Serial batch processing scheduling method based on hybrid particle swarm algorithm in fuzzy environment

Abstract

The application provides a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, and relates to the field of production scheduling.The method comprises the following steps: encoding and analyzing workpieces and machines based on machine indexes; in the case of parallel machine scheduling, initializing algorithm parameters based on the hybrid particle swarm algorithm, initializing a population by a heuristic algorithm, analyzing fitness, and updating the speed and position of particles; determining the minimization of the maximum completion time as an objective, executing a variable neighborhood descent local search strategy, and updating the local optimum and the global optimum; and in the case that an iteration index exceeds an iteration threshold, determining the end of the algorithm and outputting a target result to represent the allocation and scheduling information of the workpieces and machines.The application considers fuzzy processing time, learning effect and deterioration effect, and considers the scheduling problems of single machines and non-single machines in a fuzzy environment based on actual production conditions, which is helpful for iron and steel plants to formulate production strategies and reasonably arrange the allocation and scheduling of workpieces and machines.

Description

Technical Field

[0001] This application relates to the field of production scheduling technology, specifically to a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment. Background Technology

[0002] Production scheduling plays a crucial role in resource management, achieving specific objectives under resource constraints by determining the sequence of workpiece processing. The continuous casting and rolling process in steel typically exhibits four prominent characteristics: continuous batch production, learning effects, degradation effects, and fuzzy processing times. These four characteristics invariably lead to uncertainties in the manufacturing process, making production planning challenging.

[0003] In the continuous casting process of steel, molten steel is processed in ladles, and a ladle can be considered a batch containing a group of workpieces. This process can be carried out continuously in batches. Therefore, the processing time of a batch can be considered the total continuous casting time of the ladle. Cutting is a semi-automatic process requiring manual operation. As workers become more skilled in cutting, the cutting time required for each slab gradually decreases. Hot rolling and heat treatment operations have specific temperature requirements for the steel slabs. As the process progresses, the temperature of the molten steel decreases, thus requiring more energy and time for later-processed slabs. The crystallization rate of the steel plate is greatly affected by electromagnetic induction stirring, and the instability of the stirring process leads to uncertainty and ambiguity in processing time. For the fuzzy environment corresponding to the actual manufacturing process of continuous casting and rolling of steel, there are single-machine problems and non-parallel machine scheduling problems.

[0004] In related technologies, most focus on a portion of four practical processing characteristics: batch processing, learning effects, degradation effects, and fuzzy processing parameters. Methodologically, these technologies often rely on heuristic algorithms to design a series of realistic heuristic rules, achieving an optimal solution to a search problem in a short time. However, they fail to find optimal solutions for large-scale NP-hard problems. For example, Particle Swarm Optimization (PSO) is used for problem solving, and many studies have shown that PSO performs well in terms of convergence and ease of implementation. Nevertheless, PSO still has some drawbacks, such as inefficient local search and premature convergence, and difficulty in handling single-machine problems and non-uniform parallel machine scheduling problems in fuzzy environments. Therefore, there is an urgent need to propose a serial batch processing scheduling method based on a hybrid PSO algorithm in fuzzy environments to address the shortcomings of related technologies. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application provides a serial batch processing scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment. This method solves the problem that related technologies struggle to handle single-machine problems and non-uniform parallel machine scheduling issues in the manufacturing process scheduling of steel continuous casting and rolling.

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] In a first aspect, embodiments of this application provide a serial batch processing scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment. The method includes: scheduling the manufacturing process of continuous casting and rolling in steel; performing encoding analysis on multiple workpieces and multiple machines based on machine indices to obtain a vectorized list of workpiece processing sequences; responding to scheduling instructions, identifying the corresponding scheduling type as single-machine scheduling or parallel machine scheduling; in the case of single-machine scheduling, performing time analysis on each job and generating multiple batches sequentially for scheduling; in the case of parallel machine scheduling, based on the constructed hybrid particle swarm optimization algorithm, initializing algorithm parameters, initializing the population through the heuristic algorithm HA2 in the hybrid particle swarm optimization algorithm, analyzing fitness, and updating the velocity and position of particles; determining minimizing the maximum completion time as the objective, constructing a neighborhood structure set, executing a variable neighborhood descent local search strategy based on the neighborhood structure set, and updating the local optimum. and global optimal The neighborhood structure set includes a three-element double exchange structure, a three-element insertion structure, and a three-element reverse redistribution structure; if the iteration exponent does not exceed the iteration threshold, the velocity and position of each particle are updated again; if the iteration exponent exceeds the iteration threshold, the algorithm is terminated and the target result is output to characterize the workpiece and machine allocation and scheduling information.

[0008] Secondly, embodiments of this application provide a serial batch processing scheduling system based on a hybrid particle swarm optimization algorithm in a fuzzy environment. The system includes: an encoding module, a type identification module, a single-machine scheduling module, an initial scheduling module, a local search module, and a result output module.

[0009] Specifically, the encoding module is used for manufacturing process scheduling in continuous casting and rolling of steel. Based on machine indexing, it performs encoding analysis on multiple workpieces and multiple machines to obtain a vectorized list of workpiece processing sequences. The type identification module is used to identify the corresponding scheduling type as single-machine scheduling or parallel machine scheduling in response to scheduling instructions. The single-machine scheduling module is used to perform time analysis on each job when the scheduling type is single-machine scheduling, and generate multiple batches in sequence for scheduling. The initial scheduling module is used to initialize algorithm parameters based on the constructed hybrid particle swarm optimization algorithm when the scheduling type is parallel machine scheduling. It initializes the population through the heuristic algorithm HA2 in the hybrid particle swarm optimization algorithm, analyzes fitness, and updates the velocity and position of particles. The local search module is used to determine minimizing the maximum completion time as the objective, construct a neighborhood structure set, execute a variable neighborhood descent local search strategy based on the neighborhood structure set, and update the local optimum. and global optimal The neighborhood structure set includes a three-element double exchange structure, a three-element insertion structure, and a three-element reverse redistribution structure. The result output module is used to update the velocity and position of each particle if the iteration exponent does not exceed the iteration threshold. If the iteration exponent exceeds the iteration threshold, the algorithm is terminated and the target result is output to characterize the workpiece and machine allocation and scheduling information.

[0010] Thirdly, embodiments of this application provide an electronic device, which includes: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the serial batch scheduling method based on the hybrid particle swarm algorithm in the fuzzy environment described in the first aspect above.

[0011] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the serial batch scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment as described in the first aspect above.

[0012] This application provides a serial batch processing scheduling method based on a hybrid particle swarm optimization algorithm in fuzzy environments. Compared with existing technologies, it has the following advantages:

[0013] This application addresses the actual manufacturing process of continuous casting and rolling in steel, studying the serial batching scheduling problem under fuzzy conditions, involving both learning and deterioration effects on single machines and non-identical parallel machines. For the parallel machine scheduling scenario, due to the NP-hard nature of the problem, a hybrid particle swarm optimization (PSO) algorithm is constructed. First, the heuristic algorithm HA2 is used to obtain and modify the initial solution. Then, an approximate optimal solution is found through iterative processing using the PSO algorithm and a variable neighborhood descent local search strategy. This approach avoids the algorithm falling into local optima, improves the quality of the global search, and accelerates the convergence speed. This application embeds three local search strategies based on three neighborhood structures—a three-element double exchange structure, a three-element insertion structure, and a three-element reverse redistribution structure—into the standard PSO algorithm to improve its performance. Based on actual production conditions, this application considers the scheduling problems of single machines and non-single machines under fuzzy conditions, which helps steel plants formulate production strategies and rationally allocate and schedule workpieces and machines. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a serial batch processing scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment, as provided in an embodiment of this application.

[0016] Figure 2 This is a schematic diagram of the structure of a serial batch processing scheduling system based on a hybrid particle swarm optimization algorithm in a fuzzy environment, provided in an embodiment of this application.

[0017] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0020] This application provides a serial batch processing scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment. This method addresses the difficulty of handling single-machine problems and non-uniform parallel machine scheduling problems in the manufacturing process scheduling of steel continuous casting and rolling. It avoids the algorithm falling into local optima, thereby improving the quality of global search and accelerating the convergence speed of the algorithm, thus improving the efficiency of manufacturing process scheduling.

[0021] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0022] The following section first introduces a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, as provided in the embodiments of this application.

[0023] This application provides a flowchart illustrating a serial batch processing scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment, as shown in the embodiments below. Figure 1 As shown, the serial batch processing scheduling method based on the hybrid particle swarm algorithm in this fuzzy environment may include the following steps S110-S160.

[0024] S110. For manufacturing process scheduling of steel continuous casting and rolling, based on machine index, multiple workpieces and multiple machines are encoded and analyzed to obtain a vectorized list of workpiece processing order.

[0025] S120. In response to the scheduling instruction, identify the corresponding scheduling type as single-machine scheduling or parallel machine scheduling;

[0026] S130. When the scheduling type is single-machine scheduling, perform time analysis on each job and generate multiple batches in sequence for scheduling.

[0027] S140. When the scheduling type is parallel machine scheduling, based on the constructed hybrid particle swarm algorithm, initialize the algorithm parameters, initialize the population through the heuristic algorithm HA2 in the hybrid particle swarm algorithm, analyze the fitness, and update the velocity and position of the particles.

[0028] S150. With the objective of minimizing the maximum completion time, construct a neighborhood structure set, and execute a variable neighborhood descent local search strategy based on the neighborhood structure set to update the local optimum. and global optimal The neighborhood structure set includes three-element double exchange structure, three-element insertion structure, and three-element reverse redistribution structure.

[0029] S160. If the iteration exponent does not exceed the iteration threshold, update the velocity and position of each particle again; if the iteration exponent exceeds the iteration threshold, determine that the algorithm ends and output the target result to characterize the workpiece and machine allocation and scheduling information.

[0030] The above describes a specific implementation of a serial batch scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment, as provided in this application. It is understood that this application studies the serial batching scheduling problem with learning and deterioration effects on a single machine and different parallel machines in a fuzzy environment, specifically for the actual manufacturing process of continuous casting and rolling of steel. It should be noted that the parallel machine scheduling case exhibits NP-hard properties; NP-hard properties are a core concept in computational complexity theory, used to describe a class of extremely difficult problems to solve, for which no efficient polynomial-time algorithm has yet been found. For the parallel machine scheduling case, due to the NP-hard nature of the problem, this application constructs a hybrid particle swarm optimization algorithm. First, the heuristic algorithm HA2 is used to obtain and modify the initial solution. Then, an approximate optimal solution is found through iteration using the particle swarm optimization algorithm and a variable neighborhood descent local search strategy. This avoids the algorithm falling into local optima, improves the quality of the global search, and accelerates the convergence speed of the algorithm.

[0031] Based on this, this application incorporates local search strategies based on three neighborhood structures—three-element double exchange structure, three-element insertion structure, and three-element reverse redistribution structure—into the standard particle swarm optimization algorithm to improve its performance. This application finds the optimal scheduling plan by sorting workpieces and batches and minimizes the maximum completion time of the project. Based on actual production conditions, it considers the scheduling problems of single machines and non-single machines in fuzzy environments, which helps steel plants formulate production strategies and rationally arrange the allocation and scheduling of workpieces and machines.

[0032] It should be noted that the purpose of this application is to solve the serial batch processing scheduling problem with learning and deterioration effects on single-machine and non-identical parallel machines in a fuzzy environment. For the single-machine problem, the completion time of each batch and machine is analyzed, and the order of each workpiece in the same batch and the order of all batches are studied. For the non-identical parallel machine problem, a hybrid particle swarm optimization algorithm combining particle swarm optimization, a variable neighborhood descent local search strategy, and the heuristic algorithm HA2 is designed. A local search strategy based on three neighborhood structures is embedded in the standard particle swarm optimization algorithm to improve the algorithm's performance. This provides helpful assistance in reducing manufacturing cycle time, increasing the overall capacity of the manufacturing system, and reducing production costs.

[0033] In one example, when the scheduling type is single-machine scheduling, the aforementioned time analysis of each job and the generation of multiple batches in sequence for scheduling may include the following steps:

[0034] S410. Sort all jobs in non-decreasing order according to the fuzzy normal processing time of each job to obtain a job list;

[0035] S420. If the job list contains no less than The first assignment will be the previous one. One job is assigned to a batch; otherwise, the remaining jobs are assigned to a batch and scheduled according to the order in which the batches are generated.

[0036] In some embodiments, the hybrid particle swarm optimization algorithm is constructed by fusing the improved particle swarm optimization algorithm IPSO, the heuristic algorithm HA2, and the variable neighborhood descent local search strategy; the neighborhood structure set includes neighborhood structures NS1, NS2, and NS3, which correspond to the three-element double exchange structure, the three-element insertion structure, and the three-element reverse redistribution structure, respectively.

[0037] The workpiece processing sequence list is a particle position vector. ,in, From 1 to Any integer in the range.

[0038] The operation steps of the neighborhood structure NS1 are as follows: from the particle position vector Select three elements ; swap and Then change and .

[0039] The operation steps of the neighborhood structure NS2 are as follows: from the particle position vector Select three elements ;exist Insert before ,exist Insert after and Elements between.

[0040] The operation steps of the neighborhood structure NS3 are as follows: from the particle position vector Select three elements ;Will and The elements between are redistributed in reverse order, and then... and Repeat the operation.

[0041] In the embodiments of this application, it can be understood that, for neighborhood structure NS1, firstly, three elements of a particle are selected, then two of the three elements are swapped, and the remaining elements are swapped with the elements closest to them. For neighborhood structure NS2, firstly, three elements of a particle are selected, then the second element is inserted before the first element, and the third element is inserted after the second element. For neighborhood structure NS3, firstly, three elements of a particle are selected, and then the elements are redistributed in reverse order between the first two elements and the last two elements.

[0042] In one example, the aforementioned construction of a neighborhood structure set, and the execution of a variable neighborhood descent local search strategy based on the neighborhood structure set, may specifically include the following steps:

[0043] S210. Set the neighborhood structures NS1, NS2, and NS3 corresponding to the three-element double exchange structure, three-element insertion structure, and three-element reverse redistribution structure, and set the initial solution s to the current global optimum. or local optimum ;

[0044] S220. Perform a local search based on the neighborhood structure NS1. If a better solution s' is found than s, then let s = s'.

[0045] S230. Perform a local search based on the neighborhood structure NS2. If a better solution s'' is found than s, then let s = s''.

[0046] S240. Perform a local search based on the neighborhood structure NS3. If a better solution s''' is found than s, then let s = s'''.

[0047] It should be noted that the execution order of neighborhood structure NS1, neighborhood structure NS2, and neighborhood structure NS3 can be adjusted. When no better solution is found based on neighborhood structure NS1, neighborhood structure NS3 can be executed first, followed by neighborhood structure NS2.

[0048] In some embodiments, the serial batching machine in the steel continuous casting and rolling manufacturing process is one of the heating furnace, roughing mill and finishing mill in the continuous casting and rolling production line, and the workpiece is a steel billet or steel product.

[0049] In the case of parallel machine scheduling A workpiece is configured in Batch on different serial batching machines Processing, the first The speed of each machine is Assume all workers have the same learning rate. And the workpieces move at the same rate The situation has worsened; the preparation time for each batch of workpieces is... .

[0050] The processing time for each workpiece is represented by a trapezoidal fuzzy number and satisfies the expression: ,in, , Indicates the workpiece processing time at and Between, most likely The actual processing time of the workpiece is , It is the processing of the first One workpiece The start time, It is a constant. .

[0051] batch The fuzzy processing time satisfies the expression:

[0052] ;

[0053] Fuzzy completion time of the machine Satisfying the expression:

[0054] In the formula, Indicates the start time of machine processing. These represent the first batch, the second batch, and so on. One batch;

[0055] The maximum completion time satisfies the expression: .

[0056] In the embodiments of this application, it can be understood that the learning rate is taken into consideration. and the rate of deterioration. Trapezoidal fuzzy numbers are used to represent the processing time of each workpiece, and batch processing is analyzed. Fuzzy processing time and fuzzy completion time of the machine The maximum completion time is represented by fuzzy processing time. Based on actual production conditions, this application considers fuzzy processing time, learning effect and deterioration effect, and constructs a hybrid particle swarm algorithm to solve the corresponding problems, which is conducive to the development of the metallurgical industry. Moreover, the research results can help steel plants formulate optimal production strategies to minimize the maximum completion time of production.

[0057] Based on this, this application studies a continuous batch processing scheduling problem in a fuzzy environment, driven by the actual production process of continuous casting and rolling of steel, and considering both learning and degradation effects. Compared with related technologies, the processing environment considered in this application is more realistic.

[0058] In some embodiments, the process of initializing the population using the heuristic algorithm HA2 includes an initial solution generation phase and an initial solution modification phase; the initial solution generation phase includes:

[0059] S11: Calculate the number of positions Load an equal number of jobs onto each machine and make... ;when Can be When divisible, ;when Cannot be When divisible, ;

[0060] S12: Sort all workpieces in non-decreasing order according to the expected value of the fuzzy normal processing time for each workpiece to obtain the job list;

[0061] S13: Place the first workpiece Assign to the first position on the first machine, and continue assigning until the workpiece is... Assigned to the Taiwanese machine The first position, ;

[0062] S14: Move the next m workpieces from the machine at the second position. All the way up to the first machine ,make ;

[0063] S15: Repeat steps S13 and S14 until all workpieces have been assigned;

[0064] The initial solution modification phase includes:

[0065] S21: Calculate the completion time of the last task on each machine. ;

[0066] S22: Use This indicates the completion time for each machine relative to its processing speed;

[0067] S23: Calculation The average value, i.e. ,

[0068] if Then proceed to step S24;

[0069] S24: According to The values ​​are sorted in non-decreasing order for the machines to obtain a new list of machines. ;

[0070] S25: Select two machines with the smallest and largest indices, and randomly swap jobs at the same position.

[0071] In the embodiments of this application, it can be understood that the application uses the heuristic algorithm HA2 to generate and modify the initial solution. In the process of generating and modifying the initial solution, the application considers the fuzzy normal processing time of each workpiece. When analyzing the completion time and average completion time of each machine with respect to the processing speed, the application also uses fuzzy numbers to fully consider the fuzzy environment of the actual manufacturing process of steel continuous casting and rolling.

[0072] In some embodiments, the algorithm parameters include an iteration threshold. Population size Self-learning parameters Social learning parameters Inertia weight The initial velocity of the particles and initial position Random parameters , Set the iteration index ;in, It is a whole and represents the iteration exponent; inertia weight Employing a linear decreasing strategy, with random parameters and They follow a uniform distribution U(0,1) and are randomly regenerated in each iteration to ensure the randomness of particle updates.

[0073] In one example, the aforementioned analysis of fitness, updating the particle's velocity and position, may specifically include the following steps:

[0074] S310. Calculate the fitness of each particle and set the local optimum for the first iteration. Then select the particle with the minimum fitness as the global optimum. ;

[0075] S320. Update the velocity and position of each particle. The velocity update process satisfies the expression:

[0076] ;

[0077] The position update process satisfies the expression: ;

[0078] This indicates that the e-th particle is in the... Speed ​​at the next iteration This indicates that the e-th particle is in the... Speed ​​during the next iteration; This indicates that the e-th particle is in the... Position at the next iteration This indicates that the e-th particle is in the... The position at the next iteration.

[0079] In the embodiments of this application, it can be understood that in each iteration of the hybrid particle swarm optimization algorithm, particles track local optima. and global optimal The particle updates its velocity and position using the aforementioned velocity and position update formulas after finding two extreme values.

[0080] In some embodiments, this application provides a serial batch processing scheduling system 500 based on a hybrid particle swarm optimization algorithm in a fuzzy environment, such as... Figure 2 As shown, the serial batch processing scheduling system 500 based on the hybrid particle swarm optimization algorithm in this fuzzy environment may include the following modules:

[0081] The coding module 510 is used for manufacturing process scheduling for continuous casting and rolling of steel. It performs coding analysis on multiple workpieces and multiple machines based on machine index to obtain a vectorized list of workpiece processing order.

[0082] The type identification module 520 is used to identify the corresponding scheduling type as single-machine scheduling or parallel machine scheduling in response to scheduling instructions.

[0083] The stand-alone scheduling module 530 is used to perform time analysis on each job and generate multiple batches in sequence for scheduling when the scheduling type is stand-alone scheduling;

[0084] The initial scheduling module 540 is used to initialize algorithm parameters based on the constructed hybrid particle swarm algorithm when the scheduling type is parallel machine scheduling. It initializes the population through the heuristic algorithm HA2 in the hybrid particle swarm algorithm, analyzes fitness, and updates the velocity and position of particles.

[0085] The local search module 550 is used to determine the goal of minimizing the maximum completion time, construct a neighborhood structure set, execute a variable neighborhood descent local search strategy based on the neighborhood structure set, and update the local optimum. and global optimal The neighborhood structure set includes three-element double exchange structure, three-element insertion structure, and three-element reverse redistribution structure.

[0086] The output module 560 is used to update the velocity and position of each particle if the iteration exponent does not exceed the iteration threshold; if the iteration exponent exceeds the iteration threshold, the algorithm is terminated and the target result is output to characterize the workpiece and machine allocation and scheduling information.

[0087] According to embodiments of this application, any and multiple modules among the encoding module 510, type identification module 520, single-machine scheduling module 530, initial scheduling module 540, local search module 550, and result output module 560 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module.

[0088] Figure 2 Each module in the system shown has the function of implementing each step in the serial batch processing scheduling method based on the hybrid particle swarm algorithm in the aforementioned fuzzy environment, and can achieve its corresponding technical effect. For the sake of brevity, it will not be elaborated here.

[0089] In some embodiments, this application provides an electronic device, the structural schematic of which is shown below. Figure 3 As shown.

[0090] The electronic device may include a processor 610 and a memory 620 storing computer program instructions.

[0091] Specifically, the processor 610 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0092] Memory 620 may include mass storage for data or instructions. For example, and not limitingly, memory 620 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 620 may include removable or non-removable (or fixed) media. Where appropriate, memory 620 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 620 is non-volatile solid-state memory.

[0093] Memory 620 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory 620 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any of the serial batch scheduling methods based on hybrid particle swarm optimization algorithms in fuzzy environments described in the above embodiments.

[0094] The processor 610 reads and executes computer program instructions stored in the memory 620 to implement any of the serial batch processing scheduling methods based on the hybrid particle swarm algorithm in the above embodiments.

[0095] In one example, the electronic device may also include a communication interface 630 and a bus 600. Wherein, such as Figure 3 As shown, the processor 610, memory 620, and communication interface 630 are connected via bus 600 and communicate with each other.

[0096] The communication interface 630 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0097] Bus 600 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 600 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0098] Furthermore, in conjunction with the serial batch processing scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment described in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the serial batch processing scheduling methods based on a hybrid particle swarm optimization algorithm in a fuzzy environment described in the above embodiments.

[0099] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0100] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0101] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0102] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0103] In summary, compared with the prior art, this application has the following beneficial effects:

[0104] 1. This application addresses the actual manufacturing process of continuous casting and rolling of steel, studying the serial batching scheduling problem under fuzzy conditions, involving both learning and deterioration effects on a single machine and dissimilar parallel machines. This application constructs a hybrid particle swarm optimization (PSO) algorithm to address the NP-hard nature of parallel machine scheduling. First, the heuristic algorithm HA2 is used to obtain and modify the initial solution. Then, an approximate optimal solution is found through iterative processing using the PSO algorithm and a variable neighborhood descent local search strategy. This approach avoids the algorithm falling into local optima, improves the quality of the global search, and accelerates the convergence speed of the algorithm.

[0105] 2. This application considers the learning rate and the degradation rate, uses trapezoidal fuzzy numbers to represent the processing time of each workpiece, and analyzes the fuzzy processing time of the batch and the fuzzy completion time of the machine. The fuzzy processing time is used to characterize the maximum completion time. Based on actual production conditions, this application considers fuzzy processing time, learning effect and degradation effect. Compared with related technologies, the processing environment considered in this application is more realistic.

[0106] 3. This application uses the heuristic algorithm HA2 to generate and modify the initial solution. In the process of generating and modifying the initial solution, this application considers the fuzzy normal processing time of each workpiece. When analyzing the completion time and average completion time of each machine with respect to the processing speed, fuzzy numbers are also used to represent the results, thus fully considering the fuzzy environment of the actual manufacturing process of steel continuous casting and rolling.

[0107] 4. This application incorporates local search strategies based on three neighborhood structures—three-element double exchange structure, three-element insertion structure, and three-element reverse redistribution structure—into the standard particle swarm optimization algorithm to improve its performance. Based on actual production conditions, this application considers the scheduling problems of single-machine and non-single-machine systems under fuzzy environments, which helps steel plants formulate production strategies and rationally arrange the allocation and scheduling of workpieces and machines. This provides valuable assistance in reducing manufacturing cycles, increasing the overall capacity of the manufacturing system, and reducing production costs.

[0108] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A serial batch processing scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment, characterized in that, include: For the manufacturing process scheduling of steel continuous casting and rolling, multiple workpieces and multiple machines are encoded and analyzed based on machine index to obtain a vectorized list of workpiece processing sequences. In response to scheduling instructions, identify the corresponding scheduling type as either single-machine scheduling or parallel machine scheduling; When the scheduling type is single-machine scheduling, time analysis is performed on each job, and multiple batches are generated sequentially for scheduling. When the scheduling type is parallel machine scheduling, based on the constructed hybrid particle swarm algorithm, the algorithm parameters are initialized, the population is initialized through the heuristic algorithm HA2 in the hybrid particle swarm algorithm, fitness is analyzed, and the velocity and position of the particles are updated. With the objective of minimizing the maximum completion time, a neighborhood structure set is constructed. Based on this neighborhood structure set, a variable neighborhood descent local search strategy is executed to update the local optimum. and global optimal The neighborhood structure set includes a three-element double exchange structure, a three-element insertion structure, and a three-element reverse redistribution structure. If the iteration exponent does not exceed the iteration threshold, update the velocity and position of each particle; if the iteration exponent exceeds the iteration threshold, determine that the algorithm has ended and output the target result to characterize the workpiece and machine allocation and scheduling information. The serial batching machine in the steel continuous casting and rolling manufacturing process is one of the heating furnace, roughing mill and finishing mill in the continuous casting and rolling production line, and the workpiece is steel billet or steel product. In the case where the scheduling type is parallel machine scheduling, A workpiece is configured in Batch on different serial batching machines Processing, the first The speed of each machine is Assume all workers have the same learning rate. And the workpieces move at the same rate The situation has worsened; the preparation time for each batch of workpieces is... ; The processing time for each workpiece is represented by a trapezoidal fuzzy number and satisfies the expression: ,in, , Indicates the workpiece processing time at and Between, most likely The actual processing time of the workpiece is , It is the processing of the first One workpiece The start time, It is a constant. ; batch The fuzzy processing time satisfies the expression: ； Fuzzy completion time of the machine Satisfying the expression: In the formula, Indicates the start time of machine processing. These represent the first batch, the second batch, and so on. One batch; The maximum completion time satisfies the expression: ; The heuristic algorithm HA2 initializes the population by including an initial solution generation phase and an initial solution modification phase; the initial solution generation phase includes: S11: Calculate the number of positions Load an equal number of jobs onto each machine and make... ;when Can be When divisible, ;when Cannot be When divisible, ; S12: Sort all workpieces in non-decreasing order according to the expected value of the fuzzy normal processing time for each workpiece to obtain the job list; S13: Place the first workpiece Assign to the first position on the first machine, and continue assigning until the workpiece is... Assigned to the Taiwanese machine The first position, ; S14: Move the next m workpieces from the machine at the second position. All the way up to the first machine ,make ; S15: Repeat steps S13 and S14 until all workpieces have been assigned; The initial solution modification phase includes: S21: Calculate the completion time of the last task on each machine. ; S22: Use This indicates the completion time for each machine relative to its processing speed; S23: Calculation The average value, i.e. , if Then proceed to step S24; S24: According to The values ​​are sorted in non-decreasing order for the machines to obtain a new list of machines. ; S25: Select two machines with the smallest and largest indices, and randomly swap jobs at the same position.

2. The serial batch processing scheduling method based on hybrid particle swarm optimization algorithm in a fuzzy environment as described in claim 1, characterized in that, The hybrid particle swarm optimization algorithm is constructed by fusing the improved particle swarm optimization algorithm IPSO, the heuristic algorithm HA2, and the variable neighborhood descent local search strategy; the neighborhood structure set includes neighborhood structures NS1, NS2, and NS3, which correspond to the three-element double exchange structure, the three-element insertion structure, and the three-element reverse redistribution structure, respectively. The workpiece processing sequence list is a particle position vector. ,in, From 1 to Any integer in; The operation steps of the neighborhood structure NS1 are as follows: from the particle position vector Select three elements ; swap and Then change and ; The operation steps of the neighborhood structure NS2 are as follows: from the particle position vector Select three elements ;exist Insert before ,exist Insert after and Elements between; The operation steps of the neighborhood structure NS3 are as follows: from the particle position vector Select three elements ;Will and The elements between are redistributed in reverse order, and then... and Repeat the operation.

3. The serial batch processing scheduling method based on hybrid particle swarm optimization algorithm in a fuzzy environment as described in any one of claims 1-2, characterized in that, The construction of the neighborhood structure set, and the execution of a variable neighborhood descent local search strategy based on the neighborhood structure set, include: Set up neighborhood structures NS1, NS2, and NS3 for the corresponding three-element double-exchange structure, three-element insertion structure, and three-element reverse redistribution structure, and set the initial solution s as the current global optimum. or local optimum ; Perform a local search based on the neighborhood structure NS1. If a better solution s' than s is found, then let s = s'. Perform a local search based on the neighborhood structure NS2. If a better solution s'' is found than s, then let s = s''. Perform a local search based on the neighborhood structure NS3. If a better solution s''' is found than s, then let s = s'''.

4. The serial batch processing scheduling method based on hybrid particle swarm optimization algorithm in a fuzzy environment as described in any one of claims 1-2, characterized in that, The algorithm parameters include the iteration threshold. Population size Self-learning parameters Social learning parameters Inertia weight The initial velocity of the particles and initial position Random parameters , Set the iteration index ;in, It is a whole and represents the iteration exponent; inertia weight Employing a linear decreasing strategy, with random parameters and They follow a uniform distribution U(0,1) and are randomly regenerated in each iteration to ensure the randomness of particle updates; The analysis of fitness, updating particle velocity and position, includes: Calculate the fitness of each particle and set the local optimum for the first iteration. Then select the particle with the minimum fitness as the global optimum. ; Update the velocity and position of each particle, and the velocity update process satisfies the expression: ； The position update process satisfies the expression: ; This indicates that the e-th particle is in the... Speed ​​at the next iteration This indicates that the e-th particle is in the... Speed ​​during the next iteration; This indicates that the e-th particle is in the... Position at the next iteration This indicates that the e-th particle is in the... The position at the next iteration.

5. The serial batch processing scheduling method based on hybrid particle swarm optimization algorithm in a fuzzy environment as described in any one of claims 1-2, characterized in that, When the scheduling type is single-machine scheduling, the process involves performing time analysis on each job and generating multiple batches sequentially for scheduling, including: Sort all jobs in non-decreasing order of their fuzzy normal processing time to obtain a job list; If the assignment list contains no less than The first assignment will be the previous one. One job is assigned to a batch; otherwise, the remaining jobs are assigned to a batch and scheduled according to the order in which the batches are generated.

6. A serial batch processing scheduling system based on a hybrid particle swarm optimization algorithm in a fuzzy environment, based on the serial batch processing scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment as described in any one of claims 1-5, characterized in that, include: The coding module is used for manufacturing process scheduling for continuous casting and rolling of steel. It performs coding analysis on multiple workpieces and multiple machines based on machine index to obtain a vectorized list of workpiece processing sequences. The type identification module is used to identify the corresponding scheduling type as single-machine scheduling or parallel machine scheduling in response to scheduling instructions. The single-machine scheduling module is used to perform time analysis on each job and generate multiple batches in sequence for scheduling when the scheduling type is single-machine scheduling; The initial scheduling module is used to initialize algorithm parameters based on the constructed hybrid particle swarm algorithm when the scheduling type is parallel machine scheduling. It initializes the population through the heuristic algorithm HA2 in the hybrid particle swarm algorithm, analyzes fitness, and updates the velocity and position of particles. The local search module is used to determine the goal of minimizing the maximum completion time, construct a neighborhood structure set, and execute a variable neighborhood descent local search strategy based on the neighborhood structure set to update the local optimum. and global optimal The neighborhood structure set includes a three-element double exchange structure, a three-element insertion structure, and a three-element reverse redistribution structure. The result output module is used to update the velocity and position of each particle if the iteration exponent does not exceed the iteration threshold; if the iteration exponent exceeds the iteration threshold, the algorithm is terminated and the target result is output to characterize the workpiece and machine allocation and scheduling information.

7. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements a serial batch scheduling method based on a hybrid particle swarm optimization algorithm in a fuzzy environment as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, implement the serial batch processing scheduling method based on the hybrid particle swarm algorithm in a fuzzy environment as described in any one of claims 1 to 5.

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