CN109686431A - Based on mixing grey wolf-variable neighborhood search algorithm operating room dispatching method and device - Google Patents
Based on mixing grey wolf-variable neighborhood search algorithm operating room dispatching method and device Download PDFInfo
- Publication number
- CN109686431A CN109686431A CN201811623601.2A CN201811623601A CN109686431A CN 109686431 A CN109686431 A CN 109686431A CN 201811623601 A CN201811623601 A CN 201811623601A CN 109686431 A CN109686431 A CN 109686431A
- Authority
- CN
- China
- Prior art keywords
- solution
- operating room
- grey wolf
- day
- doctor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Biomedical Technology (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Public Health (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Epidemiology (AREA)
- Medical Treatment And Welfare Office Work (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a kind of based on mixing grey wolf-variable neighborhood search algorithm operating room dispatching method and device, this method passes through mixing grey wolf-variable neighborhood search algorithm, set algorithm parameter and initial solution is generated at random first, then calculate fitness value and determine three best solutions；Three best solutions are done again and become neighborhood search transformation, improve the quality of solution；Crossover operation is executed to population based on the fitness of solution, by iteration, the continuous renewal to population is realized, finally obtains optimal solution.In the embodiment of the present invention, mixing grey wolf-variable neighborhood search algorithm has preferable convergence rate and convergence as a result, solving the operating room scheduling problem of high complexity, improves the operating room dispatching efficiency of hospital.
Description
[technical field]
The present invention relates to field of computer technology more particularly to it is a kind of based on mixing grey wolf-variable neighborhood search algorithm hand
Art room dispatching method and device.
[background technique]
The efficiency of hospital resources scheduling decides the height of service quality, and operating room scheduling is in hospital resources scheduling
One of most important link.Operating room scheduling need systematically to consider resource constraint, doctor's quantity, clinician's types, number of patients,
The many factors such as duration of performing the operation, are the projects for having much challenge in recent years.Currently, intelligent algorithm have been widely used for it is all kinds of
In operating room scheduling problem, such as genetic algorithm, simulated annealing, tabu search algorithm etc..
In existing scheduling model, usually only consider one day operation plan of distribution, and be given each department can
Patient is allocated under the premise of the operating room quantity used.But under normal conditions, the number of patients of each department is that dynamic becomes
Change, in addition distribution of the operating room quantity to department, distribution of the doctor to the distribution and patient of operating room to operating room, this
A little factors can all have an impact the solution of operating room scheduling problem.
[summary of the invention]
In view of this, the embodiment of the invention provides a kind of based on mixing grey wolf-variable neighborhood search algorithm operating room tune
Spend method, apparatus, readable medium and electronic equipment.
In a first aspect, a kind of based on mixing grey wolf-variable neighborhood search algorithm operating room dispatching method, comprising:
Step 1, input operating room quantity, the capacity of each operating room, doctor's quantity of each type of surgery, each doctor
Number of patients and it is expected that operation duration, setting mixing grey wolf-variable neighborhood search algorithm parameter, including maximum number of iterations max_
Iter, neighborhood search number limit ρ, grey wolf quantity popsize, the number of iterations t=1；
Step 2, initialization population consider shared popsize grey wolf, and the position of every grey wolf is defined as Q=1,2 ..., popsize,Q-th of grey wolf is respectively indicated
Position in 3s-2,3s-1,3s dimension, at the same also indicate that s-th of doctor distribution operation day be
Step 3 calculates the fitness value that solution concentrates every grey wolf；
Step 4 is ranked up according to the superiority and inferiority of solution, finds out three best solution Xα, Xβ, Xδ, and record optimal solution XαIt is suitable
Answer angle value；
Step 5, to Xα, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, updates optimal solution；
Step 6, undated parameter and coefficient vector；
Step 7, the position for updating current every grey wolf；
Step 8 calculates the fitness value that solution concentrates every grey wolf, is ranked up according to the superiority and inferiority of solution, finds out best three
A solution Xα, Xβ, Xδ, and record optimal solution XαFitness value；
Step 9 enables t=t+1, judges whether t≤max_iter is true, if so, otherwise return step 5 terminates algorithm
And globally optimal solution is exported, export distribution of the optimal type of surgery to room, distribution of the doctor to operating room and operation day, disease
Operation sequence of the people to the distribution of operating room and operation day and the patient of operating room.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation calculates in step 3
Solution concentrates the fitness value of every grey wolf, specifically includes:
It is step 31, rightS-th of doctor s is assigned to operation dayOn, in each operating room day, all patients are sorted according to the non-increasing of operation risk coefficient, to obtain each hand
Patient's waiting list of art room day；
Step 32, in each operating room day, in patient's waiting list of the operating room day successively by unappropriated patient
It is assigned to operating room day, until the operating room day, the operation duration can be accommodated, distributes whole operating room days；
Step 33 sorts remaining all unappropriated patients according to the non-increasing of operation risk coefficient, to be updated
Waiting list afterwards；
Step 34, the time of last operation end time and operating room normal closing hours of calculating each operation day
Difference
Step 35, for each patient k, obtain its time difference for corresponding to 3 of doctor operation days Select optimal operation day distribution patient k；
Step 36 sorts all patients distributed in each operation day by the non-increasing of operation risk coefficient；
The sum of step 37, the waiting hospitalization cost for calculating whole patients and overtime cost of whole operating rooms, as adapt to
Angle value.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, it is right in step 5
Xα, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, optimal solution is updated, specifically includes:
Step 51, setting iter=0；
Step 52, for solve X, select neighbour structure N1Neighborhood variation is done, it is each random from type identical two operation days
It selects two doctors to exchange, obtains new explanation X '；
If fitness of the fitness of step 53, new explanation X ' due to solving X, uses new explanation X ' substitution solution X；
Step 54 selects neighbour structure N2Neighborhood variation is done, random search, which obtains, exchanges two different operation days, and (neighborhood is searched
Rope number is limited to ρ, and search is less than then stopping), obtain new explanation X "；
Step 55, the fitness of new explanation X " are more excellent, then with the original solution of new explanation X " substitution；
Step 56, to solution Xα, Xβ, XδDistribution executes step 52- step 55；
Step 57 enables iter=iter+1, judges whether iter≤max_iter is true, if so, return step 2, otherwise,
Export updated optimal solution.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation updates in step 7
The position of current every grey wolf, specifically includes:
Individual number variable q=1 is arranged in step 71, and the solution of grey wolf position is expressed as
Step 72, setting dimension variable j=1；
Step 73, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 74, the random two decimal r generated between (0,1)1And r2, calculate A2=2 × a × r1- a, C2=2 × r2,
Step 75, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 76 calculates
Step 77 enables j=j+1, judges whether j≤dim is true, if so, otherwise then return step 73 execute step
78；
Step 78 enables q=q+1, judges whether q≤popsize is true, if so, then return step 72, otherwise, output
Update result.
Second aspect, the embodiment of the invention provides a kind of based on mixing grey wolf-variable neighborhood search algorithm operating room tune
Spend device, comprising:
Computing module, for executing:
Step 1, input operating room quantity, the capacity of each operating room, doctor's quantity of each type of surgery, each doctor
Number of patients, operation risk coefficient and it is expected that operation duration, setting mixing grey wolf-variable neighborhood search algorithm parameter, including most
Big the number of iterations max_iter, neighborhood search number limit ρ, grey wolf quantity popsize, the number of iterations t=1；
Step 2, initialization population consider shared popsize grey wolf, and the position of every grey wolf is defined as Q=1,2 ..., popsize,Q-th of grey wolf is respectively indicated
Position in 3s-2,3s-1,3s dimension shows that the operation day of s-th of doctor distribution is
Step 3 calculates the fitness value that solution concentrates every grey wolf；
Step 4 is ranked up according to the superiority and inferiority of solution, finds out three best solution Xα, Xβ, Xδ, and record optimal solution XαIt is suitable
Answer angle value；
Step 5, to Xα, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, updates optimal solution；
Step 6, undated parameter and coefficient vector；
Step 7, the position for updating current every grey wolf；
Step 8 calculates the fitness value that solution concentrates every grey wolf, is ranked up according to the superiority and inferiority of solution, finds out best three
A solution Xα, Xβ, Xδ, and record optimal solution XαFitness value；
Output module, for executing:
Step 9 enables t=t+1, judges whether t≤max_iter is true, if so, otherwise return step 5 terminates algorithm
And globally optimal solution is exported, export distribution of the optimal type of surgery to room, distribution of the doctor to operating room and operation day, disease
Operation sequence of the people to the distribution of operating room and operation day and the patient of operating room.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the calculating mould
Block, to the fitness value for solving every grey wolf of concentration is calculated, specifically includes in executing step 3:
It is step 31, rightS-th of doctor s is assigned to operation dayOn, in each operating room, all patients are sorted according to the non-increasing of operation risk coefficient, to obtain each operation
The waiting list of day；
In each operation day, unappropriated patient is sequentially allocated to can accommodate the operation in waiting list for step 32
The operation day of duration, until distributing all operation days；
Step 33 sorts remaining all unappropriated patients according to the non-increasing of operation risk coefficient, to be updated
Waiting list afterwards；
Step 34, the time of last operation end time and operating room normal closing hours of calculating each operation day
Difference
Step 35, for each patient k, obtain its time difference for corresponding to 3 of doctor operation days Select optimal operation day distribution patient k；
Step 36 sorts all patients distributed in each operation day by the non-increasing of operation risk coefficient；
The sum of step 37, the waiting hospitalization cost for calculating whole patients and overtime cost of whole operating rooms, as adapt to
Angle value.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the calculating mould
Block is in executing step 5 to Xα, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, optimal solution is updated, specifically includes:
Step 51, setting iter=0；
Step 52, for solve X, select neighbour structure N1Neighborhood variation is done, it is each random from type identical two operation days
It selects two doctors to exchange, obtains new explanation X '；
If fitness of the fitness of step 53, new explanation X ' due to solving X, uses new explanation X ' substitution solution X；
Step 54 selects neighbour structure N2Do neighborhood variation: random search two different operating room days exchange, and (neighborhood is searched
Rope number is limited to ρ, and search is less than then stopping), obtain new explanation X "；
If the fitness of step 55, new explanation X " is more excellent, with the original solution of new explanation X " substitution；
Step 56, to solution Xα, Xβ, XδDistribution executes step 52- step 55；
Step 57 enables iter=iter+1, judges whether iter≤max_iter is true, if so, return step 2, otherwise,
Export updated optimal solution.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the calculating mould
Block updates the position of current every grey wolf in executing step 7, specifically includes:
Individual number variable q=1 is arranged in step 71, and the solution of grey wolf position is expressed as
Step 72, setting dimension variable j=1；
Step 73, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 74, the random two decimal r generated between (0,1)1And r2, calculate A2=2 × a × r1- a, C2=2 × r2,
Step 75, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 76 calculates
Step 77 enables j=j+1, judges whether j≤dim is true, if so, otherwise then return step 73 execute step
78；
Step 78 enables q=q+1, judges whether q≤popsize is true, if so, then return step 72, otherwise, output
Update result.
The third aspect, the present invention provides a kind of readable mediums, including execute instruction, when the processor of electronic equipment executes
Described when executing instruction, the electronic equipment executes the method as described in any in first aspect.
Fourth aspect, the present invention provides a kind of electronic equipment, comprising: processor, memory and bus；
The memory is executed instruction for storing, and the processor is connect with the memory by the bus, when
When the electronic equipment is run, the processor executes the described of memory storage and executes instruction, so that the processor
Execute the method as described in any in first aspect.
A technical solution in above-mentioned technical proposal has the following beneficial effects:
In the method for the embodiment of the present invention, for operating room scheduling problem, by mixing grey wolf-variable neighborhood search algorithm,
Set algorithm parameter and initial solution is generated at random first, then calculate fitness value and determine three best solutions；Again to best
Three solutions do become neighborhood search transformation, improve the quality of solution；Crossover operation is executed to population based on the fitness of solution, passes through weight
Multiple iteration, realizes the continuous renewal to population, finally obtains optimal solution.Mixing grey wolf-variable neighborhood search algorithm has preferably
Convergence rate and convergence improve the operating room dispatching efficiency of hospital as a result, solve the operating room scheduling problem of high complexity.
[Detailed description of the invention]
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this field
For those of ordinary skill, without any creative labor, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is surgical scheduler problem schematic diagram provided by the embodiment of the present invention；
Fig. 2 is a kind of based on mixing grey wolf-variable neighborhood search algorithm operating room scheduling provided by the embodiment of the present invention
The flow diagram of method；
Fig. 3 is a kind of based on mixing grey wolf-variable neighborhood search algorithm operating room scheduling provided by the embodiment of the present invention
The functional block diagram of device；
Fig. 4 is the structural schematic diagram of a kind of electronic equipment provided in an embodiment of the present invention.
[specific embodiment]
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment and accordingly
Technical solution of the present invention is clearly and completely described in attached drawing.Obviously, described embodiment is only a part of the invention
Embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making wound
Every other embodiment obtained under the premise of the property made labour, shall fall within the protection scope of the present invention.
For ease of understanding, the technical issues of solved to the embodiment of the present invention below with reference to Fig. 1, is described in detail.
As described in Figure 1, operating room scheduling problem, target are to minimize patient's waiting cost and operating room overtime cost.It should
Problem is described as follows: given R operating room, S doctor, and I patient needs to carry out surgical scheduler within one week time.Each
Patient i has the operation risk coefficient w assessed by attending physiciani, estimated operation duration pi.The general open duration of operating room
For U.
Problem is assumed as follows:
1) surgical procedure does not allow to interrupt, and midway does not allow patient to exit or be added；
2) patient has been admitted to hospital in scheduling decision day and has been ready for preoperative planning.Scheduling decision is usually by protecting
Scholar grows the previous day in dispatching cycle, i.e. Sunday completes；
3) time that opens of each operating room is identical；
4) operation of all patients will be completed in one-week schedule time horizon；
5) doctor of each patient has determined；
6) each doctor has weekly three days operating room times.
Based on this, the embodiment of the invention provides a kind of based on mixing grey wolf-variable neighborhood search algorithm operating room scheduling
Method, as shown in Fig. 2, method includes the following steps:
Step 1, input operating room quantity, the capacity of each operating room, doctor's quantity of each type of surgery, each doctor
Number of patients and it is expected that operation duration, setting mixing grey wolf-variable neighborhood search algorithm parameter, including maximum number of iterations max_
Iter, neighborhood search limit ρ, grey wolf quantity popsize, the number of iterations t=1；
Step 2, initialization population consider shared popsize grey wolf, and the position of every grey wolf is defined as Q=1,2 ..., popsize,Q-th of grey wolf is respectively indicated
Position in 3s-2,3s-1,3s dimension shows that the operation day of s-th of doctor distribution is
Step 3 calculates the fitness value that solution concentrates every grey wolf；
Step 4 is ranked up according to the superiority and inferiority of solution, finds out three best solution Xα, Xβ, Xδ, and record optimal solution XαIt is suitable
Answer angle value；
Step 5, to Xα, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, updates optimal solution；
Step 6, undated parameter and coefficient vector；
Step 7, the position for updating current every grey wolf；
Step 8 calculates the fitness value that solution concentrates every grey wolf, is ranked up according to the superiority and inferiority of solution, finds out best three
A solution Xα, Xβ, Xδ, and record optimal solution XαFitness value；
Step 9 enables t=t+1, judges whether t≤max_iter is true, if so, otherwise return step 5 terminates algorithm
And globally optimal solution is exported, export distribution of the optimal type of surgery to room, distribution of the doctor to operating room and operation day, disease
Operation sequence of the people to the distribution of operating room and operation day and the patient of operating room.
When it is implemented, calculating the fitness value of every grey wolf in disaggregation in step 3, specifically include:
It is step 31, rightS-th of doctor s is assigned to operation dayOn, in each operating room, all patients are sorted according to the non-increasing of operation risk coefficient, to obtain each operation
The waiting list of day；
In each operation day, unappropriated patient is sequentially allocated to can accommodate the operation in waiting list for step 32
The operation day of duration, until distributing all operation days；
Step 33 sorts remaining all unappropriated patients according to the non-increasing of operation risk coefficient, to be updated
Waiting list afterwards；
Step 34, the time of last operation end time and operating room normal closing hours of calculating each operation day
Difference
Step 35, for each patient k, obtain its time difference for corresponding to 3 of doctor operation days Select optimal operation day distribution patient k；
Step 36 sorts all patients distributed in each operation day by the non-increasing of operation risk coefficient；
The sum of step 37, the waiting hospitalization cost for calculating whole patients and overtime cost of whole operating rooms, as adapt to
Angle value.
When it is implemented, the assigning process in step 32 specifically includes:
It performs the operation in day at each, patient is sequentially allocated in the waiting list of operation day to this and is performed the operation day, until
The operation total duration of allocated patient adds next patient to perform the operation duration just greater than the generally open duration of operating room, and from hand
Allocated patient is deleted in art day waiting list.From first operating room of Monday to a last operating room, reallocate week
Two first operating room is sequentially allocated to a last operating room until the last one operating room of Friday, until having arranged
All operation days.
In step 35, for two operation days (OR of same a doctor sx, t) and (ORy, t+SD), wherein SD > 0, classification is begged for
By as follows:
Patient k:(is distributed wherein to select optimal operating room day as follows, and τ indicates that operating room is worked overtime per hour
The quotient of expense and the daily hospitalization cost of patient.)
If 1)WhenThen patient k distribution is in one's hands
Art day (ORx, t) and it is best scheduling scheme, otherwise, patient k is assigned to operation day (ORy, t+SD) and it is best scheduling scheme；
If 2)Then patient k is assigned to operation day (ORx, t) and it is best scheduling
Scheme, otherwise, patient k are assigned to operation day (ORy, t+SD) and it is best scheduling scheme；
If 3)Then patient k is assigned to operation day (ORy, t+SD) and it is best tune
Degree scheme, otherwise, patient k are assigned to operation day (ORx, t) and it is best scheduling scheme；
If 4)WhenThen patient k is assigned to operation day
(ORx, t) and it is best scheduling scheme, otherwise, patient k is assigned to operation day (ORy, t+SD) and it is best scheduling scheme；
If 5)WhenThen patient k is assigned to operation day
(ORx, t) and it is best scheduling scheme, otherwise, patient k is assigned to operation day (ORy, t+SD) and it is best scheduling scheme；
If 6)AndThen patient k is assigned to operation day (ORx, t) and it is best dispatching party
Case, otherwise, patient k are assigned to operation day (ORy, t+SD) and it is best scheduling scheme.
In step 37, fitness value=waiting hospitalization cost+overtime cost, wait hospitalization cost be operation wait number of days and
The product of daily hospitalization cost, overtime cost product of overtime cost for overtime hours number and per hour.
When it is implemented, to X in step 5α, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, is updated optimal
Solution, specifically includes:
Step 51, setting iter=0；
Step 52, for solve X, select neighbour structure N1Neighborhood variation is done, it is each random from type identical two operation days
It selects two doctors to exchange, obtains new explanation X '；
If fitness of the fitness of step 53, new explanation X ' due to solving X, uses new explanation X ' substitution solution X；
Step 54 selects neighbour structure N2Neighborhood variation is done, random search, which obtains, exchanges two different operation days, and (neighborhood is searched
Rope number is limited to ρ, and search is less than then stopping), obtain new explanation X "；
Step 55, the fitness of new explanation X " are more excellent, then with the original solution of new explanation X " substitution；
Step 56, to solution Xα, Xβ, XδDistribution executes step 52- step 55；
Step 57 enables iter=iter+1, judges whether iter≤max_iter is true, if so, return step 2, otherwise,
Export updated optimal solution.
When it is implemented, undated parameter in step 6Coefficient vector WithFor the random number between (0,1).
When it is implemented, updating the position of current every grey wolf in step 7, specifically include:
Individual number variable q=1 is arranged in step 71, and the solution of grey wolf position is expressed as
Step 72, setting dimension variable j=1；
Step 73, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 74, the random two decimal r generated between (0,1)1And r2, calculate A2=2 × a × r1- a, C2=2 × r2,
Step 75, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 76 calculates
Step 77 enables j=j+1, judges whether j≤dim is true, if so, otherwise then return step 73 execute step
78；
Step 78 enables q=q+1, judges whether q≤popsize is true, if so, then return step 72, otherwise, output
Update result.
Technical solution provided in an embodiment of the present invention has the advantages that
The embodiment of the present invention optimizes-becomes neighborhood processing by mixing grey wolf, first by operating room day in a manner of encoding, point
Then each doctor of dispensing proposes according to the property of problem for patient's distribution of each operating room day and ordering strategy, more
The in a few days optimal patient's distribution sort scheme of each operating room is obtained in the item formula time；Neighborhood search is executed to every grey wolf position
Operation；Based on the fitness of grey wolf position, selectively carries out the systematization based on grey wolf Optimizing Search and update；By repeating to change
In generation, realizes the continuous renewal to population, finally acquires optimal solution.Mixed grey wolf-variable neighborhood search algorithm has preferable
Convergence rate and convergence improve the operating room dispatching efficiency of hospital as a result, solve the operating room scheduling problem of high complexity.
The embodiment of the present invention can complete distribution of the operating room day to the distribution, doctor of type of surgery to operating room day, disease
Distribution and sequence of the people to operating room day, improve the utilization efficiency of operating room in actual complex environment, mention for operating room scheduling
Effective decision support is supplied.
The embodiment of the present invention has proposed aiming at the problem that traditional grey wolf optimization algorithm is easily trapped into local optimum for every
Patient's distribution of a operation day and ordering strategy, and the search capability for improving grey wolf is guaranteed to hunt by variable neighborhood search algorithm
The efficiency of object location not only has the diversity for optimizing convergence speed of the algorithm reconciliation, also strengthens algorithm to a certain extent
The local convergence ability in latter stage.
Based on identical design, the embodiment of the present invention, which further provides, realizes each step and method in above method embodiment
Installation practice.
Referring to FIG. 3, it is a kind of based on mixing grey wolf-variable neighborhood search algorithm hand provided by the embodiment of the present invention
Art room dispatching device, as shown, the device includes:
Computing module 310, for executing:
Step 1, input operating room quantity, the capacity of each operating room, doctor's quantity of each type of surgery, each doctor
Number of patients and it is expected that operation duration, setting mixing grey wolf-variable neighborhood search algorithm parameter, including maximum number of iterations max_
Iter, neighborhood search number limit ρ, grey wolf quantity popsize, the number of iterations t=1；
Step 2, initialization population consider shared popsize grey wolf, and the position of every grey wolf is defined as Q=1,2 ..., popsize,Q-th of grey wolf is respectively indicated
Position in 3s-2,3s-1,3s dimension shows that the operation day of s-th of doctor distribution is
Step 3 calculates the fitness value that solution concentrates every grey wolf；
Step 4 is ranked up according to the superiority and inferiority of solution, finds out three best solution Xα, Xβ, Xδ, and record optimal solution XαIt is suitable
Answer angle value；
Step 5, to Xα, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, updates optimal solution；
Step 6, undated parameter and coefficient vector；
Step 7, the position for updating current every grey wolf；
Step 8 calculates the fitness value that solution concentrates every grey wolf, is ranked up according to the superiority and inferiority of solution, finds out best three
A solution Xα, Xβ, Xδ, and record optimal solution XαFitness value；
Output module 320, for executing:
Step 9 enables t=t+1, judges whether t≤max_iter is true, if so, otherwise return step 5 terminates algorithm
And globally optimal solution is exported, export distribution of the optimal type of surgery to room, distribution of the doctor to operating room and operation day, disease
Operation sequence of the people to the distribution of operating room and operation day and the patient of operating room.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the calculating mould
Block 310, to the fitness value for solving every grey wolf of concentration is calculated, specifically includes in executing step 3:
It is step 31, rightS-th of doctor s is assigned to operation dayOn, in each operating room, all patients are sorted according to the non-increasing of operation risk coefficient, to obtain each operation
The waiting list of day；
In each operation day, unappropriated patient is sequentially allocated to can accommodate the operation in waiting list for step 32
The operation day of duration, until distributing all operation days；
Step 33 sorts remaining all unappropriated patients according to the non-increasing of operation risk coefficient, to be updated
Waiting list afterwards；
Step 34, the time of last operation end time and operating room normal closing hours of calculating each operation day
Difference
Step 35, for each patient k, obtain its time difference for corresponding to 3 of doctor operation days Select optimal operation day distribution patient k；
Step 36 sorts all patients distributed in each operation day by the non-increasing of operation risk coefficient；
The sum of step 37, the waiting hospitalization cost for calculating whole patients and overtime cost of whole operating rooms, as adapt to
Angle value.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the calculating mould
Block 310 is in executing step 5 to Xα, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, updates optimal solution, it is specific to wrap
It includes:
Step 51, setting iter=0；
Step 52, for solve X, select neighbour structure N1Neighborhood variation is done, it is each random from type identical two operation days
It selects two doctors to exchange, obtains new explanation X '；
If fitness of the fitness of step 53, new explanation X ' due to solving X, uses new explanation X ' substitution solution X；
Step 54 selects neighbour structure N2Neighborhood variation is done, random search, which obtains, exchanges two different operation days, and (neighborhood is searched
Rope number is limited to ρ, and search is less than then stopping), obtain new explanation X "；
Step 55, the fitness of new explanation X " are more excellent, then with the original solution of new explanation X " substitution；
Step 56, to solution Xα, Xβ, XδDistribution executes step 52- step 55；
Step 57 enables iter=iter+1, judges whether iter≤max_iter is true, if so, return step 2, otherwise,
Export updated optimal solution.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the calculating mould
Block 310 updates the position of current every grey wolf in executing step 7, specifically includes:
Individual number variable q=1 is arranged in step 71, and the solution of grey wolf position is expressed as
Step 72, setting dimension variable j=1；
Step 73, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 74, the random two decimal r generated between (0,1)1And r2, calculate A2=2 × a × r1- a, C2=2 × r2,
Step 75, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 76 calculates
Step 77 enables j=j+1, judges whether j≤dim is true, if so, otherwise then return step 73 execute step
78；
Step 78 enables q=q+1, judges whether q≤popsize is true, if so, then return step 72, otherwise, output
Update result.
Method shown in Fig. 2 is able to carry out by each unit module in this present embodiment, what the present embodiment was not described in detail
Part can refer to the related description to Fig. 2.
Fig. 4 is the structural schematic diagram of one embodiment of the present of invention electronic equipment.Referring to FIG. 4, in hardware view, the electricity
Sub- equipment includes processor, optionally further comprising internal bus, network interface, memory.Wherein, memory may be comprising interior
It deposits, such as high-speed random access memory (Random-Access Memory, RAM), it is also possible to further include non-volatile memories
Device (non-volatile memory), for example, at least 1 magnetic disk storage etc..Certainly, which is also possible that other
Hardware required for business.
Processor, network interface and memory can be connected with each other by internal bus, which can be ISA
(Industry Standard Architecture, industry standard architecture) bus, PCI (Peripheral
Component Interconnect, Peripheral Component Interconnect standard) bus or EISA (Extended Industry Standard
Architecture, expanding the industrial standard structure) bus etc..The bus can be divided into address bus, data/address bus, control always
Line etc..Only to be indicated with a four-headed arrow in Fig. 4, it is not intended that an only bus or a type of convenient for indicating
Bus.
Memory, for storing program.Specifically, program may include program code, and said program code includes calculating
Machine operational order.Memory may include memory and nonvolatile memory, and provide instruction and data to processor.
In a kind of mode in the cards, processor read from nonvolatile memory corresponding computer program to
It is then run in memory, corresponding computer program can also be obtained, from other equipment to form operating room on logic level
Dispatching device.Processor executes the program that memory is stored, to be realized in any embodiment of the present invention by the program executed
The operating room dispatching method of offer.
The embodiment of the present invention also proposed a kind of computer readable storage medium, the computer-readable recording medium storage one
A or multiple programs, the one or more program include instruction, which holds when by the electronic equipment including multiple application programs
When row, the electronic equipment can be made to execute the operating room dispatching method provided in any embodiment of the present invention.
It is above-mentioned as embodiment illustrated in fig. 3 of the present invention provide based on mix grey wolf-variable neighborhood search algorithm operating room tune
The method that degree device executes can be applied in processor, or be realized by processor.Processor may be a kind of integrated circuit
Chip, the processing capacity with signal.During realization, each step of the above method can pass through the hardware in processor
The instruction of integrated logic circuit or software form is completed.Above-mentioned processor can be general processor, including central processing
Device (Central Processing Unit, CPU), network processing unit (Network Processor, NP) etc.；It can also be several
Word signal processor (Digital Signal Processor, DSP), specific integrated circuit (Application Specific
Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array,
FPGA) either other programmable logic device, discrete gate or transistor logic, discrete hardware components.May be implemented or
Person executes disclosed each method, step and the logic diagram in the embodiment of the present invention.General processor can be microprocessor or
Person's processor is also possible to any conventional processor etc..
The step of method in conjunction with disclosed in the embodiment of the present invention, can be embodied directly in hardware decoding processor and execute
At, or in decoding processor hardware and software module combination execute completion.Software module can be located at random access memory,
This fields such as flash memory, read-only memory, programmable read only memory or electrically erasable programmable memory, register maturation
In storage medium.The storage medium is located at memory, and processor reads the information in memory, completes above-mentioned side in conjunction with its hardware
The step of method.
The embodiment of the present invention also proposed a kind of computer readable storage medium, the computer-readable recording medium storage one
A or multiple programs, the one or more program include instruction, which holds when by the electronic equipment including multiple application programs
When row, the electronic equipment can be made to execute the operating room dispatching method provided in any embodiment of the present invention.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity,
Or it is realized by the product with certain function.It is a kind of typically to realize that equipment is computer.Specifically, computer for example may be used
Think personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media play
It is any in device, navigation equipment, electronic mail equipment, game console, tablet computer, wearable device or these equipment
The combination of equipment.
For convenience of description, it describes to be divided into various units when apparatus above with function or module describes respectively.Certainly, exist
Implement to realize the function of each unit or module in the same or multiple software and or hardware when the present invention.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the present invention, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
In a typical configuration, calculating equipment includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology come realize information store.Information can be computer readable instructions, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage or other magnetic storage devices
Or any other non-transmission medium, can be used for storage can be accessed by a computing device information.As defined in this article, it calculates
Machine readable medium does not include temporary computer readable media (transitory media), such as the data-signal and carrier wave of modulation.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
It include so that the process, method, commodity or the equipment that include a series of elements not only include those elements, but also to wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including described want
There is also other identical elements in the process, method of element, commodity or equipment.
It will be understood by those skilled in the art that the embodiment of the present invention can provide as method, system or computer program product.
Therefore, complete hardware embodiment, complete software embodiment or embodiment combining software and hardware aspects can be used in the present invention
Form.It is deposited moreover, the present invention can be used to can be used in the computer that one or more wherein includes computer usable program code
The shape for the computer program product implemented on storage media (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
Formula.
The present invention can describe in the general context of computer-executable instructions executed by a computer, such as program
Module.Generally, program module includes routines performing specific tasks or implementing specific abstract data types, programs, objects, group
Part, data structure etc..The present invention can also be practiced in a distributed computing environment, in these distributed computing environments, by
Task is executed by the connected remote processing devices of communication network.In a distributed computing environment, program module can be with
In the local and remote computer storage media including storage equipment.
Various embodiments are described in a progressive manner in the present invention, same and similar part between each embodiment
It may refer to each other, each embodiment focuses on the differences from other embodiments.Implement especially for system
For example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring to embodiment of the method
Part illustrates.
The above description is only an embodiment of the present invention, is not intended to restrict the invention.For those skilled in the art
For, the invention may be variously modified and varied.All any modifications made within the spirit and principles of the present invention are equal
Replacement, improvement etc., should be included within scope of the presently claimed invention.
Claims (10)
1. a kind of based on mixing grey wolf-variable neighborhood search algorithm operating room dispatching method, which is characterized in that the method packet
It includes:
Step 1, input operating room quantity, the capacity of each operating room, doctor's quantity of each type of surgery, the disease of each doctor
People's quantity and it is expected that operation duration, setting mixing grey wolf-variable neighborhood search algorithm parameter, including maximum number of iterations max_
Iter, neighborhood search limit ρ, grey wolf quantity popsize, the number of iterations t=1；
Step 2, initialization population consider shared popsize grey wolf, and the position of every grey wolf is defined as Q-th of grey wolf is respectively indicated to exist
Position in 3s-2,3s-1,3s dimension shows that the operation day of s-th of doctor distribution is
Step 3 calculates the fitness value that solution concentrates every grey wolf；
Step 4 is ranked up according to the superiority and inferiority of solution, finds out three best solution Xα, Xβ, Xδ, and record optimal solution XαFitness
Value；
Step 5, to Xα, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, updates optimal solution；
Step 6, undated parameter and coefficient vector；
Step 7, the position for updating current every grey wolf；
Step 8 calculates the fitness value that solution concentrates every grey wolf, is ranked up according to the superiority and inferiority of solution, finds out three best solutions
Xα, Xβ, Xδ, and record optimal solution XαFitness value；
Step 9 enables t=t+1, judges whether t≤max_iter is true, if so, return step 5, otherwise, end algorithm are simultaneously defeated
Globally optimal solution out exports distribution of the optimal type of surgery to room, distribution of the doctor to operating room and operation day, patient couple
The operation sequence of the patient of the distribution and operating room of operating room and operation day.
2. the method according to claim 1, wherein in step 3 calculate disaggregation in every grey wolf fitness value,
It specifically includes:
It is step 31, rightS-th of doctor s is assigned to operation dayOn, in each operating room, all patients are sorted according to the non-increasing of operation risk coefficient, to obtain each operation
The waiting list of day；
In each operation day, unappropriated patient is sequentially allocated to can accommodate the operation duration in waiting list for step 32
Operation day, until distribute all operation days；
Step 33 sorts remaining all unappropriated patients according to the non-increasing of operation risk coefficient, to obtain updated
Waiting list；
Step 34, the time difference of last operation end time and operating room normal closing hours of calculating each operation day
Step 35, for each patient k, obtain its time difference for corresponding to 3 of doctor operation days Select optimal operation day distribution patient k；
Step 36 sorts all patients distributed in each operation day by the non-increasing of operation risk coefficient；
The sum of step 37, the waiting hospitalization cost for calculating whole patients and overtime cost of whole operating rooms, as fitness value.
3. the method according to claim 1, wherein to X in step 5α, Xβ, XδChange neighborhood search is done, if generating
New explanation it is more excellent, then update optimal solution, specifically include:
Step 51, setting iter=0；
Step 52, for solve X, select neighbour structure N1Neighborhood variation is done, respectively randomly chooses two from type identical two operation days
A doctor exchanges, and obtains new explanation X '；
If fitness of the fitness of step 53, new explanation X ' due to solving X, uses new explanation X ' substitution solution X；
Step 54 selects neighbour structure N2Neighborhood variation is done, random search, which obtains, exchanges two different operation days, (neighborhood search number
It is limited to ρ, search is less than then stopping), obtain new explanation X "；
Step 55, the fitness of new explanation X " are more excellent, then with the original solution of new explanation X " substitution；
Step 56, to solution Xα, Xβ, XδDistribution executes step 52- step 55；
Step 57 enables iter=iter+1, judges whether iter≤max_iter is true, if so, return step 2, otherwise, output
Updated optimal solution.
4. specifically being wrapped the method according to claim 1, wherein updating the position of current every grey wolf in step 7
It includes:
Individual number variable q=1 is arranged in step 71, and the solution of grey wolf position is expressed as
Step 72, setting dimension variable j=1；
Step 73, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 74, the random two decimal r generated between (0,1)1And r2, calculate A2=2 × a × r1- a, C2=2 × r2,
Step 75, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 76 calculates
Step 77 enables j=j+1, judges whether j≤dim is true, if so, otherwise then return step 73 execute step 78；
Step 78 enables q=q+1, judges whether q≤popsize is true, if so, then return step 72, otherwise, output update
As a result.
5. a kind of based on mixing grey wolf-variable neighborhood search algorithm operating room dispatching device, which is characterized in that described device packet
It includes:
Computing module, for executing:
Step 1, input operating room quantity, the capacity of each operating room, doctor's quantity of each type of surgery, the disease of each doctor
People's quantity and it is expected that operation duration, setting mixing grey wolf-variable neighborhood search algorithm parameter, including maximum number of iterations max_
Iter, neighborhood search limit ρ, grey wolf quantity popsize, the number of iterations t=1；
Step 2, initialization population consider shared popsize grey wolf, and the position of every grey wolf is defined as Q-th of grey wolf is respectively indicated to exist
Position in 3s-2,3s-1,3s dimension shows that the operation day of s-th of doctor distribution is
Step 3 calculates the fitness value that solution concentrates every grey wolf；
Step 4 is ranked up according to the superiority and inferiority of solution, finds out three best solution Xα, Xβ, Xδ, and record optimal solution XαFitness
Value；
Step 5, to Xα, Xβ, XδChange neighborhood search is done, if the new explanation generated is more excellent, updates optimal solution；
Step 6, undated parameter and coefficient vector；
Step 7, the position for updating current every grey wolf；
Step 8 calculates the fitness value that solution concentrates every grey wolf, is ranked up according to the superiority and inferiority of solution, finds out three best solutions
Xα, Xβ, Xδ, and record optimal solution XαFitness value；
Output module, for executing:
Step 9 enables t=t+1, judges whether t≤max_iter is true, if so, return step 5, otherwise, end algorithm are simultaneously defeated
Globally optimal solution out exports distribution of the optimal type of surgery to room, distribution of the doctor to operating room and operation day, patient couple
The operation sequence of the patient of the distribution and operating room of operating room and operation day.
6. device according to claim 5, which is characterized in that the computing module is in executing step 3 to calculating disaggregation
In every grey wolf fitness value, specifically include:
It is step 31, rightS-th of doctor s is assigned to operation dayOn, in each operating room, all patients are sorted according to the non-increasing of operation risk coefficient, to obtain each operation
The waiting list of day；
In each operation day, unappropriated patient is sequentially allocated to can accommodate the operation duration in waiting list for step 32
Operation day, until distribute all operation days；
Step 33 sorts remaining all unappropriated patients according to the non-increasing of operation risk coefficient, to obtain updated
Waiting list；
Step 34, the time difference of last operation end time and operating room normal closing hours of calculating each operation day
Step 35, for each patient k, obtain its time difference for corresponding to 3 of doctor operation days Select optimal operation day distribution patient k；
Step 36 sorts all patients distributed in each operation day by the non-increasing of operation risk coefficient；
The sum of step 37, the waiting hospitalization cost for calculating whole patients and overtime cost of whole operating rooms, as fitness value.
7. device according to claim 5, which is characterized in that the computing module is in executing step 5 to Xα, Xβ, XδIt does
Become neighborhood search, if the new explanation generated is more excellent, updates optimal solution, specifically include:
Step 51, setting iter=0；
Step 52, for solve X, select neighbour structure N1Neighborhood variation is done, respectively randomly chooses two from type identical two operation days
A doctor exchanges, and obtains new explanation X '；
If fitness of the fitness of step 53, new explanation X ' due to solving X, uses new explanation X ' substitution solution X；
Step 54 selects neighbour structure N2Neighborhood variation is done, random search, which obtains, exchanges two different operation days, (neighborhood search number
It is limited to ρ, search is less than then stopping), obtain new explanation X "；
Step 55, the fitness of new explanation X " are more excellent, then with the original solution of new explanation X " substitution；
Step 56, to solution Xα, Xβ, XδDistribution executes step 52- step 55；
Step 57 enables iter=iter+1, judges whether iter≤max_iter is true, if so, return step 2, otherwise, output
Updated optimal solution.
8. device according to claim 5, which is characterized in that the computing module updates current every in executing step 7
The position of grey wolf, specifically includes:
Individual number variable q=1 is arranged in step 71, and the solution of grey wolf position is expressed as
Step 72, setting dimension variable j=1；
Step 73, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 74, the random two decimal r generated between (0,1)1And r2, calculate A2=2 × a × r1- a, C2=2 × r2,
Step 75, the random two decimal r generated between (0,1)1And r2, calculate A1=2 × a × r1- a, C1=2 × r2,
Step 76 calculates
Step 77 enables j=j+1, judges whether j≤dim is true, if so, otherwise then return step 73 execute step 78；
Step 78 enables q=q+1, judges whether q≤popsize is true, if so, then return step 72, otherwise, output update
As a result.
9. a kind of readable medium, which is characterized in that including executing instruction, when electronic equipment processor execute described in execute instruction
When, the electronic equipment executes the method as described in any in Claims 1-4.
10. a kind of electronic equipment characterized by comprising processor, memory and bus；The memory is held for storing
Row instruction, the processor are connect with the memory by the bus, when electronic equipment operation, the processor
It executes the described of memory storage to execute instruction, so that the processor is executed as described in any in Claims 1-4
Method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811623601.2A CN109686431B (en) | 2018-12-28 | 2018-12-28 | Operating room scheduling method based on mixed wolf-change neighborhood search algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811623601.2A CN109686431B (en) | 2018-12-28 | 2018-12-28 | Operating room scheduling method based on mixed wolf-change neighborhood search algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109686431A true CN109686431A (en) | 2019-04-26 |
| CN109686431B CN109686431B (en) | 2021-04-06 |
Family
ID=66190861
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811623601.2A Active CN109686431B (en) | 2018-12-28 | 2018-12-28 | Operating room scheduling method based on mixed wolf-change neighborhood search algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109686431B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113011612A (en) * | 2021-03-23 | 2021-06-22 | 合肥工业大学 | Production and maintenance scheduling method and system based on improved wolf algorithm |
| CN113066568A (en) * | 2021-04-13 | 2021-07-02 | 安徽医科大学第二附属医院 | PETCT inspection scheduling method based on GVNS-OP algorithm |
| CN113345559A (en) * | 2021-04-15 | 2021-09-03 | 山东师范大学 | Multi-objective flexible element heuristic optimization method and system for scheduling surgical cases |
| CN113555096A (en) * | 2021-06-10 | 2021-10-26 | 合肥工业大学 | Operating room scheduling method and system considering doctor scheduling condition |
| CN115331793A (en) * | 2022-10-12 | 2022-11-11 | 华中科技大学同济医学院附属协和医院 | Operation information management system and method based on lean medical treatment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107730065A (en) * | 2017-06-12 | 2018-02-23 | 合肥工业大学 | Based on the production scheduling method and system for improving variable neighborhood search algorithm |
| CN107730072A (en) * | 2017-09-11 | 2018-02-23 | 合肥工业大学 | Based on the parallel machine gang scheduling method and system for improving Artificial Immune Algorithm |
| CN109087698A (en) * | 2018-07-16 | 2018-12-25 | 合肥工业大学 | Based on the operating room dispatching method of dragonfly algorithm under weighted completion time minimum |
-
2018
- 2018-12-28 CN CN201811623601.2A patent/CN109686431B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107730065A (en) * | 2017-06-12 | 2018-02-23 | 合肥工业大学 | Based on the production scheduling method and system for improving variable neighborhood search algorithm |
| CN107730072A (en) * | 2017-09-11 | 2018-02-23 | 合肥工业大学 | Based on the parallel machine gang scheduling method and system for improving Artificial Immune Algorithm |
| CN109087698A (en) * | 2018-07-16 | 2018-12-25 | 合肥工业大学 | Based on the operating room dispatching method of dragonfly algorithm under weighted completion time minimum |
Non-Patent Citations (1)
| Title |
|---|
| 姜天华: ""混合灰狼优化算法求解柔性作业车间调度问题"", 《控制与决策》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113011612A (en) * | 2021-03-23 | 2021-06-22 | 合肥工业大学 | Production and maintenance scheduling method and system based on improved wolf algorithm |
| CN113011612B (en) * | 2021-03-23 | 2022-09-20 | 合肥工业大学 | Production and maintenance scheduling method and system based on improved wolf algorithm |
| CN113066568A (en) * | 2021-04-13 | 2021-07-02 | 安徽医科大学第二附属医院 | PETCT inspection scheduling method based on GVNS-OP algorithm |
| CN113345559A (en) * | 2021-04-15 | 2021-09-03 | 山东师范大学 | Multi-objective flexible element heuristic optimization method and system for scheduling surgical cases |
| CN113345559B (en) * | 2021-04-15 | 2023-04-18 | 山东师范大学 | Multi-objective flexible element heuristic optimization method and system for surgical case scheduling |
| CN113555096A (en) * | 2021-06-10 | 2021-10-26 | 合肥工业大学 | Operating room scheduling method and system considering doctor scheduling condition |
| CN113555096B (en) * | 2021-06-10 | 2023-06-30 | 合肥工业大学 | Operating room scheduling method and system considering doctor scheduling condition |
| CN115331793A (en) * | 2022-10-12 | 2022-11-11 | 华中科技大学同济医学院附属协和医院 | Operation information management system and method based on lean medical treatment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109686431B (en) | 2021-04-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109686431A (en) | Based on mixing grey wolf-variable neighborhood search algorithm operating room dispatching method and device | |
| Li et al. | Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions | |
| Li et al. | Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm | |
| Wang | Multi-objective optimization based on decomposition for flexible job shop scheduling under time-of-use electricity prices | |
| Hamzadayi et al. | A genetic algorithm based approach for simultaneously balancing and sequencing of mixed-model U-lines with parallel workstations and zoning constraints | |
| Guo et al. | Mathematical model and genetic optimization for the job shop scheduling problem in a mixed-and multi-product assembly environment: A case study based on the apparel industry | |
| CN106934580A (en) | Method for inventory control and device | |
| CN109144699A (en) | Distributed task dispatching method, apparatus and system | |
| Rahman et al. | Real-time order acceptance and scheduling problems in a flow shop environment using hybrid GA-PSO algorithm | |
| Burdett et al. | A flexible job shop scheduling approach with operators for coal export terminals | |
| CN107391527A (en) | A kind of data processing method and equipment based on block chain | |
| Lei et al. | A unified approach for location-allocation analysis: integrating GIS, distributed computing and spatial optimization | |
| CN109582452A (en) | A kind of container dispatching method, dispatching device and electronic equipment | |
| Li et al. | Bi-objective hybrid flow shop scheduling with common due date | |
| Subramoney et al. | Multi-swarm PSO algorithm for static workflow scheduling in cloud-fog environments | |
| CN1973263A (en) | Bank assignment for partitioned register banks | |
| CN107870866A (en) | I/O command dispatching method and NVM interface controller | |
| Chen et al. | A modified harmony search algorithm for solving the dynamic vehicle routing problem with time windows | |
| CN108920183A (en) | A kind of operational decision making method, device and equipment | |
| CN106874079A (en) | A kind of method and device of tasks carrying | |
| CN109787836A (en) | Based on the determination method and device of the VNF scheduling scheme of genetic algorithm | |
| CN109032789A (en) | A kind of dispositions method of logistics network, device and server | |
| CN109376988A (en) | A kind for the treatment of method and apparatus of business datum | |
| CN115237592B (en) | Privacy-aware hybrid cloud service flow scheduling method | |
| CN104657087B (en) | A kind of method, apparatus and system of virtual disk mapping |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |