CN117474174A - Scheduling rule optimization method, scheduling rule optimization device, computer equipment, storage medium and product - Google Patents
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Abstract
The application relates to a scheduling rule optimization method, a scheduling rule optimization device, computer equipment, a storage medium and a product. The method comprises the following steps: determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights; and carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the weights thereof. According to the method and the device, the specific multi-optimization targets are scheduled through the linear combination scheduling rule, training is conducted based on the constructed scheduling problem set, so that the linear weight of the linear combination scheduling rule is optimized, the optimal linear combination scheduling rule applicable to the specific multi-optimization targets can be obtained, and a better optimization effect can be achieved on scheduling problems.
Description
Technical Field
The present disclosure relates to the field of workshop production scheduling technologies, and in particular, to a scheduling rule optimization method, apparatus, computer device, storage medium, and product.
Background
The flexible job shop scheduling problem is a typical combinatorial optimization problem that relates to how to arrange the processing order of workpieces and the allocation of equipment to achieve certain optimization goals in the case where multiple workpieces need to be processed on multiple pieces of equipment. The optimization objectives of the flexible shop scheduling problem are usually multiple, such as maximum finishing time, production cost, equipment load, etc., and there are often conflicts and contradictions between these objectives, so that a multi-objective optimization method needs to be adopted for solving.
At present, for a multi-objective optimization method of flexible job shop scheduling, a scheduling rule action library formed by one manually defined scheduling rule or a plurality of scheduling rules is often relied on, so that the adaptability to specific multi-optimization targets is lacking, the scheduling rules cannot be constructed and adjusted according to the specific multi-optimization targets, and the optimization effect on the multi-objective optimization scheduling problem is required to be improved.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a scheduling rule optimizing method, apparatus, computer device, storage medium, and product capable of improving the optimizing effect.
In a first aspect, the present application provides a scheduling rule optimization method, where the method includes:
Determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights;
and carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
In one embodiment, the optimizing the weights corresponding to the scheduling rules based on the multiple optimization targets to obtain the optimized weights includes:
starting iteration from the initial weight until the termination condition, and performing disturbance adjustment on the weight corresponding to each scheduling rule in each iteration step;
determining a plurality of first scheduling schemes according to the weights and the scheduling rules before disturbance, and determining a plurality of second scheduling schemes according to the weights and the scheduling rules after disturbance;
and carrying out optimizing processing according to the multiple optimizing targets, the multiple first scheduling schemes and the multiple second scheduling schemes to obtain optimized weights.
In one embodiment, the optimizing process according to the multiple optimizing targets, the multiple first scheduling schemes and the multiple second scheduling schemes to obtain the optimized weight includes:
Determining a comprehensive performance index according to the multiple optimization targets;
for the comprehensive performance index, respectively determining first performance information of a plurality of first scheduling schemes and second performance information of a plurality of second scheduling schemes;
and determining the optimized weight according to the comparison result of the first performance information and the second performance information.
In one embodiment, the determining the optimized weight according to the comparison result of the first performance information and the second performance information includes:
under the condition that the first scheduling scheme is determined to be better than the second scheduling scheme according to the first performance information and the second performance information, determining the weight before disturbance as the weight after optimizing;
and under the condition that the second scheduling scheme is determined to be better than the first scheduling scheme according to the first performance information and the second performance information, determining the disturbed weight as the optimized weight.
In one embodiment, the determining of the first performance information includes:
respectively determining performance data of each first scheduling scheme;
performing data processing on the performance data of each first scheduling scheme to obtain processed performance data;
and carrying out average calculation on the plurality of processed performance data to obtain first performance information.
In one embodiment, the determining of the second performance information includes:
respectively determining performance data of each second scheduling scheme;
performing data processing on the performance data of each second scheduling scheme to obtain processed performance data;
and carrying out average calculation on the plurality of processed performance data to obtain second performance information.
In a second aspect, the present application further provides a scheduling rule optimizing apparatus. The device comprises:
a scheme confirmation module for determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
the weight determining module is used for optimizing the weights corresponding to the scheduling rules based on the multiple optimization targets to obtain optimized weights;
and the scheduling application module is used for carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the following steps:
Determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights;
and carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights;
and carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, performs the steps of:
Determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights;
and carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
The scheduling rule optimizing method, the scheduling rule optimizing device, the computer equipment, the storage medium and the product are characterized in that firstly, a plurality of scheduling schemes are determined based on a pre-constructed scheduling problem set, then, the weights corresponding to the scheduling rules are optimized based on a plurality of optimization targets, the optimized weights are obtained, and finally, the dynamic scheduling application of the plurality of optimization targets is carried out in an actual scene according to the plurality of scheduling rules and the corresponding optimized weights. According to the embodiment of the application, the specific multi-optimization targets are scheduled through the linear combination scheduling rule, and training is performed based on the constructed scheduling problem set, so that the linear weight of the linear combination scheduling rule is optimized, the optimal linear combination scheduling rule applicable to the specific multi-optimization targets can be obtained, and a better optimization effect can be achieved on scheduling problems.
Drawings
FIG. 1 is an application environment diagram of a scheduling rule optimization method in one embodiment;
FIG. 2 is a flow diagram of a scheduling rule optimization method in one embodiment;
FIG. 3 is a flow chart of obtaining optimized weights in one embodiment;
FIG. 4 is a flow chart of the optimization process steps in one embodiment;
FIG. 5 is a flow chart of determining optimized weights in one embodiment;
FIG. 6 is a flow diagram of a process for determining first performance information in one embodiment;
FIG. 7 is a flow diagram of a process for determining second performance information in one embodiment;
FIG. 8 is a block diagram of a scheduling rule optimizing apparatus in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
First, before a technical solution of an embodiment of the present application is specifically described, a description is first given of a technical background on which the embodiment of the present application is based.
Job shop scheduling refers to optimizing job processing decisions to maximize production efficiency and resource utilization, shortening manufacturing cycle. Conventional shop scheduling problems are typically studied in a static environment, assuming that all job tasks of a job shop are known in advance and that the relationships and constraints between jobs do not change throughout the scheduling process. However, job shops in actual production environments are typically dynamic, and the job tasks within them may change, such as insertion of slips, production planning adjustments, rework, or equipment downtime. Thus, dynamic job shop scheduling is an important issue in manufacturing. The scheduling problem is an NP-hard (non-deterministic polynomial) problem, and the solution space of the problem increases according to the power-of-power function with the scale and latitude of the problem, so that it is difficult to accurately solve the optimal solution in a limited time.
Scheduling rules are a robust and efficient practical method for all dynamic scheduling problems. The scheduling rules are also called priority rules and dispatch rules, and the priority relation among the jobs is calculated according to the characteristics of the jobs so as to determine the scheduling sequence of the jobs. Common scheduling rules include earliest deadline first (Earliest Due Date, EDD), shortest processing time first (Shortest Processing Time, SPT), critical Ratio rules (CR), etc., typically different rules have different optimization effects on different optimization objectives for which corresponding scheduling rules can be selected, e.g., selecting SPT rules helps to optimize average flow time and overrun.
In the existing workshop job scheduling technology, the scheduling rule is used for pre-scheduling production of production tasks, and the production tasks are further optimized through algorithms such as genetic algorithm and the like. The selection model of the dispatching rules is obtained through training by a deep reinforcement learning method and a graph neural network method respectively, and the optimal dispatching rules are selected from a dispatching rule base to execute dispatching by the optimal actions corresponding to state decisions, so that dispatching according to the real-time state selection dispatching rules can be realized.
However, the existing dynamic scheduling methods have some defects in solving the multi-objective scheduling problem, the methods often depend on a manually defined scheduling rule or a scheduling rule action library formed by a plurality of scheduling rules, scheduling decisions lack flexibility and intelligence, and lack adaptability to specific multi-optimization objectives, and the optimization effect on the multi-objective optimal scheduling problem needs to be improved. Therefore, a new method capable of considering specific multiple optimization objectives, having adaptive scheduling capability, and intelligently adjusting scheduling rules according to the optimization objectives is needed to solve the above problems.
Based on the above, the application provides a scheduling rule optimization method, a scheduling rule optimization device, computer equipment, a storage medium and a product, and aims to solve the technical problems.
The scheduling rule optimization method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 determines a plurality of scheduling schemes based on a pre-constructed scheduling problem set; optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights; and carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the weights thereof. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and internet of things devices. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In an exemplary embodiment, as shown in fig. 2, an embodiment of the present application provides a scheduling rule optimizing method, which is described by taking an example that the method is applied to the terminal in fig. 1, and includes the following S201 to S203.
Wherein:
s201, determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set.
Wherein, each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to each scheduling rule. The scheduling problem set (JSSP set) may consist of 100 randomly generated scheduling problems, each scheduling problem having a randomly generated task amount, a task procedure number, a lead time, and a selectable device number, and the generation parameters of the random scheduling problem being designed according to the characteristics of the specific shop scheduling scenario. A specific example is as follows:
the number of tasks obeys a uniform distribution U [10, 30];
the number of working procedures of each task is subject to uniform distribution U5, 15;
working hours t of each working procedure obey uniform distribution U0, 40, and the unit is minutes;
the number of optional equipment in each procedure is subject to uniform distribution U1, 3;
lead time d=δ×Σtfor each task, where δ is a loose factor, δ e [1.1,1.2,1.5].
In this embodiment of the present application, the terminal may generate a corresponding scheduling scheme according to each scheduling problem in the scheduling problem set. Specifically, for each scheduling problem in the scheduling problem set, a scheduling rule algorithm, such as a shortest job priority rule, an earliest deadline priority rule, and the like, is called to schedule each problem, and a corresponding scheduling scheme is generated.
S202, optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights.
The multiple optimization objectives may include, among others, shortest time, lowest cost, highest quality, smallest delay, etc. The scheduling rules may include, but are not limited to, scheduling rules commonly used in the following prior art methods: first-in-first-out (FIFO), time-of-Arrival (AT), earliest expiration date (EDD), relaxation of the remaining process (SOPN), cost over time (cover), modified expiration date (MDD), shortest Process Time (SPT). Each scheduling rule in the embodiment of the present application corresponds to a weight, and for each scheduling rule, the embodiment of the present application may linearly combine the scheduling rules, based on a scheduling rule model of linear combination of basic scheduling rules. The expression of the scheduling rule model is shown in formula (1):
PX(X j )＝∑(X j SPR)(1)
wherein SPR is j Is the normalized value of each basic dispatching rule index, the value range is 0-1, x j Is SPR j Corresponding linear weights, x j ∈[0，1]. PR is the combined scheduling rule index.
In the embodiment of the application, the terminal can perform optimizing processing on the weights corresponding to each scheduling rule to obtain the optimized weights so as to achieve a multi-optimization target. The optimizing process may adopt genetic algorithm, annealing algorithm, etc., and the optimizing process mode is not particularly limited in this application.
S203, performing dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
In this embodiment of the present application, based on the foregoing embodiment, a plurality of scheduling rules and corresponding optimized weights are obtained, and the terminal may input the weights and the scheduling rules to the foregoing scheduling rule model to generate a corresponding scheduling scheme, and perform dynamic scheduling on a production scenario. And selecting the working procedure with the highest priority to process according to the scheduling rule based on the real-time job task.
According to the scheduling rule optimizing method, firstly, a plurality of scheduling schemes are determined based on a pre-constructed scheduling problem set, then, optimizing processing is carried out on weights corresponding to each scheduling rule based on a plurality of optimizing targets, the weights after optimizing are obtained, and finally, dynamic scheduling application of the plurality of optimizing targets is carried out in an actual scene according to the plurality of scheduling rules and the weights thereof. According to the embodiment of the application, the specific multi-optimization targets are scheduled through the linear combination scheduling rule, and training is performed based on the constructed scheduling problem set, so that the linear weight of the linear combination scheduling rule is optimized, the optimal linear combination scheduling rule applicable to the specific multi-optimization targets can be obtained, and a better optimization effect can be achieved on scheduling problems.
In an exemplary embodiment, based on the foregoing embodiment, please refer to fig. 3, and the embodiment of the present application relates to performing optimization processing on weights corresponding to each scheduling rule based on multiple optimization targets, so as to obtain optimized weights, which includes the following S301 to S303. Wherein:
s301, starting iteration from the initial weight to the termination condition, and performing disturbance adjustment on the weight corresponding to each scheduling rule in each iteration step.
In this embodiment of the present application, the terminal may randomly increase or decrease the weight corresponding to each obtained scheduling rule in the initial optimizing temperature, and after the number of times of disturbance is set for each optimizing temperature, cool down, where the current temperature matches with the terminated optimizing temperature, and stop the disturbance.
Illustratively, random perturbation is performed on the weight X j Adding or subtracting a random number between 0 and 1 to the random number elements beyond [0,1 ] after perturbation]The element of the interval is corrected to 0 or 1 according to the numerical value to obtain the weight X after disturbance j ’。
Embodiments of the present application may reduce the value of temperature T, optionally using an exponential decay function such as t=t 0 * alpha k, where alpha is the decay factor between (0, 1). If the optimal temperature of termination is reached, ending the calculation and outputting X j As the optimal linear weight.
S302, determining a plurality of first scheduling schemes according to weights and scheduling rules before disturbance, and determining a plurality of second scheduling schemes according to weights and scheduling rules after disturbance;
in the embodiment of the application, each scheduling problem in the scheduling problem set JSSP set is regularly scheduled according to the weight before disturbance, so as to obtain a first scheduling scheme. A kind of electronic device with a high-pressure air-conditioning system.
And carrying out regular scheduling on each scheduling problem in the scheduling problem set JSSP set according to the disturbed weight to obtain a second scheduling scheme.
Alternatively, the scheduling method may employ the following procedure: according to PR (X) j ) The rule sequences the priority of each task, determines the priority of each task, sequentially randomly selects one optional device of each process of each task according to the priority sequence, and calculates the processing time of the process.
S303, optimizing according to the multiple optimization targets, the multiple first scheduling schemes and the multiple second scheduling schemes to obtain optimized weights.
In this embodiment of the present application, based on the multiple optimization targets, the multiple first scheduling schemes, and the multiple second scheduling schemes obtained in the foregoing embodiment, the terminal may find an optimal scheme among the multiple first scheduling schemes and the multiple second scheduling schemes according to the multiple optimization targets, and determine a weight of the optimal scheme as the optimized weight.
The embodiment of the application searches the optimal weight by continuously adjusting the weight. In the optimization problem, it is easier to find a globally optimal solution.
In an exemplary embodiment, referring to fig. 4, referring to the foregoing embodiment, the embodiment of the present application relates to performing a optimizing process according to multiple optimization targets, multiple first scheduling schemes and multiple second scheduling schemes, to obtain weights after optimizing, which includes the following S401 to S403. Wherein:
s401, determining the comprehensive performance index according to the multiple optimization targets.
In this embodiment of the present application, based on the multiple optimization objectives in the foregoing embodiment, the terminal may determine, according to the multiple optimization objectives, a comprehensive performance index of each scheduling scheme on the problem set. Wherein the comprehensive performance index is F= Σobj i Wherein Obj is i Is the value of each minimization optimization objective.
S402, first performance information of a plurality of first scheduling schemes and second performance information of a plurality of second scheduling schemes are respectively determined according to the comprehensive performance indexes.
In this embodiment of the present application, based on the comprehensive performance indexes obtained in the foregoing embodiments, the terminal may determine, according to the comprehensive performance indexes, first performance information of the plurality of first scheduling schemes and second performance information of the plurality of second scheduling schemes, respectively.
S403, determining the optimized weight according to the comparison result of the first performance information and the second performance information.
In this embodiment of the present application, based on the first performance information and the second performance information obtained in the foregoing embodiment, the terminal may compare the first performance information and the second performance information to obtain a comparison result, and determine a result after optimizing according to the comparison result, for example, if the first performance information is smaller than the second performance information, then the weight after optimizing is set as the weight corresponding to the first scheduling scheme.
In the embodiment of the application, the optimal weight meeting multiple optimization targets can be obtained by determining the first performance information of the first scheduling scheme and the second performance information of the multiple second scheduling schemes.
In an exemplary embodiment, referring to fig. 5, referring to the foregoing embodiment, the embodiment of the present application refers to determining the weights after optimizing according to the comparison result of the first performance information and the second performance information, which includes the following S501 to S502. Wherein:
s501, determining the weight before disturbance as the weight after optimizing when the first scheduling scheme is determined to be better than the second scheduling scheme according to the first performance information and the second performance information.
In this embodiment of the present application, based on the first performance information and the second performance information obtained in the foregoing embodiment, when it is determined that the first scheduling scheme is better than the second scheduling scheme according to the first performance information and the second performance information, the weight before disturbance is determined as the weight after optimizing. For example, if the first performance information>Second performance information->The weight X after optimizing j X after =perturbation j '. Otherwise a random number r between 0 and 1 is generated, if->Let X j ＝X j ’。
S502, determining the disturbed weight as the optimized weight under the condition that the second scheduling scheme is superior to the first scheduling scheme according to the first performance information and the second performance information.
In this embodiment of the present application, based on the first performance information and the second performance information obtained in the foregoing embodiment, when it is determined that the second scheduling scheme is better than the first scheduling scheme according to the first performance information and the second performance information, the weight after disturbance is determined as the weight after optimizing.
In this embodiment of the present application, by comparing the first performance information of the first scheduling scheme and the second performance information of the plurality of second scheduling schemes, an optimal weight that meets multiple optimization objectives may be determined.
In an exemplary embodiment, please refer to fig. 6 based on the above embodiment, the embodiment of the present application relates to a determination process of the first performance information, which includes the following S601 to S603. Wherein:
s601, determining performance data of each first scheduling scheme.
In this embodiment of the present application, based on the multiple first scheduling schemes obtained in the foregoing embodiment, the terminal may determine, according to multiple optimization objectives, a value of each minimum optimization objective corresponding to each first scheduling scheme, that is, performance data.
S602, performing data processing on the performance data of each first scheduling scheme to obtain the processed performance data.
In this embodiment of the present application, based on the performance data of each first scheduling scheme obtained in the foregoing embodiment, the terminal may normalize the performance data of the first scheduling schemes to obtain the processed performance data.
And S603, carrying out average calculation on the plurality of processed performance data to obtain first performance information.
In this embodiment of the present application, based on the plurality of processed performance data obtained in the foregoing embodiment, the terminal may perform average calculation on the plurality of processed performance data to obtain the first performance information
The embodiment of the application obtains the optimal weight by determining the first performance information of the scheduling scheme obtained on each scheduling problem in the problem set JSSP set.
In an exemplary embodiment, please refer to fig. 7 based on the above embodiment, the embodiment of the present application relates to a determination process of the second performance information, which includes the following S701 to S703. Wherein:
s701, determining performance data of each second scheduling scheme.
In this embodiment of the present application, based on the plurality of second scheduling schemes obtained in the foregoing embodiment, the terminal may determine, according to multiple optimization objectives, a value of each minimum optimization objective corresponding to each second scheduling scheme, that is, performance data.
S702, performing data processing on the performance data of each second scheduling scheme to obtain the processed performance data.
In this embodiment of the present application, based on the performance data of each second scheduling scheme obtained in the foregoing embodiment, the terminal may normalize the performance data of each second scheduling scheme to obtain the processed performance data.
And S703, carrying out average calculation on the plurality of processed performance data to obtain second performance information.
In this embodiment of the present application, based on the plurality of processed performance data obtained in the foregoing embodiment, the terminal may perform average calculation on the plurality of processed performance data to obtain the second performance information
The embodiment of the application obtains the optimal weight by determining the second performance information of the scheduling scheme obtained on each scheduling problem in the problem set JSSP set.
In an exemplary embodiment, based on the foregoing embodiment, the method according to the embodiment of the present application further includes the following steps:
step 1: determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set;
step 2: starting iteration from the initial weight until the termination condition, and performing disturbance adjustment on the weight corresponding to each scheduling rule in each iteration step; determining a plurality of first scheduling schemes according to the weights and the scheduling rules before disturbance, and determining a plurality of second scheduling schemes according to the weights and the scheduling rules after disturbance;
step 3: determining a comprehensive performance index according to the multiple optimization targets;
step 4: respectively determining performance data of each first scheduling scheme aiming at the comprehensive performance index; performing data processing on the performance data of each first scheduling scheme to obtain processed performance data; performing average calculation on the plurality of processed performance data to obtain first performance information; respectively determining performance data of each second scheduling scheme aiming at each comprehensive performance index; performing data processing on the performance data of each second scheduling scheme to obtain processed performance data; performing average calculation on the plurality of processed performance data to obtain second performance information;
Step 5: under the condition that the first scheduling scheme is determined to be better than the second scheduling scheme according to the first performance information and the second performance information, determining the weight before disturbance as the weight after optimizing; under the condition that the second scheduling scheme is determined to be better than the first scheduling scheme according to the first performance information and the second performance information, determining the disturbed weight as the optimized weight;
step 6: and carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
In the embodiment of the application, a scheduling rule model PR (x) based on a linear combination of a plurality of basic scheduling rules is first defined j ) Wherein each basic scheduling rule has a corresponding linear combination weight x j . Aiming at the job task characteristics of a specific dynamic scheduling scene, a scheduling problem set JSSP set for training is constructed, a large number of scheduling problems are contained in the set, each scheduling problem has different numbers of tasks and working procedures, and the scheduling problem set JSSP set has different delivery times, optional equipment and other attributes. By scheduling on a problem set and evaluating the average overall performance index, the constructed scheduling rules PR (x j ) Evaluation and optimization were performed. Specifically, xj may be optimized using a simulated annealing algorithm to obtain an optimal scheduling rule PR (x j ). In the optimization process, the weight x is continuously adjusted through a plurality of iterations j So as to gradually obtain the optimal scheduling rule and apply the optimal scheduling rule to the dynamic scheduling scene.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a scheduling rule optimizing device for realizing the above related scheduling rule optimizing method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the one or more scheduling rule optimizing apparatus provided below may be referred to the limitation of the scheduling rule optimizing method hereinabove, and will not be described herein.
In one embodiment, as shown in fig. 8, there is provided a scheduling rule optimizing apparatus 800, comprising:
a solution confirmation module 801, configured to determine a plurality of scheduling solutions based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
the weight determining module 802 is configured to perform optimizing processing on weights corresponding to the scheduling rules based on multiple optimization targets, so as to obtain optimized weights;
the scheduling application module 803 is configured to perform dynamic scheduling application of multiple optimization targets in an actual scenario according to multiple scheduling rules and corresponding optimized weights.
In one embodiment, the weight determining module 802 includes:
the weight disturbance unit is used for starting iteration from the initial weight to the termination condition, and carrying out disturbance adjustment on the weight corresponding to each scheduling rule in each iteration step;
a scheme determining unit, configured to determine a plurality of first scheduling schemes according to the weights and the scheduling rules before the disturbance, and determine a plurality of second scheduling schemes according to the weights and the scheduling rules after the disturbance;
the weight determining unit is used for carrying out optimizing processing according to the multiple optimizing targets, the multiple first scheduling schemes and the multiple second scheduling schemes to obtain optimized weights.
In one embodiment, the weight determining unit includes:
the index determining subunit is used for determining the comprehensive performance index according to the multi-optimization targets;
an information determining subunit, configured to determine, for the comprehensive performance index, first performance information of the plurality of first scheduling schemes and second performance information of the plurality of second scheduling schemes, respectively;
and the weight determining subunit is used for determining the optimized weight according to the comparison result of the first performance information and the second performance information.
In one embodiment, the weight determining subunit is specifically configured to: under the condition that the first scheduling scheme is determined to be better than the second scheduling scheme according to the first performance information and the second performance information, determining the weight before disturbance as the weight after optimizing; and under the condition that the second scheduling scheme is determined to be better than the first scheduling scheme according to the first performance information and the second performance information, determining the disturbed weight as the optimized weight.
In one embodiment, the apparatus further comprises:
the first data determining module is used for determining performance data of each first scheduling scheme respectively;
the first data processing module is used for carrying out data processing on the performance data of each first scheduling scheme to obtain processed performance data;
And the first information determining module is used for carrying out average calculation on the plurality of processed performance data to obtain first performance information.
In one embodiment, the apparatus further comprises:
the second data determining module is used for determining the performance data of each second scheduling scheme respectively;
the second data processing module is used for carrying out data processing on the performance data of each second scheduling scheme to obtain processed performance data;
and the second information determining module is used for carrying out average calculation on the plurality of processed performance data to obtain second performance information.
The above-mentioned respective modules in the scheduling rule optimizing apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 9. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a scheduling rule optimization method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights;
and carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
In one embodiment, the processor when executing the computer program further performs the steps of:
starting iteration from the initial weight until the termination condition, and performing disturbance adjustment on the weight corresponding to each scheduling rule in each iteration step;
Determining a plurality of first scheduling schemes according to the weights and the scheduling rules before disturbance, and determining a plurality of second scheduling schemes according to the weights and the scheduling rules after disturbance;
and carrying out optimizing processing according to the multiple optimizing targets, the multiple first scheduling schemes and the multiple second scheduling schemes to obtain optimized weights.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining a comprehensive performance index according to the multiple optimization targets;
for the comprehensive performance index, respectively determining first performance information of a plurality of first scheduling schemes and second performance information of a plurality of second scheduling schemes;
and determining the optimized weight according to the comparison result of the first performance information and the second performance information.
In one embodiment, the processor when executing the computer program further performs the steps of:
under the condition that the first scheduling scheme is determined to be better than the second scheduling scheme according to the first performance information and the second performance information, determining the weight before disturbance as the weight after optimizing;
and under the condition that the second scheduling scheme is determined to be better than the first scheduling scheme according to the first performance information and the second performance information, determining the disturbed weight as the optimized weight.
In one embodiment, the processor when executing the computer program further performs the steps of:
respectively determining performance data of each first scheduling scheme;
performing data processing on the performance data of each first scheduling scheme to obtain processed performance data;
and carrying out average calculation on the plurality of processed performance data to obtain first performance information.
In one embodiment, the processor when executing the computer program further performs the steps of:
respectively determining performance data of each second scheduling scheme;
performing data processing on the performance data of each second scheduling scheme to obtain processed performance data;
and carrying out average calculation on the plurality of processed performance data to obtain second performance information.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights;
And carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
In one embodiment, the computer program when executed by the processor further performs the steps of:
starting iteration from the initial weight until the termination condition, and performing disturbance adjustment on the weight corresponding to each scheduling rule in each iteration step;
determining a plurality of first scheduling schemes according to the weights and the scheduling rules before disturbance, and determining a plurality of second scheduling schemes according to the weights and the scheduling rules after disturbance;
and carrying out optimizing processing according to the multiple optimizing targets, the multiple first scheduling schemes and the multiple second scheduling schemes to obtain optimized weights.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a comprehensive performance index according to the multiple optimization targets;
for the comprehensive performance index, respectively determining first performance information of a plurality of first scheduling schemes and second performance information of a plurality of second scheduling schemes;
and determining the optimized weight according to the comparison result of the first performance information and the second performance information.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Under the condition that the first scheduling scheme is determined to be better than the second scheduling scheme according to the first performance information and the second performance information, determining the weight before disturbance as the weight after optimizing;
and under the condition that the second scheduling scheme is determined to be better than the first scheduling scheme according to the first performance information and the second performance information, determining the disturbed weight as the optimized weight.
In one embodiment, the computer program when executed by the processor further performs the steps of:
respectively determining performance data of each first scheduling scheme;
performing data processing on the performance data of each first scheduling scheme to obtain processed performance data;
and carrying out average calculation on the plurality of processed performance data to obtain first performance information.
In one embodiment, the computer program when executed by the processor further performs the steps of:
respectively determining performance data of each second scheduling scheme;
performing data processing on the performance data of each second scheduling scheme to obtain processed performance data;
and carrying out average calculation on the plurality of processed performance data to obtain second performance information.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
Determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
optimizing weights corresponding to each scheduling rule based on multiple optimization targets to obtain optimized weights;
and carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
In one embodiment, the computer program when executed by the processor further performs the steps of:
starting iteration from the initial weight until the termination condition, and performing disturbance adjustment on the weight corresponding to each scheduling rule in each iteration step;
determining a plurality of first scheduling schemes according to the weights and the scheduling rules before disturbance, and determining a plurality of second scheduling schemes according to the weights and the scheduling rules after disturbance;
and carrying out optimizing processing according to the multiple optimizing targets, the multiple first scheduling schemes and the multiple second scheduling schemes to obtain optimized weights.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a comprehensive performance index according to the multiple optimization targets;
for the comprehensive performance index, respectively determining first performance information of a plurality of first scheduling schemes and second performance information of a plurality of second scheduling schemes;
And determining the optimized weight according to the comparison result of the first performance information and the second performance information.
In one embodiment, the computer program when executed by the processor further performs the steps of:
under the condition that the first scheduling scheme is determined to be better than the second scheduling scheme according to the first performance information and the second performance information, determining the weight before disturbance as the weight after optimizing;
and under the condition that the second scheduling scheme is determined to be better than the first scheduling scheme according to the first performance information and the second performance information, determining the disturbed weight as the optimized weight.
In one embodiment, the computer program when executed by the processor further performs the steps of:
respectively determining performance data of each first scheduling scheme;
performing data processing on the performance data of each first scheduling scheme to obtain processed performance data;
and carrying out average calculation on the plurality of processed performance data to obtain first performance information.
In one embodiment, the computer program when executed by the processor further performs the steps of:
respectively determining performance data of each second scheduling scheme;
performing data processing on the performance data of each second scheduling scheme to obtain processed performance data;
And carrying out average calculation on the plurality of processed performance data to obtain second performance information.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of scheduling rule optimization, the method comprising:
determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
optimizing the weights corresponding to the scheduling rules based on multiple optimization targets to obtain optimized weights;
And carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
2. The method of claim 1, wherein the optimizing the weights corresponding to the scheduling rules based on the multiple optimization objectives to obtain optimized weights comprises:
starting iteration from the initial weight until the termination condition, and performing disturbance adjustment on the weight corresponding to each scheduling rule in each iteration step;
determining a plurality of first scheduling schemes according to the weights before disturbance and the scheduling rules, and determining a plurality of second scheduling schemes according to the weights after disturbance and the scheduling rules;
and carrying out optimizing processing according to the multi-optimizing target, the plurality of first scheduling schemes and the plurality of second scheduling schemes to obtain the optimized weight.
3. The method of claim 2, wherein the optimizing according to the multiple optimization objectives, the multiple first scheduling schemes, and the multiple second scheduling schemes to obtain the optimized weights includes:
determining a comprehensive performance index according to the multi-optimization targets;
For the comprehensive performance index, respectively determining first performance information of a plurality of first scheduling schemes and second performance information of a plurality of second scheduling schemes;
and determining the optimized weight according to the comparison result of the first performance information and the second performance information.
4. The method of claim 3, wherein the determining the optimized weights based on the comparison of the first performance information and the second performance information comprises:
determining the pre-disturbance weight as the optimized weight under the condition that the first scheduling scheme is determined to be better than the second scheduling scheme according to the first performance information and the second performance information;
and determining the perturbed weight as the optimized weight when the second scheduling scheme is determined to be better than the first scheduling scheme according to the first performance information and the second performance information.
5. A method according to claim 3, wherein the determining of the first performance information comprises:
respectively determining performance data of each first scheduling scheme;
performing data processing on the performance data of each first scheduling scheme to obtain processed performance data;
And carrying out average calculation on the plurality of processed performance data to obtain the first performance information.
6. A method according to claim 3, wherein the determining of the second performance information comprises:
respectively determining performance data of each second scheduling scheme;
performing data processing on the performance data of each second scheduling scheme to obtain processed performance data;
and carrying out average calculation on the plurality of processed performance data to obtain the second performance information.
7. A scheduling rule optimizing apparatus, the apparatus comprising:
a scheme confirmation module for determining a plurality of scheduling schemes based on a pre-constructed scheduling problem set; each scheduling scheme is based on a plurality of scheduling rules and weights corresponding to the scheduling rules;
the weight determining module is used for carrying out optimizing processing on the weights corresponding to the scheduling rules based on the multiple optimization targets to obtain optimized weights;
and the scheduling application module is used for carrying out dynamic scheduling application of multiple optimization targets in an actual scene according to the multiple scheduling rules and the corresponding optimized weights.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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