CN115577997B - Scheduling control method, device, equipment and computer readable storage medium - Google Patents
Scheduling control method, device, equipment and computer readable storage medium Download PDFInfo
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Abstract
The application discloses a scheduling control method, a scheduling control device, scheduling control equipment and a computer readable storage medium. Wherein the method comprises the following steps: acquiring experimental operation flows corresponding to n experimental samples respectively, wherein the experimental operation flows comprise a plurality of operations executed by the experimental samples according to a preset execution sequence and expected operation duration of the operations; and determining a first start execution time and a first execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm by taking the minimum sum of the start execution times of all the operations in the experimental operation flows corresponding to the n experimental samples as an objective function and taking the execution sequence constraint and the execution time constraint between the operations corresponding to the same experimental operation flow as constraint conditions, so as to obtain a first experimental schedule. According to the scheduling control method, the use efficiency and the experimental flux of the equipment can be improved, and the experimental efficiency is further improved.
Description
Technical Field
The application belongs to the field of intelligent laboratories, and particularly relates to a scheduling control method, a scheduling control device, scheduling control equipment and a computer readable storage medium.
Background
In the field of life science and physicochemical experiments, the main mode of the current experiment is mainly manual work, and an experimenter completes the experiment by transferring samples among a plurality of different instruments. However, human factors have led to limitations in the throughput of the experiment and in the reproducibility of the experimental results to varying degrees.
In order to overcome the above-mentioned problems caused by human factors, in the field of life science and physical and chemical automation, robots are often used to replace laboratory staff to transfer samples between a plurality of different instruments and equipment to complete the experiment. In order to ensure that as many experiments as possible are completed within a certain period of time, the use efficiency of the equipment is improved, and a plurality of samples are required to be run simultaneously to improve the experimental flux. However, in the prior art, the use efficiency of the equipment and the experimental flux are low, so that the experimental efficiency is low.
Disclosure of Invention
The embodiment of the application provides a scheduling control method, a scheduling control device, scheduling control equipment, computer readable storage media and computer program products, which can improve the use efficiency and experimental flux of equipment and further improve the experimental efficiency.
In a first aspect, an embodiment of the present application provides a scheduling control method, including:
acquiring experimental operation flows corresponding to n experimental samples respectively, wherein the experimental operation flows comprise a plurality of operations executed by the experimental samples according to a preset execution sequence and expected operation duration of the operations, and n is a positive integer;
taking the minimum sum of the starting execution time of all operations in the experimental operation flows corresponding to the n experimental samples as an objective function, taking the constraint of the execution sequence and the constraint of the execution time among the operations corresponding to the same experimental operation flow as constraint conditions, and determining the first starting execution time and the first execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm to obtain a first experimental schedule;
the execution time constraint is a time interval constraint between a target time of a first operation and a target time of a second operation, wherein the first operation and the second operation are any one of a plurality of operations corresponding to the same experimental operation flow, and the target time comprises a start execution time or an end execution time.
In one possible implementation manner, the execution time constraint includes a target difference value not smaller than an expected operation duration of the first operation, where the target difference value is a difference value between a start execution time of the first operation and a start execution time of the second operation, which corresponds to the experimental sample, and the start execution time of the second operation is later than the start execution time of the first operation.
In one possible implementation manner, the execution time constraint includes a target difference value not smaller than an expected operation duration of the second operation, where the target difference value is a difference value between an execution end time of the first operation and an execution end time of the second operation, which corresponds to the experimental sample, and the execution end time of the second operation is later than the execution end time of the first operation.
In one possible implementation, after the obtaining the first experimental schedule, the method further includes:
sequentially executing the operations in the experimental operation flow according to the first start execution time and the first execution sequence in the first experimental schedule;
after the third operation is executed, updating the expected operation duration corresponding to the third operation according to the actual operation duration corresponding to the third operation, and obtaining an updated experimental operation flow; the third operation is any one of the plurality of operations;
taking the minimum sum of the start execution time of all operations in the updated experimental operation flows corresponding to the n experimental samples as an objective function, taking the execution sequence constraint and the execution time constraint among the operations corresponding to the same experimental operation flow as constraint conditions, and determining a second start execution time and a second execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm to obtain a second experimental schedule;
the execution time constraint is a time interval constraint between a target time of a first operation and a target time of a second operation, wherein the first operation and the second operation are any one of a plurality of operations corresponding to the same experimental operation flow, and the target time comprises a start execution time or an end execution time.
In one possible implementation, after the obtaining the second experimental schedule, the method further includes:
and under the condition that m experimental samples in the n experimental samples are executed according to the first experimental schedule, executing subsequent operations on the rest n-m experimental samples according to the second experimental schedule, wherein m is a positive integer, and m is smaller than n.
In one possible implementation, the optimization algorithm includes at least one of a branch-and-bound method, a column generation method, a genetic algorithm, and an ant colony algorithm.
In a second aspect, embodiments of the present application provide a scheduling control apparatus, including:
the experimental operation flow comprises a plurality of operations executed by the experimental samples according to a preset execution sequence and the expected operation duration of the operations, wherein n is a positive integer;
the first determining module is configured to determine, based on an optimization algorithm, a first start execution time and a first execution sequence of operations corresponding to each of the n experimental samples, with a minimum sum of start execution times of all operations in the experimental operation flows corresponding to each of the n experimental samples as an objective function, and with execution sequence constraints and execution time constraints between operations corresponding to the same experimental operation flow as constraint conditions, to obtain a first experimental schedule;
the execution time constraint is a time interval constraint between a target time of a first operation and a target time of a second operation, wherein the first operation and the second operation are any one of a plurality of operations corresponding to the same experimental operation flow, and the target time comprises a start execution time or an end execution time.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the method of any one of the possible implementation methods of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement a method according to any one of the possible implementation methods of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product, the instructions in which, when executed by a processor of an electronic device, cause the electronic device to perform a method as in any one of the possible implementation methods of the first aspect described above.
According to the scheduling control method, the scheduling control device, the scheduling control equipment, the computer readable storage medium and the computer program product, the execution sequence and the execution time interval between operations are determined to be constraint conditions, so that the experiment can be ensured to be carried out smoothly. In addition, based on an optimization design algorithm, the starting execution time and the execution sequence of the operations corresponding to the n experimental samples are determined by taking the minimum sum of the starting execution time of all the operations in the experimental operation flows corresponding to the n experimental samples as an objective function and taking the execution sequence constraint and the execution time constraint between the operations corresponding to the same experimental operation flow as constraint conditions, so that a first experimental schedule is obtained, and the execution sequence and the starting execution time of each operation can be reasonably arranged under the condition that the successful verification is ensured. Therefore, by reasonably arranging the execution sequence and the start execution time of each operation, the use efficiency and the experimental flux of the equipment can be improved, and the experimental efficiency can be further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a scheduling control method according to an embodiment of the present application;
FIG. 2 is a flow chart of another scheduling control method provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a scheduling control device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application are described in detail below to make the objects, technical solutions and advantages of the present application more apparent, and to further describe the present application in conjunction with the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative of the application and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
In the field of life sciences and physical and chemical automation, robots are often used to transfer samples between a number of different instruments and equipment to complete an experiment instead of laboratory staff. In order to ensure that as many experiments as possible are completed within a certain period of time, the use efficiency of the equipment is improved, and a plurality of samples are required to be run simultaneously to improve the experimental flux. Furthermore, how to reasonably arrange the sample introduction time of each sample and the operation time sequence of each instrument device in the laboratory automation software are important. In addition, on the basis of improving the experimental flux, certain constraint is also satisfied. For example, if only one sample is processed at a time for a single device, then other samples can use the device only after the device has processed the current sample. As another example, during transfer of the device, some samples cannot wait empty, which can result in cells not surviving and thus in failure of the experiment.
However, in the prior art, the sample injection time of the sample and the operation time sequence of all devices in the system cannot be controlled by a software algorithm under the condition of meeting certain constraint conditions, so as to achieve the purpose of maximum experimental flux.
To solve the problems in the prior art, embodiments of the present application provide a scheduling control method, apparatus, device, computer readable storage medium, and computer program product.
The scheduling control method provided in the embodiment of the present application is first described below.
Fig. 1 shows a flow chart of a scheduling control method according to an embodiment of the present application. As shown in fig. 1, the scheduling control method provided in the embodiment of the present application includes the following steps:
s110, acquiring experimental operation flows corresponding to n experimental samples respectively, wherein the experimental operation flows comprise a plurality of operations executed by the experimental samples according to a preset execution sequence and expected operation duration of the operations, and n is a positive integer;
s120, determining a first start execution time and a first execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm by taking the minimum sum of start execution time of all the operations in the experimental operation flows corresponding to the n experimental samples as an objective function and taking the execution sequence constraint and the execution time constraint between the operations corresponding to the same experimental operation flow as constraint conditions, so as to obtain a first experimental schedule;
the execution time constraint is a time interval constraint between a target time of a first operation and a target time of a second operation, wherein the first operation and the second operation are any one of a plurality of operations corresponding to the same experimental operation flow, and the target time comprises a start execution time or an end execution time.
According to the scheduling control method, the execution sequence and the execution time interval between the operations are determined to be constraint conditions, so that the experiment can be ensured to be carried out smoothly. In addition, based on an optimization design algorithm, the starting execution time and the execution sequence of the operations corresponding to the n experimental samples are determined by taking the minimum sum of the starting execution time of all the operations in the experimental operation flows corresponding to the n experimental samples as an objective function and taking the execution sequence constraint and the execution time constraint between the operations corresponding to the same experimental operation flow as constraint conditions, so that a first experimental schedule is obtained, and the execution sequence and the starting execution time of each operation can be reasonably arranged under the condition that the successful verification is ensured. Therefore, by reasonably arranging the execution sequence and the start execution time of each operation, the use efficiency and the experimental flux of the equipment can be improved, and the experimental efficiency can be further improved.
A specific implementation of each of the above steps is described below.
In some embodiments, in S110, the experimental operation procedures corresponding to each of the n experimental samples may be identical, may be completely different, may be partially identical, and are not limited herein. For example, if there are a total of 3 test samples, a, b, and C, and a total of 3 experimental runs, respectively, A, B, C, the correspondence between the test samples and the experimental runs may be a-A, b-A, C-a, a-A, b-B, C-C, a-A, b-B, C-a, or the like.
In addition, any operation may have a corresponding device, and a device may correspond to one or more operations. However, only one operation can be performed by one device at a time. For example, in the case where the apparatus is a centrifuge, turning on the centrifuge, centrifuging with the centrifuge, and turning off the centrifuge may be three different operations. Based on this, the corresponding operations between the experimental operation flows may have the same portions. For example, experimental operation flow a may include three operations A1, A2, A3, and experimental operation flow B may include six operations B1, B2, B3, A1, A2, A3, and so on.
As an example, the experimental operation procedure a corresponding to the experimental sample a may be: firstly, executing an operation A1, wherein the operation time is 5 minutes; secondly, executing an operation A2, wherein the operation time is 1 hour; and finally, executing the operation A3, wherein the operation time is 1 minute, and ending. Wherein, if the experimental operation flow a has not been performed on the experimental sample a, the operation duration may be an expected operation duration.
In some embodiments, in S120, the same operation may be performed in different experimental operation flows, that is, different experimental operation flows may correspond to the same apparatus. Therefore, the operation corresponding to each of the n experimental samples can be reasonably arranged, so that the use efficiency and the experimental flux of the equipment are improved, and the experimental efficiency is further improved. Based on this, the first experimental schedule may include a schedule of execution orders of operations and start execution times of operations corresponding to each experimental sample.
Based on this, in some embodiments, the optimization algorithm may include at least one of a branch-and-bound method, a column generation method, a genetic algorithm, and an ant colony algorithm.
As an example, the objective function may be:
here, M and N are both positive integers, where M may be the number of experimental samples, and N may be the number of operations in the experimental operation flow corresponding to the ith experimental sample. In addition, x ij The start execution time of the j-th operation step corresponding to the i-th experimental sample may be set.
In some embodiments, the execution time constraint may include that the target difference is not less than the expected operation duration of the first operation, the target difference may be a difference between a start execution time of the first operation corresponding to the experimental sample and a start execution time of the second operation, and the start execution time of the second operation may be later than the start execution time of the first operation.
Here, the matrix representation of the execution time constraint may be:
AX≥B (2)
wherein A can be coefficient matrix and X can be X ij The component vector, B, may be B ij A component vector. Wherein b ij Can be the j-th corresponding to the i-th experimental sampleThe expected operating time of the operating step.
For example, if a certain experimental operational flow includes 5 operations, the objective function may be:
minf T x=x 11+ x 12+ x 13+ x 14+ x 15 (3)
one of the constraints may be
x 21 - x 11 ≥b 11 (4)
Here, x 21 The execution order of the corresponding operations may be located at x 11 The execution sequence of the corresponding operations follows. In addition, other constraint conditions are the same and are not described in detail herein.
In other embodiments, the execution time constraint may include that the target difference is not less than the expected operation duration of the second operation, the target difference may be a difference between an execution end time of the first operation corresponding to the experimental sample and an execution end time of the second operation, and the execution end time of the second operation may be later than the execution end time of the first operation.
In still other embodiments, the execution time constraint may include that the target difference is not less than a sum of expected operation durations of the first operation and the second operation, the target difference may be a difference between a start execution time of the first operation and an execution end time of the second operation corresponding to the experimental sample, and the execution end time of the second operation may be later than the start execution time of the first operation.
In still other embodiments, the execution time constraint may further include that the target difference is not greater than a preset operation duration, the target difference may be a difference between a start execution time of the first operation corresponding to the experimental sample and an execution end time of the second operation, and the execution end time of the second operation may be later than the start execution time of the first operation. For example, for some experimental samples, the experimental operation procedure must be completed within a fixed time from the first operation to the third operation, and if the time is exceeded, the experimental sample may fail, resulting in experimental failure. Thus, the execution time constraint may be that the target difference is less than or equal to the preset operation duration, wherein the target difference may be the execution end time of the third step operation minus the start execution time of the first step operation.
Based on this, in some embodiments, to improve the accuracy of the experimental scheduling result, after S120, it may further include:
sequentially executing the operations in the experimental operation flow according to the first starting execution time and the first execution sequence in the first experimental schedule;
after the third operation is executed, updating the expected operation duration corresponding to the third operation according to the actual operation duration corresponding to the third operation, so as to obtain an updated experimental operation flow, wherein the third operation is any one of a plurality of operations;
taking the minimum sum of the start execution time of all operations in the updated experimental operation flows corresponding to the n experimental samples as an objective function, taking the execution sequence constraint and the execution time constraint between the operations corresponding to the same experimental operation flow as constraint conditions, and determining a second start execution time and a second execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm to obtain a second experimental schedule;
the execution time constraint is a time interval constraint between a target time of a first operation and a target time of a second operation, wherein the first operation and the second operation are any one of a plurality of operations corresponding to the same experimental operation flow, and the target time comprises a start execution time or an end execution time.
Here, the estimated operation period and the actual operation period may be different. Thus, preemption of the device may result during execution of the operations according to the first start execution time and the first execution order. Thus, the subsequent experimental sample needs to wait for the device to complete the operation of the current experimental sample before the device can be used, thereby causing an empty waiting of the experimental sample.
As an example, in order to avoid the occurrence of the above, in the case where the actual operation duration is different from the expected operation duration, the expected operation duration may be updated according to the actual operation duration.
In this way, the accuracy of the experimental scheduling result can be improved by determining the second experimental schedule by using the minimum sum of the start execution time of all operations in the updated experimental operation flow corresponding to each of the n experimental samples as the objective function.
Based on this, in some embodiments, to further improve the accuracy of the experimental schedule results, after obtaining the second experimental schedule, it may further include:
and under the condition that m experimental samples in the n experimental samples are executed according to the first experimental schedule, the rest n-m experimental samples execute subsequent operations according to the second experimental schedule, wherein m is a positive integer, and m is smaller than n.
Here, in the case where the first experimental schedule is obtained, if 5 experimental samples are scheduled at the start of the experimental operation and 3 experimental samples have been performed, the expected operation duration may be updated according to the actual operation durations of the corresponding operations of the first 3 samples. Further, after the second experimental schedule is obtained, the subsequent 2 experimental samples may be performed according to the second experimental schedule. In addition, if other experimental samples are added in the process, the experimental operation can be performed according to the second experimental schedule. And, after the execution of the subsequent 2 experimental samples, the expected operation period can be continuously updated in the above-described manner.
Thus, the updated experimental schedule can be applied to the experimental process in real time, and the effect of dynamic scheduling can be achieved. Furthermore, with the continuous operation of the experimental operation, the accuracy of the experimental scheduling result can be further improved.
In order to better describe the whole solution, some specific examples are given based on the above embodiments.
For example, a flow diagram of a scheduling control method as shown in fig. 2.
In some specific examples, after the experimental operation flows corresponding to the n experimental samples are obtained, the experimental schedule may be determined according to the experimental operation flows corresponding to the n experimental samples. The experimental operational procedure may include, among other things, the order of execution among the operations and the expected operational duration of each operation. Further, after determining the experimental schedule, the experimental schedule may be performed, that is, experimental operations may be performed according to the experimental schedule. In the experimental operation process, the experimental operation flow can be updated according to the actual operation time length. After that, if the experiment is completed, the flow is ended. However, if the experiment is not completed, that is, there are more experimental samples to perform the experimental operation, it can be judged whether to update the experimental schedule. The experiment can be continued according to the existing experiment schedule without updating the experiment schedule. If the experimental schedule is updated, returning to execute the experimental operation flow corresponding to each of the n experimental samples, and determining the experimental schedule.
Therefore, the updated experimental schedule is applied to the experimental process in real time, and the effect of dynamic scheduling can be achieved. Furthermore, with the continuous operation of the experimental operation, the accuracy of the experimental scheduling result can be further improved. Furthermore, as the accuracy of the experimental scheduling result is continuously improved, the execution sequence and the start execution time of each operation can be continuously and reasonably arranged, and the experimental efficiency is improved.
Based on the scheduling control method provided by the embodiment, correspondingly, the application also provides a specific implementation mode of the scheduling control device. Please refer to the following examples.
As shown in fig. 3, the scheduling control apparatus 300 provided in the embodiment of the present application includes the following modules:
the obtaining module 310 is configured to obtain experimental operation flows corresponding to n experimental samples, where the experimental operation flows include a plurality of operations performed by the experimental samples according to a preset execution sequence and expected operation durations of the operations, and n is a positive integer;
the first determining module 320 is configured to determine, based on an optimization algorithm, a first start execution time and a first execution order of operations corresponding to each of the n experimental samples, with a minimum sum of start execution times of all operations in the experimental operation flows corresponding to each of the n experimental samples as an objective function, and with execution order constraints and execution time constraints between operations corresponding to the same experimental operation flow as constraint conditions, to obtain a first experimental schedule;
the execution time constraint is a time interval constraint between a target time of a first operation and a target time of a second operation, wherein the first operation and the second operation are any one of a plurality of operations corresponding to the same experimental operation flow, and the target time comprises a start execution time or an end execution time.
The following describes the scheduling control apparatus 300 in detail, and is specifically described as follows:
in some embodiments, the execution time constraint may include that the target difference is not less than the expected operation duration of the first operation, the target difference may be a difference between a start execution time of the first operation corresponding to the experimental sample and a start execution time of the second operation, and the start execution time of the second operation may be later than the start execution time of the first operation.
In one possible implementation, the execution time constraint may include that the target difference is not less than an expected operation duration of the second operation, the target difference may be a difference between an execution end time of the first operation corresponding to the experimental sample and an execution end time of the second operation, and the execution end time of the second operation may be later than the execution end time of the first operation.
In some of these embodiments, the scheduling control apparatus 300 may further include:
the first execution module is used for sequentially executing the operations in the experimental operation flow according to the first starting execution time and the first execution sequence in the first experimental schedule;
the updating module is used for updating the expected operation duration corresponding to the third operation according to the actual operation duration corresponding to the third operation after the third operation is executed, so as to obtain an updated experimental operation flow; the third operation is any one of a plurality of operations;
the second determining module is configured to determine, after obtaining the updated experimental operation flows, a second start execution time and a second execution order of operations corresponding to the n experimental samples based on an optimization algorithm by using a minimum sum of start execution times of all operations in the updated experimental operation flows corresponding to the n experimental samples as an objective function and using execution order constraints and execution time constraints between operations corresponding to the same experimental operation flow as constraint conditions, so as to obtain a second experimental schedule;
the execution time constraint is a time interval constraint between a target time of a first operation and a target time of a second operation, wherein the first operation and the second operation are any one of a plurality of operations corresponding to the same experimental operation flow, and the target time comprises a start execution time or an end execution time.
In some of these embodiments, the scheduling control apparatus 300 may further include:
and the second execution module is used for executing subsequent operations on the remaining n-m experimental samples according to the second experimental schedule under the condition that m experimental samples in the n experimental samples are executed according to the first experimental schedule after the second experimental schedule is obtained, wherein m is a positive integer and is smaller than n.
In some of these embodiments, the optimization algorithm includes at least one of a branch-and-bound method, a column generation method, a genetic algorithm, and an ant colony algorithm.
The scheduling control device provided by the embodiment of the invention can ensure the smooth performance of the experiment by determining the execution sequence and the execution time interval between the operations as the constraint conditions. In addition, based on an optimization design algorithm, the starting execution time and the execution sequence of the operations corresponding to the n experimental samples are determined by taking the minimum sum of the starting execution time of all the operations in the experimental operation flows corresponding to the n experimental samples as an objective function and taking the execution sequence constraint and the execution time constraint between the operations corresponding to the same experimental operation flow as constraint conditions, so that a first experimental schedule is obtained, and the execution sequence and the starting execution time of each operation can be reasonably arranged under the condition that the successful verification is ensured. Therefore, by reasonably arranging the execution sequence and the start execution time of each operation, the use efficiency and the experimental flux of the equipment can be improved, and the experimental efficiency can be further improved.
Based on the scheduling control method provided by the above embodiment, the embodiment of the application further provides a specific implementation mode of the electronic device. Fig. 4 shows a schematic diagram of an electronic device 400 according to an embodiment of the present application.
The electronic device 400 may include a processor 410 and a memory 420 storing computer program instructions.
In particular, the processor 410 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory comprises one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to the method according to the first aspect of the present application.
The processor 410 implements any of the scheduling methods of the above embodiments by reading and executing computer program instructions stored in the memory 420.
In one example, electronic device 400 may also include communication interface 430 and bus 440. As shown in fig. 4, the processor 410, the memory 420, and the communication interface 430 are connected and communicate with each other through a bus 440.
The communication interface 430 is mainly used to implement communication between each module, apparatus, unit and/or device in the embodiments of the present application.
By way of example, electronic device 400 may be a cell phone, tablet, notebook, palm, in-vehicle electronic device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (personal digital assistant, PDA), or the like.
The electronic device may execute the scheduling control method in the embodiment of the present application, thereby implementing the scheduling control method and apparatus described in connection with fig. 1 to 3.
In addition, in combination with the scheduling control method in the above embodiment, the embodiment of the application may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the scheduling control methods of the above embodiments.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be different from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present application are described above 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 block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, which are intended to be included in the scope of the present application.
Claims (8)
1. A scheduling control method, comprising:
acquiring experimental operation flows corresponding to n experimental samples respectively, wherein the experimental operation flows comprise a plurality of operations executed by the experimental samples according to a preset execution sequence and expected operation duration of the operations, and n is a positive integer;
taking the minimum sum of the starting execution time of all operations in the experimental operation flows corresponding to the n experimental samples as an objective function, taking the constraint of the execution sequence and the constraint of the execution time among the operations corresponding to the same experimental operation flow as constraint conditions, and determining the first starting execution time and the first execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm to obtain a first experimental schedule;
after the first experimental schedule is obtained, the method further comprises:
sequentially executing the operations in the experimental operation flow according to the first start execution time and the first execution sequence in the first experimental schedule;
after the third operation is executed, updating the expected operation duration corresponding to the third operation according to the actual operation duration corresponding to the third operation, and obtaining an updated experimental operation flow; the third operation is any one of the plurality of operations;
taking the minimum sum of the start execution time of all operations in the updated experimental operation flows corresponding to the n experimental samples as an objective function, taking the execution sequence constraint and the execution time constraint among the operations corresponding to the same experimental operation flow as constraint conditions, and determining a second start execution time and a second execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm to obtain a second experimental schedule;
the execution time constraint is a time interval constraint between a target time of a first operation and a target time of a second operation, wherein the first operation and the second operation are any one of a plurality of operations corresponding to the same experimental operation flow, and the target time comprises a start execution time or an end execution time.
2. The method of claim 1, wherein the execution time constraint comprises a target difference value that is a difference between a start execution time of the first operation and a start execution time of the second operation corresponding to the experimental sample, the start execution time of the second operation being later than the start execution time of the first operation, being not less than an expected operation duration of the first operation.
3. The method of claim 1, wherein the execution time constraint comprises a target difference value that is a difference between an execution end time of the first operation and an execution end time of the second operation corresponding to the experimental sample that is later than the execution end time of the first operation being not less than an expected operation duration of the second operation.
4. The method of claim 1, wherein after the second experimental schedule is obtained, the method further comprises:
and under the condition that m experimental samples in the n experimental samples are executed according to the first experimental schedule, executing subsequent operations on the rest n-m experimental samples according to the second experimental schedule, wherein m is a positive integer, and m is smaller than n.
5. The method of claim 1, wherein the optimization algorithm comprises at least one of a branch-and-bound method, a column generation method, a genetic algorithm, and an ant colony algorithm.
6. A scheduling control apparatus, the apparatus comprising:
the experimental operation flow comprises expected operation duration of a plurality of operations executed by the experimental samples according to a preset execution sequence, wherein n is a positive integer;
the first determining module is configured to determine, based on an optimization algorithm, a first start execution time and a first execution sequence of operations corresponding to each of the n experimental samples, with a minimum sum of start execution times of all operations in the experimental operation flows corresponding to each of the n experimental samples as an objective function, and with execution sequence constraints and execution time constraints between operations corresponding to the same experimental operation flow as constraint conditions, to obtain a first experimental schedule;
the first execution module is used for sequentially executing the operations in the experimental operation flow according to the first starting execution time and the first execution sequence in the first experimental schedule;
the updating module is used for updating the expected operation duration corresponding to the third operation according to the actual operation duration corresponding to the third operation after the third operation is executed, so as to obtain an updated experimental operation flow; the third operation is any one of a plurality of operations;
the second determining module is configured to determine, after obtaining the updated experimental operation flows, a second start execution time and a second execution order of operations corresponding to the n experimental samples based on an optimization algorithm by using a minimum sum of start execution times of all operations in the updated experimental operation flows corresponding to the n experimental samples as an objective function and using execution order constraints and execution time constraints between operations corresponding to the same experimental operation flow as constraint conditions, so as to obtain a second experimental schedule;
the execution time constraint is a time interval constraint between a target time of a first operation and a target time of a second operation, wherein the first operation and the second operation are any one of a plurality of operations corresponding to the same experimental operation flow, and the target time comprises a start execution time or an end execution time.
7. An electronic device, the device comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the scheduling control method of any one of claims 1-5.
8. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon computer program instructions, which when executed by a processor, implement the scheduling control method of any one of claims 1-5.
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