A method, device and equipment for scheduling workshop operations

Abstract

The present invention provides a scheduling method, device and equipment for workshop operations, wherein the scheduling method includes: obtaining an initial population corresponding to an initial scheduling plan for workpieces to be processed in a workshop and parameter information of workshop operations; determining a target optimization function of a constraint scheduling plan based on the parameter information of the workshop operations; and screening the initial scheduling plans in the initial population based on the optimization target value of the target optimization function and the machine utilization rate in each initial scheduling plan to obtain a first scheduling plan; obtaining workshop real-time status information at the same time; and optimizing the first scheduling plan based on the workshop real-time status information, the target optimization function and a preset rescheduling strategy to obtain a target scheduling plan. The scheduling method of the present invention optimizes the initial scheduling plan based on dynamic power and workshop real-time status information, which can reduce the production cost of the enterprise and improve the flexibility and stability of the scheduling plan.

Description

Technical Field

[0001] The present invention relates to the technical field of computer information processing, and in particular to a scheduling method, device and equipment for workshop operations. Background Art

[0002] With the intensification of market competition in the global manufacturing industry, the reduction of production energy, and the gradual diversification of customer needs, more and more manufacturing companies have begun to consider how to formulate reasonable scheduling methods to cope with the changing production environment and changing production tasks, so as to shorten the production cycle, reduce production costs and achieve the most stable scheduling, thereby improving their own competitiveness and customer satisfaction.

[0003] In the manufacturing industry, with the reduction of energy and the advancement of renewable energy technology, more and more companies are gradually building microgrids belonging to their own industrial parks in order to cope with the energy crisis, achieve energy conservation and emission reduction, and reduce production costs. However, due to the characteristics of its energy source, renewable energy often changes with changes in the environment and time, and the static workshop production environment is no longer suitable for today's changing production environment. Therefore, how to solve the problem of flexible workshops facing dynamic power and production environments has gradually become a hot topic of research.

[0004] In the prior art, the invention patent application with publication number CN116644902A proposes a method for multi-objective dynamic flexible job shop scheduling based on deep reinforcement learning for energy consumption. The method proposes to apply deep reinforcement learning to flexible job shop scheduling, divide the deep reinforcement learning network into high-level and low-level networks, and use the training results of the high-level network to capture the optimal experience value, and optimize the parameters of the low-level network, and finally select the optimal scheduling plan according to the optimal target value. This method optimizes the scheduling plan of the workshop as a whole through the optimization results of the high-level neural network. However, this method does not propose the dynamic production environment faced by the enterprise, and no abnormal events will occur in the idealized workshop scheduling process.

[0005] In the prior art, the invention patent application with publication number CN109784603A proposes a method for solving flexible job shop scheduling based on a hybrid whale swarm algorithm. The method uses random key encoding and rotation mechanism for mapping conversion, optimizes the initial population using the genetic method of the whale swarm algorithm, and finally optimizes the machine allocation and calculates the optimal fitness value through an improved genetic algorithm. This method solves the problems of low solution accuracy and slow convergence speed in the flexible job shop scheduling problem. However, the invention does not take into account the processing environment of dynamic power and dynamic production workshops, and fails to build a dynamic, energy-related production workshop.

[0006] In the prior art, the invention patent with publication number CN110836423A proposes a design technology for a green and energy-saving intelligent manufacturing workshop. This method takes into account the impact of harsh production environment on workshop production, and avoids energy waste caused by long-term equipment startup through the setting of controllers, humidity sensors and gas sensors, thereby achieving the purpose of energy saving. However, this method does not mention the dynamic energy consumption problem and multi-objective optimization problem in the production process.

[0007] In summary, although existing research has adopted deep reinforcement learning and swarm intelligence optimization algorithms to solve the scheduling problems of flexible job shops and effectively promoted the research on green factory scheduling problems, further innovation and improvement are still needed. It is necessary to consider both the adaptability of the enterprise's production workshop and the impact of external factors on the enterprise's production workshop. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a scheduling method, device and equipment for workshop operations to reduce the production cost of the enterprise and improve the flexibility and stability of the scheduling plan.

[0009] In order to solve the above technical problems, an embodiment of the present invention provides a scheduling method for workshop operations, comprising:

[0010] Acquire an initial population corresponding to an initial scheduling scheme for a workpiece to be processed in a workshop operation and parameter information of the workshop operation, wherein the initial population includes a plurality of different initial scheduling schemes for processing the workpiece to be processed;

[0011] Determine the target optimization function of the constraint scheduling scheme according to the parameter information of the workshop operation, wherein the optimization target values ​​corresponding to the target optimization function include minimizing the completion time, minimizing the energy consumption, minimizing the production power cost, and minimizing the stability of the scheduling scheme;

[0012] Based on the optimization target value of the target optimization function and the machine utilization rate in each initial scheduling plan, the initial scheduling plans in the initial population are screened to obtain a first scheduling plan, wherein the machine utilization rate is determined based on the insertability of the process corresponding to the initial scheduling plan and the number of insertable processes during the execution of the initial scheduling plan;

[0013] Get real-time status information of the workshop;

[0014] The first scheduling plan is optimized according to the real-time status information of the workshop, the target optimization function and the preset rescheduling strategy to obtain a target scheduling plan.

[0015] In one embodiment, the objective optimization function is constructed by the following formula: ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] represents the minimization of electricity cost; where, represents the jth process among the total number of processing processes N, =1, 2, 3, ..., N; represents the kth machine among the total number of machines m, =1, 2, 3, ..., m; Indicates The process is Processing energy consumption on the machine; express Idle energy consumption on machines; , , They represent the first electricity price during peak periods, the second electricity price during off-peak periods, and the third electricity price during off-peak periods; , , Respectively represent The first processing time of the process on machine k during the peak electricity price period, the second processing time during the off-peak electricity price period, and the third processing time during the off-peak electricity price period; , , They represent the first idling time of machine k during the peak electricity price period, the second idling time during the flat electricity price period, and the third idling time during the valley electricity price period; represents the fixed energy consumption of the workshop; represents the average unit electricity price; Indicates The transfer energy consumption generated by transferring the process to the machine for processing;

[0021] represents minimizing energy consumption; where, express No. The processing energy consumption of the machine to process the workpiece; express No. Idle energy consumption of the machine;

[0022] represents the minimum completion time; where is the completion time of the last process to complete the last workpiece, represents the first completion time corresponding to the initial scheduling scheme;

[0023] represents the minimization stability; where It indicates the second completion time corresponding to the completion of all workpieces after the abnormal event occurs. Indicates the number of available machines after an abnormal event occurs, Indicates that the initial scheduling scheme corresponds to Number of processing steps of machine No.; Indicates that after an abnormal event occurs The number of processing steps of the machine.

[0024] In one embodiment, based on the optimization target value of the target optimization function and the machine utilization rate in each initial scheduling solution, the initial scheduling solutions in the initial population are screened to obtain a first scheduling solution, including:

[0025] Based on the optimization target value of the target optimization function, the initial population is initialized to obtain the optimal population corresponding to the initial population;

[0026] A first scheduling solution in the optimal population is determined according to the machine utilization in each initial scheduling solution in the optimal population.

[0027] In one embodiment, based on the optimization target value of the target optimization function, the initial population is initialized to obtain the optimal population corresponding to the initial population, including:

[0028] Based on the optimization target value of the target optimization function, performing non-dominated quick sorting and congestion calculation on the initial population, and determining the congestion and level information of each initial scheduling scheme in the initial population;

[0029] According to the congestion degree and level information of each initial scheduling scheme in the initial population, and based on a preset competition algorithm, the initial scheduling schemes in the initial population are processed by competition to obtain a population after competition;

[0030] The population after the competition is iteratively initialized based on a preset initialization algorithm and a preset number of iterations to obtain the optimal population.

[0031] In one embodiment, the real-time status information of the workshop represents a collection of abnormal event information and dynamic power information generated when the job workshop processes the workpiece based on the first scheduling scheme;

[0032] The scheduling method for workshop operations further includes:

[0033] Based on the dynamic power information and the power system energy supply limitation, and according to the scheduling information in the first scheduling plan, the first scheduling plan is optimized to obtain a first optimized scheduling plan.

[0034] In one embodiment, according to the scheduling information in the first scheduling scheme, the first scheduling scheme is optimized for the first time to obtain a first optimized scheduling scheme, including:

[0035] Determine, based on the scheduling information, a first power cost of idling the machine for processing the workpiece to be processed during an idle time period and a second power cost of executing a preset on / off strategy;

[0036] The first scheduling plan is optimized for the first time according to the first electricity cost and the second electricity cost to obtain the first optimized scheduling plan.

[0037] In one embodiment, the first scheduling scheme is optimized according to the real-time status information of the workshop, the target optimization function and the preset rescheduling strategy to obtain a target scheduling scheme, including:

[0038] According to the abnormal event information, the preset rescheduling strategy and the target optimization function, the first optimized scheduling scheme is optimized to obtain a second optimized scheduling scheme, and the second optimized scheduling scheme is used as the target scheduling scheme.

[0039] In one embodiment, the first optimization scheduling scheme is optimized according to the abnormal event information, the preset rescheduling strategy and the target optimization function to obtain a second optimization scheduling scheme, including:

[0040] Determine, according to the abnormal event information and the preset rescheduling strategy, a plurality of rescheduling schemes corresponding to the unprocessed workpiece information corresponding to the workpiece processed based on the first optimization scheduling scheme;

[0041] According to the optimization target value of the target optimization function, the plurality of rescheduling schemes are optimized to obtain a second scheduling scheme among the plurality of rescheduling schemes, wherein the second scheduling scheme is a rescheduling scheme that satisfies the optimization target value of the target optimization function;

[0042] According to the first optimization scheduling plan and the second scheduling plan, the second optimization scheduling plan is obtained.

[0043] An embodiment of the present invention further provides a scheduling device for workshop operations, comprising:

[0044] A first acquisition module is used to acquire an initial population corresponding to an initial scheduling scheme for workpieces to be processed in a job shop and parameter information of the job shop operations, wherein the initial population includes a plurality of different initial scheduling schemes for scheduling the job shop operations;

[0045] The second processing module is used to determine the target optimization function of the constrained scheduling scheme according to the workshop operation parameter information, wherein the optimization target value of the target optimization function includes minimizing the completion time, minimizing the energy consumption, minimizing the production power cost, and minimizing the stability of the scheduling scheme; based on the optimization target value of the target optimization function and the machine utilization rate in each initial scheduling scheme, the initial scheduling schemes in the initial population are screened to obtain the first scheduling scheme, wherein the machine utilization rate is determined based on the insertability of the process corresponding to the initial scheduling scheme during the execution of the initial scheduling scheme and the number of insertable processes;

[0046] The second acquisition module is used to obtain real-time status information of the workshop;

[0047] The second processing module optimizes the first scheduling plan according to the real-time status information of the workshop, the target optimization function and the preset rescheduling strategy to obtain a target scheduling plan.

[0048] An embodiment of the present invention further provides a computing device, comprising:

[0049] A memory for storing one or more programs;

[0050] One or more processors are used to execute the one or more programs to implement the above-mentioned method.

[0051] The above solution of the present invention includes at least the following beneficial effects:

[0052] The above scheme of the present invention provides a scheduling method, device and equipment for workshop operations, wherein the scheduling method includes: obtaining an initial population corresponding to an initial scheduling scheme for a workpiece to be processed in the workshop operation and parameter information of the workshop operation, wherein the initial population includes multiple different initial scheduling schemes for processing the workpiece to be processed; determining the target optimization function of the constraint scheduling scheme according to the parameter information of the workshop operation, wherein the optimization target value of the target optimization function includes minimizing the completion time, minimizing energy consumption, minimizing the production power cost and minimizing the stability of the scheduling scheme; based on the optimization target value of the target optimization function, screening the initial scheduling schemes in the initial population to obtain a first scheduling scheme; and obtaining the real-time status information of the workshop; and optimizing the first scheduling scheme according to the real-time status information of the workshop, the target optimization function and the preset rescheduling strategy to obtain the target scheduling scheme. The scheduling method provided by the above scheme of the present invention dynamically optimizes the initial scheduling scheme based on dynamic power and real-time status information of the workshop to improve the flexibility, stability and adaptability of the scheduling scheme, thereby enabling the enterprise to achieve the production goals of energy conservation, emission reduction, cost reduction and efficiency improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of a scheduling method for workshop operations provided by an embodiment of the present invention;

[0054] Figure 2 is a schematic diagram of a flexible job shop scheduling model provided by an optional embodiment of the present invention;

[0055] Figure 3 is a flow chart for determining the execution of a preset switching strategy by a machine based on the electricity cost under a time-of-use electricity price provided by an optional embodiment of the present invention;

[0056] Figure 4 is a power consumption comparison curve diagram of an initial scheduling scheme and a scheme based on power consumption adjustment provided by an optional embodiment of the present invention;

[0057] Figure 5 is a convergence diagram of power costs of rescheduling schemes under various initialization algorithms provided by an optional embodiment of the present invention;

[0058] Figure 6 is a convergence diagram of the stability of the rescheduling scheme under various initialization algorithms provided by an optional embodiment of the present invention;

[0059] Figure 7 is a convergence diagram of the maximum completion time of the rescheduling scheme under various initialization algorithms provided by an optional embodiment of the present invention;

[0060] Figure 8It is a module block diagram of a scheduling device for workshop operations provided by an optional embodiment of the present invention;

[0061] Fig. 9 is a schematic block diagram of an electronic device provided by an optional embodiment of the present invention; and

[0062] Fig.10 is a schematic block diagram of a computing device provided by an optional embodiment of the present invention. DETAILED DESCRIPTION

[0063] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0064] In the following description, certain specific details are set forth for the purpose of illustrating the various disclosed embodiments to provide a thorough understanding of the various disclosed embodiments. However, those skilled in the relevant art will recognize that the embodiments may be practiced without one or more of these specific details. In other cases, well-known devices, structures, and techniques associated with the present application may not be shown or described in detail to avoid unnecessarily obscuring the description of the embodiments.

[0065] References throughout the specification to "one embodiment" or "an embodiment" indicate that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of "in one embodiment" or "in an embodiment" in various places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any manner in one or more embodiments.

[0066] In the following description, in order to clearly show the structure and working mode of the present invention, many directional words will be used for description, but the words "front", "back", "left", "right", "outside", "inside", "outward", "inward", "up", "down", etc. should be understood as convenient terms and should not be understood as restrictive terms.

[0067] like Figure 1 As shown, an embodiment of the present invention provides a scheduling method 10 for workshop operations, comprising:

[0068] Step 11, obtaining an initial population corresponding to an initial scheduling scheme for workpieces to be processed in a workshop operation and parameter information of the workshop operation, wherein the initial population includes a plurality of different initial scheduling schemes for processing the workpieces to be processed;

[0069] Step 12, determining the target optimization function of the constraint scheduling scheme according to the parameter information of the workshop operation, the optimization target values ​​of the target optimization function include minimizing the completion time, minimizing the energy consumption, minimizing the production power cost, and minimizing the stability of the scheduling scheme;

[0070] Step 13, based on the optimization target value of the target optimization function and the machine utilization rate in each initial scheduling plan, the initial scheduling plans in the initial population are screened to obtain a first scheduling plan, wherein the machine utilization rate is determined based on the insertability of the process corresponding to the initial scheduling plan and the number of insertable processes during the execution of the initial scheduling plan;

[0071] Step 14, obtaining real-time status information of the workshop;

[0072] Step 15, optimizing the first scheduling solution according to the real-time status information, the target optimization function and the preset rescheduling strategy to obtain the target scheduling solution.

[0073] In this embodiment, the real-time status information of the workshop is the generated environment information during the process of the workshop processing the workpiece to be processed, including but not limited to various types of abnormal events occurring during the processing;

[0074] The parameter information of the workshop operation includes but is not limited to the total number of workpieces to be processed, the total number of machines, the total number of processes for processing workpieces, the workshop operation energy consumption, the machine operation time and dynamic power information, etc.; here, the dynamic power information may include the real-time energy consumption of the machine during the processing, the energy supply information of the power grid environment center and the power consumption cost, the workshop operation energy consumption may include the processing energy consumption, idling energy consumption, workshop fixed energy consumption, transfer energy consumption, etc. of the machine, and the machine operation time includes the processing time and idling time of the machine, etc.;

[0075] Based on the parameter information of the workshop operation, the target optimization function of the constraint scheduling scheme is determined, and the dynamically changing parameters in the processing scheduling process are used as the optimization target. The initial scheduling scheme is screened in combination with the machine utilization rate in each initial scheduling scheme to ensure the accuracy of the subsequent optimization of the initial scheduling scheme, so as to enhance the adaptability and flexibility of the final target scheduling scheme. Here, the machine utilization rate is determined based on the insertable process corresponding to the initial scheduling scheme during the execution of the initial scheduling scheme and the number of insertable processes, which will be explained in detail later.

[0076] Here, the following constraints need to be met during the optimization process:

[0077] At any given moment, a machine can only process one operation of a workpiece;

[0078] There is no processing priority constraint for different workpieces;

[0079] At any given moment, the total energy consumption of the machine must meet a threshold, exceeding which processing adjustments are required;

[0080] The next process of a workpiece cannot affect the processing of the previous process of the workpiece.

[0081] It should be known that for all the workpieces to be processed in any order information, without considering the dynamic power information during the workshop processing and the influence of the workshop production environment, all the workpieces to be processed can have multiple different initial scheduling schemes, and each initial scheduling scheme corresponds to different scheduling information; it should be known that when the workpieces to be processed in any order information are determined, the number of machines for processing each workpiece in the batch of workpieces, the number of processes for the corresponding machines to process the workpiece, and the processing time of each process are also determined;

[0082] In a realizable example, the description of all workpieces to be processed in any order information is as follows:

[0083] The current order information has workpieces to be processed, each workpiece is indexed Said that The workpiece is represented as , , each artifact is independent of each other; each artifact Both Each workpiece Each process is represented by , , ; corresponding to Machines for processing, each machine with index For each process There is a set of candidate machines ( ) is capable of carrying out the process; In the machine The processing time on , and the purpose of scheduling is to assign machines that meet the conditions to each process so that the optimization goal corresponding to the scheduling result is optimal; it should be known that at any time, the machine cannot process two processes at the same time;

[0084] In order to ensure the quality of the first scheduling solution determined later, the processing procedures can be randomly arranged through the Piecewise (segmented) chaotic mapping strategy to obtain multiple different initial scheduling solutions corresponding to the batch of workpieces, and the set of multiple different initial scheduling solutions is used as the initial population; the specific description of randomly arranging the processing procedures through the Piecewise (segmented) chaotic mapping strategy is as follows:

[0085] Step 111, traverse the process set of the batch of workpieces , The sequential processing steps of the batch of workpieces are included, that is, each step of each workpiece is traversed sequentially;

[0086] Step 112, construct a dimension , the size is Chaotic sequence , its size and process set The size is equal. The Piecewise chaos mapping function expression is as follows:

[0087] ;

[0088] in is a chaotic variable, initialized Randomly select a value in [0, 1], represents the ordinal number of the chaotic variable, is a piecewise control factor, which takes values ​​in [0, 0.5] and is used to divide the piecewise function into four parts. , the chaotic orbit value range is: (0, 1);

[0089] Step 113, map the Piecewise chaotic sequence to the solution space to generate an initial process set , It can be obtained through the chaos mapping function:

[0090]

[0091] in, and They represent the lower and upper bounds of the random values ​​of the workpiece position sequence respectively;

[0092] Step 114, converting the position sequence into process information.

[0093] In this embodiment, the first scheduling scheme is the optimal solution for the initial population. It should be known that the first scheduling scheme is an initial scheduling scheme that is not affected by abnormal events. Furthermore, the workpieces to be processed are processed and scheduled based on the scheduling information of the first scheduling scheme to reduce the cost and energy consumption of production and processing in the workshop. Here, the scheduling information of the first scheduling scheme may include multiple processing procedures for each of the multiple workpieces to be processed, the machines corresponding to each processing procedure, and the processing time corresponding to each processing procedure. In the process of processing and scheduling the workpieces to be processed through the first scheduling scheme, the first scheduling scheme is dynamically adjusted and optimized based on the real-time status information of the workshop and the target optimization function, which can improve the flexibility and adaptability of the first scheduling scheme, thereby improving the ability of the workshop to respond to sudden abnormal events, while ensuring the cost and energy consumption of production and processing in the workshop.

[0094] Here, the real-time status information of the workshop is a collection of abnormal events and dynamic power information that occur in the workshop during the process of processing and scheduling workpieces according to the first scheduling plan; here, the dynamic power information may include: the real-time energy consumption information corresponding to the scheduling plan at any time and the energy supply information of the microgrid system at the corresponding time. Due to the various abnormal events and power supply fluctuations that may occur in the production process (the energy supply information of the microgrid system at the corresponding time), the subsequent scheduling of the first scheduling plan and the subsequent processing scheduling process of the unprocessed scheduled workpieces will be affected; here, based on the changes in the production environment caused by the abnormal events and power supply fluctuations corresponding to the real-time status information of the workshop, the preset rescheduling strategy, and the constraints of the target optimization function in the processing process, the first scheduling plan is adjusted and optimized in real time and dynamically to obtain a target scheduling plan that meets the optimization target value of the target optimization function, so as to improve the flexibility, stability and adaptability of the scheduling plan; processing workpieces according to the target scheduling plan can optimize the production and processing plan of the enterprise and effectively reduce the production cost of the enterprise, so that the enterprise can achieve the production goals of energy conservation, emission reduction, cost reduction and efficiency improvement, which is of practical significance for promoting industrial green production.

[0095] The scheduling method 10 provided in the above embodiment of the present invention, when applied in a specific application, Figure 2As shown, first, a flexible job shop scheduling model that can be rescheduled under dynamic power information and real-time status information of the workshop is constructed in the computer system. The workshop scheduling model includes a workshop environment with various abnormal events, a power grid environment center, and a workshop scheduling center. Among them, the power supply is provided by the power grid environment center, which includes a microgrid system and a public power grid system; when the workshop is producing and processing, the microgrid system is given priority to supply power. When the workshop scheduling center determines that the microgrid system is insufficiently powered, it considers purchasing from the public power grid system; further, the workshop scheduling center will optimize the initial scheduling plan based on the power information and real-time status information of the workshop during the production and processing process and based on the constraint objective function to obtain a target scheduling plan to help manufacturing companies reduce production costs and save energy, and improve their ability to respond to emergencies;

[0096] Here, the microgrid system refers to a small system that uses distributed power sources of renewable green energy in a certain area to provide electricity according to user needs. It can be connected to the external power grid or operated in isolation. Its energy supply is affected by the environment and seasons. The dynamic power output power of the microgrid system is represented by the following function:

[0097] ;

[0098] in, Indicated in , the total power of wind, solar, hydroelectric and other renewable energy sources in the microgrid system; Represents time. The capacity and proportion of renewable energy will change over time. express The power generation of wind energy in renewable energy at the moment, express The power generation of solar energy in renewable energy at any moment, express The power generation of hydroelectric power among renewable energy sources at the moment, Indicates the power generation of other types of energy, such as biomass power generation, geothermal power generation, tidal power generation, etc.;

[0099] The public power grid system can be divided into four links: power generation, transmission, distribution and sales. Since the production mode of the industrial park will have a large demand for electricity at certain time points, which will cause the public power grid system to be overloaded, the public power grid system adopts a time-of-use electricity price strategy to alleviate the load of the public power grid system, stimulate the development of power storage technology and promote the development of green energy technology. The time-of-use electricity price strategy corresponding to the public power grid system is specifically represented by the following time-of-use electricity price function:

[0100] ;

[0101] in, Indicated in The electricity price, Indicated in time, The electricity price, Indicated in time, electricity price.

[0102] In an optional embodiment of the present invention, the target optimization function is constructed by the following formula: ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] represents the minimization of electricity cost; where, represents the jth process among the total number of processing processes N, =1, 2, 3, ..., N; represents the kth machine among the total number of machines m, =1, 2, 3, ..., m; Indicates The process is Processing energy consumption on the machine; express Idle energy consumption on machines; , , They represent the first electricity price during peak periods, the second electricity price during off-peak periods, and the third electricity price during off-peak periods; , , Respectively represent The first processing time of the process on machine k during the peak electricity price period, the second processing time during the off-peak electricity price period, and the third processing time during the off-peak electricity price period; , , They represent the first idling time of machine k during the peak electricity price period, the second idling time during the flat electricity price period, and the third idling time during the valley electricity price period; represents the fixed energy consumption of the workshop; represents the average unit electricity price; Indicates The transfer energy consumption generated by transferring the process to the machine for processing;

[0108] represents minimizing energy consumption; where, express No. The processing energy consumption of the machine to process the workpiece; express No. Idle energy consumption of the machine;

[0109] represents the minimum completion time; where is the completion time of the last process to complete the last workpiece, represents the first completion time corresponding to the initial scheduling plan;

[0110] represents the minimization stability; where It indicates the second completion time corresponding to the completion of all workpieces after the abnormal event occurs. Indicates the number of available machines after an abnormal event occurs, Indicates the initial scheduling scheme corresponding to Number of processing steps of machine No.; Indicates that after an abnormal event occurs The number of processing steps of the machine.

[0111] Here, it should be known that since each initial scheduling scheme in the initial population is not affected by abnormal events in the workshop scheduling process, the stability of each initial scheduling scheme in the initial population is 0.

[0112] In an optional embodiment of the present invention, the above step 13 may include:

[0113] Step 131, based on the optimization target value of the target optimization function, the initial population is initialized to obtain the optimal population corresponding to the initial population;

[0114] Here, the initial population is initialized based on the optimization target value to perform a first screening of the initial scheduling schemes in the initial population to obtain a representative optimal population, thereby ensuring the accuracy of the subsequent determination of the first scheduling scheme.

[0115] Furthermore, in an optional embodiment of the present invention, the above step 131 may include:

[0116] Step 1311, based on the optimization target value of the target optimization function, perform non-dominated quick sorting and congestion calculation on the initial population to determine the congestion and level information of each initial scheduling solution in the initial population;

[0117] Step 1312, according to the congestion degree and level information of each initial scheduling scheme in the initial population, and based on a preset competition algorithm, the initial scheduling schemes in the initial population are processed by competition to obtain a population after competition;

[0118] Step 1313, iteratively initialize the population after the competition based on a preset initialization algorithm and a preset number of iterations to obtain the optimal population.

[0119] In this embodiment, the non-dominated sorting genetic algorithm NSGA-II can be used to perform non-dominated quick sorting and congestion calculation of the initial population to ensure the diversity of the initial population and prevent the algorithm from falling into a local optimal solution, so as to ensure the accuracy of subsequent processing steps and further ensure the accuracy of the optimization of the initial scheduling scheme;

[0120] Further, according to the congestion and level information of each initial scheduling scheme, the initial population can be processed by a competition system through a preset competition algorithm. Here, the preset competition algorithm can be a tournament algorithm. The tournament algorithm and the initial scheduling schemes in the initial population are processed by competition based on the congestion and level information of each initial scheduling scheme, so as to achieve preliminary screening of the initial population, ensure that the corresponding initial scheduling schemes in the population after the competition can basically meet the requirements of the target optimization function, and further ensure the accuracy of the subsequent dynamic optimization adjustment of the scheduling scheme;

[0121] Furthermore, according to the ranking information corresponding to the initial scheduling scheme in the population after the competition (the higher the ranking, the more advantageous the scheduling information of the corresponding initial scheduling scheme), and through a preset initialization algorithm and a preset number of iterations, the population after the competition is iteratively initialized to obtain the optimal population that meets the target optimization function; here, the preset initialization algorithm can be a gray wolf algorithm, an improved gray wolf algorithm, or an improved golden jackal algorithm; when the population after the competition is initialized by the preset initialization algorithm, the initial scheduling scheme with a higher ranking in the population after the competition (for example, the top three initial scheduling schemes) is selected to iteratively initialize the remaining initial scheduling schemes to obtain the optimal population.

[0122] In an achievable example, the specific process of performing non-dominated quick sorting and crowding calculation of the initial population by using the non-dominated sorting genetic algorithm NSGA-II is as follows:

[0123] First set the constraint target , decision variables: ;

[0124] in, represents the size of the initial population, Represents the number of optimization target values ​​of the target optimization function; since there are no abnormal events in each initial scheduling plan in the initial population, the optimization target value minimizes the stability of the scheduling plan ,The objectives of the required optimization constraints are: minimizing the completion time, minimizing the energy consumption, and minimizing the cost of produced electricity; Represents a decision variable. The fitness value of each initial scheduling solution is stored in the scheduling solution of the initial population. Determine the location of the optimized fitness value.

[0125] Furthermore, based on the non-dominated sorting genetic algorithm NSGA-II, the Pareto non-dominated solution sorting of the initial population is calculated and the congestion degree of each initial scheduling scheme is calculated, including:

[0126] Step 13111, calculate the number of levels of the initial population, and loop through each level, specifically:

[0127] Divide the initial population into levels, each level contains optimization target values, and obtain the maximum value of each optimization target value at each level and minimum value , set the congestion of the initial scheduling scheme corresponding to the maximum and minimum values ​​of each optimization target value at each level to .

[0128] Step 13112, calculate In the level The congestion of other initial scheduling schemes under the optimization target value is as follows:

[0129] Get the current initial scheduling plan, The first Under the optimization target values ​​(where a=1, 2, 3, ..., A; f=1, 2, 3, ..., F), the optimization target value of the previous initial scheduling solution and the optimization target value of the next initial scheduling solution , the calculation formula of congestion is as follows:

[0130] ;

[0131] in, Indicates Next level The optimization target value The congestion degree of the initial scheduling schemes (s=1, 2, 3, ..., S represents the sth initial scheduling scheme among the S scheduling schemes in the initial population), Indicates Next level The optimization target value The optimization target value of the initial scheduling scheme, Indicates Next level The optimization bundle objective value The optimization target value of the initial scheduling scheme, Representative The first The maximum constraint target value under the optimization target value, Representative The first The minimum optimization target value under the optimization target values.

[0132] The congestion of each initial scheduling solution is stored in middle, It contains the processing order, fitness value, Pareto grade and congestion degree of each scheduling scheme;

[0133] The above steps are repeated to calculate the congestion degree of each initial scheduling scheme in each level under multiple optimization target values, which is used as the input of the preset competition algorithm.

[0134] Furthermore, before the tournament algorithm is used to process the initial scheduling schemes in the initial population, the state variables in the competition are first set: the number of players in the tournament and mating pool size ;

[0135] In an implementable example, the above step 1312 may specifically include:

[0136] Step 13121, initialize the number of tournament participants based on the level information and congestion of each initial scheduling scheme in the population and mating pool size , get the population after the competition, specifically:

[0137] According to the level information, one initial scheduling scheme is selected as a competition individual, and another initial scheduling scheme, that is, another competition individual, is randomly selected and the two are competed, ensuring that each initial scheduling scheme can participate in the competition and increase the randomness of the competition, ensuring the complexity and diversity of the initial population; here, the initial scheduling scheme with a high Pareto level is preferentially selected as the winner; for the initial scheduling schemes with the same level information, the competition is carried out according to the congestion, and the initial scheduling scheme with a larger congestion is selected as the winner; for the initial scheduling schemes with the same level information and the same congestion, the competition is carried out according to the serial number of the initial scheduling scheme, and the population after the competition is output and obtained.

[0138] In an optional embodiment of the present invention, the above step 13 may further include:

[0139] Step 132, determining a first scheduling solution in the optimal population according to the machine utilization in each initial scheduling solution in the optimal population.

[0140] In this embodiment, based on the utilization rate of the corresponding machine in each initial scheduling scheme in the optimal population, the initial scheduling scheme with the highest utilization rate of all machines in the initial scheduling scheme is determined as the first scheduling scheme; here, when determining the first scheduling scheme according to the machine utilization rate, specifically, whether there is a process that can be inserted in the decoding process (the specific process when executing the initial scheduling scheme) of each initial scheduling scheme and the number of processes that can be inserted are considered, and the initial scheduling scheme with the largest number of processes that can be inserted is determined as the first scheduling scheme, so as to shorten the maximum completion time of the scheduling scheme, improve the utilization rate of the machine, and reduce energy waste;

[0141] Furthermore, the specific judgment process of whether the decoding process step of the initial scheduling scheme can be inserted is as follows:

[0142] Step 1321, according to the scheduling information of the initial scheduling plan, obtain the process Processing time , that is, The first The process is assigned The processing time of machine No. is ;

[0143] Step 1322, based on the start processing time of the process of the workpiece constraint, determine the process The start processing time is , specifically: calculate the process The end processing time is ,if , then the process Start processing time is 0, otherwise the process The start processing time is ;

[0144] Step 1323, determine the start processing time of the process based on the machine constraints The final processing time of the last process of the machine , and its calculation formula is as follows:

[0145] ;

[0146] in, Indicated in The end processing time of each process on the machine number; if There is no processing task on machine No. ;

[0147] Step 1324, determine Does the machine have a position to insert?

[0148] if , and obtain the judgment result that the machine may have an insertable position. The specific judgment process is as follows:

[0149] Step 13241, calculate the space of the idle time period of the machine, the specific calculation formula is as follows:

[0150] ;in, Represents all idle time periods of the machine, Indicates the end time of all idle time of the machine. Indicates the start time of all idle time of the machine;

[0151] Step 13242, determine whether the idle position can be inserted. , that is, the length of the idle time period is not less than the processing time and the end time of the idle time period is not less than the end processing time of the workpiece, indicating that there is at least one insertable position, and the first position is selected for insertion; at this time, the calculation formula for the completion time of the processing operation is as follows:

[0152] ;

[0153] ;

[0154] in, Indicates the process Start processing time, Indicates the process End processing time, Indicates the process The start time based on the workpiece constraints, Indicates the start time of the inserted idle time period. Show process Processing time.

[0155] if , and the result is that there is no insertable position in the machine, then the process The start processing time is: ;

[0156] Then calculate The final start time. Determine the size of the start time based on the process start time and idle time position, Final start time: ;

[0157] in: Indicates the process The start time of represents the start time determined based on the process constraints, Indicates the start time based on machine constraints.

[0158] calculate The completion time is calculated as follows:

[0159] ;

[0160] in, express The completion time, express The start time of express Processing time;

[0161] Based on the above process, the decoding process of each initial scheduling plan involves the insertability of the workpiece process, and when it is judged that the process can be inserted, the corresponding process is inserted to shorten the maximum completion time of the scheduling plan, increase the utilization rate of the machine, and reduce energy waste.

[0162] In an optional embodiment of the present invention, the above step 13 may further include:

[0163] Step 133, based on the dynamic power information and the power system energy supply limitation, and according to the scheduling information in the first scheduling scheme, optimizing the first scheduling scheme to obtain a first optimized scheduling scheme;

[0164] In this embodiment, after determining the first scheduling scheme, considering the impact of the dynamic energy supply of the microgrid system on workshop production, it is necessary to dynamically adjust the first scheduling scheme to ensure that the energy consumption at each moment of the processing process does not exceed the processing power consumption of a machine;

[0165] Considering the impact of dynamic power consumption of microgrid system, when When the power consumption exceeds the limit at any moment, the start processing time of the process is gradually extended, and the remaining unprocessed processes are rescheduled so that the scheduling scheme that adapts to dynamic power consumption can be met in the end; specifically: based on dynamic power information, the dynamic power consumption limit parameters of the microgrid system are input: when When Real-time energy consumption information of the workshop at all times Exceeds the energy supply information of the microgrid system at the corresponding time l (Energy supply information of the microgrid system at the corresponding time l is the dynamic power output power of the microgrid system, where ), the start time of the processing workpiece at the current moment needs to be adjusted. Here, the unprocessed process information is rescheduled, that is, the processing order of the remaining processes and their corresponding processing machines are rearranged to ensure the complexity of the population, and then the processed information and the rescheduled unprocessed information are spliced ​​to ensure the integrity of the scheduling plan;

[0166] Add 1 to the start time of the process being scheduled to ensure The power consumption at the moment is within a certain threshold. Due to excessive adjustments that cause the processing time to be postponed, which will greatly extend the completion time and the urgency of some workpieces, a small amount of power consumption excess is allowed, and its value will not exceed the processing power consumption of the machine. The excess electricity will be purchased from the public power grid system.

[0167] Here, the dynamic power consumption limit parameters of the microgrid system are input, and the first scheduling scheme is optimized and adjusted according to the dynamic power consumption limit parameters, and the energy consumption at each moment after the optimization and adjustment is calculated; Figure 4 As shown in the figure, the power consumption before and after optimization and adjustment is compared. It can be seen from the figure that the power consumption after optimizing and adjusting the first scheduling scheme based on dynamic power consumption limitation is significantly lower than the power consumption of the initial scheduling scheme, and the power consumption of the optimized and adjusted scheduling scheme at each moment meets the requirements of dynamic power consumption limitation.

[0168] Here, the real-time energy consumption information of the workshop can be confirmed by the following steps:

[0169] Step 1331, traverse the machines in the order of the machines assigned according to the processing sequence of the process. When the machine is processing the workpiece, its processing power consumption is determined, which is calculated by the following formula:

[0170] ;

[0171] in, express time The sum of the processing power consumption of the machines, express time The processing power consumption of the machine;

[0172] Step 1332, traverse the machines in the order in which they are assigned according to the processing sequence of the process. When the machine is not processing workpieces, the sum of its idling power consumption is determined, which is calculated by the following formula:

[0173] ;

[0174] in, express time The sum of the idling power consumption of the machines, express time No. The idling power consumption of the machine;

[0175] Step 1333, confirm The sum of the power consumption at each moment is calculated using the following formula:

[0176] ;

[0177] in, express The sum of the processing power consumption and idling power consumption of all machines at any given moment.

[0178] Further, optimizing the first scheduling scheme according to the scheduling information in the first scheduling scheme to obtain a first optimized scheduling scheme may include:

[0179] Step 1334, determining, based on the scheduling information, a first power cost of idling the machine for processing the workpiece to be processed in an idle time period and a second power cost of executing a preset power on / off strategy, wherein the idling time period means that the machine is running in the time period and does not process the workpiece;

[0180] Step 1335: Perform a first optimization process on the first scheduling plan according to the first electricity cost and the second electricity cost to obtain a first optimized scheduling plan.

[0181] In this embodiment, according to the scheduling information in the first scheduling scheme, the idle time period of the machine when processing the workpiece can be determined in the first scheduling scheme; considering the impact of the dynamic energy supply of the microgrid system on the workshop production, the first power cost required when the machine continues to idle during the idle time period is compared with the second power cost required when the machine executes the preset power on / off strategy (shuts down the machine) during the idle time period, and determines whether to execute the power on / off strategy for the machine during the idle time period, thereby ensuring that the energy consumption and power cost of the entire processing scheduling process are minimized, and more intuitively showing the effectiveness of the preset power on / off strategy based on power cost for the enterprise;

[0182] See also Figure 3 Specifically, the above step 1334 may include:

[0183] Step 13341, traverse the scheduling information and confirm all idle time periods in the scheduling information;

[0184] Step 13342, confirm that the machine is The first power cost of idling operation during the idle time period at the time is calculated by the following formula:

[0185] ;

[0186] in, Represents the machine in the The first electricity cost of idling during the entire idle time period, Indicates that it contains The end time of the idle time period at the moment, Indicates that it contains The start time of the idle time period at the moment, express Idle energy consumption of the machine, Indicates that during this idle time period The electricity price corresponding to the time, , The value follows the time-of-use electricity price strategy.

[0187] Step 13343, confirm that the machine is The second power cost of executing the preset power on / off strategy during the idle time period at the time is calculated by the following formula:

[0188] ;

[0189] ;

[0190] in, express The end time of the machine from shutdown state to startup state capable of processing, express Average machine startup time, express Average downtime of the machine, Indicates that it contains The start time of the idle time period of the moment; Represents the machine in the The second power cost of executing the preset switching strategy during the entire switching time, express The average power of the machine, represent The average shutdown power of the machine, Indicates that during this idle time period The electricity price corresponding to the time, , The value follows the time-of-use electricity price strategy;

[0191] Here, the preset power-on / off strategy can be expressed as a binary decision variable , when the machine that executes the preset switching strategy changes from the shutdown state to the startup state capable of processing Greater than the end time of this idle time period When calculating the production electricity cost, otherwise keep the machine turned on.

[0192] Furthermore, the above step 1335 may include:

[0193] Step 13351a, if , indicating that the first power cost of the machine idling during the idle period is greater than the second power cost of executing the preset power on / off strategy, and the machine needs to be shut down to save power costs;

[0194] Step 13352a, Update The sum of the power consumption of the power-on / off strategy executed at all times is calculated using the following formula:

[0195] ;

[0196] in, express The sum of the power consumption of each machine that always executes the preset power on / off strategy, express The sum of the power consumption of each machine that does not execute the preset power on / off strategy at any time. Indicates that the preset switch strategy is executed No. The idling power consumption of the machine, Indicates the machine that executed the preset switching strategy.

[0197] Step 13351b, if , which means that executing the preset power on / off strategy cannot bring direct reduction in production costs for the enterprise, and there is no need to execute the preset power on / off strategy.

[0198] Step 1336, updating all processing information, including: the affected idle time period after the current process is moved, the processing information of the affected machines, and the processing information of the process of the affected workpiece.

[0199] Step 1337, loop and calculate the above The power consumption at each moment is calculated until the decoding of the first scheduling scheme is completed.

[0200] In an optional embodiment of the present invention, the above step 15 may include:

[0201] Step 151, optimize the first optimized scheduling plan according to the abnormal event information, the preset rescheduling strategy and the target optimization function to obtain the second optimized scheduling plan, and use the second optimized scheduling plan as the target scheduling plan. The real-time status information of the workshop represents the set of abnormal events generated when the work shop processes the workpiece based on the first optimized scheduling plan.

[0202] In this embodiment, various abnormal events occurring in the workshop are taken into consideration during the execution of the first optimization scheduling plan. It is necessary to optimize the workpieces that are not scheduled for processing and the partial plan for scheduling the workpieces that are not scheduled for processing in the first optimization scheduling plan after the occurrence of various abnormal events, based on the types of the corresponding abnormal events and the target optimization function, so as to realize dynamic adjustment of the first optimization scheduling plan, improve the ability of the workshop to deal with sudden abnormal events, and at the same time ensure that the optimized second optimization scheduling plan can meet the requirements of the target optimization function.

[0203] Here, various abnormal events in the workshop may include:

[0204] Assume that at time t1, the processing The first process of the workpiece Sometimes, due to worker operation, machine processing failure, etc., the second process needs special processing, such as the appearance of burrs needs manual grinding, etc. At this time, it is necessary to define the grinding time as fuzzy processing time based on the worker operation, machine processing failure, etc. as abnormal events, and update the processing information of the affected process;

[0205] Assume that at time t2, a new processing order appears. At this time, the processing order information of the workshop needs to be updated based on the new processing order as an abnormal event, including the number of processes of the new workpiece, the processing time of each process of the new workpiece, and the candidate machine set corresponding to each process.

[0206] Assume that at time t3, a new processing machine is activated; at this time, it is necessary to update the processing information of the workshop based on the activation of the new processing machine as an abnormal event, including the number of processing machines in the factory, the energy consumption of the newly activated machine, the processable workpieces of the newly activated machine and its processing time, and other information.

[0207] Here, the fuzzy processing time is explained:

[0208] Assuming that And specific machines , During the processing, due to the wear and tear of the machine, the machine failure occurs, and the maintenance of the machine will extend the completion time of the workpiece. Assuming that the maintenance time of the machine follows the fuzzy time theory, the fuzzy processing time is defined as: , then the processing time calculation formula is:

[0209] ;

[0210] in, Indicates the processing time closest to the actual time. Indicates the optimal processing time, represents the most likely processing time, Indicates the longest processing time, The value is obtained based on workshop production experience. When a machine is damaged, update its start processing time:

[0211] ;

[0212] in, Indicates the processing time that is closest to the actual time;

[0213] Update its completion time:

[0214] .

[0215] In an optional embodiment of the present invention, the above step 151 may include:

[0216] Step 1511, according to the abnormal event information and the preset rescheduling strategy, determine multiple rescheduling schemes based on the information of the unprocessed workpiece corresponding to the workpiece processed by the first optimized scheduling scheme;

[0217] Step 1512, optimizing the plurality of rescheduling schemes according to the optimization target value of the target optimization function to obtain a second rescheduling scheme among the plurality of rescheduling schemes, the second rescheduling scheme being a rescheduling scheme that satisfies the optimization target value of the target optimization function;

[0218] Step 1513, obtaining a second optimized scheduling plan according to the first optimized scheduling plan and the second scheduling plan.

[0219] In this embodiment, the information about the processed workpiece may include the sequence of processed processes, the set of processing machines corresponding to the processed processes, and the sequence of unprocessed processes;

[0220] After various abnormal events occur, based on the information of the unprocessed workpieces in the scheduling process of the first optimization scheduling scheme and the corresponding available machine set, the unprocessed workpieces are rescheduled based on a preset rescheduling strategy, and multiple rescheduling schemes corresponding to the unprocessed workpieces can be determined;

[0221] Here, the preset rescheduling strategy is to reschedule the unprocessed workpieces based on different heuristic algorithms. It should be noted that the preset rescheduling strategy will be applied to the entire abnormal event scheduling process; here, the different heuristic algorithms include:

[0222] Minimum processing time heuristic (χ1): For operation There is a set of candidate machines, and the processing time of each machine is often different. In the rescheduling stage, the machine with the smaller processing time is selected from the candidate machine set to balance the completion time, but this method may cause a machine with strong processing capacity to be overloaded;

[0223] Minimum Work Heuristic (χ2): For operations , calculate the processing task volume of each machine in the candidate machine set. When the processing task volume of the selected machine is too large, select the machine with the smallest task volume to process the process;

[0224] Minimum workload heuristic (χ3): For each operation , when the difference in the processing task volume of the selected machine is within the threshold, the machine with smaller energy consumption is selected to balance the energy consumption of the factory;

[0225] When rescheduling unprocessed workpieces based on different heuristic algorithms, the priority order considered by different heuristic algorithms is: .

[0226] Furthermore, multiple rescheduling schemes are used as rescheduling populations, and the rescheduling population is optimized based on the optimization target value of the target optimization function to obtain a second scheduling scheme that meets the optimization target value of the target optimization function; specifically, the rescheduling population can be initialized based on the optimization target value of the target optimization function and a preset initialization algorithm; here, the specific process of initializing the rescheduling population is the same as the above-mentioned process of optimizing the initial population through a preset initialization algorithm, and will not be repeated here.

[0227] Furthermore, after obtaining the second scheduling plan, the executed part of the scheduling plan in the first optimized scheduling plan is spliced ​​with the second scheduling plan to obtain a complete second optimized scheduling plan to ensure the integrity of the target scheduling plan.

[0228] Here, the grey wolf algorithm (GWO), improved grey wolf algorithm (IGWO) and improved golden jackal algorithm (IGJO) can be used to reschedule the population for initialization processing, and the corresponding second scheduling schemes under different initialization algorithms are obtained respectively. The power cost, stability and maximum completion time of the second scheduling schemes under different initialization algorithms are compared with those of any rescheduled schemes. Figures 5 to 7 It can be seen from the comparative convergence diagram that with the increase in the number of initialization iterations, the improved golden jackal algorithm has a better convergence degree corresponding to each optimization target value, indicating that the improved golden jackal algorithm has great advantages in dealing with workshop scheduling problems in dynamic energy supply and production environments.

[0229] The present invention constructs a scheduling method for workshop operations under a dynamic power and production environment, optimizes and updates the initial population formed by the initial scheduling scheme of the workpieces to be processed in the workshop based on the target optimization function, and ensures the complexity and integrity of the population; at the same time, based on the target optimization function and the preset rescheduling strategy, the processing adjustment of the workshop under dynamic energy supply constraints and the occurrence of various abnormal events is realized, and the processing environment of the traditional static processing workshop is innovated. When an abnormal event occurs, the production and processing information is divided into processed information and unprocessed information, and multiple corresponding rescheduling schemes of the new processing information are obtained by rescheduling the unprocessed information according to the preset rescheduling strategy, and the rescheduling scheme is optimized and updated based on the target optimization function and the preset initialization algorithm, so as to realize the dynamic adjustment and optimization of the scheduling scheme; when an abnormal event occurs, the fuzzy time theory is used as the processing time when the factory responds to the abnormal event, which is closer to reality. Compared with the traditional static processing workshop, the method provided by the present invention can respond to the energy consumption supply of the microgrid changes and the occurrence of various abnormal events in a timely manner, and make corresponding adjustments to the processing tasks according to the corresponding energy consumption constraints and the corresponding abnormal events, and reasonably allocate the processing tasks of the machine in real time.

[0230] like Figure 8 As shown, an embodiment of the present invention further provides a scheduling device 20 for workshop operations, comprising:

[0231] A first acquisition module 21 is used to acquire an initial population corresponding to an initial scheduling scheme for workpieces to be processed in a workshop operation, wherein the initial population includes a plurality of different initial scheduling schemes for processing the workpieces to be processed;

[0232] The first processing module 22 is used to determine the target optimization function of the constraint scheduling scheme according to the parameter information of the workshop operation, and the optimization target value of the target optimization function includes minimizing the completion time, minimizing the energy consumption, minimizing the production power cost, and minimizing the stability of the scheduling scheme; based on the optimization target value of the target optimization function and the machine utilization rate in each initial scheduling scheme, the initial scheduling schemes in the initial population are screened to obtain the first scheduling scheme, and the machine utilization rate is determined based on the insertability of the process corresponding to the initial scheduling scheme during the execution of the initial scheduling scheme and the number of insertable processes;

[0233] The second acquisition module 23 is used to obtain real-time status information of the workshop;

[0234] The second processing module 24 optimizes the first scheduling solution according to the real-time status information of the workshop, the target optimization function and the preset rescheduling strategy to obtain the target scheduling solution.

[0235] like Fig. 9As shown, an embodiment of the present invention further provides an electronic device 30, including: a memory 31 for storing one or more computer programs; one or more processors 32 for executing one or more computer programs, and when the computer program is run by the processor, the scheduling method 10 of the workshop operation as described above is executed. All implementations in the above method embodiment are applicable to this embodiment, and can also achieve the same technical effect. The electronic device 30 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown in the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required in the present invention.

[0236] like Fig.10 As shown, the electronic device 30 is a computing device, or a computer system, which may include a CPU 301 (computing unit), which can perform various appropriate actions and processes according to a computer program stored in a ROM 302 (read-only memory) or a computer program loaded from a storage unit 308 into a random access RAM 303 (memory). In the RAM 303, various programs and data required for the operation of the device 30 may also be stored. The CPU 501, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An I / O interface 305 (input / output interface) is also connected to the bus 304.

[0237] A number of components in the electronic device 30 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 30 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0238] CPU 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of CPU 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. CPU 301 performs the various methods and processes described above. For example, in some embodiments, the scheduling method 10 for workshop operations may be implemented as a computer software program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 30 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by CPU 301, one or more steps of the scheduling method 10 for workshop operations described above may be performed. Alternatively, in other embodiments, CPU 301 may be configured to execute the scheduling method 10 for workshop operations in any other appropriate manner (e.g., by means of firmware).

[0239] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the above-mentioned scheduling method 10 for workshop operations. All implementations in the above-mentioned method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0240] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0241] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0242] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0243] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0244] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0245] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or optical disk, etc., which can store program codes.

[0246] In addition, it should be pointed out that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understandable that all or any steps or components of the method and apparatus of the present invention can be implemented in hardware, firmware, software or a combination thereof in any computing device (including processors, storage media, etc.) or a network of computing devices, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0247] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code for implementing a method or device. That is to say, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0248] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for scheduling workshop operations, characterized in that: include: Acquire an initial population corresponding to an initial scheduling scheme for a workpiece to be processed in a workshop operation and parameter information of the workshop operation, wherein the initial population includes a plurality of different initial scheduling schemes for processing the workpiece to be processed; Determine the target optimization function of the constraint scheduling scheme according to the parameter information of the workshop operation, wherein the optimization target values ​​corresponding to the target optimization function include minimizing the completion time, minimizing the energy consumption, minimizing the production power cost, and minimizing the stability of the scheduling scheme; Based on the optimization target value of the target optimization function and the machine utilization rate in each initial scheduling plan, the initial scheduling plans in the initial population are screened to obtain a first scheduling plan, wherein the machine utilization rate is determined based on the insertability of the process corresponding to the initial scheduling plan and the number of insertable processes during the execution of the initial scheduling plan; Get real-time status information of the workshop; Optimizing the first scheduling solution according to the real-time status information of the workshop, the target optimization function and the preset rescheduling strategy to obtain a target scheduling solution; The objective optimization function is constructed as follows: ; ； ； ； ； represents the minimization of electricity cost; where, represents the jth process among the total number of processing processes N, =1, 2, 3, ..., N; represents the kth machine among the total number of machines m, =1, 2, 3, ..., m; Indicates The process is Processing energy consumption on the machine; express Idle energy consumption on machines; , , They represent the first electricity price during peak periods, the second electricity price during off-peak periods, and the third electricity price during off-peak periods; , , Respectively represent The first processing time of the process on machine k during the peak electricity price period, the second processing time during the off-peak electricity price period, and the third processing time during the off-peak electricity price period; , , They represent the first idling time of machine k during the peak electricity price period, the second idling time during the flat electricity price period, and the third idling time during the valley electricity price period; represents the fixed energy consumption of the workshop; represents the average unit electricity price; Indicates The transfer energy consumption generated by transferring the process to the machine for processing; represents minimizing energy consumption; where, express The energy consumption of machining workpieces; express Idle energy consumption of the machine; represents the minimum completion time; where is the completion time of the last process to complete the last workpiece, represents the first completion time corresponding to the initial scheduling scheme; represents the minimization stability; where It indicates the second completion time corresponding to the completion of all workpieces after the abnormal event occurs. Indicates the number of available machines after an abnormal event occurs, Indicates that the initial scheduling scheme corresponds to Number of processing steps of machine No.; Indicates that after an abnormal event occurs The number of processing steps of the machine.

2. The method for scheduling workshop operations according to claim 1, characterized in that: Based on the optimization target value of the target optimization function and the machine utilization rate in each initial scheduling solution, the initial scheduling solutions in the initial population are screened to obtain a first scheduling solution, including: Based on the optimization target value of the target optimization function, the initial population is initialized to obtain the optimal population corresponding to the initial population; A first scheduling solution in the optimal population is determined according to the machine utilization in each initial scheduling solution in the optimal population.

3. The method for scheduling workshop operations according to claim 2, characterized in that: Based on the optimization target value of the target optimization function, the initial population is initialized to obtain the optimal population corresponding to the initial population, including: Based on the optimization target value of the target optimization function, performing non-dominated quick sorting and congestion calculation on the initial population, and determining the congestion and level information of each initial scheduling scheme in the initial population; According to the congestion degree and level information of each initial scheduling scheme in the initial population, and based on a preset competition algorithm, the initial scheduling schemes in the initial population are processed by competition to obtain a population after competition; The population after the competition is iteratively initialized based on a preset initialization algorithm and a preset number of iterations to obtain the optimal population.

4. The method for scheduling workshop operations according to claim 2, characterized in that: The real-time status information of the workshop represents a collection of abnormal event information and dynamic power information generated when the work shop processes the workpiece based on the first scheduling scheme; The scheduling method for workshop operations further includes: Based on the dynamic power information and the power system energy supply limitation, and according to the scheduling information in the first scheduling plan, the first scheduling plan is optimized to obtain a first optimized scheduling plan.

5. The method for scheduling workshop operations according to claim 4, characterized in that: According to the scheduling information in the first scheduling scheme, the first scheduling scheme is optimized for the first time to obtain a first optimized scheduling scheme, including: Determine, based on the scheduling information, a first power cost of idling the machine for processing the workpiece to be processed during an idle time period and a second power cost of executing a preset on / off strategy; The first scheduling plan is optimized according to the first electricity cost and the second electricity cost to obtain the first optimized scheduling plan.

6. The method for scheduling workshop operations according to claim 5, characterized in that: According to the real-time status information of the workshop, the target optimization function and the preset rescheduling strategy, the first scheduling plan is optimized to obtain a target scheduling plan, including: According to the abnormal event information, the preset rescheduling strategy and the target optimization function, the first optimized scheduling scheme is optimized to obtain a second optimized scheduling scheme, and the second optimized scheduling scheme is used as the target scheduling scheme.

7. The method for scheduling workshop operations according to claim 6, characterized in that: According to the abnormal event information, the preset rescheduling strategy and the target optimization function, the first optimization scheduling scheme is optimized to obtain a second optimization scheduling scheme, including: Determine, according to the abnormal event information and the preset rescheduling strategy, a plurality of rescheduling schemes corresponding to the unprocessed workpiece information corresponding to the workpiece processed based on the first optimization scheduling scheme; According to the optimization target value of the target optimization function, the plurality of rescheduling schemes are optimized to obtain a second scheduling scheme among the plurality of rescheduling schemes, wherein the second scheduling scheme is a rescheduling scheme that satisfies the optimization target value of the target optimization function; According to the first optimization scheduling plan and the second scheduling plan, the second optimization scheduling plan is obtained.

8. A scheduling device for workshop operations, characterized in that: include: A first acquisition module is used to acquire an initial population corresponding to an initial scheduling scheme for a workpiece to be processed in a workshop operation and parameter information of the workshop operation, wherein the initial population includes a plurality of different initial scheduling schemes for processing the workpiece to be processed; A first processing module is used to determine the target optimization function of the constraint scheduling scheme according to the parameter information of the workshop operation, wherein the optimization target values ​​of the target optimization function include minimizing the completion time, minimizing the energy consumption, minimizing the production power cost, and minimizing the stability of the scheduling scheme; Based on the optimization target value of the target optimization function and the machine utilization rate in each initial scheduling plan, the initial scheduling plans in the initial population are screened to obtain a first scheduling plan, wherein the machine utilization rate is determined based on the insertability of the process corresponding to the initial scheduling plan and the number of insertable processes during the execution of the initial scheduling plan; The second acquisition module is used to obtain real-time status information of the workshop; A second processing module optimizes the first scheduling solution according to the real-time status information of the workshop, the target optimization function and a preset rescheduling strategy to obtain a target scheduling solution; The objective optimization function is constructed as follows: ; ； ； ； ； represents the minimization of electricity cost; where, represents the jth process among the total number of processing processes N, =1, 2, 3, ..., N; represents the kth machine among the total number of machines m, =1, 2, 3, ..., m; Indicates The process is Processing energy consumption on the machine; express Idle energy consumption on machines; , , They represent the first electricity price during peak periods, the second electricity price during off-peak periods, and the third electricity price during off-peak periods; , , Respectively represent The first processing time of the process on machine k during the peak electricity price period, the second processing time during the off-peak electricity price period, and the third processing time during the off-peak electricity price period; , , They represent the first idling time of machine k during the peak electricity price period, the second idling time during the flat electricity price period, and the third idling time during the valley electricity price period; represents the fixed energy consumption of the workshop; represents the average unit electricity price; Indicates The transfer energy consumption generated by transferring the process to the machine for processing; represents minimizing energy consumption; where, express The energy consumption of machining workpieces; express Idle energy consumption of the machine; represents the minimum completion time; where is the completion time of the last process to complete the last workpiece, represents the first completion time corresponding to the initial scheduling scheme; represents the minimization stability; where It indicates the second completion time corresponding to the completion of all workpieces after the abnormal event occurs. Indicates the number of available machines after an abnormal event occurs, Indicates that the initial scheduling scheme corresponds to Number of processing steps of machine No.; Indicates that after an abnormal event occurs The number of processing steps of the machine.

9. A computing device, characterized in that include: A memory for storing one or more programs; One or more processors, configured to execute the one or more programs to implement the method according to any one of claims 1 to 7.

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