CN111353675B - Job scheduling method and device - Google Patents

Job scheduling method and device Download PDF

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CN111353675B
CN111353675B CN201811580285.5A CN201811580285A CN111353675B CN 111353675 B CN111353675 B CN 111353675B CN 201811580285 A CN201811580285 A CN 201811580285A CN 111353675 B CN111353675 B CN 111353675B
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陈凯
时均见
冯杰
苏瑞文
周璐
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China Mobile Communications Group Co Ltd
China Mobile Hangzhou Information Technology Co Ltd
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Abstract

The invention discloses a job scheduling method and a job scheduling device, which are used for solving the problem of how to minimize the time for manufacturing workpieces by a plurality of devices. The method comprises the following steps: determining a Shannon entropy index value according to each target execution sequence in a first sequence group to be evolved and each execution sequence in a second sequence group; judging whether the shannon entropy index value is larger than a predetermined first value or not; if so, determining a mutation execution sequence by adopting a DE/rand/1 mutation strategy; otherwise, determining a mutation execution sequence by adopting a DE/best/1 mutation strategy; performing cross selection, and determining the execution sequence in the third sequence group; and when the evolution termination condition is met, outputting the execution sequence with the minimum duration value in the third sequence group. And determining the optimal process execution sequence corresponding to the target workpiece according to the Shannon entropy index value and the DE algorithm so as to minimize the time for completing the target workpiece among a plurality of devices.

Description

Job scheduling method and device
Technical Field
The invention relates to the technical field of manufacturing industry, in particular to a job scheduling method and device.
Background
The development level of the manufacturing industry is the important embodiment of national economic strength, and with the continuous promotion of intelligent manufacturing, the optimized dispatching of a job shop is used as the key technology and the core content of a flexible manufacturing production plan, thereby playing a vital role in the aspects of improving the production efficiency, reducing the production cost and the like. The main research content of optimizing and scheduling the job shop is to design a reasonable scheduling scheme under the existing resources so as to efficiently organize production and operation. Specifically, it is possible to use a task of manufacturing a workpiece, which requires a plurality of steps with a constraint on the execution order between the steps, for example, 5 steps are required for manufacturing a workpiece, which are steps 1 to 5, step 2 must be after step 1, and step 3 must be before step 5. The devices in the operation workshop correspond to the processes executed by the devices one by one, and after the devices execute the corresponding processes, the executed processes need to be transmitted to the next device, so that the next device continues to execute until all the processes are completed, and the workpiece can be considered to be produced. The duration of one process completed by the equipment can be understood as the sum of the duration of the corresponding process executed by the equipment and the duration of the time for the equipment to transmit the executed process to the next equipment. Taking a certain device as an example, after the device itself executes the corresponding process, according to the execution sequence constraint condition between the processes, there may be a plurality of devices to which the executed process is transmitted, and the time length for the device to transmit to each device meeting the execution sequence constraint condition is different. Based on this, a reasonable scheduling scheme, that is, a reasonable process execution sequence, needs to be designed to minimize the time for manufacturing the workpiece by the multiple devices.
Disclosure of Invention
The embodiment of the invention discloses a job scheduling method and a job scheduling device, which are used for solving the problem of how to minimize the time for manufacturing workpieces by a plurality of devices.
In order to achieve the above object, an embodiment of the present invention discloses a job scheduling method, where the method includes:
when a first sequence group to be evolved exists, determining a Shannon entropy index value according to each target execution sequence in the first sequence group and each execution sequence in a second sequence group; wherein the first order group is a previous order group of the second order group;
judging whether the shannon entropy index value is larger than a predetermined first value or not;
if so, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/rand/1 variation strategy;
if not, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/best/1 variation strategy;
aiming at each target execution sequence, generating an experiment execution sequence corresponding to the target execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and a corresponding variation execution sequence thereof; identifying a first time length value corresponding to the target execution sequence which is saved in advance, determining a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with the smaller time length value as the execution sequence in a third sequence group;
when the third sequence group is determined to meet the evolution termination condition, outputting the execution sequence with the minimum duration value in the third sequence group; otherwise, the third order group is taken as the first order group to be evolved.
Further, determining a shannon entropy index value according to each target execution order in the first order group and each execution order in the second order group, comprising:
determining the target execution sequence belonging to each class in the first sequence group according to each pre-stored time length clustering center and a first time length value corresponding to each target execution sequence in the first sequence group;
determining a transfer matrix between a second sequence group and a first sequence group according to a target execution sequence belonging to each class in the first sequence group and an execution sequence belonging to each class in a predetermined second sequence group;
and determining the Shannon entropy index value according to the transfer matrix and the number of the duration clustering centers.
Further, according to the transition matrix and the number of the duration clustering centers, determining a shannon entropy index value, comprising:
Figure BDA0001917616610000031
wherein k is the number of the time length clustering centers, trans is the transfer matrix, trans ij Representing the value in the ith row and jth column of the transition matrix, E g+1 The numerical ranges of i and j are both 0-k, which is the Shannon entropy index value.
Further, determining whether the third order group satisfies an evolution termination condition comprises:
determining whether an evolution algebra corresponding to the third sequence group reaches a threshold value or not, and determining whether an execution sequence with a time length value smaller than a preset time length threshold value exists in the third sequence group or not;
if at least one is yes, determining that the third order group meets an evolution termination condition;
and if the first order group and the second order group do not meet the evolution termination condition, determining that the third order group does not meet the evolution termination condition.
Further, if the second order group is the initial order group, the first order group is the second order group;
the process of determining the second order group includes:
generating a second sequence group according to the number of execution sequences in the pre-stored sequence group and process execution sequence constraint conditions corresponding to the target workpiece, wherein each execution sequence in the second sequence group conforms to the execution sequence constraint conditions;
the process of determining the first order group includes:
determining a variation execution sequence corresponding to each target execution sequence in the second sequence group by adopting a DE/rand/1 variation strategy, and generating an experiment execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and the variation execution sequence corresponding to the target execution sequence; and determining a first time length value corresponding to the target execution sequence and a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with less time length values as the execution sequence in the first sequence group.
Further, the process of determining the first value in advance includes:
and generating a random number in a preset interval, and determining the generated random number as a first numerical value.
The embodiment of the invention discloses a job scheduling device, which comprises:
the shannon entropy determination module is used for determining a shannon entropy index value according to each target execution sequence in the first sequence group and each execution sequence in the second sequence group when the first sequence group to be evolved exists; wherein the first order group is a previous order group of the second order group;
the variation module is used for judging whether the Shannon entropy index value is larger than a first predetermined value; if so, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/rand/1 variation strategy; if not, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/best/1 variation strategy;
the crossing module is used for generating an experiment execution sequence corresponding to each target execution sequence based on the crossing processing of the DE algorithm according to the target execution sequence and the corresponding variation execution sequence thereof;
the selection module is used for identifying a first time length value corresponding to the target execution sequence which is saved in advance, determining a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with the smaller time length value as the execution sequence in the third sequence group;
the termination module is used for outputting the execution sequence with the minimum duration value in the third sequence group when the third sequence group is determined to meet the evolution termination condition; otherwise, the third order group is taken as the first order group to be evolved.
The embodiment of the invention discloses electronic equipment, which comprises: a processor and a memory;
the processor is used for reading the program in the memory and executing the following processes:
when a first sequence group to be evolved exists, determining a Shannon entropy index value according to each target execution sequence in the first sequence group and each execution sequence in a second sequence group; wherein the first order group is a previous order group of the second order group;
judging whether the Shannon entropy index value is larger than a predetermined first value or not;
if yes, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/rand/1 variation strategy;
if not, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/best/1 variation strategy;
aiming at each target execution sequence, generating an experiment execution sequence corresponding to the target execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and a corresponding variation execution sequence thereof; identifying a first time length value corresponding to the target execution sequence which is saved in advance, determining a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with the smaller time length value as the execution sequence in a third sequence group;
when the third sequence group is determined to meet the evolution termination condition, outputting the execution sequence with the minimum duration value in the third sequence group; otherwise, the third order group is taken as the first order group to be evolved.
The embodiment of the invention discloses an electronic device, which comprises: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory has stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of any of the above-described job scheduling methods.
The embodiment of the invention discloses a computer readable storage medium, which stores a computer program executable by an electronic device, and when the program runs on the electronic device, the program causes the electronic device to execute the steps of any one of the above job scheduling methods.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic diagram of a job scheduling process according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a job scheduling process according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a job scheduling process according to an embodiment of the present invention;
fig. 4 is a structural diagram of an operation scheduling apparatus according to an embodiment of the present invention;
fig. 5 is an electronic device according to an embodiment of the present invention;
fig. 6 is an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The job scheduling method provided by the embodiment of the invention can be applied to electronic equipment. The electronic device may determine an optimal process execution sequence corresponding to the target workpiece according to the shannon entropy index value and a Differential Evolution (DE) algorithm, so as to minimize time consumed for completing the target workpiece among the multiple devices. For simplicity, the sequence of execution of the processes may be referred to herein as the order of execution. In addition, it should be noted that "order group" in the present application may be understood as "population" in combination with conventional descriptions in DE algorithm, such as population, individual, population scale, initial population, second generation population, target individual, variant individual, experimental individual, etc.; an "execution order" in this application is understood to be one "individual" in a population; the "number of execution orders in an order group" in the present application is understood as "population size"; the "initial order group" in this application is understood to be the "initial population"; "second generation order group" in this application is understood to be a "second generation population"; the "target execution order" in the present application may be understood as "target individual"; the term "order of execution of variants" in this application is understood as "individual variants"; the "experiment execution order" in the present application is understood as "experimental individual".
In this application, the execution sequence may be referred to as an execution sequence, and may also be referred to as an execution sequence.
Fig. 1 is a schematic diagram of a job scheduling process according to an embodiment of the present invention, where the process includes the following steps:
s101: and generating an initial sequence group according to the number of execution sequences in the pre-stored sequence group and the process execution sequence constraint condition corresponding to the target workpiece, wherein each execution sequence in the initial sequence group meets the execution sequence constraint condition.
The electronic device stores a plurality of processes required for manufacturing a target workpiece in advance, and stores a process execution order constraint condition of the plurality of processes required for manufacturing the target workpiece and the number of execution orders in an order group. When determining the optimal execution sequence corresponding to the target workpiece, an initial sequence group may be generated first, and each execution sequence in the initial sequence group meets the execution sequence constraint condition.
After knowing the number of execution sequences in the sequence group and the constraint condition of the process execution sequence, the specific process of generating the initial sequence group according to the number of execution sequences in the sequence group stored in advance and the constraint condition of the process execution sequence corresponding to the target workpiece belongs to the prior art, and is not described in detail in the embodiment of the present invention.
S102: determining whether the initial order group meets an evolution termination condition; if yes, S103 is performed, and if no, S104 is performed.
The electronic device pre-stores an evolution termination condition, and after each generation of a sequence group, the electronic device can judge whether the evolution termination condition is met to determine whether to continue to evolve the sequence group, and if the evolution termination condition is met, the electronic device can output an optimal execution sequence in the current sequence group, and if the evolution termination condition is not met, the electronic device can continue to evolve the current sequence group.
S103: and outputting the execution sequence with the minimum duration value in the initial sequence group.
When the electronic device determines that the initial sequence group meets the evolution termination condition, the electronic device may output an optimal execution sequence in the initial sequence group, where the optimal execution sequence may be understood as a minimum execution sequence in total use. The electronic device may calculate a total time corresponding to each execution order in the initial order group, which may be referred to as a duration value, and identify an execution order with the smallest duration value as an optimal execution order, that is, output the execution order with the smallest duration value in the initial order group.
S104: determining a variation execution sequence corresponding to each target execution sequence in the initial sequence group by adopting a DE/rand/1 variation strategy, and generating an experiment execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and the variation execution sequence corresponding to the target execution sequence; and determining a first time length value corresponding to the target execution sequence and a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with less time length values as the execution sequence in the second generation sequence group.
When the electronic device determines that the initial sequence group does not satisfy the evolution termination condition, the electronic device may evolve the initial sequence group to determine a second-generation sequence group. The evolution process is mainly divided into mutation, crossover and selection, and is specifically explained as follows:
mutation: the DE/rand/1 mutation policy is pre-stored in the electronic device, and the electronic device may determine a mutation execution order corresponding to each target execution order according to the DE/rand/1 mutation policy, with each execution order in the initial order group being used as the target execution order.
The DE/rand/1 mutation strategy may be understood as randomly selecting two execution orders from a current generation (initial) order group, using a difference vector of the randomly selected two execution orders as a variation source of a randomly selected third execution order, and summing the weighted difference vector with the third execution order according to a certain rule to generate a mutation execution order.
And (3) crossing: and generating an experimental execution sequence based on the cross processing of the DE algorithm according to the target execution sequence and the corresponding variant execution sequence.
Selecting: when the experiment execution order is determined, an optimal execution order may be selected as the execution order in the next-generation (second-generation) order group from among the experiment execution order and the target execution order. Specifically, a first duration value corresponding to the target execution order and a second duration value corresponding to the experiment execution order corresponding to the target execution order may be determined, and an execution order with a smaller duration value of the first duration value and the second duration value may be used as an execution order in the next-generation (second-generation) order group.
After each generation of the sequence group, the electronic device can judge whether an evolution termination condition is met or not to determine whether the sequence group is to be evolved continuously or not, if the evolution termination condition is met, the electronic device can output the optimal execution sequence in the current sequence group, and if the evolution termination condition is not met, the electronic device can continue to evolve the current sequence group. The electronic device may cycle until a certain generation meets the evolution termination condition.
The electronic device may perform steps S105-S107 after determining the second order group.
S105: determining whether the second generation order group satisfies an evolution termination condition; if yes, S106 is performed, and if no, S107 is performed.
S106: and outputting the execution sequence with the minimum time length value in the second generation sequence group.
S107: the second generation order group was evolved.
The execution sequence contained in each generation sequence group obtained by the evolution of the initial sequence group meets the constraint condition of the procedure execution sequence.
In order to improve the speed of determining the optimal execution sequence, when the sequence group is changed, different variation strategies can be selected by combining shannon entropy index values, and the optimal execution sequence can be determined more quickly.
Fig. 2 is a schematic diagram of a job scheduling process according to an embodiment of the present invention, where the process includes the following steps:
s201: when a first sequence group to be evolved exists, determining a Shannon entropy index value according to each target execution sequence in the first sequence group and each execution sequence in a second sequence group; wherein the first order group is a previous order group of the second order group.
In the embodiment of the present invention, any one generation order group may be determined as the second order group, and the next generation order group of the second order group may be determined as the first order group, that is, the first order group is the previous generation order group of the second order group. Preferably, starting from the initial order group, the initial order group is first determined as the second order group and the second order group is determined as the first order group.
The electronic device can determine a shannon entropy index value according to the execution sequence in the two adjacent generation sequence groups, and select different variation strategies according to the shannon entropy index value. When the electronic equipment identifies that a first sequence group to be evolved exists, the shannon entropy index value is determined according to each target execution sequence in the first sequence group and each execution sequence in the second sequence group. The mutation strategy can comprise a DE/rand/1 mutation strategy and a DE/best/1 mutation strategy, when the DE/best/1 mutation strategy is adopted for mutation, difference vectors of two randomly selected execution sequences can be used as a change source of a third execution sequence with the minimum time length in the first sequence group, and the difference vectors are weighted and then summed with the third execution sequence according to a certain rule to generate a mutation execution sequence.
S202: and judging whether the shannon entropy index value is larger than a first numerical value predetermined for the first sequence group, if so, performing S203, and if not, performing S204.
S203: and determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/rand/1 variation strategy.
S204: and determining a variant execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/best/1 variant strategy.
The smaller the shannon entropy index value is, the more stable the evolution is, otherwise, the more unstable the evolution is, the more stable and unstable the evolution can be understood as the convergence rate of the DE algorithm, and the larger the shannon entropy index value is, the faster the convergence rate of the evolution is, and the smaller the convergence rate of the evolution is, the slower the convergence rate of the evolution is.
The electronic device pre-stores a first numerical value predetermined for the first order group, and can determine whether the shannon entropy index value is greater than the first numerical value, if so, the convergence rate of the evolution can be considered to be fast, and at this time, a DE/rand/1 variation strategy can be adopted for variation, and if not, the convergence rate of the evolution can be considered to be slow, and at this time, a DE/best/1 variation strategy can be adopted for variation.
After the execution sequence of the mutation is determined, the crossover and selection can be performed, which is specifically referred to as step S205 below.
S205: aiming at each target execution sequence, generating an experiment execution sequence corresponding to the target execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and a corresponding variation execution sequence thereof; and identifying a first time length value corresponding to the target execution sequence which is saved in advance, determining a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with the smaller time length value as the execution sequence in the third sequence group.
Selecting: after the experiment execution order is determined, an optimal execution order can be selected from the experiment execution order and the target execution order as an execution order in a next-generation order group, and for convenience of distinction, the next-generation order group of the first order group is referred to as a third order group. Specifically, a first duration value corresponding to a predetermined target execution sequence may be identified, a second duration value corresponding to an experimental execution sequence corresponding to the target execution sequence may be determined, and an execution sequence with a smaller duration value of the first duration value and the second duration value may be used as an execution sequence in the third sequence group.
The electronic device can store the time length value corresponding to each execution sequence after determining the time length value corresponding to each execution sequence for each generation sequence group, so as to be directly used subsequently. After determining each execution sequence in the third sequence group, the electronic device may store a duration value corresponding to each execution sequence in the third sequence group.
After each generation of the sequence group, the electronic device can judge whether an evolution termination condition is met or not to determine whether the sequence group is to be evolved continuously or not, if the evolution termination condition is met, the electronic device can output the optimal execution sequence in the current sequence group, and if the evolution termination condition is not met, the electronic device can continue to evolve the current sequence group. The electronic device may cycle until a certain generation meets the evolution termination condition.
After determining the third order group, the electronic device may perform steps S206-S208.
S206: it is determined whether the third sequential group satisfies the evolution termination condition, and if so, S207 is performed, and if not, S208 is performed.
S207: and outputting the execution sequence with the minimum time length value in the third sequence group.
S208: and taking the third order group as the first order group to be evolved, and returning to the S201.
In a possible implementation manner, the first numerical value compared with the shannon entropy index value may be a numerical value configured by the user in the electronic device for each generation of order group, or may be a random number located in a preset interval generated by the electronic device for an advanced order group (first order group) after the electronic device has evolved each generation of order group, and the generated random number is determined as the first numerical value. The general shannon entropy index value is between 0 and 1, and the preset interval can be 0 to 1 when the random number is generated.
In a possible implementation manner, the evolution termination condition may be an evolution algebra, and the electronic device may update the accumulated evolution algebra after each evolution generation, specifically, while the third order group is used as the first order group to be evolved in S208, the electronic device may update the accumulated evolution algebra, and in general, the value may be increased by 1.
The threshold of the evolution algebra can be preset in the electronic device, when whether the sequence group meets the evolution termination condition or not is determined, whether the evolution algebra corresponding to the sequence group is larger than the preset threshold or not can be judged, if yes, the evolution termination condition is met, and if not, the evolution termination condition is not met.
In a possible implementation manner, the evolution termination condition may be that an execution sequence with a sufficiently small duration value is found, the electronic device may pre-store a duration threshold, the electronic device may compare the duration value corresponding to each execution sequence in the third sequence group with a preset duration threshold, determine whether there is an execution sequence with a duration value smaller than the preset duration threshold, if yes, the evolution termination condition is satisfied, and if not, the execution termination condition is not satisfied.
In a possible implementation manner, when determining whether the sequence group meets the evolution termination condition, the electronic device may determine whether an evolution algebra corresponding to the sequence group reaches a threshold, and determine whether an execution sequence with a duration value smaller than a preset duration threshold exists in the sequence group;
if at least one is yes, determining that the order group meets the evolution termination condition;
and if not, determining that the sequence group does not meet the evolution termination condition.
In one possible implementation, the determining the shannon entropy index value according to each target execution order in the first order group and each execution order in the second order group includes:
determining the target execution sequence belonging to each class in the first sequence group according to each pre-stored time length clustering center and a first time length value corresponding to each target execution sequence in the first sequence group;
determining a transfer matrix between a second sequence group and a first sequence group according to a target execution sequence belonging to each class in the first sequence group and an execution sequence belonging to each class in a predetermined second sequence group;
and determining the Shannon entropy index value according to the transfer matrix and the number of the duration clustering centers.
In the embodiment of the present invention, a plurality of cluster centers are pre-stored in the electronic device, and each cluster center may be understood as each duration value, where each duration value is a duration value of an execution sequence that meets an execution sequence constraint condition, a plurality of execution sequences may be selected from the initial sequence group, and the corresponding duration value is used as a cluster center, or the electronic device may generate a plurality of execution sequences that meet an execution sequence constraint condition in addition to the initial population, and use the duration value corresponding to the generated execution sequence as a cluster center. Since the cluster center is a duration value, the cluster center may be referred to as a duration cluster center for ease of understanding.
The electronic device may determine, based on a k-means algorithm, a target execution order belonging to each class in the first order group, that is, determine and store a class to which each target execution order belongs, according to a first time length value corresponding to each target execution order in the first order group and each pre-stored time length clustering center.
The electronic device may store the execution sequence of each class in the sequence group for subsequent use, the second sequence group is used as a previous sequence group of the first sequence group, and the electronic device generally stores the execution sequence of each class in the second sequence group.
The electronic device may determine a transition matrix between the second order group and the first order group according to a target execution order belonging to each class in the first order group and an execution order belonging to each class in the second order group. The process belongs to the prior art, and is not described in detail in the embodiment of the invention.
After the electronic device determines the transfer matrix, the shannon entropy index value can be determined according to the transfer matrix and the number of the duration clustering centers, wherein the transfer matrix is k rows and k columns, and the following formula can be specifically referred to:
Figure BDA0001917616610000121
wherein k is the number of time length clustering centers, trans is the transfer matrix, trans ij Representing the value in the ith row and jth column of the transition matrix, E g+1 The numerical ranges of i and j are both 0-k, which is the Shannon entropy index value.
The embodiments provided in this application may also include the embodiment provided in fig. 3 below (the numbers of the embodiments provided in this section do not have an explicit correspondence with the numbers of the previous embodiments, and are only for convenience of description in this section).
Fig. 3 is a schematic diagram of a job scheduling process according to an embodiment of the present invention, in the process, an electronic device first generates an initial population according to parameters, generates a (background point) clustering center, and clusters individuals in the initial population according to the background point. And evolving the initial population to generate a next generation (second generation) population, clustering individuals in the next generation population according to background points, determining a Shannon entropy index value according to individuals positioned in each class in two adjacent generations of populations, selecting to adopt a DE/rand/1 variation strategy or a DE/best/1 variation strategy for variation according to the Shannon entropy index value, then performing cross processing, and decoding (time length value corresponding to the individual) to select the optimal individual as the individual in the next generation population. And determining whether the evolution termination condition is met, if so, outputting an optimal solution, if not, evolving the second generation population to generate a next generation (third generation) population, and repeating the steps in sequence until the evolution termination condition is met, and outputting the optimal solution after the evolution is finished.
Based on the same inventive concept as the job scheduling method, fig. 4 is a structural diagram of a job scheduling apparatus according to an embodiment of the present invention, where the apparatus includes:
a shannon entropy determination module 41, configured to determine a shannon entropy index value according to each target execution order in the first order group and each execution order in the second order group when there is a first order group to be evolved; wherein, the first order group is a previous generation order group of the second order group;
a variation module 42, configured to determine whether the shannon entropy index value is greater than a predetermined first value; if so, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/rand/1 variation strategy; if not, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/best/1 variation strategy;
a crossover module 43, configured to generate, for each target execution sequence, an experiment execution sequence corresponding to the target execution sequence based on a crossover process of the DE algorithm according to the target execution sequence and a variation execution sequence corresponding to the target execution sequence;
a selecting module 44, configured to identify a first duration value corresponding to the target execution sequence that is pre-saved, determine a second duration value corresponding to the experiment execution sequence corresponding to the target execution sequence, and use the execution sequence with the smaller duration value as an execution sequence in a third sequence group;
a termination module 45, configured to output an execution sequence with a minimum duration value in the third order group when it is determined that the third order group meets the evolution termination condition; otherwise, the third order group is taken as the first order group to be evolved.
Further, the shannon entropy determining module 41 is specifically configured to determine, according to each pre-stored time-length cluster center and a first time-length value corresponding to each target execution order in the first order group, a target execution order belonging to each class in the first order group; determining a transfer matrix between a second sequence group and a first sequence group according to a target execution sequence belonging to each class in the first sequence group and an execution sequence belonging to each class in a predetermined second sequence group; and determining the Shannon entropy index value according to the transfer matrix and the number of the time length clustering centers.
Further, the shannon entropy determination module 41 is specifically configured to determine the shannon entropy
Figure BDA0001917616610000141
Wherein k is the number of time length clustering centers, trans is the transfer matrix, trans ij Representing the value in the ith row and jth column of the transfer matrix, E g+1 The value ranges of i and j are both 0-k, which is the Shannon entropy index value.
Further, the termination module 45 is specifically configured to determine whether an evolution algebra corresponding to the third order group reaches a threshold, and determine whether an execution sequence with a duration value smaller than a preset duration threshold exists in the third order group;
if at least one is yes, determining that the third order group meets the evolution termination condition;
and if the first order group and the second order group are not satisfied, determining that the third order group does not satisfy the evolution termination condition.
Further, the apparatus further comprises:
a determining module 46 configured to determine that the first order group is the second order group if the second order group is the initial order group;
generating a second sequence group according to the number of execution sequences in the pre-stored sequence group and process execution sequence constraint conditions corresponding to the target workpiece, wherein each execution sequence in the second sequence group conforms to the execution sequence constraint conditions;
adopting a DE/rand/1 variation strategy aiming at each target execution sequence in the second sequence group to determine a variation execution sequence corresponding to the target execution sequence, and generating an experiment execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and the variation execution sequence corresponding to the target execution sequence; and determining a first time length value corresponding to the target execution sequence and a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with the smaller time length value as the execution sequence in the first sequence group.
Further, the mutation module 42 is specifically configured to generate a random number located in a preset interval, and determine the generated random number as the first numerical value.
Fig. 5 is an electronic device provided in an embodiment of the present invention, where the electronic device includes: a processor 51 and a memory 52;
in fig. 5, the bus architecture may include any number of interconnected buses and bridges, with one or more processors 51, represented by processor 51, and various circuits of memory 52, represented by memory 52, being linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The processor 51 is responsible for managing the bus architecture and general processing, and the memory 52 may store data used by the processor 51 in performing operations.
Alternatively, the processor 51 may be a CPU (central processing unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a CPLD (Complex Programmable Logic Device).
The processor 51 is configured to read the program in the memory 52, and execute the following processes:
when a first sequence group to be evolved exists, determining a Shannon entropy index value according to each target execution sequence in the first sequence group and each execution sequence in a second sequence group; wherein the first order group is a previous order group of the second order group;
judging whether the Shannon entropy index value is larger than a predetermined first value or not;
if so, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/rand/1 variation strategy;
if not, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/best/1 variation strategy;
aiming at each target execution sequence, generating an experiment execution sequence corresponding to the target execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and a corresponding variation execution sequence thereof; identifying a first time length value corresponding to the target execution sequence which is saved in advance, determining a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with the smaller time length value as the execution sequence in a third sequence group;
when the third sequence group is determined to meet the evolution termination condition, outputting the execution sequence with the minimum duration value in the third sequence group; otherwise, the third order group is taken as the first order group to be evolved.
Further, the processor 51 is specifically configured to determine, according to each pre-stored time-length cluster center and a first time-length value corresponding to each target execution order in the first order group, a target execution order belonging to each class in the first order group;
determining a transfer matrix between a second sequence group and a first sequence group according to a target execution sequence belonging to each class in the first sequence group and an execution sequence belonging to each class in a predetermined second sequence group;
and determining the Shannon entropy index value according to the transfer matrix and the number of the time length clustering centers.
Further, the processor 51 is specifically configured for
Figure BDA0001917616610000161
Wherein k is the number of time length clustering centers, trans is the transfer matrix, trans ij Representing the value in the ith row and jth column of the transfer matrix, E g+1 The numerical ranges of i and j are both 0-k, which is the Shannon entropy index value.
Further, the processor 51 is specifically configured to determine whether an evolution algebra corresponding to the third order group reaches a threshold, and determine whether an execution sequence with a duration value smaller than a preset duration threshold exists in the third order group;
if at least one is yes, determining that the third order group meets an evolution termination condition;
and if the first order group and the second order group are not satisfied, determining that the third order group does not satisfy the evolution termination condition.
Further, the processor 51 is further configured to, if the second order group is an initial order group, the first order group is a second order group;
the process of determining the second order group includes:
generating a second sequence group according to the number of execution sequences in the pre-stored sequence group and the process execution sequence constraint condition corresponding to the target workpiece, wherein each execution sequence in the second sequence group meets the execution sequence constraint condition;
the process of determining the first order group comprises:
determining a variation execution sequence corresponding to each target execution sequence in the second sequence group by adopting a DE/rand/1 variation strategy, and generating an experiment execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and the variation execution sequence corresponding to the target execution sequence; and determining a first time length value corresponding to the target execution sequence and a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with less time length values as the execution sequence in the first sequence group.
Further, the processor 51 is specifically configured to generate a random number located in a preset interval, and determine the generated random number as a first numerical value.
Fig. 6 is an electronic device provided in an embodiment of the present invention, including: the system comprises a processor 61, a communication interface 62, a memory 63 and a communication bus 64, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
the memory has stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of any of the above-described job scheduling methods.
The communication bus mentioned in the electronic device in each of the above embodiments may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
And the communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a central processing unit, a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an application specific integrated circuit, a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like.
An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program executable by an electronic device, and when the program runs on the electronic device, causes the electronic device to execute the steps of any one of the above-mentioned job scheduling methods.
The computer readable storage medium in the above embodiments may be any available medium or data storage device that can be accessed by a processor in an electronic device, including but not limited to magnetic memory such as floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc., optical memory such as CDs, DVDs, BDs, HVDs, etc., and semiconductor memory such as ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), solid State Disks (SSDs), etc.
For the system/apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.
It is to be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or operation from another entity or operation without necessarily requiring or implying any actual such relationship or order between such entities or operations.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely application embodiment, or an embodiment combining application and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims (10)

1. A job scheduling method, comprising:
when a first sequence group to be evolved exists, determining a Shannon entropy index value according to each target execution sequence in the first sequence group and each execution sequence in a second sequence group; wherein the first order group is a previous order group of the second order group;
judging whether the Shannon entropy index value is larger than a predetermined first value or not;
if so, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/rand/1 variation strategy;
if not, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/best/1 variation strategy;
aiming at each target execution sequence, generating an experiment execution sequence corresponding to the target execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and a corresponding variation execution sequence thereof; identifying a first time length value corresponding to the target execution sequence which is saved in advance, determining a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with the smaller time length value as the execution sequence in a third sequence group;
when the third sequence group is determined to meet the evolution termination condition, outputting the execution sequence with the minimum duration value in the third sequence group; otherwise, the third order group is taken as the first order group to be evolved.
2. The method of claim 1, wherein determining a shannon entropy index value based on each target execution order in a first order group and each execution order in a second order group comprises:
determining the target execution sequence belonging to each class in the first sequence group according to each pre-stored time length clustering center and a first time length value corresponding to each target execution sequence in the first sequence group;
determining a transfer matrix between a second sequence group and a first sequence group according to a target execution sequence belonging to each class in the first sequence group and an execution sequence belonging to each class in a predetermined second sequence group;
and determining the Shannon entropy index value according to the transfer matrix and the number of the time length clustering centers.
3. The method of claim 2, wherein determining a shannon entropy index value based on the transition matrix and the number of duration cluster centers comprises:
Figure FDA0001917616600000021
wherein k is the number of the time length clustering centers, trans is the transfer matrix, trans ij Representing the value in the ith row and jth column of the transfer matrix, E g+1 The value ranges of i and j are both 0-k, which is the Shannon entropy index value.
4. The method of claim 1, wherein determining whether the third order group satisfies an evolution termination condition comprises:
determining whether an evolution algebra corresponding to the third sequence group reaches a threshold value or not, and determining whether an execution sequence with a duration value smaller than a preset duration threshold value exists in the third sequence group or not;
if at least one is yes, determining that the third order group meets the evolution termination condition;
and if the first order group and the second order group are not satisfied, determining that the third order group does not satisfy the evolution termination condition.
5. The method of claim 1, wherein if the second order group is an initial order group, the first order group is a second order group;
the process of determining the second order group includes:
generating a second sequence group according to the number of execution sequences in the pre-stored sequence group and process execution sequence constraint conditions corresponding to the target workpiece, wherein each execution sequence in the second sequence group conforms to the execution sequence constraint conditions;
the process of determining the first order group comprises:
determining a variation execution sequence corresponding to each target execution sequence in the second sequence group by adopting a DE/rand/1 variation strategy, and generating an experiment execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and the variation execution sequence corresponding to the target execution sequence; and determining a first time length value corresponding to the target execution sequence and a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with less time length values as the execution sequence in the first sequence group.
6. The method of claim 1, wherein the predetermined first value comprises:
and generating a random number in a preset interval, and determining the generated random number as a first numerical value.
7. A job scheduling apparatus, characterized in that the apparatus comprises:
the shannon entropy determination module is used for determining a shannon entropy index value according to each target execution sequence in the first sequence group and each execution sequence in the second sequence group when the first sequence group to be evolved exists; wherein the first order group is a previous order group of the second order group;
the variation module is used for judging whether the Shannon entropy index value is larger than a first predetermined value; if so, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/rand/1 variation strategy; if not, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/best/1 variation strategy;
the crossing module is used for generating an experiment execution sequence corresponding to each target execution sequence based on the crossing processing of the DE algorithm according to the target execution sequence and the corresponding variation execution sequence thereof;
the selection module is used for identifying a first time length value corresponding to the target execution sequence which is saved in advance, determining a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with the smaller time length value as the execution sequence in the third sequence group;
the termination module is used for outputting the execution sequence with the minimum duration value in the third sequence group when the third sequence group is determined to meet the evolution termination condition; otherwise, the third order group is taken as the first order group to be evolved.
8. An electronic device, characterized in that the electronic device comprises: a processor and a memory;
the processor is used for reading the program in the memory and executing the following processes:
when a first sequence group to be evolved exists, determining a Shannon entropy index value according to each target execution sequence in the first sequence group and each execution sequence in a second sequence group; wherein the first order group is a previous order group of the second order group;
judging whether the Shannon entropy index value is larger than a predetermined first value or not;
if so, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/rand/1 variation strategy;
if not, determining a variation execution sequence corresponding to each target execution sequence in the first sequence group by adopting a DE/best/1 variation strategy;
aiming at each target execution sequence, generating an experiment execution sequence corresponding to the target execution sequence based on the cross processing of a DE algorithm according to the target execution sequence and a corresponding variation execution sequence thereof; identifying a first time length value corresponding to the target execution sequence which is saved in advance, determining a second time length value corresponding to the experiment execution sequence corresponding to the target execution sequence, and taking the execution sequence with the smaller time length value as the execution sequence in a third sequence group;
when the third sequence group is determined to meet the evolution termination condition, outputting the execution sequence with the minimum duration value in the third sequence group; otherwise, the third order group is taken as the first order group to be evolved.
9. An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
the memory has stored therein a computer program which, when executed by the processor, causes the processor to carry out the steps of the method of any one of claims 1-6.
10. A computer-readable storage medium, characterized in that it stores a computer program executable by an electronic device, which program, when run on the electronic device, causes the electronic device to carry out the steps of the method according to any one of claims 1-6.
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