CN115577997A - Scheduling control method, device, equipment and computer readable storage medium - Google Patents
Scheduling control method, device, equipment and computer readable storage medium Download PDFInfo
- Publication number
- CN115577997A CN115577997A CN202211588465.4A CN202211588465A CN115577997A CN 115577997 A CN115577997 A CN 115577997A CN 202211588465 A CN202211588465 A CN 202211588465A CN 115577997 A CN115577997 A CN 115577997A
- Authority
- CN
- China
- Prior art keywords
- experimental
- execution
- time
- operations
- experiment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0633—Workflow analysis
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- General Factory Administration (AREA)
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 experiment operation flows corresponding to the n experiment samples, wherein the experiment operation flows comprise a plurality of operations executed by the experiment samples according to a preset execution sequence and predicted operation duration of the operations; the method comprises the steps of determining a first starting execution time and a first execution sequence of operations corresponding to n experimental samples based on an optimization algorithm by taking the minimum sum of the starting 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, and obtaining a first experimental schedule. According to the scheduling control method, the service efficiency and the experimental flux of the equipment can be improved, and the experimental efficiency is further improved.
Description
Technical Field
The present application relates to the field of intelligent laboratories, and in particular, to a scheduling control method, apparatus, device, and computer-readable storage medium.
Background
In the field of life science and physicochemical experiments, the main mode of the current experiments is still manual, and a laboratory technician completes the experiments by transferring samples among a plurality of different instruments and devices. However, the throughput and reproducibility of experimental results are limited to different degrees by human factors.
In order to overcome the above problems caused by human factors, in the fields of life sciences and physical and chemical automation, a robot is generally used to replace a laboratory technician to transfer samples among a plurality of different instruments and apparatuses to complete an experiment. In order to ensure that as many experiments as possible are completed in a certain time and the use efficiency of the equipment is improved, a plurality of samples need to be run simultaneously to improve the experiment flux. However, in the prior art, the use efficiency and the experimental flux of the equipment 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, a scheduling control apparatus, a computer-readable storage medium and a computer program product, which can improve the use efficiency of the apparatus and the experimental flux, and further improve the experimental efficiency.
In a first aspect, an embodiment of the present application provides a scheduling control method, including:
acquiring an experiment operation flow corresponding to each of n experiment samples, wherein the experiment operation flow comprises a plurality of operations executed by the experiment samples according to a preset execution sequence and a predicted operation duration of the operations, and n is a positive integer;
determining a first starting execution time and a first execution sequence of the operations corresponding to the n experimental samples respectively based on an optimization algorithm by taking a minimum sum of starting execution times of all the operations in the experimental operation flows corresponding to the n experimental samples respectively as an objective function and taking an execution sequence constraint and an execution time constraint between the 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 execution end time.
In a possible implementation manner, the execution time constraint includes a target difference not less than a predicted operation duration of the first operation, where the target difference 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, and the start execution time of the second operation is later than the start execution time of the first operation.
In a possible implementation manner, the execution time constraint includes that a target difference is not less than an expected operation duration of the second operation, the target difference 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, and the execution end time of the second operation is later than the execution end time of the first operation.
In a possible implementation manner, after the obtaining the first experiment schedule, the method further includes:
sequentially executing the operations in the experiment operation flow according to the first starting execution time and the first execution sequence in the first experiment schedule;
after the third operation is executed, updating the expected operation time length corresponding to the third operation according to the actual operation time length corresponding to the third operation to obtain an updated experimental operation flow; the third operation is any one of the plurality of operations;
determining a second starting execution time and a second execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm by taking the minimum sum of the starting execution times of all the operations in the updated 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 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 execution end time.
In a possible implementation manner, after the obtaining of the second experiment 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 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, an embodiment of the present application provides a scheduling control apparatus, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring experiment operation flows corresponding to n experiment samples, the experiment operation flows comprise a plurality of operations executed by the experiment samples according to a preset execution sequence and predicted operation duration of the operations, and n is a positive integer;
a first determining module, 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 the operations in an experimental operation flow corresponding to each of the n experimental samples as an objective function, and with an execution sequence constraint and an 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 execution end 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 described above.
In a fourth aspect, the present application provides a computer-readable storage medium, on which computer program instructions are stored, and the computer program instructions, when executed by a processor, implement the method in any one of the possible implementation methods of the first aspect.
In a fifth aspect, the present application provides a computer program product, where instructions of the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method in any one of the possible implementation methods as described in the first aspect.
The scheduling control method, apparatus, device, computer-readable storage medium, and computer program product according to the embodiments of the present application can ensure smooth progress of an experiment by determining the execution sequence and the execution time interval between operations as constraint conditions. In addition, based on the optimization design algorithm, the minimum sum of the starting execution times of all the operations in the experiment operation flows corresponding to the n experiment samples is taken as an objective function, the execution sequence constraint and the execution time constraint between the operations corresponding to the same experiment operation flow are taken as constraint conditions, the starting execution times and the execution sequences of the operations corresponding to the n experiment samples are determined, and the first experiment schedule is obtained. Therefore, by reasonably arranging the execution sequence and the starting execution time of each operation, the use efficiency and the experiment flux of the equipment can be improved, and the experiment efficiency is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a scheduling control method according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another scheduling control method according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a scheduling control apparatus 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 of various aspects and exemplary embodiments of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only 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 illustrating examples thereof.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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 another like element in a process, method, article, or apparatus that comprises the element.
In the fields of life sciences and physicochemical automation, it is common to use robots to transfer samples between a number of different instruments and devices to complete experiments instead of laboratory staff. In order to ensure that as many experiments as possible are completed in a certain time and the use efficiency of the equipment is improved, a plurality of samples need to be run simultaneously to improve the experiment flux. Furthermore, how to reasonably arrange the sample injection time of each sample and the operation time sequence of each instrument and equipment in laboratory automation software is very important. In addition, certain constraints are also required to be met on the basis of improving experimental throughput. For example, for a single device, if only one sample can be processed at a time, that other sample can only be used after the device has processed the current sample. For another example, during the transfer of the device, some samples cannot wait empty, which may result in the cell not being viable and thus in the failure of the experiment.
However, in the prior art, the purpose of maximum experimental throughput cannot be achieved by controlling the sample introduction time of a sample and the operation time sequence of all equipment in a system through a software algorithm under the condition of meeting certain constraint conditions.
In order 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.
First, a scheduling control method provided in the embodiments of the present application will be described below.
Fig. 1 is a flowchart illustrating a scheduling control method according to an embodiment of the present disclosure. As shown in fig. 1, the scheduling control method provided in the embodiment of the present application includes the following steps:
s110, obtaining experiment operation flows corresponding to n experiment samples, wherein the experiment operation flows comprise a plurality of operations executed by the experiment samples according to a preset execution sequence and predicted operation duration of the operations, and n is a positive integer;
s120, determining a first starting execution time and a first execution sequence of the operation corresponding to each of the n experimental samples based on an optimization algorithm by taking the minimum sum of the starting 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 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 execution end time.
The scheduling control method of the embodiment of the application can ensure the smooth progress of the experiment by determining the execution sequence and the execution time interval between the operations as the constraint conditions. In addition, based on the optimization design algorithm, the minimum sum of the starting execution times of all the operations in the experiment operation flows corresponding to the n experiment samples is taken as an objective function, the execution sequence constraint and the execution time constraint between the operations corresponding to the same experiment operation flow are taken as constraint conditions, the starting execution times and the execution sequences of the operations corresponding to the n experiment samples are determined, and the first experiment schedule is obtained. Therefore, by reasonably arranging the execution sequence and the starting execution time of each operation, the use efficiency and the experiment flux of the equipment can be improved, and the experiment efficiency is further improved.
Specific implementations of the above steps are described below.
In some embodiments, in S110, the experimental operation flows corresponding to the n experimental samples may be completely the same, may be completely different, or may be partially the same, and are not limited herein. For example, if there are 3 total test samples, designated as a, b, and C, and 3 total test procedures, designated as A, B, C, the correspondence between the test samples and the test procedures may be a-A, b-A, C-a, a-A, b-B, C-C, or a-A, b-B, C-a.
In addition, any operation may have a corresponding device, and a device may correspond to one or more operations. However, one device can perform only one operation at a time. For example, where the device 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 parts. For example, experimental operational flow a may include three operations A1, A2, A3, etc., and experimental operational flow B may include six operations B1, B2, B3, A1, A2, A3, etc.
As an example, the experimental operation flow 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 finishing. Wherein, if the experimental operation flow a has not been executed on the experimental sample a, the operation time length may be an expected operation time length.
In some embodiments, in S120, different experimental operation flows may have the same operation, that is, different experimental operation flows may correspond to the same equipment. Therefore, the operation that n experimental samples correspond respectively can be rationally arranged to improve the availability factor and the experiment flux of equipment, and then improve experimental efficiency. Based on this, the first experiment schedule may include an execution order of operations corresponding to each experiment sample and a schedule of start execution times of the operations.
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 execution time for the beginning of the j operation step corresponding to the i experimental sample can 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 and a start execution time of the second operation corresponding to the experimental sample, 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 may be a coefficient matrix and X may be X ij The constituent vector B may be B ij The vectors of the components. Wherein, b ij The expected operation time of the j operation step corresponding to the i experimental sample can be taken.
For example, if a certain experimental operation 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 order of execution of the corresponding operations may be in x 11 The order of execution of the corresponding operations. In addition, other constraints are similar and will not be described herein again.
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 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 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 the preset operation duration, 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. For example, for a certain experimental operation flow corresponding to an experimental sample, the first step operation to the third step operation must be completed within a fixed time, and if the time is exceeded, the experimental sample may fail, resulting in failure of the experiment. Therefore, the execution time constraint may be that the target difference is less than or equal to the preset operation time length, 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, in order to improve the accuracy of the experiment scheduling result, after S120, the method may further include:
sequentially executing the operations in the experiment operation flow according to the first starting execution time and the first execution sequence in the first experiment 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 to obtain an updated experimental operation flow, wherein the third operation is any one of a plurality of operations;
determining a second starting execution time and a second execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm by taking the minimum sum of the starting execution times of all the operations in the updated 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 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 execution end time.
Here, the expected operation time period and the actual operation time period may be different. Therefore, in the course of executing the operations in accordance with the first start execution time and the first execution order, preemption of the devices may be caused. Therefore, the subsequent experimental sample needs to wait for the device to complete the operation of the current experimental sample before using the device, thereby resulting in the empty waiting of the experimental sample.
As an example, in order to avoid the occurrence of the above-described situation, in the case where the actual operation time length is not the same as the expected operation time length, the expected operation time length may be updated according to the actual operation time length.
In this way, the accuracy of the experiment scheduling result can be improved by determining the second experiment schedule by taking the minimum sum of the starting execution times of all the operations in the updated experiment operation flow corresponding to each of the n experiment samples as the objective function.
Based on this, in some embodiments, in order to further improve the accuracy of the experiment scheduling result, after obtaining the second experiment schedule, the method 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, m is a positive integer and is smaller than n.
Here, in the case of obtaining the first experiment schedule, if 5 experiment samples are scheduled at the time of starting the experiment operation and 3 experiment samples have been already executed, the expected operation time period may be updated according to the actual operation time period of the operation corresponding to the first 3 samples. Furthermore, after obtaining the second experiment schedule, the subsequent 2 experiment samples can be executed according to the second experiment 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 predicted operation time period can be continuously updated in the above manner.
Therefore, the effect of dynamic scheduling can be achieved by applying the updated experiment scheduling to the experiment process in real time. Furthermore, with the continuous operation of the experiment operation, the accuracy of the experiment scheduling result can be further improved.
In order to better describe the whole scheme, specific examples are given based on the above embodiments.
For example, fig. 2 shows a flowchart of a scheduling control method.
In some specific examples, after obtaining the experimental operation flows corresponding to the n experimental samples, the experimental schedule may be determined according to the experimental operation flows corresponding to the n experimental samples. The experimental operation flow may include an execution sequence between operations and a predicted operation duration of each operation. Further, after determining the experiment schedule, the experiment schedule may be executed, that is, the experiment operation may be performed according to the experiment schedule. In the experimental operation process, the experimental operation flow can be updated according to the actual operation duration. Thereafter, if the experiment is completed, the flow ends. However, if the experiment is not completed, that is, there are still experimental samples to be performed, it can be determined whether to update the experimental schedule. Under the condition of not updating the experiment schedule, the experiment can be continued according to the existing experiment schedule. And if the experiment schedule is updated, returning to execute the experiment operation flow corresponding to each of the n experiment samples, and determining the experiment schedule.
Therefore, the effect of dynamic scheduling can be achieved by applying the updated experiment scheduling to the experiment process in real time. Furthermore, with the continuous operation of the experiment operation, the accuracy of the experiment scheduling result can be further improved. Furthermore, with the continuous improvement of the accuracy of the experiment scheduling result, the execution sequence and the execution starting time of each operation can be continuously and reasonably arranged, and the experiment efficiency is improved.
Based on the scheduling control method provided in the foregoing embodiment, correspondingly, the present application further provides a specific implementation manner of the scheduling control device. Please see the examples below.
As shown in fig. 3, the scheduling control apparatus 300 according to the embodiment of the present invention includes the following modules:
an obtaining module 310, configured to obtain an experiment operation flow corresponding to each of n experiment samples, where the experiment operation flow includes multiple operations executed by the experiment samples according to a preset execution sequence and a predicted operation duration of the operations, and n is a positive integer;
a first determining module 320, 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 the operations in the experimental operation flows corresponding to each of the n experimental samples as an objective function, and with an execution sequence constraint and an 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 execution end time.
The following describes the scheduling control device 300 in detail, specifically 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 and a start execution time of the second operation corresponding to the experimental sample, 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 the expected operation duration of the second operation, the target difference may be 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, and the execution end time of the second operation may be later than the execution end time of the first operation.
In some embodiments, the scheduling control apparatus 300 may further include:
the first execution module is used for sequentially executing the operations in the experiment operation flow according to the first starting execution time and the first execution sequence in the first experiment schedule;
the updating module is used for updating the predicted 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;
a second determining module, configured to, after obtaining the updated experimental operation flow, determine a second start execution time and a second execution order of the operations corresponding to each of the n experimental samples based on an optimization algorithm by taking a minimum sum of start execution times of all the operations in the updated experimental operation flow corresponding to each of the n experimental samples as an objective function and taking an execution sequence constraint and an execution time constraint between the 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 execution end time.
In some 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 experiment schedule under the condition that m experimental samples in the n experimental samples are executed according to the first experiment schedule after the second experiment 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 according to the embodiment of the present application can ensure smooth progress of an experiment by determining the execution sequence and the execution time interval between operations as constraint conditions. In addition, based on the optimized design algorithm, the starting execution time and the execution sequence of the operation corresponding to each of the n experimental samples are determined by taking the minimum sum of the starting execution times of all the operations in the experimental operation flows corresponding to each of 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. Therefore, by reasonably arranging the execution sequence and the starting execution time of each operation, the use efficiency and the experiment flux of the equipment can be improved, and the experiment efficiency is further improved.
Based on the scheduling control method provided in the foregoing embodiments, embodiments of the present application further provide a specific implementation of the electronic device. Fig. 4 shows a schematic diagram of an electronic device 400 provided in an embodiment of the present application.
In particular, the processor 410 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the 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 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) 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 application.
The processor 410 reads and executes the computer program instructions stored in the memory 420 to implement any of the scheduling control methods in the above embodiments.
In one example, electronic device 400 may also include a communication interface 430 and a bus 440. As shown in fig. 4, the processor 410, the memory 420, and the communication interface 430 are connected via a bus 440 to complete communication therebetween.
The communication interface 430 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application.
The bus 440 includes hardware, software, or both to couple the components of the electronic device to one another. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 440 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
Illustratively, the electronic device 400 may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like.
The electronic device may execute the scheduling control method in the embodiment of the present application, so as to implement the scheduling control method and apparatus described in conjunction with fig. 1 to 3.
In addition, in combination with the scheduling control method in the foregoing embodiments, the embodiments of the present application may provide a computer storage medium to implement the method. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any of the scheduling control methods in the above embodiments.
It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. 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 the steps, after comprehending the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented as 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, plug-in, 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 by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, 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 so forth. The code segments may be downloaded via computer networks such as the internet, intranet, 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 performed in an order different from the order in the embodiments, or 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, 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 for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. 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, and these modifications or substitutions should be covered within the scope of the present application.
Claims (10)
1. A method for scheduling control, comprising:
acquiring an experiment operation flow corresponding to each of n experiment samples, wherein the experiment operation flow comprises a plurality of operations executed by the experiment samples according to a preset execution sequence and a predicted operation duration of the operations, and n is a positive integer;
determining a first starting 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 starting 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 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 execution end time.
2. The method of claim 1, wherein the execution time constraint comprises a target difference not less than an expected operation duration of the first operation, the target difference being 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.
3. The method of claim 1, wherein the execution time constraint comprises a target difference not less than an expected operation duration of the second operation, the target difference being 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, the execution end time of the second operation being later than the execution end time of the first operation.
4. The method of claim 1, wherein after the obtaining the first experimental schedule, the method further comprises:
sequentially executing the operations in the experiment operation flow according to the first starting execution time and the first execution sequence in the first experiment 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 to obtain an updated experimental operation flow; the third operation is any one of the plurality of operations;
determining a second starting execution time and a second execution sequence of the operations corresponding to the n experimental samples based on an optimization algorithm by taking the minimum sum of the starting execution times of all the operations in the updated 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 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 execution end time.
5. The method of claim 4, wherein after obtaining the second experimental schedule, 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 is smaller than n.
6. 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.
7. A scheduling control apparatus, the apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an experiment operation flow corresponding to each of n experiment samples, the experiment operation flow comprises the predicted operation duration of a plurality of operations executed by the experiment samples according to a preset execution sequence, and n is a positive integer;
a first determining module, 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 the operations in an experimental operation flow corresponding to each of the n experimental samples as an objective function, and with an execution sequence constraint and an 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 execution end time.
8. An electronic device, characterized in that the device comprises: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the scheduling control method of any of claims 1-6.
9. A computer-readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the scheduling control method of any one of claims 1-6.
10. A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the scheduling control method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211588465.4A CN115577997B (en) | 2022-12-12 | 2022-12-12 | Scheduling control method, device, equipment and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211588465.4A CN115577997B (en) | 2022-12-12 | 2022-12-12 | Scheduling control method, device, equipment and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115577997A true CN115577997A (en) | 2023-01-06 |
| CN115577997B CN115577997B (en) | 2023-06-09 |
Family
ID=84590545
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211588465.4A Active CN115577997B (en) | 2022-12-12 | 2022-12-12 | Scheduling control method, device, equipment and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115577997B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116440976A (en) * | 2023-03-31 | 2023-07-18 | 上海奔曜科技有限公司 | Laboratory workflow control method, electronic device and readable storage medium |
| CN116882691A (en) * | 2023-07-19 | 2023-10-13 | 特维沃(上海)智能科技有限责任公司 | Automatic scheduling processing method, device and equipment for experiment plan and readable medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8555281B1 (en) * | 2011-02-16 | 2013-10-08 | Google Inc. | Scheduling of tasks based upon historical execution times |
| CN107860931A (en) * | 2016-09-21 | 2018-03-30 | 豪夫迈·罗氏有限公司 | Automatic arrangement program device for laboratory equipment |
| CN108805764A (en) * | 2018-05-31 | 2018-11-13 | 上海与德科技有限公司 | A kind of job scheduling monitoring method, device, terminal and readable medium |
| US20190130330A1 (en) * | 2017-10-31 | 2019-05-02 | Huntington Ingalls Incorporated | Method and system for management and control of highly complex projects |
| US20190286478A1 (en) * | 2016-12-07 | 2019-09-19 | Tata Consultancy Services Limited | Systems and methods for scheduling tasks and managing computing resource allocation for closed loop control systems |
| CN110751352A (en) * | 2018-07-24 | 2020-02-04 | 上汽通用汽车有限公司 | Test scheduling method and computer-readable storage medium |
| CN112379982A (en) * | 2020-11-12 | 2021-02-19 | 北京字跳网络技术有限公司 | Task processing method and device, electronic equipment and computer readable storage medium |
| CN113139710A (en) * | 2021-01-05 | 2021-07-20 | 中国电子科技集团公司第二十九研究所 | Multi-resource parallel task advanced plan scheduling method based on genetic algorithm |
| US20210240533A1 (en) * | 2020-01-31 | 2021-08-05 | Hewlett Packard Enterprise Development Lp | Dynamic adjustment of response time |
| CN113726595A (en) * | 2021-08-06 | 2021-11-30 | 视联动力信息技术股份有限公司 | Detection method and device of timeout client, electronic equipment and storage medium |
| US20220208364A1 (en) * | 2019-07-24 | 2022-06-30 | Siemens Healthcare Diagnostics Inc. | Methods and apparatus of maintenance scheduling in automated testing over a planning period |
-
2022
- 2022-12-12 CN CN202211588465.4A patent/CN115577997B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8555281B1 (en) * | 2011-02-16 | 2013-10-08 | Google Inc. | Scheduling of tasks based upon historical execution times |
| CN107860931A (en) * | 2016-09-21 | 2018-03-30 | 豪夫迈·罗氏有限公司 | Automatic arrangement program device for laboratory equipment |
| US20190286478A1 (en) * | 2016-12-07 | 2019-09-19 | Tata Consultancy Services Limited | Systems and methods for scheduling tasks and managing computing resource allocation for closed loop control systems |
| US20190130330A1 (en) * | 2017-10-31 | 2019-05-02 | Huntington Ingalls Incorporated | Method and system for management and control of highly complex projects |
| CN108805764A (en) * | 2018-05-31 | 2018-11-13 | 上海与德科技有限公司 | A kind of job scheduling monitoring method, device, terminal and readable medium |
| CN110751352A (en) * | 2018-07-24 | 2020-02-04 | 上汽通用汽车有限公司 | Test scheduling method and computer-readable storage medium |
| US20220208364A1 (en) * | 2019-07-24 | 2022-06-30 | Siemens Healthcare Diagnostics Inc. | Methods and apparatus of maintenance scheduling in automated testing over a planning period |
| US20210240533A1 (en) * | 2020-01-31 | 2021-08-05 | Hewlett Packard Enterprise Development Lp | Dynamic adjustment of response time |
| CN112379982A (en) * | 2020-11-12 | 2021-02-19 | 北京字跳网络技术有限公司 | Task processing method and device, electronic equipment and computer readable storage medium |
| CN113139710A (en) * | 2021-01-05 | 2021-07-20 | 中国电子科技集团公司第二十九研究所 | Multi-resource parallel task advanced plan scheduling method based on genetic algorithm |
| CN113726595A (en) * | 2021-08-06 | 2021-11-30 | 视联动力信息技术股份有限公司 | Detection method and device of timeout client, electronic equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| MARCUS FRANTZEN等: "A simulation-based scheduling system for real-time optimization and decision making support", ROBITCS AND COMPUTER-INTEGRATED MANUFACTURIING * |
| 孟凡睿等: "考虑多因素条件下的择期手术排程约束规划模型", 计算机应用与软件 * |
| 杨志: "不确定条件下船舶平面分段流水线调度方法研究", 中国优秀硕士论文 工程科技Ⅱ辑 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116440976A (en) * | 2023-03-31 | 2023-07-18 | 上海奔曜科技有限公司 | Laboratory workflow control method, electronic device and readable storage medium |
| CN116882691A (en) * | 2023-07-19 | 2023-10-13 | 特维沃(上海)智能科技有限责任公司 | Automatic scheduling processing method, device and equipment for experiment plan and readable medium |
| CN116882691B (en) * | 2023-07-19 | 2024-06-21 | 特维沃(上海)智能科技有限责任公司 | Automatic scheduling processing method, device and equipment for experiment plan and readable medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115577997B (en) | 2023-06-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115577997A (en) | Scheduling control method, device, equipment and computer readable storage medium | |
| US20210083484A1 (en) | Method, apparatus, device and medium for equalization control of battery packs | |
| KR102074961B1 (en) | Method and apparatus for efficient scheduling for asymmetrical execution units | |
| CN101901133B (en) | Information processing apparatus and branch prediction method | |
| US20160092224A1 (en) | Checkpoints for a simultaneous multithreading processor | |
| CN105190540A (en) | Optimizing performance for context-dependent instructions | |
| CN114780338A (en) | Host information processing method and device, electronic equipment and computer readable medium | |
| CN115312127B (en) | Pre-training method of recognition model, recognition method, device, medium and equipment | |
| CN108280513B (en) | Model generation method and device | |
| CN112712795A (en) | Method, device, medium and electronic equipment for determining label data | |
| CN115277261A (en) | Abnormal machine intelligent identification method, device and equipment based on industrial control network virus | |
| CN113419841B (en) | Message scheduling method and device, electronic equipment and computer readable medium | |
| US20240118984A1 (en) | Prediction method and apparatus for faulty gpu, electronic device and storage medium | |
| US20190103093A1 (en) | Method and apparatus for training acoustic model | |
| CN113392018A (en) | Traffic distribution method, traffic distribution device, storage medium, and electronic device | |
| GB2594498A (en) | Instruction scheduling | |
| CN113435528B (en) | Method, device, readable medium and electronic equipment for classifying objects | |
| CN113320431A (en) | Method, device and system for dynamically adjusting temperature protection point of charging module of electric vehicle | |
| CN112965827B (en) | Information scheduling method and device, electronic equipment and computer medium | |
| CN116887257B (en) | Abuse identification method and device for vehicle-to-vehicle network card, electronic equipment and storage medium | |
| CN116343905B (en) | Pretreatment method, pretreatment device, pretreatment medium and pretreatment equipment for protein characteristics | |
| CN110009109B (en) | Model prediction method for predicting overdue repayment probability of user and related equipment | |
| CN115381455A (en) | Electromyographic signal acquisition device, control method and electronic equipment | |
| CN117762494A (en) | Data processor, conditional instruction processing method, electronic device, and storage medium | |
| CN118171080A (en) | Fault level determining method and related device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |