CN110570937A - Method for scheduling by doctors and assistants - Google Patents
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- G—PHYSICS
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- 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
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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- G06Q10/06311—Scheduling, planning or task assignment for a person or group
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
Abstract
the invention discloses a method for scheduling by doctors and assistants, which aims to solve the problem in the conventional hospital scheduling. The method comprises the following specific steps: collecting personnel information, shift information, hard constraint conditions and historical shift records; performing primary modeling on the personnel information, the shift information, the hard constraint conditions and the historical shift record through a greedy algorithm to obtain a primary shift result; and step three, carrying out secondary modeling on the first scheduling result according to the soft constraint condition, the warning condition and the genetic algorithm to obtain a final scheduling result. The invention converts the scheduling problem of the doctor assistant into a multi-objective optimization problem, adopts a multi-objective evolutionary algorithm to process the medical scheduling problem, and meets the humanized scheduling requirement; the invention detects the rationality of the shift arrangement of the doctor assistant through the difference of the number of the medical care personnel, enables the shift arrangement to meet the requirements of the hospital more under the condition of certain number of the medical care personnel as much as possible, and fully utilizes the limited resources of the hospital to serve patients.
Description
Technical Field
The invention relates to the field of doctor scheduling, in particular to a method for scheduling by doctors and assistants.
Background
With the development of society, the problem of assistant scheduling of doctors in hospitals is more and more concerned by students, and hospitals spend a lot of energy to schedule doctors and assistants every month because hospitals need a lot of high-quality doctors and assistants, and although the problem of assistant scheduling of doctors is a problem which is combined with practice and is difficult to optimize, a lot of students are attracted to study the problem.
With the rapid development of medical treatment, how to reasonably utilize the existing resources of hospitals to provide services for patients is extremely important. However, among the numerous resources, the physician assistant plays a very important role, since it is extremely important to optimize the scheduling of the physician and assistant (hereinafter referred to as medical assistance) in the case of limited hospital resources. Therefore, it is very important to reasonably utilize hospital staff and reasonably arrange the shift for display, so that good service can be provided for patients, the serious illness state can be prevented, and the hospital staff can have sufficient rest.
it is a very complex problem due to the many constraints and conflicts that exist in the medical assistance shift problem. In many large or internet hospitals, the medical assistance shift problem is a significant challenge for researchers. Medical assistance scheduling requires which doctors and assistants to work each day, and each shift must meet certain constraints, such as coverage requirements, healthcare workload, consecutive assigned shifts, work and rest requirements, and weekend related requirements. The medical shift problem has been studied intensively during the last years and some more classical algorithms have been proposed, which mainly fall into two categories: one is an accurate algorithm, mainly adopts a mathematical programming technology, has high calculation complexity, limits the wide application of the algorithm, and can only calculate small-scale examples; the other is a heuristic algorithm, which is mainly a variable domain search algorithm proposed by Burke et al. There are also many solutions to the physician assistant scheduling problem, such as Huang et al propose to solve the physician assistant scheduling problem with an evolutionary algorithm. Medical assistance shift scheduling problems are widely researched in recent decades, the research is particularly intensive in recent decades, and in numerous solving algorithms, two major categories of precise algorithms and heuristic algorithms are mainly provided. The precise algorithm comprises an integer programming method used by Isken, Glass and the like and a branch pricing method proposed by Belien and the like. In addition, He and the like use a column generation algorithm to model and solve the scheduling problem of the doctor assistant, Burke and the like propose mixed taboo search aiming at the scheduling problem of the doctor assistant, and then successively propose a modulo-cause algorithm, variable-field search, decentralized search and variable-depth search to solve the scheduling problem of the doctor assistant. In addition, there are many solutions to the medical shift scheduling problem, such as a hybrid evolutionary algorithm proposed by Bai and the like, a hyper-heuristic algorithm proposed by Anwar and the like, and an evolutionary algorithm proposed by Huang and the like.
However, when the number of the doctor assistants, the scheduling period and the number of the constraint conditions are increased and the solving scale is increased, the optimal solution is difficult to obtain in linear time by the method, and even the solution cannot be completed; in addition, in more and more medical care shift-scheduling researches, in addition to consideration of meeting the operation management objective of a hospital, the individual needs of medical care are added into the optimization objective, including the work shift preference and the work partner preference of the medical care individual. The medical shift scheduling problem is changed from the previous single-target problem to a multi-target problem, and people are also researching related aspects.
disclosure of Invention
An object of an embodiment of the present invention is to provide a method for scheduling for doctors and assistants, so as to solve the problems in the background art.
in order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
a method for scheduling doctors and assistants comprises the following specific steps:
Collecting personnel information, shift information, hard constraint conditions and historical shift records;
Secondly, modeling the personnel information, the shift information, the hard constraint conditions, the greedy algorithm and the historical shift record for the first time to obtain a first shift result;
And step three, carrying out secondary modeling on the first scheduling result according to the soft constraint condition, the warning condition and the genetic algorithm to obtain a final scheduling result.
As a further scheme of the embodiment of the invention: the doctor's shift was: a early shift (7:00-15:30), A shift (8:00-16:30), B shift (9:30-19:00), C shift (14:00-22:00), D shift (15:00-23:00), X shift (16:00-24:00), E shift (18:00-1:00) and L shift (24:00-8:00), wherein the hard constraint conditions comprise that the shift is scheduled one day before the L shift, the shift is scheduled one day after the L shift, the shift cannot be scheduled A or A early after the C shift, the shift cannot be scheduled A or A early after the D shift, the shift cannot be scheduled A or A early after the X shift, the shift cannot be scheduled for more than 5 days after the C shift, the shift cannot be scheduled for more than 5 days after the D shift, the shift cannot be scheduled for more than 5 days after the X shift, and the shift cannot be scheduled for more than 5 days after the E shift.
As a further scheme of the embodiment of the invention: the procedure of the genetic algorithm is as follows: initializing a population, evaluating the individual fitness in the population, selecting, crossing and mutating, then evaluating the individual fitness in the population again, and repeating the steps until a result is obtained.
As a further scheme of the embodiment of the invention: the soft constraint conditions comprise scheduling after the shift E when the manpower is sufficient, scheduling balance by a doctor assistant, scheduling balance in the morning and evening, scheduling balance of each doctor every month, avoiding scheduling the same shift in the same workplace every day, scheduling the later shift by redundant personnel when the personnel are sufficient, and reducing the relative balance of the scheduling shift between the shift A and each workplace when the manpower is insufficient.
As a further scheme of the embodiment of the invention: the soft constraints also include the daily balance between class A and early class A and between class D and X for each workplace, 2 days of continuous rest as possible, and the lowest labor cost.
As a further scheme of the embodiment of the invention: hard constraints also include that major medicine and major surgery must have one shift, L or E, per day and that each person only goes to one shift per day.
As a further scheme of the embodiment of the invention: the selection is made using a non-dominated sorting algorithm of the NSGA-2 algorithm.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
According to the medical staff scheduling method, the scheduling problem of the doctor assistant is converted into a multi-objective optimization problem, the medical staff scheduling problem is processed by adopting a multi-objective evolutionary algorithm, the humanized scheduling requirement is met, and the balance among the number of shifts, the assistant and the doctor is realized;
The invention detects the reasonability of the scheduling of the doctor assistant through the difference of the number of the medical care personnel, enables the scheduling to better meet the requirements of the hospital under the condition of certain number of the medical care personnel as much as possible, can fully utilize the limited resources of the hospital to serve patients, has good performance in the scheduling process of 3-100 doctors and more, and meets the requirements of users on the scheduling.
Drawings
FIG. 1 is a flow chart illustrating a method for scheduling by a physician and an assistant.
FIG. 2 is a flow chart of a genetic algorithm in a method for scheduling by a doctor and an assistant.
Detailed Description
The technical solution of the present patent will be described in further detail with reference to the following embodiments.
Example 1.
the doctor's shift was: early class A (7:00-15:30), class A (8:00-16:30), class B (9:30-19:00), class C (14:00-22:00), class D (15:00-23:00), class X (16:00-24:00), class E (18:00-1:00) and class L (24:00-8:00), wherein the hard constraint conditions comprise that the class A or the class A cannot be scheduled one day before the class L, the class L is scheduled one day after the class L, the class A or the class A cannot be scheduled after the class C, the class A or the class A cannot be scheduled after the class D, the class X cannot be scheduled for more than 5 days after the class X, the class X cannot be scheduled for more than 5 days, the class E cannot be scheduled for more than 5 days, the class Davidae and major surgery must have one class L or E each day and each person is scheduled for one class each day. The soft constraint conditions comprise scheduling after E shift when the manpower is sufficient, scheduling balance by a doctor assistant, scheduling balance in morning and evening, scheduling balance of each doctor every month, avoiding the same shift from being arranged in the same workplace every day, scheduling late shift by redundant personnel when the personnel is sufficient, reducing A shift when the manpower is insufficient, relative balance of scheduling shift between each workplace, balance between A shift and A morning shift and between D shift and X shift of each workplace every day, 2 days of continuous rest as much as possible and lowest labor cost.
The mathematical model of the multi-objective optimization problem consisting of m objective functions F ═ F1, F2, …, fm } is as follows:
min y＝F(x)＝min(f1(x),f2(x),…,fm(x))，
s.t. gk(x)≤0,k＝1,2,…,p (1)
hl(x)＝0,l＝1,2,…,q
Wherein the content of the first and second substances,Is an n-dimensional decision variable, generally referred to as X is a decision space,Is a target vector of the multi-objective optimization problem, generally called Y as the target space, f1(x), f2(x), …, fm (x) is m sub-targets to be optimizedThe function gk (x) ≦ 0, k ≦ 1,2 …, p being the defined p inequalities, hl (x) ≦ 0, l ≦ 1,2, …, q being the defined q equality constraints. When p is 0, the method is called an unconstrained multiobjective optimization problem, and conversely, the method is called a constrained multiobjective optimization problem. For any X ∈ X, when X satisfies 2 constraints in the formula (1.0), it is called a feasible solution of the multi-objective optimization problem. The whole of all possible solutions in X is defined as the set of possible solutions, and is generally expressed as Ω ∈ X | gk ≦ 0, k ∈ 1,2, …, p and hl (X) ═ 0, and l ═ 1,2, …. q }. If x1, x2 belong to any 2 individuals in the feasible solution, and the relationship between them satisfies equation (2), then x2 is said to be Pareto dominant over x1 or x1 dominates x 2:
fi(x1)≤fi(x2)andfj (x1) < f (x2) (2). Commonly denoted as x1<x 2. If and only if not presentThen, the representation X ∈ X is called Pareto optimal solution.
the Pareto optimal solution set is a set of all Pareto optimal solutions, and is defined as follows:
The curved surface formed by the target vectors corresponding to all Pareto optimal solutions is called Pareto front edge (PF), that is, the Pareto front edge
PF＝{F(X*)＝(f1(x*),….,fm(x*))|x*∈P*}。
scheduling thought: firstly, a target is formed for more important hard constraint conditions, then other soft constraint conditions form a target in an average weighting mode, and the condition with the lowest labor cost forms another multi-objective optimization problem, so that the medical shift scheduling problem can be solved by adopting a multi-objective evolutionary algorithm. The medical assistant shift scheduling problem is provided with 11 hard constraints, the 11 hard constraints are met during initialization, and the 11 hard constraints are also met during later selection, crossing and mutation. For 11 soft constraints, all the soft constraints should be met as much as possible in each shift, 11 hard constraints and 11 soft constraints form one target through average weighting, and the minimum labor cost forms another target, so that the method is converted into a multi-target optimization problem for 2 targets.
the parameter assumptions are:
i 1,2, … denotes the physician index;
j-1, 2, ….30 represents the number of days of the shift cycle, representing the time of one month;
k 1,2 …, type number indicating shift per day;
m represents the maximum number of work shifts per doctor during the scheduling period;
n represents the maximum number of consecutive night shifts per doctor during the shift scheduling period;
ci is 1,3 and 5, which represents the wage grade of the ith doctor and respectively corresponds to the wage cost of the doctors with low level, the doctors with middle level and the doctors with high level;
pijk represents the work satisfaction of the ith doctor on the k shift of day j, and is noted as pijk ═ 1 (very unsatisfactory), 2 (unsatisfactory), 3 (general), 4 (satisfactory), 5 (very satisfactory) }.
Collection
N represents a whole doctor set;
T represents the set of all shift days;
k represents a shift type set;
Decision variable definition
xijk-1 indicates that the ith doctor scheduled the kth shift on the jth day, and the word xijk-0.
MinF(x)＝∑Ni＝1∑Tj＝1∑Kk＝1cijkxijk
MaxF(x)＝∑Ni＝1∑Tj＝1∑Kk＝1pijkxijk (1.0)
equation 1.0 is the objective function of physician scheduling, i.e. minimizing the cost of physician payroll and physician satisfaction in the hospital. Equation 1.1 is that each doctor is assigned at most one shift of work per day. Equation 1.2 is that the number of work shifts per doctor cannot exceed the specified upper limit, and the upper limit on the work time of a month required to meet the requirements specified in the labor code is at most 21 days. Equation 1.3 is that the number of night shifts that each doctor works continuously cannot exceed a prescribed upper limit. Equations 1.4 and 1.5 are constraints that limit the arrangement of 2 adjacent day shifts to ensure that no shift combination violating the scheduling plan is generated, and the 2 constraints sequentially indicate that the doctor cannot work in the early shift and the middle shift next day after the night shift on a certain day; and doctors cannot work at night immediately the next day after going to middle shift on a certain day. Equation 1.6 represents an upper limit on the number of doctor's work shifts over 4 consecutive days. Equation 1.7 represents the lower limit on the number of doctor's rest shifts for 4 consecutive days. Equation 1.8 is used to limit the number of shifts for the doctor to work and rest as continuously as possible, which can ensure adequate rest for the doctor.
In evolutionary algorithms, each argument must first be encoded and then grouped into an individual. For the medical care scheduling problem, the individual refers to a medical care scheduling table, and the gene is a basic element in the medical care scheduling table, such as a doctor, an assistant, the shift order and the like.
Initializing a population: the medical care shift individuals represent a matrix, all hard constraints should be met for the initialization population, H1, H2 and H9L shifts are scheduled front and back, the L shifts are scheduled preferentially, and then the shift is scheduled front and back fixedly. H3, H4 and H5 are arranged by arranging A behind the rest in the morning or fixedly arranging A, H6, H7, H8 and H9, the rest is distributed to each day in a balanced mode, the situation that the rest is continuously on duty for 6 days does not occur, then A is arranged behind the rest, the rest is not continuously on the last 5 night shifts, and H11 is automatically met when the individuals are represented by a two-dimensional matrix. Therefore, at initialization, it is guaranteed that the hard constraints are all satisfied, and that the hard constraints are satisfied at a later time for selection, crossover, and mutation.
Selecting: 2 optimization targets established for medical care shift adopt a non-dominated sorting algorithm in NSGA-2 algorithm to ensure that individuals entering the next generation each time are excellent individuals in the population, so that good genetic inheritance can be ensured.
And (3) crossing: all hard constraints and all soft constraints as far as possible must be met each time crossover is performed, and 2 individual exchange shifts are adopted during crossover.
Mutation: at each mutation, the mutation of the medical staff shift needs to satisfy all hard constraints and as far as possible soft constraints.
And (3) keeping elite: the elite retention strategy is to allow good gene spread and to move in a good direction during each evolution. In the medical shift scheduling problem, parent optimal individuals and child optimal individuals are combined in each evolution, and then optimal solutions are selected through non-dominated sorting. This ensures that the inherited genes of the previous generation are retained each time they are selected.
optimizing and setting: the total group size is 20-100; the iteration number is 200-50000; if the setting is too large, the calculation overhead is large, the setting is too small, and the result is not ideal.
the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (7)
1. A method for scheduling doctors and assistants is characterized by comprising the following specific steps:
Collecting personnel information, shift information, hard constraint conditions and historical shift records;
Secondly, modeling the personnel information, the shift information, the hard constraint conditions, the greedy algorithm and the historical shift record for the first time to obtain a first shift result;
and step three, carrying out secondary modeling on the first scheduling result according to the soft constraint condition, the warning condition and the genetic algorithm to obtain a final scheduling result.
2. The physician and assistant shift scheduling method of claim 1 wherein the physician's shift is: a early shift (7:00-15:30), A shift (8:00-16:30), B shift (9:30-19:00), C shift (14:00-22:00), D shift (15:00-23:00), X shift (16:00-24:00), E shift (18:00-1:00) and L shift (24:00-8:00), wherein the hard constraint conditions comprise that the shift is scheduled one day before the L shift, the shift is scheduled one day after the L shift, the shift cannot be scheduled A or A early after the C shift, the shift cannot be scheduled A or A early after the D shift, the shift cannot be scheduled A or A early after the X shift, the shift cannot be scheduled for more than 5 days after the C shift, the shift cannot be scheduled for more than 5 days after the D shift, the shift cannot be scheduled for more than 5 days after the X shift, and the shift cannot be scheduled for more than 5 days after the E shift.
3. The physician and assistant shift scheduling method according to claim 1, wherein the genetic algorithm is as follows: initializing a population, evaluating the individual fitness in the population, selecting, crossing and mutating, then evaluating the individual fitness in the population again, and repeating the steps until a result is obtained.
4. The physician and assistant scheduling method of claim 2 wherein the soft constraints include scheduling after E shift when sufficient manpower is available, physician assistant scheduling, morning and evening shift, monthly per physician shift scheduling, avoiding scheduling the same shift at the same time, scheduling the same shift at night when sufficient personnel is available, reducing a shift when insufficient manpower is available, relative balancing of scheduling shifts between each time of day.
5. The physician and assistant scheduling method of claim 4 wherein the soft constraints further include a daily balance between class A and early class A and between class D and X for each workplace, a 2 day rest with the lowest possible labor cost.
6. the physician and assistant shift scheduling method according to claim 2, wherein the hard constraints further include that major internal medicine and major surgery must have one L shift or E shift per day and that each person is only one shift per day.
7. The physician and assistant shift scheduling method according to claim 3, wherein the selection is performed by using a non-dominant ranking algorithm of the NSGA-2 algorithm.
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| CN116646068A (en) * | 2023-07-27 | 2023-08-25 | 四川互慧软件有限公司 | Nurse Scheduling Method Based on Demand Selection |
| CN116646068B (en) * | 2023-07-27 | 2024-02-13 | 四川互慧软件有限公司 | Nurse scheduling method based on demand selection |
| CN117114373A (en) * | 2023-10-24 | 2023-11-24 | 中铁发展投资有限公司 | Intelligent building site personnel management system |
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