Experimental resource allocation method based on APS

Abstract

The invention discloses an experimental resource allocation method based on an APS, and relates to the technical field of experimental resource allocation, and the method comprises the steps: building a resource list in advance, calibrating experimental task demands, setting constraint conditions according to the resource list and the experimental task demands, and building an APS model according to the resource list, the experimental task demands and personnel constraints; and a target function is calibrated, a resource allocation scheme is generated according to an APS model, and the method is compared with a traditional manual scheduling mode and a mode that experimenters autonomously select equipment reservation time. According to the method, the scheduling resources of the laboratory are uniformly incorporated into the APS model for resource allocation, the experimental resources and experimental tasks are overall planned, the experimental scheduling work is executed by the computer, and the scheduling personnel only need to finally confirm the feasibility and rationality of the scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of experimental resource allocation, and in particular to an experimental resource allocation method based on APS. Background Art

[0002] In a large laboratory, managing the scheduling of experimental tasks is a complex task. The laboratory has multiple resources, such as personnel, equipment, and samples, which need to be effectively allocated in different time periods to complete various experimental tasks. The working hours of the laboratory are limited. The allocation of tasks must be carried out within the working hours.

[0003] At present, most laboratories schedule experimental tasks manually, but as the scale of laboratories increases, this kind of work handled manually will undoubtedly greatly increase the difficulty of operation, and it is also difficult to ensure that the scheduling results can achieve the optimal solution within the visible range. Some laboratories use instrument sharing platforms (public technology platforms) to achieve a certain degree of experimental task scheduling. The principle is to let the experimenters go to the platform to find the experimental instruments they need to use, check the instrument schedule, choose the right time and make an appointment for the equipment. However, this method often needs to occur after the task schedule is released, or determine the task schedule based on the instrument schedule, and cannot really determine complex experimental scheduling issues.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related technology, the present invention proposes an APS-based experimental resource allocation method to overcome the above-mentioned technical problems existing in the existing related technology.

[0006] The technical solution of the present invention is achieved in this way:

[0007] An APS-based experimental resource allocation method comprises the following steps:

[0008] Establish a resource list in advance, including: collecting experimental equipment resources, experimental personnel resources and experimental material resources;

[0009] Conduct calibration of experimental task requirements, including: calibration of the equipment, personnel and materials required for the experimental task, and calibration of the time requirements and priorities of the current experimental task;

[0010] Set constraints according to the resource list and experimental task requirements, including: setting equipment constraints, setting personnel constraints and setting material constraints;

[0011] The APS model was constructed based on the resource list, experimental task requirements, and personnel constraints, and the objective function was calibrated, including: minimizing the experimental completion time and maximizing resource utilization;

[0012] Generate a resource allocation plan according to the APS model, including: running the APS model and generating an experimental resource allocation plan through a built-in heuristic algorithm.

[0013] Furthermore, the generation of the resource allocation plan according to the APS model further includes the following steps:

[0014] Evaluate and optimize the resource allocation plan, including the following steps:

[0015] Obtain evaluation results for the current resource allocation plan, including: evaluating whether the experimental tasks are completed on time, evaluating whether resources are fully utilized, and evaluating whether there are resource conflicts;

[0016] Optimize the current resource allocation plan based on the evaluation results, including: adjusting the order of experimental tasks, reallocating resources, and extending the experimental time span.

[0017] Furthermore, the setting of device constraints includes: calibrating the device set as E = {e 1 ,e 2 ,…,e p}, experimental task t i For device k The usage time is UT ik , equipment k The available time is AT, then there is a constraint

[0018] Furthermore, the setting of personnel constraints includes: personnel working hours and skill matching;

[0019] The working time of the personnel includes: the calibration personnel set is P = {p 1 ,p 2 ,…,p q}, experimental task t i For personnel i The working time requirement is PT il , the available working time of personnel is AW l , then

[0020]

[0021] Among them, for skill matching, if the experimental task t i Required skill set S i Skills, personnel i The skill set is SP l, then it is necessary to satisfy The task can be assigned to that person.

[0022] Furthermore, the setting of material constraints includes: calibrating the material set as M = {m 1 ,m 2 ,…,m r}, experimental task t i For material m s The demand is MT is , material m s The stock is ST s , then

[0023] Further, the calibration minimizes the experiment completion time, including:

[0024] The sum of the completion time of all experimental tasks is calibrated to minimize the experimental completion time, including the following steps:

[0025] The set of calibration experimental tasks is expressed as: T = {t 1 ,t 2 ,…,t n};

[0026] Experimental task i The completion time is CT i , then the objective function is expressed as

[0027] Furthermore, the calibration to minimize the experiment completion time also includes:

[0028] The calibration of the longest experimental task completion time is expressed as minimizing the experimental completion time, including the following steps:

[0029] The set of calibration experimental tasks is expressed as: T = {t 1 ,t 2 ,…,t n};

[0030] The completion time of the experimental task ti is CTi, then the objective function is expressed as Z = max{CT 1 ,CT 2 ,…,CT n}.

[0031] Furthermore, the maximum resource utilization is expressed as:

[0032] Among them, the experimental task t i For resources j The demand is D ij , resources j The total supply is Sj , resource utilization U j , expressed as:

[0033] Beneficial effects of the present invention:

[0034] The present invention establishes a resource list in advance, calibrates the experimental task requirements, sets constraints based on the resource list and the experimental task requirements, and constructs an APS model based on the resource list, experimental task requirements and personnel constraints, and calibrates the objective function. A resource allocation plan is generated based on the APS model. Compared with traditional manual scheduling and the way in which experimenters independently select equipment reservation times, the present invention integrates the laboratory's scheduling resources into the APS model for resource allocation, globally coordinates experimental resources and experimental tasks, and leaves the experimental scheduling work to computers. Schedulers only need to ultimately confirm the feasibility and rationality of the scheduling.

[0035] At the same time, the APS model of the present invention performs resource allocation, and its set constraints and objective functions are constructed as configuration parameters, that is, the laboratory resources that need to be considered for resource allocation do not need to be fixed as equipment, people or samples, etc., but these resources are abstracted as configuration parameters, and only the input parameters are calculated, and the scheduling results are output. Such a setting can be compatible with more types of laboratories, such as scientific research laboratories, teaching laboratories, production and testing laboratories, inspection and testing institutions, etc. The dimensions considered in these laboratories during scheduling are different, and the present invention can be as compatible as possible with laboratory scheduling scenarios.

[0036] In addition, the present invention can select scheduling criteria during scheduling, such as cost priority (reducing labor costs, equipment usage costs, etc. as much as possible before the planned completion time), time priority (reducing the weight of cost factors and advancing the completion time as much as possible), etc. These scheduling criteria can give laboratory administrators or laboratory schedulers a certain degree of autonomy and selectivity to meet various experimental scenarios in the laboratory. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a flowchart of an APS-based experimental resource allocation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0040] According to an embodiment of the present invention, an APS-based experiment resource allocation method is provided.

[0041] like Figure 1 As shown, the APS-based experimental resource allocation method according to an embodiment of the present invention includes the following steps:

[0042] Step S1, pre-establishing a resource list, including: collecting experimental equipment resources, experimental personnel resources and experimental material resources;

[0043] This technical solution, specifically, experimental equipment resources, including experimental equipment, such as high-precision microscopes, centrifuges, etc., record the model, function, quantity, and maintenance plan information. For example, for a specific model of centrifuge, its maximum speed, the number of samples it can accommodate, and the maintenance cycle (such as a comprehensive maintenance is required every 1,000 hours of operation) must be clearly stated.

[0044] Among them, the experimental personnel resources include: professional skills, work schedule. For example, a certain experimental personnel has a background in biochemistry, is good at protein analysis experiments, works from Monday to Friday, and works 8 hours a day.

[0045] Among them, experimental material resources, including chemical reagents and biological samples, should record their types, stock, shelf life, etc. For example, a special enzyme reagent has a stock of 100 ml and a shelf life of 6 months.

[0046] Step S2, calibrating the experimental task requirements, including: calibrating the equipment, personnel and materials required for the experimental task, and calibrating the time requirements and priority of the current experimental task;

[0047] This technical solution determines the equipment, personnel and materials required for each experimental task. For example, a cell culture experiment requires a cell culture box, a sterile operating table, experimental personnel with cell culture skills, and materials such as culture medium and cell lines.

[0048] Among them, in terms of calibrating the time requirements and priorities of the current experimental tasks, when applied, some experiments may be urgent scientific research projects that need to be arranged first, while some may be regular teaching experiments with relatively loose time requirements. For example, an experiment in the clinical trial stage of drug development has a high priority because it involves the progress of the drug's launch, and it may be required to complete a specific stage of testing within one month.

[0049] Step S3, setting constraints according to the resource list and experimental task requirements, including:

[0050] Set device constraints, including: the device set is E = {e 1 ,e 2 ,…,e p}, the experimental task ti uses the device ek for a time period of UT ik , equipment k The available time is AT, then there is a constraint

[0051] This technical solution specifically requires that a gene sequencer needs to reserve one day per week for system updates and maintenance, and can only be used for specific types of gene sequencing experiments.

[0052] Set personnel constraints, including: personnel working hours and skill matching.

[0053] Among them, the working time of personnel includes: the personnel set is P = {p 1 ,p 2 ,…,p q}, experimental task ti for personnel p i The working time requirement is PT il , the available working time of personnel is AW l , then

[0054] Among them, for skill matching, if the experimental task t i Required skill set S i Skills, personnel i The skill set is SP l , then it is necessary to satisfy The task can be assigned to that person.

[0055] Specifically, experimenters cannot conduct complex experiments during non-working hours, and must ensure that experimental tasks are assigned to personnel with corresponding skills.

[0056] Set material constraints, including: material set M = {m 1 ,m 2 ,…,m r}, the demand for material ms in experimental task ti is MT is , material m s The stock is ST s , then

[0057] Specifically, if the stock of a certain key reagent is insufficient, it is necessary to consider whether it can be purchased and replenished in a timely manner, and the impact this will have on the progress of the experiment.

[0058] Step S4, constructing the APS model according to the resource list, experimental task requirements and personnel constraints, and calibrating the objective function, which includes: minimizing the experiment completion time and maximizing resource utilization.

[0059] Among them, the calibration minimizes the experiment completion time, including:

[0060] The sum of the completion time of all experimental tasks is calibrated to minimize the experimental completion time, including the following steps:

[0061] The set of calibration experimental tasks is expressed as: T = {t 1 ,t 2 ,…,t n};

[0062] Experimental task i The completion time is CT i , then the objective function is expressed as

[0063] The calibration of the longest experimental task completion time is expressed as minimizing the experimental completion time, including the following steps:

[0064] The set of calibration experimental tasks is expressed as: T = {t 1 ,t 2 ,…,t n};

[0065] The completion time of the experimental task ti is CTi, then the objective function is expressed as Z = max{CT 1 ,CT 2 ,…,CT n}.

[0066] Among them, maximizing resource utilization is expressed as:

[0067] Among them, the experimental task t i For resources j The demand is D ij , resources j The total supply is S j , resource utilization U j , expressed as:

[0068] Step S5, generating a resource allocation plan according to the APS model, including: running the APS model and generating an experimental resource allocation plan through a built-in heuristic algorithm.

[0069] Specifically, the heuristic algorithm allocates specific equipment, personnel, and material usage time to each experimental task based on the equipment's idle time, personnel's skills and schedule, and material availability.

[0070] Specifically, when applying, a resource allocation plan is generated according to the APS model. For example: Experiment A uses equipment 1 from 9:00 a.m. to 12:00 p.m. on December 1st, operated by experimenter A, and uses materials a and b; Experiment B uses equipment 2 from 1:00 p.m. to 3:00 p.m. on December 1st, operated by experimenter B, and uses material c, etc.

[0071] Step S6 further includes the following steps:

[0072] Evaluate and optimize the resource allocation plan, including the following steps:

[0073] Obtain evaluation results for the current resource allocation plan, including: evaluating whether the experimental tasks are completed on time, evaluating whether resources are fully utilized, and evaluating whether there are resource conflicts;

[0074] Optimize the current resource allocation plan based on the evaluation results, including: adjusting the order of experimental tasks, reallocating resources, and extending the experimental time span.

[0075] Specifically, when applying, for example, if a certain experimental device is too busy in a certain period of time, causing other experiments to wait too long, you can consider postponing some experiments with lower priority, or transferring some experiments to other available devices.

[0076] In addition, some practices have proved that due to the introduction of the present invention, laboratory scheduling will be carried out in a more efficient and reasonable way. This has played a positive role in improving the utilization rate of experimental equipment, reducing the proportion of delayed experimental tasks, and balancing the work of experimental personnel. For example, after the laboratory management system of Company A introduced the present invention, the utilization rate of important large instruments in the laboratory increased from 35% to the current 80% or more, and the proportion of delayed experimental tasks decreased significantly.

[0077] In summary, with the help of the above technical solution of the present invention, the following effects can be achieved:

[0078] The present invention establishes a resource list in advance, calibrates the experimental task requirements, sets constraints based on the resource list and the experimental task requirements, and constructs an APS model based on the resource list, experimental task requirements and personnel constraints, and calibrates the objective function. A resource allocation plan is generated based on the APS model. Compared with traditional manual scheduling and the way in which experimenters independently select equipment reservation times, the present invention integrates the laboratory's scheduling resources into the APS model for resource allocation, globally coordinates experimental resources and experimental tasks, and leaves the experimental scheduling work to computers. Schedulers only need to ultimately confirm the feasibility and rationality of the scheduling.

[0079] At the same time, the APS model of the present invention performs resource allocation, and its set constraints and objective functions are constructed as configuration parameters, that is, the laboratory resources that need to be considered for resource allocation do not need to be fixed as equipment, people or samples, etc., but these resources are abstracted as configuration parameters, and only the input parameters are calculated, and the scheduling results are output. Such a setting can be compatible with more types of laboratories, such as scientific research laboratories, teaching laboratories, production and testing laboratories, inspection and testing institutions, etc. The dimensions considered in these laboratories during scheduling are different, and the present invention can be as compatible as possible with laboratory scheduling scenarios.

[0080] In addition, the present invention can select scheduling criteria during scheduling, such as cost priority (reducing labor costs, equipment usage costs, etc. as much as possible before the planned completion time), time priority (reducing the weight of cost factors and advancing the completion time as much as possible), etc. These scheduling criteria can give laboratory administrators or laboratory schedulers a certain degree of autonomy and selectivity to meet various experimental scenarios in the laboratory.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. After considering the disclosure of the specification and the examples, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and the examples are only regarded as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

[0082] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An APS-based experimental resource allocation method, characterized in that: The following steps are involved: Establish a resource list in advance, including: collecting experimental equipment resources, experimental personnel resources and experimental material resources; Conduct calibration of experimental task requirements, including: calibration of the equipment, personnel and materials required for the experimental task, and calibration of the time requirements and priorities of the current experimental task; Set constraints according to the resource list and experimental task requirements, including: setting equipment constraints, setting personnel constraints and setting material constraints; The APS model was constructed based on the resource list, experimental task requirements, and personnel constraints, and the objective function was calibrated, including: minimizing the experimental completion time and maximizing resource utilization; Generate a resource allocation plan according to the APS model, including: running the APS model and generating an experimental resource allocation plan through a built-in heuristic algorithm.

2. The APS-based experimental resource allocation method according to claim 1, characterized in that: The generation of the resource allocation plan according to the APS model also includes the following steps: Evaluate and optimize the resource allocation plan, including the following steps: Obtain evaluation results for the current resource allocation plan, including: evaluating whether the experimental tasks are completed on time, evaluating whether resources are fully utilized, and evaluating whether there are resource conflicts; Optimize the current resource allocation plan based on the evaluation results, including: adjusting the order of experimental tasks, reallocating resources, and extending the experimental time span.

3. The APS-based experimental resource allocation method according to claim 1, characterized in that: The setting of device constraints includes: calibrating the device set to be E = {e1, e2, ..., e p }, experimental task t i For device k The usage time is UT ik , equipment k The available time is AT, then there is a constraint 4. The APS-based experimental resource allocation method according to claim 3, characterized in that: The setting of personnel constraints includes: personnel working hours and skill matching; The working time of the personnel includes: the calibration personnel set is P = {p1, p2, ..., p q }, experimental task t i For personnel i The working time requirement is PT il , the available working time of personnel is AW l , then Among them, for skill matching, if the experimental task t i Required skill set S i Skills, personnel i The skill set is SP l , then it is necessary to satisfy The task can be assigned to that person.

5. The APS-based experimental resource allocation method according to claim 4, characterized in that: The setting of material constraints includes: calibrating the material set to M = {m1, m2, ..., m r }, experimental task t i For material m s The demand is MT is , material m s The stock is ST s , then 6. The APS-based experimental resource allocation method according to claim 1, characterized in that: The calibration minimizes the experiment completion time and includes: The sum of the completion time of all experimental tasks is calibrated to minimize the experimental completion time, including the following steps: The set of calibration experimental tasks is expressed as: T = {t1, t2, …, t n }; Experimental task i The completion time is CT i , then the objective function is expressed as 7. The APS-based experimental resource allocation method according to claim 1, characterized in that: The calibration minimizes the experiment completion time and also includes: The calibration of the longest experimental task completion time is expressed as minimizing the experimental completion time, including the following steps: The set of calibration experimental tasks is expressed as: T = {t1, t2, …, t n }; Experimental task i The completion time is CT i , then the objective function is expressed as Z = max{CT1, CT2,…, CT n }.

8. The APS-based experimental resource allocation method according to claim 1, characterized in that: The maximum resource utilization is expressed as: Among them, the experimental task t i For resources j The quantity demanded is D ij , resources j The total supply is S j , resource utilization U j , expressed as:

Images