment that is first in an idle state.

[0050] Based on the above method, the present invention provides a chemical production scheduling system, including: a model building module and an update module.

[0051] The model building module transforms the chemical production scheduling problem into a mixed flow shop scheduling problem, and constructs a mathematical model based on the definition of the mixed flow shop scheduling problem and the chemical production process with the goal of minimizing the production cycle. The update module generates an initial solution and a file set for the chemical production task based on the mathematical model, updates the file set with the initial solution, obtains the current optimal solution, and performs a variable neighborhood search on the initial solution when the current optimal solution does not meet the termination condition, updates the optimal solution, and uses the updated optimal solution as the chemical production scheduling scheme.

[0052] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the processor performs the steps of a chemical production scheduling method.

[0053] According to the disclosed embodiments, the computer device can communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth communication, etc.) or with any device that enables the computing device to communicate with one or more other computing devices (e.g., router, demodulator, etc.).

[0054] The present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a chemical production scheduling method.

[0055] According to the disclosed embodiments, the storage medium can be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0056] The above description, in conjunction with specific preferred embodiments, provides a more detailed explanation of the present invention. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention.

Claims

1. A chemical production scheduling method, characterized in that, Includes the following steps: The chemical production scheduling problem is transformed into a mixed flow shop scheduling problem, and a mathematical model is constructed based on the definition of the mixed flow shop scheduling problem and the chemical production process with the goal of minimizing the production cycle. Based on the mathematical model, an initial solution and file set for chemical production tasks are generated. The file set is then updated using the initial solution to obtain the current optimal solution. If the current optimal solution does not meet the termination condition, a variable neighborhood search is performed on the initial solution to update the optimal solution. The updated optimal solution is then used as the chemical production scheduling scheme.

2. The chemical production scheduling method as described in claim 1, characterized in that, The transformation of the chemical production scheduling problem into a mixed flow workshop scheduling problem is specifically as follows: A set of chemical production tasks in the chemical production scheduling problem J Transformed into sequential process Each process is carried out in a series of steps, and a system is set up at each step. Use the same equipment, and at least one process must meet the requirements. The scheduling problem of mixed assembly line workshops.

3. The chemical production scheduling method as described in claim 1, characterized in that, The mathematical model, which aims to minimize the production cycle, is constructed based on the definition of the mixed flow shop scheduling problem and the chemical production process, including: Based on the processing sequence and equipment selection of each step in the chemical production process, the objective function for the mixed flow workshop scheduling problem and the chemical production process is obtained, and its specific expression is as follows: ； The objective function is to minimize the maximum completion time, i.e., the production cycle. For the maximum completion time; Based on the objective function, construct the constraints for the hybrid flow workshop scheduling problem and the chemical production process; A mathematical model is constructed based on the definition of the hybrid flow workshop scheduling problem and the objective function and constraints of the chemical production process; The constraints mentioned above are specifically: The maximum completion time must be greater than or equal to the completion time of any chemical production task in its final stage. The specific expression is: ； Chemical production tasks in process k Only one machine can be selected for processing. The specific expression is: ； The start time of a chemical production task must be greater than or equal to 0, as expressed in the following expression: ； The completion time of a chemical production task at a certain process stage is the sum of the start time and the processing time, expressed as follows: ； In chemical production, if the start time of the subsequent process is greater than or equal to the completion time of the preceding process, the specific expression is as follows: ； On the same equipment, the start time of a subsequent chemical production task must be greater than or equal to the completion time of the previous task. The specific expression is as follows: ； Priority constraints require that two tasks processed on the same device must have a sequential relationship, specifically expressed as: ； ； ； in, Number of processes Chemical production task numbers Process The number of devices, Process Index , Chemical production task index , Device index, , :batch A single item in Processing time for each stage Maximum completion time Given a very large number, the decision variable is: Chemical production tasks In the process The start time, Chemical production tasks In the process The completion time, : Binary variable; if chemical production task In the process Assigned to equipment If processed as described above, the value will be 1. : Binary variable; assuming a chemical production task and In the process All allocated to equipment The above processing, and Compare If processed first, the value is 1.

4. The chemical production scheduling method as described in claim 1, characterized in that, The process of encoding the solution into a one-dimensional vector, representing the processing sequence of a chemical production task in the first step, and obtaining the processing sequence and equipment selection for subsequent chemical production tasks through decoding rules, includes: Using a one-dimensional vector Indicate the solution, Index for chemical production tasks This represents the total number of chemical production tasks. In the first step, through encoding vectors The processing sequence of chemical production tasks is determined by the position of the chemical production task in the vector. In subsequent stages, the processing sequence of chemical production tasks is determined according to the first-come, first-served rule, and chemical production tasks are preferentially assigned to equipment that is in an idle state.

5. A chemical production scheduling method as described in claim 1, characterized in that, The initial solutions and archives for generating chemical production tasks include: Constructing vectors The chemical production tasks are arranged in ascending order according to their total processing time. Construct an empty vector and sequentially from vector Take a chemical production task and place it into an empty vector. The optimal position in the vector minimizes the completion time, until the vector... Empty; empty vector after completion This is the initial solution and the current main solution. ; Generate an archive set, in which each solution is generated randomly and uses the current master solution. Update the archive set, replace the worst solution in the archive set with the objective function, and select the current best solution.

6. A chemical production scheduling method as described in claim 5, characterized in that, The step of performing a variable neighborhood search on the initial solution and updating the optimal solution when the current optimal solution does not meet the termination condition includes: Using the current main solution Perform a variable neighborhood search within the nth neighborhood structure among multiple neighborhood structures. If a better solution is found, replace the current main solution with the better solution. Update archives and optimal solutions and the number of searches Set to 0 to use the new main solution. Restart the variable neighborhood search; A dynamic acceptance criterion is used to determine whether to accept a new master solution, and when accepting, the new master solution is taken as the optimal solution.

7. A chemical production scheduling method as described in claim 1, characterized in that, The step of using the updated optimal solution as a chemical production scheduling scheme includes: The optimal solution is encoded as a one-dimensional vector, which represents the processing sequence of the chemical production task in the first process. The processing sequence and equipment selection of the chemical production task in the subsequent processes are obtained through decoding rules.

8. A chemical production scheduling system, characterized in that, include: The model building module is used to transform the chemical production scheduling problem into a mixed flow shop scheduling problem, and to build a mathematical model based on the definition of the mixed flow shop scheduling problem and the chemical production process with the goal of minimizing the production cycle. The update module is used to generate initial solutions and archive sets for chemical production tasks based on mathematical models, update the archive sets with the initial solutions, obtain the current optimal solution, and perform a variable neighborhood search on the initial solutions when the current optimal solution does not meet the termination conditions, update the optimal solution, and use the updated optimal solution as the chemical production scheduling scheme.

9. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores a program that, when executed by the processor, causes the processor to perform the steps of a chemical production scheduling method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of a chemical production scheduling method according to any one of claims 1 to 7.

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