Calculation-efficient discrete industrial production arrangement optimization method

Abstract

The invention provides a discrete industrial production arrangement optimization method with efficient calculation, and relates to the technical field of industrial production process arrangement management, and the method comprises the steps: employing continuous variables to represent the operation arrangement and processing progress of production equipment; on the basis of continuous variables, a unified mathematical form is adopted to describe the production process, constraint conditions are constructed, and the constraint conditions comprise constraint conditions for the general industrial production process and discrete characteristic constraints for the discrete production process; production process parameters are obtained, numerical values of the parameters in constraint conditions are calculated, and based on discrete characteristic constraints, an optimal production arrangement problem with the lowest energy consumption cost as a target is constructed; and calling a commercial mixed integer linear programming solver to solve an optimal production arrangement problem, and executing the solved optimal production arrangement through a factory automatic production management system. By the adoption of the scheme, production arrangement optimization can have better calculation performance and expandability.

Description

Technical Field

[0001] The present application relates to the technical field of industrial production process scheduling and management, and in particular to a computationally efficient discrete industrial production scheduling optimization method and device. Background Art

[0002] Energy consumption during the production process is a major component of overall energy consumption for typical industrial users, and the resulting electricity costs are a significant cost source for typical energy-intensive industries. Under time-of-use electricity pricing, industrial users can plan their production ahead of time, thereby avoiding peak electricity prices and reducing their electricity costs. This can be achieved by mathematically describing the industrial production process and solving the production scheduling optimization problem using computers. The theoretical basis for this is the mathematical modeling of the technical constraints of the industrial production process. Specifically, based on the characteristics of the product handling, typical industrial production processes can be divided into two categories: continuous and discrete. In discrete production processes, product volume is not a continuous function of time, but rather requires a long period of input to produce a batch of steel. For example, in the secondary steelmaking process, an electric arc furnace requires 80 minutes of heating to fully melt a batch of steel. Until the steel is completely melted, the intermediate material cannot be fed into the next process. The mathematical models of discrete industrial production are more complex, requiring more computationally efficient discrete industrial production scheduling optimization methods.

[0003] Existing discrete industrial production scheduling optimization methods are mainly based on resource-task networks. Resource-task networks regard manufacturing equipment and products as resources and production and transportation processes as tasks. They can accurately mathematically express the complex operational requirements of discrete production processes and provide standardized constraints for optimization problems such as production scheduling. Therefore, resource-task networks have been widely used in various scenarios. On this basis, the existing technology has expanded the resource-task network model to represent flexible adjustment patterns in the production process. In order to represent the complex constraints in the operation of discrete production processes, traditional resource-task network models introduce many binary variables to model the process of resource conversion and task execution, which results in low computational efficiency of production scheduling optimization methods based on these models. In addition, typical resource-task networks model each batch of products separately, which means that the scale of constraints is proportional to the production target, further limiting the scalability of resource-task network models.

[0004] With the large-scale implementation of industrial demand response, the actual scalability requirements of resource task network models must be reasonably considered, especially in scenarios such as when multiple industrial users coordinate and optimize their production processes. To this end, the second prior art focuses on the computational complexity of production scheduling problems based on resource task networks, making the calculations more tractable through methods such as adding cuts. However, the modeling technology of resource task networks has not been optimized. The third prior art proposes a continuous time-based modeling method, which focuses on the accuracy of load tracking without addressing the scalability of the model.

[0005] In summary, existing research lacks sufficient attention to the computational performance scalability of resource-task network models. It is necessary to fundamentally solve the computational complexity problem by improving modeling technology, so as to achieve computationally efficient discrete industrial production scheduling optimization without affecting the representability of the original model. Summary of the Invention

[0006] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, the first purpose of this application is to propose a computationally efficient discrete industrial production scheduling optimization method, which enables the computer to give the optimal production schedule for the discrete production process in a manner with fewer binary variables, shorter solution time, and better scalability, thereby greatly improving computational efficiency.

[0008] The second object of this application is to provide a computer device.

[0009] A third object of the present application is to provide a non-transitory computer-readable storage medium.

[0010] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a computationally efficient discrete industrial production scheduling optimization method, including: using continuous variables to represent the operation schedule and processing progress of production equipment; based on continuous variables, using a unified mathematical form to describe the production process and construct constraints, wherein the constraints include constraints for general industrial production processes and discrete characteristic constraints for discrete production processes; obtaining production process parameters, and calculating the values ​​of the parameters in the constraints, and based on the discrete characteristic constraints, constructing an optimal production scheduling problem with the goal of minimizing energy costs; calling a commercial mixed integer linear programming solver to solve the optimal production scheduling problem, and executing the solved optimal production schedule through the factory automation production management system.

[0011] To achieve the above-mentioned purpose, the second embodiment of the present invention proposes a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned computationally efficient discrete industrial production scheduling optimization method is implemented.

[0012] In order to achieve the above objectives, a third aspect of the present invention provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by a processor, can perform a computationally efficient discrete industrial production scheduling optimization method.

[0013] The computationally efficient discrete industrial production scheduling optimization method and device of the embodiment of the present application uses continuous variables to represent the operating time and processing progress of production equipment in a unified manner, and adopts a unified modeling method for ordinary production processes and flexible and adjustable production processes. Based on these continuous variables, a computationally efficient mathematical form is designed for the technical constraints in the discrete production process, including resource balance, task execution, waiting time and production target constraints. Finally, based on the proposed method, the constraints of the discrete production process are constructed, and the optimal production scheduling problem is constructed with the goal of minimizing the production energy cost. This embodiment provides a discrete production process production scheduling optimization method with fewer binary variables, shorter solution time and better scalability, which greatly improves the computational efficiency without affecting the optimality of the production schedule.

[0014] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0016] Figure 1 A flowchart of a computationally efficient discrete industrial production scheduling optimization method provided in Example 1 of the present application. DETAILED DESCRIPTION

[0017] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0018] The following describes a computationally efficient discrete industrial production scheduling optimization method and apparatus according to an embodiment of the present application with reference to the accompanying drawings.

[0019] Figure 1 A flowchart of a computationally efficient discrete industrial production scheduling optimization method provided in Example 1 of the present application.

[0020] like Figure 1 As shown, the computationally efficient discrete industrial production scheduling optimization method includes the following steps:

[0021] Step 101, using continuous variables to represent the operation schedule and processing progress of production equipment;

[0022] In this embodiment, i and r represent the task index and resource index respectively, k represents the operating status index of the production equipment, and the production schedule of the factory is optimized in discrete time intervals t = 1, 2, ..., T, where T is the number of time periods;

[0023] In this embodiment, the operating conditions of the production equipment in each time period are modeled as the normalized operating time in each operating state, thereby more accurately modeling and controlling the operation of the equipment. The processing progress is modeled as the normalized processing completion amount of the production batch, so as to monitor and control the progress of the production process. The specific process includes:

[0024] 1) Model the operation of production equipment in each period as the normalized operation time in each operating state: use the continuous variable D i,k,t Indicates the time that task i is in state k within time period t; the task-resource association matrix G is introduced to represent the resource changes of each task. The matrix element g r,i,k It represents the change (generation or consumption) of resource r caused by task i in state k per unit time.

[0025] For the convenience of description, this embodiment assumes by default that the state k=0 corresponds to the idle state of the task (g r,i,0 =0), k=1 and k=2 represent the states corresponding to the minimum and maximum processing speeds after the task starts running. For a task with only one processing speed, only two states need to be modeled: k=0 (off) and k=1 (on).

[0026] 2) Model the processing progress as the standardized processing completion amount of resources: use the continuous variable R r,t To express the amount of resources, R r,t It can be understood as the task progress of generating resource r. r,t =0(1) means no progress (task completed), while R r,t ∈(0,1) indicates that processing is in progress.

[0027] Step 102: Based on continuous variables, a unified mathematical form is used to describe the production process and construct constraints. The constraints include constraints for general industrial production processes and discrete characteristic constraints for discrete production processes.

[0028] In this embodiment, a unified mathematical form is used to describe common production processes and flexible and adjustable production processes to characterize their energy consumption and material conversion characteristics. For general industrial production processes, constraints such as resource balance, task execution, and production targets are established. For the unique characteristics of discrete production processes, a computationally efficient discrete characteristic modeling method is designed based on continuous variables, including non-interruptible characteristics, batch processing characteristics, and waiting time constraints. The specific process includes:

[0029] (1) For general industrial production processes, construct constraints such as resource balance, task execution, and production goals.

[0030] 1) Under the continuous decision variable model of the task, the resource changes brought about by the task are also continuous. The resource balance constraint can be expressed as:

[0031]

[0032] Tasks can only process products in batches, and generally do not allow multiple batches to accumulate at the same stage, so there are:

[0033]

[0034] 2) According to the definition, D i,k,t is non-negative and does not exceed the length of the time interval, and the total running time of task i in time period t is equal to the length of the time interval δ, which can be expressed as the task execution constraint:

[0035]

[0036] 3) In the last time period t end At the end, the final product end The number of products needed to achieve the target This can be expressed as a production target constraint:

[0037]

[0038] (2) In view of the unique characteristics of discrete production processes, a computationally efficient discrete characteristic modeling method is designed based on continuous variables, including non-interruptible characteristics, batch processing characteristics, waiting time constraints, etc.

[0039] 1) Once a non-interruptible task starts running, it cannot be stopped until the batch is processed. This type of task needs to meet where r i+ is the resource generated by task i. In order to avoid bilinear terms, a binary variable u is introduced i,t To characterize the processing status of the task, where u i,t =1 means the current process is in progress. The above non-interruptible characteristic constraint can be rewritten as:

[0040]

[0041] 2) In discrete production processes, subsequent tasks can only be completed after the previous task is completely processed (i.e., R r,t=1). To reflect this feature, "output" and "input" tasks are added between the waiting task and the processing task. An output (input) task remains idle until the resource it consumes reaches 1; in the same time period when the previous task is completed, it is set to 0, and the resource it generates is set to 1, thus meeting the discrete requirements of batch processing. i,t = 1, the batch processing characteristics of the output (input) task can be expressed as:

[0042]

[0043] Among them, the resources consumed by the output (input) task r i- ,make For the resource r generated by the output (input) task i+ ,set up

[0044] 3) Waiting time constraint: The waiting time between the intermediate product production stages is modeled as a task. The waiting task consumes the resources transferred by the previous output task and generates resources for the input task. Waiting itself does not actually generate resources, so the resources here only represent the progress of waiting. The parameter setting of the consumption (generation) rate is to ensure that the sum of the transfer and waiting time of the intermediate products meets the requirements. To achieve this, the minimum and maximum generation rates of the waiting task are set to 1 / (w i +W i ) and 1 / w i , where w i and W i are the transport time and the maximum waiting time of task i, respectively. Naturally, waiting tasks are also non-interruptible tasks. Finally, since the waiting time has been taken into account by waiting tasks, other tasks must start running immediately when their input materials arrive and cannot remain idle. This can be expressed as:

[0045]

[0046] Step 103: Obtain production process parameters, calculate the values ​​of the parameters in the constraint conditions, and construct an optimal production scheduling problem with the goal of minimizing energy costs based on discrete characteristic constraints;

[0047] In this embodiment, production process parameters are obtained, including production equipment rated power, production process processing time, material transmission time, maximum waiting time, production target, etc., and the values ​​of the parameters in the constraint conditions are calculated accordingly.

[0048] In this embodiment, let the electricity price in time period t be λ t , the power consumption of device i when running in state k is P i,k, the objective function of the optimal production scheduling problem is to minimize the electricity cost, then the objective function can be expressed as:

[0049]

[0050] Step 104 : Invoke a commercial mixed integer linear programming solver to solve the optimal production scheduling problem, and execute the optimal production scheduling obtained through the factory automation production management system.

[0051] The computationally efficient discrete industrial production scheduling optimization method of the embodiment of the present application uses continuous variables to represent the operating time and processing progress of production equipment in a unified manner, and adopts a unified modeling method for ordinary production processes and flexible and adjustable production processes. Based on these continuous variables, a computationally efficient mathematical form is designed for the technical constraints in the discrete production process, including resource balancing, task execution, waiting time and production target constraints. Finally, based on the proposed method, the constraints of the discrete production process are constructed, and the optimal production scheduling problem is constructed with the goal of minimizing the production energy cost. This embodiment provides a discrete production process production scheduling optimization method with fewer binary variables, shorter solution time and better scalability, which greatly improves the computational efficiency without affecting the optimality of the production arrangement.

[0052] In order to implement the above embodiments, the present invention further proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in the above embodiments is implemented.

[0053] In order to implement the above embodiments, the present invention further proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method of the above embodiments is implemented.

[0054] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0055] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0056] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0057] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0058] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0059] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0060] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0061] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A computationally efficient method for optimizing discrete industrial production schedules, characterized in that: include: Continuous variables are used to represent the operation schedule and processing progress of production equipment; Based on continuous variables, a unified mathematical form is used to describe the production process and construct constraints, wherein the constraints include constraints for general industrial production processes and discrete characteristic constraints for discrete production processes; Obtaining production process parameters, calculating the values ​​of the parameters in the constraint conditions, and constructing an optimal production scheduling problem with the goal of minimizing energy costs based on the discrete characteristic constraints; A commercial mixed-integer linear programming solver is used to solve the optimal production scheduling problem, and the optimal production schedule obtained is executed through the factory automation production management system.

2. The method according to claim 1, wherein The method further comprises: Let i and r represent the task index and resource index respectively, and k represents the operating status index of the generating equipment. The production schedule of the factory is optimized in the discrete time interval t = 1, 2, ..., T, where T is the number of time periods.

3. The method according to claim 2, wherein The continuous variables used to represent the operation schedule and processing progress of the production equipment include: The operation status of the production equipment in each period is modeled as the normalized operation time in each operating state, including: using the continuous variable D i,k,t represents the time that task i is in state k during time period t; The task-resource association matrix G is introduced to represent the resource changes of each task, and the matrix element g is used r,i,k represents the change in resource r per unit time caused by task i running in state k, where the change is the generated amount or consumed amount; Setting k = 0 corresponds to the idle state of the task, at which time g r,i,0 =0, k=1 and k=2 represent the states corresponding to the minimum and maximum processing speeds after the task starts running, respectively. For tasks with only one processing speed, k=0 represents closed and k=1 represents open during modeling; Model the processing progress as the standardized processing completion of resources, including: using the continuous variable R r,t Indicates the amount of resources, R r,t is the progress of the task of generating resource r, R r,t =0 means no progress, R r,t =1 means the task is completed, R r,t ∈(0,1) indicates that processing is in progress.

4. The method according to claim 3, wherein For common industrial production processes, build constraints, including: Assuming that the resource changes brought about by the task are continuous under the continuous decision variable modeling, the resource balance constraint is constructed as follows: Set the task to process products in batches, and do not allow multiple batches to accumulate at the same stage. The corresponding constraints are: Among them, R i,t Indicates the progress of task i; Setting D i,k,t is non-negative and does not exceed the length of the time interval. The total running time of task i in time period t is equal to the length of the time interval δ. The task execution constraint is constructed as follows: Set in the last time period t end At the end, the final product end Number of products reaching the target The production target constraints are constructed as follows:

5. The method according to claim 4, wherein For discrete production processes, construct discrete characteristic constraints, including: Once a non-interruptible task starts running, it cannot be stopped until the batch is processed. This is expressed as: r i+ is the resource generated by task i, introducing binary variable u i,t Represents the processing status of the task, u i,t =1 indicates that the task is in progress. The non-interruptible characteristic constraint is rewritten as: Set the discrete production process so that subsequent tasks can only be performed after the previous task is processed. Add output tasks and input tasks between waiting tasks and processing tasks. The output task or input task remains idle until the resource it consumes reaches 1. In the same time period when the previous task is completed, it is set to 0 and the resource it generates is set to 1. Introduce u i,t =1, the batch processing characteristics of the output task or input task are expressed as: For the resources consumed by the output task or input task r i- ,make For the resource r generated by the output task or input task i+ ,set up The waiting time between the intermediate product production stages is modeled as a task. The waiting task is set to consume the resources transferred by the previous output task and generate resources for the input task. Waiting itself does not generate resources. Resources represent the progress of waiting. The parameters of the consumption rate or generation rate are set to ensure that the sum of the transfer and waiting time of the intermediate product meets the requirements. The minimum and maximum generation rates of the waiting task are set to 1 / (w i +W i ) and 1 / w i , w i and W i The transportation time and the maximum waiting time of task i are set. The waiting task is set to be an uninterruptible task. Since the waiting time is taken into account by the waiting task, other tasks must start running immediately when their input materials arrive and cannot remain idle. The corresponding constraints are constructed as follows:

6. The method according to claim 1, wherein The production process parameters include the rated power of the production equipment, the production process processing time, the material transmission time, the maximum waiting time and the production target.

7. The method according to claim 3, wherein Set the electricity price for period t to λ t , the power consumption of device i when running in state k is P i,k , the objective function of the optimal production scheduling problem is to minimize the electricity cost, which can be expressed as:

8. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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