CN116092653A - Comprehensive hospital medical distribution dynamic scheduling method - Google Patents

Comprehensive hospital medical distribution dynamic scheduling method Download PDF

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CN116092653A
CN116092653A CN202310163488.9A CN202310163488A CN116092653A CN 116092653 A CN116092653 A CN 116092653A CN 202310163488 A CN202310163488 A CN 202310163488A CN 116092653 A CN116092653 A CN 116092653A
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邵骏扬
王小明
吴庆镇
郑桐
张烜华
林坚满
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Guangdong University of Technology
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Abstract

The invention provides a dynamic dispatching method for medical delivery of a comprehensive hospital, which comprises the following steps: acquiring dynamic information of a hospital; constructing a scheduling decision model; and inputting the dynamic information of the hospital into an optimized scheduling decision model, and outputting an optimal scheduling scheme by the scheduling decision model. The method has the advantages of intelligent decision making, higher scheduling decision making efficiency, capability of effectively reducing task deadlines and balancing the task quantity of personnel.

Description

Comprehensive hospital medical distribution dynamic scheduling method
Technical Field
The invention relates to the technical field of medical informatization, in particular to a comprehensive hospital medical distribution dynamic scheduling method.
Background
In recent years, the development problem of medical and health industries in China has been attracting attention, and the state is pushing the intelligent medical strategy. In large integrated hospitals, the scheduling of logistics personnel and caregivers is an important content of the operation process of hospitals. However, most of the hospitals in China have a relatively late current central distribution scheduling system, and a relatively large optimization and lifting space from the aspects of operation efficiency and cost.
Comprehensive hospital medical delivery refers to the delivery of related medical supplies, specimens, etc. by logistic personnel, caregivers, nurses, etc. inside a hospital, or the examination or operation of accompanying patients, etc. The medical delivery schedule of the large comprehensive hospital covers the whole hospital, cross delivery exists among different departments, the number of delivery tasks is more every day, and related delivery personnel are more. However, most hospitals currently have distribution tasks manually arranged by a central dispatching room through communication means such as interphones, telephones, weChats and the like. This conventional scheduling method has the following two problems:
the scheduling effect is poor. At present, the method mainly depends on manual experience decision, generally arranges tasks according to a nearby principle, lacks scientific basis, and easily causes the problems of untimely emergency task treatment, unbalanced workload of workers, serious task delay and the like.
The scheduling efficiency is low. When the distribution tasks are more, a great deal of communication time is required for manual arrangement one by one, mainly because communication facilities limit the transmission efficiency of the dispatching information and the real-time effective supervision of the central dispatching cannot be realized.
Disclosure of Invention
The invention aims to provide a comprehensive hospital medical distribution dynamic scheduling method which is intelligent in decision making and high in scheduling decision making efficiency, and can effectively reduce task deadlines and balance personnel task amounts.
A comprehensive hospital medical delivery dynamic scheduling method comprises the following steps:
acquiring dynamic information of a hospital;
constructing a scheduling decision model;
and inputting the dynamic information of the hospital into an optimized scheduling decision model, and outputting an optimal scheduling scheme by the scheduling decision model.
The dynamic information of the hospital includes:
current hospital task information: the method comprises the steps of collecting tasks to be completed currently, starting departments of each task, ending departments of each task, earliest delivery time and latest delivery time;
current hospital personnel information: the real-time position of the personnel and the current accumulated workload of each personnel.
The construction of the scheduling decision model comprises the following steps:
defining decision variables of a scheduling decision model;
establishing an objective function of a scheduling decision model;
and establishing constraint conditions of a scheduling decision model.
The decision variables defining the scheduling decision model include:
Figure BDA0004095029410000021
representing task a E A t And task b e A t Whether { a } is processed by personnel K E K, K is the number of people in the hospital, A t A hospital task set at the moment t, wherein a is arranged at an adjacent position before the task b;
introducing a virtual task 0 as a front task of a first task and a rear task of a last task;
for any person K e K, the virtual starting task end point f 0 ＝ID k ，ID k The current department of the worker k is indicated, the starting point and the end point of the virtual ending task are the end point of the last non-virtual task, namely, the worker k stays on site for standby after finishing the last task.
Establishing an objective function of the scheduling decision model includes:
let r a E {0,1} represents a non-virtual task a E A t Whether it can be completed within 2 hours after execution, the relationship with its completion time is as follows:
Figure BDA0004095029410000022
then, the number of tasks performed over 2 hours is:
Figure BDA0004095029410000023
let z a E {0,1} represents task a E A t Whether or not to be delayed, the relationship between it and the finishing time is as follows:
Figure BDA0004095029410000024
then, the number of deadlines is weighted:
Figure BDA0004095029410000025
W a weighting for task urgency
The maximum time required to complete all tasks is
Figure BDA0004095029410000026
C a For the completion time of task a
Maximum workload difference between persons
Figure BDA0004095029410000027
Wherein TW k Representing the cumulative workload of person K e K
Figure BDA0004095029410000028
CW k For the current workload of worker k, S a For the department of task a starting point, F b For the end point department of task b, P b Processing time after sampling and delivering for task b, +.>
Figure BDA0004095029410000029
Representing the time required from the start department of task a to the end department of task b;
finally, combining the multiple targets into a total target by using the weight coefficient
Figure BDA00040950294100000210
Wherein h= |a t |w max I.e. the number of tasks multiplied by the maximum task weight.
Establishing constraint conditions of the scheduling decision model comprises establishing constraint condition lower limits, specifically:
order the
Figure BDA00040950294100000211
The scheduling criteria and scheduling constraints for personnel are uniformly specified by the following constraints:
the very end of each people list has only one non-virtual task:
Figure BDA0004095029410000031
the forefront of each personnel list has only one non-virtual task:
Figure BDA0004095029410000032
each non-virtual task can only be handled by one person:
Figure BDA0004095029410000033
each task can only have one immediately preceding and immediately following task:
Figure BDA0004095029410000034
defining a time window for each task, and obtaining the finishing time of each task through condition constraint, wherein M is a maximum value:
lower bound of task completion time variable:
Figure BDA0004095029410000035
e a for task a earliest start time
B starts after the non-virtual task a is finished:
Figure BDA0004095029410000036
the non-virtual task b is first handled by person k:
Figure BDA0004095029410000037
IT k for time t
Person k earliest idle time.
Establishing constraint conditions of the scheduling decision model comprises establishing constraint condition upper limits, specifically:
if the previous task of non-virtual task b is non-virtual task a, then there is
Figure BDA0004095029410000038
Defining a new binary variable sigma b It is connected with
Figure BDA0004095029410000039
The relationship of (2) is as follows:
Figure BDA00040950294100000310
Figure BDA00040950294100000311
Figure BDA00040950294100000312
if the previous task of the non-virtual task b is virtual task 0 and is performed by person K e K, then there are:
Figure BDA00040950294100000313
if it is
Figure BDA00040950294100000314
Sigma is then b =1, otherwise σ b =0, i.e.: />
Figure BDA0004095029410000041
Figure BDA0004095029410000042
Figure BDA0004095029410000043
r a E {0,1} represents a non-virtual task a E A t Whether it can be completed within 2 hours after execution, the relationship between it and the completion time is as follows:
if the process is completed after two hours, the reinforcement order r a ＝1
Figure BDA0004095029410000044
If the process is completed within two hours, the reinforcement command r a ＝0
Figure BDA0004095029410000045
z a E {0,1} represents task a E A t Whether or not to be delayed, the relationship between it and the finishing time is as follows:
if in a late stage, make z stronger a ＝1
Figure BDA0004095029410000046
If not, force z a ＝0
Figure BDA0004095029410000047
LF a The latest completion time for task a.
After the scheduling decision model is constructed, the method further comprises the step of optimizing the scheduling decision model by using a simulated annealing algorithm, and specifically comprises the following steps:
through the simulated annealing process, iteration is carried out continuously, new solutions are generated each time and compared with the objective function of the current solution;
if the difference is less than 0, accepting the new solution;
if the difference is greater than 0, the acceptance probability is represented by generating a random number r between 0 and 1;
if: e, e -(Δf/T) R accepts new solution, otherwise does not accept new solution;
and finally obtaining a task scheduling scheme of the optimal solution.
The dynamic information of the hospital is input into an optimized scheduling decision model, and the scheduling decision model outputs an optimal scheduling scheme, which comprises the following steps:
after the task arrangement is obtained at the time t, the scheduling decision model calculates the number of the tasks in the drags by calculating the difference value between the finishing time of each task and the designated latest finishing time of the task;
calculating the number of tasks with the deadline exceeding two hours, and giving different weight coefficients according to the priority degree of the tasks;
calculating the task amount of each person and the current accumulated workload, and calculating the difference value between the maximum workload and the minimum workload of different persons, so as to finally balance the workload of each person;
the objective function formula is as follows:
Figure BDA0004095029410000048
wherein A is t Representing the total number of tasks, ω, at time t max Representation A t The maximum weight value of the task in (c),
Figure BDA0004095029410000049
indicating the number of tasks with execution time exceeding 2 hours, < >>
Figure BDA0004095029410000051
Representing the weighted number of deadlines, C max Indicating all time, TW, required to complete all tasks k Indicating the workload of the kth person, and alpha and beta correspond to the corresponding weights.
An integrated hospital medical delivery dynamic scheduling system comprising:
the data acquisition module is used for acquiring dynamic information of a hospital;
the data processing module is used for calculating and obtaining an optimal scheduling scheme according to the dynamic information of the hospital;
and the data output module is used for outputting the optimal scheduling scheme.
According to the invention, by establishing the scheduling decision model, a multi-objective optimized mathematical model and a solving algorithm of efficient scheduling decision can be performed according to the dynamically-changed distribution task and personnel information, so that scientific and efficient comprehensive medical distribution dynamic scheduling of the hospital is realized, the problem of comprehensive medical distribution scheduling decision can be solved, the task delay is reduced, the distribution time is shortened, and the workload of workers is balanced.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of a dynamic scheduling method for medical delivery of a comprehensive hospital;
FIG. 2 is a diagram of a system for dynamic dispatching of medical delivery in a comprehensive hospital;
FIG. 3 is a simulation model diagram of a dynamic dispatching method for medical delivery of a comprehensive hospital;
FIG. 4 is a diagram showing experimental parameter comparison of a dynamic dispatching method for medical delivery in a comprehensive hospital;
FIG. 5 is an Insert strategy diagram of a comprehensive hospital medical delivery dynamic scheduling method according to the present invention;
FIG. 6 is a SWAP strategy 1 chart of a comprehensive hospital medical delivery dynamic scheduling method provided by the invention;
fig. 7 is a view of a SWAP strategy 2 of a comprehensive hospital medical delivery dynamic scheduling method according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Example 1
A comprehensive hospital medical delivery dynamic scheduling method comprises the following steps:
s100, acquiring dynamic information of a hospital;
s200, constructing a scheduling decision model;
s300, inputting the dynamic information of the hospital into an optimized scheduling decision model, and outputting an optimal scheduling scheme by the scheduling decision model.
The dynamic information of the hospital includes:
current hospital task information: the method comprises the steps of collecting tasks to be completed currently, starting departments of each task, ending departments of each task, earliest delivery time and latest delivery time;
current hospital personnel information: the real-time position of the personnel and the current accumulated workload of each personnel.
S200, constructing a scheduling decision model comprises the following steps:
s201, defining decision variables of a scheduling decision model;
s202, establishing an objective function of a scheduling decision model;
s203, constraint conditions of a scheduling decision model are established.
S201 defines decision variables of the scheduling decision model including:
Figure BDA0004095029410000061
representing task a E A t And task b e A t Whether all { a } are peopleK is treated by K E, K is the number of people in a hospital, A t A hospital task set at the moment t, wherein a is arranged at an adjacent position before the task b;
introducing a virtual task 0 as a front task of a first task and a rear task of a last task;
for any person K e K, the virtual starting task end point f 0 ＝ID k ，ID k The current department of the worker k is indicated, the starting point and the end point of the virtual ending task are the end point of the last non-virtual task, namely, the worker k stays on site for standby after finishing the last task.
S202 establishes an objective function of the scheduling decision model comprising:
let r a E {0,1} represents a non-virtual task a E A t Whether it can be completed within 2 hours after execution, the relationship with its completion time is as follows:
Figure BDA0004095029410000062
then, the number of tasks performed over 2 hours is:
Figure BDA0004095029410000063
let z a E {0,1} represents task a E A t Whether or not to be delayed, the relationship between it and the finishing time is as follows:
Figure BDA0004095029410000064
then, the number of deadlines is weighted:
Figure BDA0004095029410000065
W a weighting for task urgency
The maximum time required to complete all tasks is
Figure BDA0004095029410000071
C a For the completion time of task a
Maximum workload difference between persons
Figure BDA0004095029410000072
Wherein TW k Representing the cumulative workload of person K e K
Figure BDA0004095029410000073
CW k For the current workload of worker k, S a For the department of task a starting point, F b For the end point department of task b, P b Processing time after sampling and delivering for task b, +.>
Figure BDA0004095029410000074
Representing the time required from the start department of task a to the end department of task b;
finally, combining the multiple targets into a total target by using the weight coefficient
Figure BDA0004095029410000075
Wherein h= |a t |w max I.e. the number of tasks multiplied by the maximum task weight.
S203, establishing constraint conditions of the scheduling decision model comprises establishing constraint condition lower limits, specifically:
order the
Figure BDA0004095029410000076
The scheduling criteria and scheduling constraints for personnel are uniformly specified by the following constraints:
the very end of each people list has only one non-virtual task:
Figure BDA0004095029410000077
the forefront of each personnel list has only one non-virtual task:
Figure BDA0004095029410000078
each non-virtual task can only be handled by one person:
Figure BDA0004095029410000079
each task can only have one immediately preceding and immediately following task:
Figure BDA00040950294100000710
defining a time window for each task, and obtaining the finishing time of each task through condition constraint, wherein M is a maximum value:
lower bound of task completion time variable:
Figure BDA00040950294100000711
e a for task a earliest start time
B starts after the non-virtual task a is finished:
Figure BDA00040950294100000712
the non-virtual task b is first handled by person k:
Figure BDA00040950294100000713
IT k for time t
Person k earliest idle time.
S203, establishing constraint conditions of the scheduling decision model comprises establishing constraint condition upper limits, specifically:
if the previous task of non-virtual task b is non-virtual task a, then there is
Figure BDA0004095029410000081
Defining a new binary variable sigma b It is connected with
Figure BDA0004095029410000082
The relationship of (2) is as follows:
Figure BDA0004095029410000083
Figure BDA0004095029410000084
Figure BDA0004095029410000085
if the previous task of the non-virtual task b is virtual task 0 and is performed by person K e K, then there are:
Figure BDA0004095029410000086
if it is
Figure BDA0004095029410000087
Sigma is then b =1, otherwise σ b =0, i.e.:
Figure BDA0004095029410000088
Figure BDA0004095029410000089
Figure BDA00040950294100000810
r a e {0,1} represents a non-virtual task a E A t Whether it can be completed within 2 hours after execution, the relationship between it and the completion time is as follows:
if the process is completed after two hours, the reinforcement order r a ＝1
Figure BDA00040950294100000811
If the process is completed within two hours, the reinforcement command r a ＝0
Figure BDA00040950294100000812
z a E {0,1} represents task a E A t Whether or not to be delayed, the relationship between it and the finishing time is as follows:
if in a late stage, make z stronger a ＝1
Figure BDA00040950294100000813
If not, force z a ＝0
Figure BDA00040950294100000814
LF a The latest completion time for task a.
After the scheduling decision model is built in S200, S210 further includes optimizing the scheduling decision model by using a simulated annealing algorithm, and specifically includes:
through the simulated annealing process, iteration is carried out continuously, new solutions are generated each time and compared with the objective function of the current solution;
if the difference is less than 0, accepting the new solution;
if the difference is greater than 0, the acceptance probability is represented by generating a random number r between 0 and 1;
if: e, e -(Δf/T) R accepts new solution, otherwise does not accept new solution;
and finally obtaining a task scheduling scheme of the optimal solution.
The neighborhood generates new orchestration schemes (three generate new solutions):
(1) The implement strategy, as in fig. 5:
step1: randomly extracting one task in a personnel task arrangement;
step2: randomly extracting another person and randomly selecting any position in the task schedule;
step3: inserting the extracted task into the selected location;
step4: forming a new task orchestration;
(2) SWAP strategy 1: randomly selecting a task of a person, exchanging position with the previous task, as shown in FIG. 6
Step1: randomly extracting a person;
step2: randomly extracting a task of the personnel task arrangement;
step3: exchanging the task with a previous task;
step4: if no position is in front of the task, re-extracting the task;
(3) SWAP strategy 2: two tasks, exchange positions, of two persons randomly selected, as shown in FIG. 7
Step1: randomly extracting a person;
step2: randomly extracting a task of the person;
step3: randomly extracting another person;
step4: randomly extracting a task of the person;
step5: exchanging the extracted two tasks to generate a new arrangement scheme;
s300, inputting dynamic information of a hospital into an optimized scheduling decision model, and outputting an optimal scheduling scheme by the scheduling decision model comprises the following steps:
after the task arrangement is obtained at the time t, the scheduling decision model calculates the number of the tasks in the drags by calculating the difference value between the finishing time of each task and the designated latest finishing time of the task;
calculating the number of tasks with the deadline exceeding two hours, and giving different weight coefficients according to the priority degree of the tasks;
calculating the task amount of each person and the current accumulated workload, and calculating the difference value between the maximum workload and the minimum workload of different persons, so as to finally balance the workload of each person;
the objective function formula is as follows:
Figure BDA0004095029410000091
wherein A is t Representing the total number of tasks, ω, at time t max Representation A t The maximum weight value of the task in (c),
Figure BDA0004095029410000092
indicating the number of tasks with execution time exceeding 2 hours, < >>
Figure BDA0004095029410000093
Representing the weighted number of deadlines, C max Indicating all time, TW, required to complete all tasks k Indicating the workload of the kth person, and alpha and beta correspond to the corresponding weights.
According to the MIP model, the item group writes the c# language to call the Gurobi solver to solve the model so as to verify the scheduling algorithm. A total of 9 examples were tested by simulation experiments (Gurobi uses 100 seconds of single core time resolution), and the test results of the two were compared as follows:
table 1-1 comparison table of test results
Figure BDA0004095029410000101
It can be seen that the scheduling algorithm is very close to the optimal solution of Gurobi, and after the problem size is slightly larger, the Gurobi can hardly find the optimal solution within 100 seconds, and the calculation speed of the scheduling algorithm is very high. When the problem size increases (e.g., 4 workers for 20 tasks and 5 workers for 25 tasks), the scheduling algorithm results in a significantly better result than the best result obtained by the Gurobi at the time limit of 100 seconds. This also shows that the scheduling algorithm proposed herein has a very good optimizing effect, while meeting the requirements of actual decisions on timeliness.
Embedding the scheduling optimization algorithm into a simulation model and setting a simulation experiment to verify the optimization effect of the algorithm, wherein the simulation experiment takes a problem target as a research premise, and takes four indexes: the number of tasks outstanding in 2 hours, obj 1C 2hrs, weighted accumulation of the tared task weights and Obj2 TWN, time required to complete all tasks, obj3 Cmax, difference between the workload of the most accumulated workload and the least accumulated workload of the logistic personnel, obj 4G, are taken as experimental factors and the time period is scheduled with an algorithm: the actual worker scheduling rules, 1 minute/time, 2 minutes/time, 5 minutes/time and 10 minutes/time are used as experimental variables, and the change conditions of four indexes under different factor levels are obtained through simulation operation to develop comparison experimental analysis, so that an experimental optimal solution is obtained.
According to the model application scene and combining the functional characteristics of simulation software, the functions mainly realized by the basic model are divided into the following two main modules: information determination of the order (delivery worker, etc.), delivery processing of the order. According to the basic layout of an investigation hospital and the number proportion of departments and logistics, a basic Plant Simulation model is established, a workbench Pool object is used for simulating a distribution total Station, 2 FootPath objects are used for simulating stairs among floors of the hospital, 9 FootPath objects are used for simulating sidewalks of all floors of the hospital, two source objects, one Station object and five Buffer objects are used for combining and simulating a department of the hospital, and finally, an effect diagram (simulating three floors of the hospital, three departments are arranged on each floor, and four medical staff are shared by the distribution team) of the Simulation model of the hospital is established and obtained as shown in FIG. 3.
The simulation experiment takes a problem target as a research premise, and four indexes are adopted: the number of tasks outstanding in 2 hours, obj 1C 2hrs, weighted accumulation of the tared task weights and Obj2 TWN, time required to complete all tasks, obj3 Cmax, difference between the workload of the most accumulated workload and the least accumulated workload of the logistic personnel, obj 4G, are taken as experimental factors and the time period is scheduled with an algorithm: the actual worker scheduling rules, 1 minute/time, 2 minutes/time, 5 minutes/time and 10 minutes/time are used as experimental variables, and the change conditions of four indexes under different factor levels are obtained through simulation operation to develop comparison experimental analysis, so that an experimental optimal solution is obtained.
As shown in FIG. 4, when the scheduling period is 1 minute, the amount of incomplete tasks is reduced by 22.58% in 2 hours, the accumulation of the weight of the delayed tasks is reduced by 10.92%, the time required for completing all tasks is reduced by 6.7%, and the workload of workers is balanced, so that the scheduling period is the scheduling period of the most fit model target. Therefore, the algorithm parameters are adjusted according to the experimental results.
Through simulation test, the calculation speed of the scheduling algorithm is superior to that of a solver, and the scheduling efficiency is also superior to that of an actual manual scheduling simulation result.
By the scheme of the invention, the problem of dynamic scheduling of medical delivery of the comprehensive hospital can be better solved.
Example 2
An integrated hospital medical delivery dynamic scheduling system comprising:
the data acquisition module is used for acquiring dynamic information of a hospital;
the data processing module is used for calculating and obtaining an optimal scheduling scheme according to the dynamic information of the hospital;
and the data output module is used for outputting the optimal scheduling scheme.
According to the invention, by establishing the scheduling decision model, a multi-objective optimized mathematical model and a solving algorithm of efficient scheduling decision can be performed according to the dynamically-changed distribution task and personnel information, so that scientific and efficient comprehensive medical distribution dynamic scheduling of the hospital is realized, the problem of comprehensive medical distribution scheduling decision can be solved, the task delay is reduced, the distribution time is shortened, and the workload of workers is balanced.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. The utility model provides a comprehensive hospital medical delivery dynamic scheduling method which is characterized by comprising the following steps:
acquiring dynamic information of a hospital;
constructing a scheduling decision model;
and inputting the dynamic information of the hospital into an optimized scheduling decision model, and outputting an optimal scheduling scheme by the scheduling decision model.
2. The method for dynamically scheduling medical delivery of a comprehensive hospital according to claim 1, wherein the dynamic information of the hospital comprises:
current hospital task information: the method comprises the steps of collecting tasks to be completed currently, starting departments of each task, ending departments of each task, earliest delivery time and latest delivery time;
current hospital personnel information: the real-time position of the personnel and the current accumulated workload of each personnel.
3. The method for dynamically scheduling medical delivery of an integrated hospital according to claim 1, wherein the constructing a scheduling decision model comprises:
defining decision variables of a scheduling decision model;
establishing an objective function of a scheduling decision model;
and establishing constraint conditions of a scheduling decision model.
4. A method of dynamic scheduling of medical delivery of an integrated hospital according to claim 3, wherein said decision variables defining a scheduling decision model comprise:
Figure FDA0004095029400000011
representing task a E A t And task b e A t Whether { a } is processed by personnel K E K, K is the number of people in the hospital, A t A hospital task set at the moment t, wherein a is arranged at an adjacent position before the task b;
introducing a virtual task 0 as a front task of a first task and a rear task of a last task;
for any person K e K, the virtual starting task end point f 0 ＝ID k ，ID k The current department of the worker k is indicated, the starting point and the end point of the virtual ending task are the end point of the last non-virtual task, namely, the worker k stays on site for standby after finishing the last task.
5. A method of dynamic scheduling of medical delivery of an integrated hospital according to claim 3, wherein said establishing an objective function of a scheduling decision model comprises:
let r a E {0,1} represents a non-virtual task a E A t Whether it can be completed within 2 hours after execution, the relationship with its completion time is as follows:
Figure FDA0004095029400000012
then, the number of tasks performed over 2 hours is:
Figure FDA0004095029400000013
let z a E {0,1} represents task a E A t Whether or not to be delayed, the relationship between it and the finishing time is as follows:
Figure FDA0004095029400000021
then, the number of deadlines is weighted:
Figure FDA0004095029400000022
W a weight for task urgency->
The maximum time required to complete all tasks is
Figure FDA0004095029400000023
C a For the completion time of task a
Maximum workload difference between persons
Figure FDA0004095029400000024
Wherein TW k Representing the cumulative workload of person K e K
Figure FDA0004095029400000025
CW k For the current workload of worker k, S a For the department of task a starting point, F b For the end point department of task b, P b Processing time after sampling and delivering for task b, +.>
Figure FDA00040950294000000213
Representing the time required from the start department of task a to the end department of task b;
finally, combining the multiple targets into a total target by using the weight coefficient
Figure FDA0004095029400000026
Wherein h= |a t |w max I.e. the number of tasks multiplied by the maximum task weight.
6. A comprehensive hospital medical delivery dynamic scheduling method according to claim 3, wherein the establishment of the constraint condition of the scheduling decision model comprises establishment of a constraint condition lower limit, specifically:
order the
Figure FDA0004095029400000027
The scheduling criteria and scheduling constraints for personnel are uniformly specified by the following constraints:
the very end of each people list has only one non-virtual task:
Figure FDA0004095029400000028
the forefront of each personnel list has only one non-virtual task:
Figure FDA0004095029400000029
each non-virtual task can only be handled by one person:
Figure FDA00040950294000000210
each task can only have one immediately preceding and immediately following task:
Figure FDA00040950294000000211
defining a time window for each task, and obtaining the finishing time of each task through condition constraint, wherein M is a maximum value:
lower bound of task completion time variable:
Figure FDA00040950294000000212
e a for task a earliest start time
B starts after the non-virtual task a is finished:
Figure FDA0004095029400000031
the non-virtual task b is first handled by person k:
Figure FDA0004095029400000032
IT k the earliest idle time of worker k at time t. />
7. A comprehensive hospital medical delivery dynamic scheduling method according to claim 3, wherein the establishment of the constraint condition of the scheduling decision model comprises establishment of a constraint condition upper limit, specifically:
if the previous task of non-virtual task b is non-virtual task a, then there is
Figure FDA0004095029400000033
Defining a new binary variable sigma b It is connected with
Figure FDA0004095029400000034
The relationship of (2) is as follows:
Figure FDA0004095029400000035
Figure FDA0004095029400000036
Figure FDA0004095029400000037
if the previous task of the non-virtual task b is virtual task 0 and is performed by person K e K, then there are:
Figure FDA0004095029400000038
if it is
Figure FDA0004095029400000039
Sigma is then b =1, otherwise σ b =0, i.e.:
Figure FDA00040950294000000310
Figure FDA00040950294000000311
Figure FDA00040950294000000312
r a e {0,1} represents a non-virtual task a E A t Whether it can be completed within 2 hours after execution, the relationship between it and the completion time is as follows:
if the process is completed after two hours, the reinforcement order r a ＝1
Figure FDA00040950294000000313
If the process is completed within two hours, the reinforcement command r a ＝0
Figure FDA00040950294000000314
z a E {0,1} represents task a E A t Whether or not to be delayed, the relationship between it and the finishing time is as follows:
if in a late stage, make z stronger a ＝1
Figure FDA00040950294000000315
If not, force z a ＝0
Figure FDA0004095029400000041
LF a The latest completion time for task a.
8. The method for dynamically scheduling medical delivery of integrated hospitals according to claim 1, wherein after the scheduling decision model is constructed, the method further comprises optimizing the scheduling decision model by using a simulated annealing algorithm, and specifically comprises the following steps:
through the simulated annealing process, iteration is carried out continuously, new solutions are generated each time and compared with the objective function of the current solution;
if the difference is less than 0, accepting the new solution;
if the difference is greater than 0, the acceptance probability is represented by generating a random number r between 0 and 1;
if: e, e -(Δf/T) R accepts new solution, otherwise does not accept new solution;
and finally obtaining a task scheduling scheme of the optimal solution.
9. The method for dynamically scheduling medical delivery of a comprehensive hospital according to claim 1, wherein the step of inputting the dynamic information of the hospital into an optimized scheduling decision model, and the step of outputting an optimal scheduling scheme by the scheduling decision model comprises the steps of:
after the task arrangement is obtained at the time t, the scheduling decision model calculates the number of the tasks in the drags by calculating the difference value between the finishing time of each task and the designated latest finishing time of the task;
calculating the number of tasks with the deadline exceeding two hours, and giving different weight coefficients according to the priority degree of the tasks;
calculating the task amount of each person and the current accumulated workload, and calculating the difference value between the maximum workload and the minimum workload of different persons, so as to finally balance the workload of each person;
the objective function formula is as follows:
Figure FDA0004095029400000042
wherein A is t Representing the total number of tasks, ω, at time t max Representation A t The maximum weight value of the task in (c),
Figure FDA0004095029400000043
indicating the number of tasks with execution time exceeding 2 hours, < >>
Figure FDA0004095029400000044
Representing the weighted number of deadlines, C max Indicating all time, TW, required to complete all tasks k Indicating the workload of the kth person, and alpha and beta correspond to the corresponding weights.
10. A comprehensive hospital medical delivery dynamic scheduling system, comprising:
the data acquisition module is used for acquiring dynamic information of a hospital;
the data processing module is used for calculating and obtaining an optimal scheduling scheme according to the dynamic information of the hospital;
and the data output module is used for outputting the optimal scheduling scheme.
CN202310163488.9A 2023-02-24 2023-02-24 Comprehensive hospital medical distribution dynamic scheduling method Pending CN116092653A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117936055A (en) * 2024-03-25 2024-04-26 四川互慧软件有限公司 Medical project distribution method and system based on workload

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117936055A (en) * 2024-03-25 2024-04-26 四川互慧软件有限公司 Medical project distribution method and system based on workload
CN117936055B (en) * 2024-03-25 2024-05-24 四川互慧软件有限公司 Medical project distribution method and system based on workload

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