CN108960585A - Service role dispatching method under a kind of remote health monitoring line with hard time window - Google Patents
Service role dispatching method under a kind of remote health monitoring line with hard time window Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
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- 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]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Abstract
Service role dispatching method under the invention proposes a kind of remote health monitoring line with hard time window.The present invention initially sets up the mathematical model that service role under remote health monitoring line is dispatched, and then proposes a kind of mould because algorithm solves.Specific steps: (1) mathematical model that service role is dispatched under remote health monitoring line is established；(2) parameter initialization；(3) position of random initializtion firefly individual determines the corresponding self brightness of each firefly individual by calculating the target function value of each firefly individual；(4) the opposite Attraction Degree and brightness of each firefly individual are calculated；(5) firefly position is updated；(6) brightness of each firefly individual is updated；(7) local search is carried out using chaos neighborhood solution of the simulated annealing to optimal solution；(8) judge that the number of iterations either with or without maximum value is met, obtains optimal solution.The present invention can effectively solve service role scheduling problem under remote health monitoring line.
Description
Technical field
The invention belongs to wisdom health neighborhood, it is related to service role tune under a kind of remote health monitoring line with hard time window
Degree method.
Background technique
Service is the core function of remote health monitoring under remote health monitoring line, and the quality of task schedule quality is directly
Influence quality, efficiency and the cost serviced under remote health monitoring line.Service role scheduling problem can under remote health monitoring line
There are several service objects there are in the system of demand relation for one to be briefly described, there are several service dispatch center kimonos
Business point, it is desirable that for the route of reasonable design service trip with the time for arranging trip, the target function value for reaching certain is optimal.It
It is a kind of with the academic research project that value is quite widely used, theoretically belongs to complicated combinatorial optimization problem.
The shortcomings that prior art will appear relatively slow convergence, long operational time using genetic algorithm；Particle swarm algorithm is because of it
Fast convergence rate, the disadvantages of be easy to causeing low precision, easy diverging.
Summary of the invention
For service role dispatching technique under existing remote health monitoring line the speed of service it is slow, convergence capabilities are poor, optimizing
The problems such as low efficiency, that the present invention provides a kind of speeds of service is fast, convergence capabilities are strong, Searching efficiency is high with the remote of hard time window
Service role dispatching method under journey health supervision line, this method are based on improvement firefly and to improve the mould of simulated annealing because calculating
Method.
To achieve the goals above, the technical solution of the present invention is as follows:
Service role dispatching method under a kind of remote health monitoring line with hard time window, comprising the following steps:
S1, the mathematical model that service role is dispatched under remote health monitoring line is established:
eti≤Ti≤etiI=1,2 ... N (9)
xijk∈ { 0,1 } i, j=1,2 ... N；i≠j (12)
yik∈ { 0,1 } i=1,2 ... N (13)
Wherein, Z (unit: km) indicates the total distance of vehicle service；K (unit :) indicate to use in scheduling process
Vehicle fleet；N (unit: a) indicates the patient populations for needing to service；dij(unit: km) indicates vehicle from patient's point i row
Sail to the total distance of patient's point j；DkThe maximum travel distance of (unit: km) expression vehicle；qi(unit: kilogram) indicate vehicle
Drug weight to be used is needed at patient's point i；Qk(unit: kilogram) indicate vehicle dead weight；Decision variable: xijkWith
yikExpression is two non-zero i.e. 1 decision variables；
Formula (3) is the objective function of service role scheduling problem under the Med Reg center line with hard time window, with linear
The traveling total path length for changing minimum vehicle is target, indicates this position of Med Reg center using i=0；Formula (4) and formula
(5) it indicates that each vehicle will be from medical control centre, and is eventually returned to Med Reg center；Formula (6) indicates each
Patient's point only has a trolley offer service and each patient can access service；(7) maximum of every vehicle driving is indicated
Distance limitation；Formula (8), which indicates that the dead weight of vehicle is greater than, needs drug weight to be used equal to vehicle to patient's point
The sum of；The time that formula (9) expression vehicle reaches patient service point i must strictly observe formula；(10) and formula (11) illustrates two
The correlation of decision variable；Formula (12) and formula (13) illustrate that two decision variables are all non-zero i.e. 1 variables；
S2, parameter initialization；Firefly initial population quantity M, the number of iterations D are setmax, maximum Attraction Degree β0, fluorescein
Regulation rate γ, minimum step factor-alphamin；Initial temperature Wmax；Minimum temperature Wmin；Mapkob chain length L etc..
The position of S3, random initializtion firefly individual take it by calculating the target function value of each firefly individual
Derivative is as the corresponding self brightness of each firefly individual；
S4, the opposite Attraction Degree that firefly is calculated according to the opposite Attraction Degree of glowworm swarm algorithm and brightness-formula and bright
Degree.
S5, firefly position is updated according to firefly location update formula is improved.Specific step is as follows:
S5-1, propose a kind of improvement inertia weight strategy, by way of a kind of automatic adjusument as formula (14) come pair
Standard firefly location update formula improves.Wherein w0For initial value；wtTo improve inertia weight value；DmaxMaximum is represented to change
Generation number；DtRepresent the t times iteration；Random number of the random number rand () between [0,0.5].
In formula, inertia weight wtRepresent the influence mobile to current firefly position of previous generation firefly individual.Repeatedly
When for initial stage, a biggish inertia weight value is needed, inertia weight value range is considered larger between 0.7-1 in the present case
, so that firefly individual has stronger ability of searching optimum；And with the increase of the number of iterations, it needs to be gradually reduced (logical
Formula (14) are crossed, with the increase of the number of iterations, inertia weight can be gradually decreased to wmin) inertia weight value, make its local search
Ability enhancing, to avoid falling into locally optimal solution.
S5-2, a kind of adaptive step is proposed because of substrategy, by a kind of dynamic mode such as formula (15) come adjustment parameter α
Value.Wherein α0For the initial step length factor；ImaxFor the maximum firefly individual brightness value of present intensity, IiIt (t) is the current light of firefly
The brightness value of worm individual；DmaxRepresent maximum number of iterations；DtRepresent the t times iteration.
In formula, step factor is influenced by firefly individual brightness value and the number of iterations simultaneously.At iteration initial stage, currently
Firefly individual brightness value and the maximum firefly individual brightness value of present intensity differ greatly (in first time iteration, firefly
It is individual with the biggest gap with the maximum individual brightness value of all group's brightness, it can be calculated by brightness-formula, as algorithm changes
In generation, deeply all firefly individuals all can be mobile toward the maximum individual of group's brightness, and this gap will reduce), pass through formula
(15), step-length factor-alpha can be allowed big as far as possible with dynamic regulation, α value is [0.3,0.5] in this case, and in iteration later period, current firefly
Slowly close to most bright firefly individual, its own brightness value differs very fireworm individual with the maximum firefly individual brightness value of brightness
It is small that (with going deep into for iteration, closer to meaning that gap is smaller, step factor is minimum value 0.1 at this time, and firefly individual is mobile
Distance is most short, can calculate its value by formula (15)), step factor α needs smaller at this time, avoids missing because step-length is too big
Optimal solution.Meanwhile step factor α is influenced by the number of iterations, with DtIncrease, step factor α is gradually reduced.
S6, the brightness for updating each firefly individual again according to brightness-formula, the maximum individual of brightness is optimal solution.
S7, local search is carried out using chaos neighborhood solution of the modified-immune algorithm to optimal solution.Specific step is as follows:
S7-1, chaos neighborhood solution strategy is used.
S7-2, it is jumped using transition probability progress new explanation.
S7-3, it proposes to improve and heat up again annealing strategy, simulated annealing is allowed to stop search falling into local optimum
When, its external temperature is increased, increases the probability for receiving new explanation, to jump out locally optimal solution.Steps are as follows for specific improvement:
1, record does not occur the frequency n of more excellent solution, and the tactful threshold of heating is that number reaches Mapkob/t again for setting1It is secondary,
Mapkob is interior cycle-index, t1General random takes the number between [4,6]；
2, as n >=Mapkob/t1When, illustrate that algorithm the case where not searching more excellent solution repeatedly occurs and falls into office
Portion is optimal, at this moment using the mechanism that heats up again, by ambient temperature W according to W/t2It is heated up again, t2General random takes [0.9,0.95]
Between number, activate transition probability P, search the probability of optimal solution to increase.When more less than then illustrating that algorithm does not occur also
Secondary the case where not searching more excellent solution, algorithm continues iteration operation at this time, if the more excellent solution of appearance, is replaced；If continueing to
Do not occur more excellent solution Mapkob/t1 times, then illustrate that algorithm falls into local optimum, weight warming temperature need to be carried out, improve external temperature,
Increase jumps probability.
S8, judge Dt< DmaxIt is whether true, the D if setting upt=Dt+ 1, S4 is jumped, if invalid reach greatest iteration
Number jumps S9.
S9, terminate algorithm, export optimal solution, the optimal solution corresponds to derivative (each iteration institute of algorithm of target function value
Obtained solution can all have a corresponding fitness value, and specific value is the derivative of target function value), show target function value
Smaller, vehicle running path is shorter, and cost is lower, and corresponding optimal solution is bigger, and fitness value is higher, as firefly individual
Self brightness it is brighter.
Service role dispatching method under a kind of remote health monitoring line with hard time window proposed by the present invention, builds first
Then the mathematical model that service role is dispatched under vertical remote health monitoring line proposes a kind of mould because algorithm solves.Specific steps:
(1) mathematical model that service role is dispatched under remote health monitoring line is established；(2) parameter initialization；(3) the random initializtion light of firefly
The position of worm individual, by calculating the target function value of each firefly individual, determine each firefly it is individual it is corresponding itself
Brightness；(4) the opposite Attraction Degree and brightness of each firefly individual are calculated；(5) firefly position is updated；(6) each firefly is updated
The brightness of fireworm individual；(7) local search is carried out using chaos neighborhood solution of the simulated annealing to optimal solution；(8) judgement changes
Generation number obtains optimal solution either with or without maximum value is met.The present invention can effectively solve service under remote health monitoring line and appoint
Business scheduling problem.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System.
Fig. 1 is the process of service role dispatching method under a kind of remote health monitoring line with hard time window of the invention
Figure；
Fig. 2 is correspondence period hodograph employed in specific embodiments of the present invention；
Fig. 3 is the shortest path of service role dispatching method under a kind of remote health monitoring line with hard time window of the invention
Diameter figure.
Specific embodiment
The embodiment of the invention will now be described in detail with reference to the accompanying drawings, and as part of this specification passes through
Embodiment illustrates the principle of the present invention, and other aspects of the present invention, feature and its advantage will become by the detailed description
It is very clear.In the attached drawing of institute's reference, the same or similar component is indicated using identical drawing reference numeral in different figures.
In order to prove superiority of the invention, implements the present invention under the remote health monitoring line in certain city and service below
In task schedule, its step are as follows:
S1, the mathematical model that service role is dispatched under remote health monitoring line is established:
eti≤Ti≤etiI=1,2 ... N (9)
xijk∈ { 0,1 } i, j=1,2 ... N；i≠j (12)
yik∈ { 0,1 } i=1,2 ... N (13)
Wherein, Z (unit: km) indicates the total distance of vehicle driving；K (unit :) indicate to use in scheduling process
Vehicle fleet be 3；N (unit: a) indicates that the sum of service point has 10；dij(unit: km) indicates vehicle from service
Point i drives to the distance of service point j, and coordinate is shown in Table 1；DkThe maximum travel distance of (unit: km) expression vehicle
260km；qi(unit: kilogram) indicating that vehicle needs drug weight to be used at service point i, parameter is shown in Table 2；QkIt is (single
Position: kilogram) indicate vehicle dead weight 20kg.
The coordinate number of service role dispatching method under a kind of remote health monitoring line with hard time window of table 1
The service point of service role dispatching method uses drug weight under a kind of remote health monitoring line with hard time window of table 2
Amount and service time data
S2, parameter initialization.Setting number of particles is M=30；The number of iterations Dmax=200 times；Maximum Attraction Degree β0=1；
Fluorescein regulation rate γ=0.9；Initial temperature Tmax=5000；Minimum temperature Tmin=0.0001；Mapkob chain length L=500.
The position of S3, random initializtion firefly individual take it by calculating the target function value of each firefly individual
Derivative is as the corresponding self brightness of each firefly individual, i.e. I=1/Z.
S4, the opposite Attraction Degree and brightness that each firefly individual is calculated using formula (14) and formula (15).
S5, firefly position is updated using firefly location update formula (16) are improved.Specific step is as follows:
xi+1=wtxi+β(xj-xi)+α(rand-1/2) (16)
S5-1, propose a kind of improvement inertia weight strategy, by way of a kind of automatic adjusument as formula (17) come pair
Standard firefly location update formula improves.
In formula, initial inertia weight w0=1, wminThe random number of=0.5, rand () between [0,0.5].
S5-2, a kind of adaptive step is proposed because of substrategy, by a kind of dynamic mode such as formula (18) come adjustment parameter α
Value.
In formula, initial step length factor-alpha0=0.1.
S6, the brightness for updating each firefly individual again according to brightness-formula, the maximum individual of brightness is optimal solution.
S7, local search is carried out using chaos neighborhood solution of the modified-immune algorithm to optimal solution.Specific step is as follows:
S7-1, using a kind of chaos neighborhood solution strategy, select classics Logistic chaotic maps as Chaos Variable
Generation mechanism, specific formula are as follows:
In formula, ZiChaos Variable is represented, u represents control parameter, and d represents population serial number, under normal circumstances, when u takes 4,0
≤Z0When≤1, system represented by formula is complete chaos state, it can generate an arbitrary size between 0 to 1
Output quantity, corresponding chaos system just has the ergodic of complete meaning at this time.We are complete using under this state by the present invention
State generates corresponding Chaos Variable Zi, to carry out the initialization of chaos neighborhood solution.
S7-2, it is jumped using a kind of progress new explanation of transition probability, uses formula (20) Metropolis algorithm transition probability P
To determine whether from the position a to b position transfer.When the adaptive value Y (a) of the position a is less than the adaptive value Y (b) of the position b, a is represented
Position is more excellent, but still mobile to the position b with certain probability, is laid out optimal predicament to jump out to fall into；When fitting for the position a
Should value Y (a) when being greater than the adaptive value Y (b) of the position b, it is more excellent to represent the position b, and a is bound to mobile to the position b.
S7-3, it proposes that a kind of improvement heats up annealing strategy again, simulated annealing is allowed to stop falling into local optimum
When search, its external temperature is increased, increases the probability for receiving new explanation, to jump out locally optimal solution.Steps are as follows for specific improvement:
S7-3-1, record do not occur the frequency n of more excellent solution, and the tactful threshold of heating is that number reaches Mapkob/t again for setting1
Secondary, Mapkob is interior cycle-index, t1General random takes the number between [4,6]；
S7-3-2, as n >=Mapkob/t1When, illustrate that algorithm the case where not searching more excellent solution repeatedly occurs and falls
Local optimum is entered, at this moment using the mechanism that heats up again, by ambient temperature W according to W/t2It is heated up again, t2General random takes
Number between [0.9,0.95] activates transition probability P, to increase the probability for searching optimal solution.
S8, judge Dt< DmaxIt is whether true, the D if setting upt=Dt+ 1, S4 is jumped, if invalid reach greatest iteration
Number jumps S9.
S9, terminate algorithm, export optimal result.
Mould proposed by the present invention based on improvement firefly and improvement simulated annealing is as shown in Figure 1 because of the process of algorithm.
K=3 in the embodiment of the present invention, N=10, vehicle driving expense is 3 yuan/km, and it is 0.3 that vehicle parking, which takes,
Member/minute, it is 2 yuan/minute that doctor, which services time cost, because being related to time window, the speed pair of Vehicle Speed
It is most important that patient service point can be reached on time, and vehicle is in speed in different time periods as shown in Fig. 2, by mentioning to the present invention
Service role scheduling problem is solved under the remote health monitoring line with hard time window out, obtains simulation result such as 3 institute of table
Show, the minimum cost spent is 4791.77 yuan.Its most short driving path passes through as shown in figure 3, the service path that number is 1
The time of each patient service point is respectively 12:21 minutes, 13:07 minutes, 14:08 minutes, 15:37 minutes；The service path that number is 2,
Time by each patient service point is respectively 12:43 minutes, 13:25 minutes, 14:19 minutes；The service path that number is 3, passes through
The time of each patient service point is respectively 11:45 minutes, 13:11 minutes, 14:30 minutes, in strict conformity with each patient service when
Between window requirement.The present invention can effectively solve service role scheduling problem under the remote health monitoring line with hard time window.
The simulation result of service role dispatching method under a kind of remote health monitoring line with hard time window of table 3
Claims (6)
1. service role dispatching method under a kind of remote health monitoring line with hard time window, which is characterized in that including following step
It is rapid:
S1, the mathematical model that service role is dispatched under remote health monitoring line is established:
eti≤Ti≤etiI=1,2 ... N (9)
xijk∈ { 0,1 } i, j=1,2 ... N；i≠j (12)
yik∈ { 0,1 } i=1,2 ... N (13)
Wherein, Z (unit: km) indicates the total distance of vehicle service；K (unit :) indicate the vehicle used in scheduling process
Sum；N (unit: a) indicates the patient populations for needing to service；dij(unit: km) indicates that vehicle drives to from patient's point i
The total distance of patient's point j；DkThe maximum travel distance of (unit: km) expression vehicle；qi(unit: kilogram) indicate that vehicle is being suffered from
Drug weight to be used is needed at person's point i；Qk(unit: kilogram) indicate vehicle dead weight；Decision variable: xijkAnd yikTable
Show it is two non-zero i.e. 1 decision variables；
Formula (3) is the objective function of service role scheduling problem under the Med Reg center line with hard time window, to linearize most
The traveling total path length of smallization vehicle is target, indicates this position of Med Reg center using i=0；Formula (4) and formula (5)
It indicates that each vehicle will be from medical control centre, and is eventually returned to Med Reg center；Formula (6) indicates each patient
Point only has a trolley offer service and each patient can access service；(7) maximum distance of every vehicle driving is indicated
Limitation；Formula (8), which indicates that the dead weight of vehicle is greater than, needs the sum of drug weight to be used equal to vehicle to patient's point；
The time that formula (9) expression vehicle reaches patient service point i must strictly observe formula；(10) and formula (11) illustrates that two decisions become
The correlation of amount；Formula (12) and formula (13) illustrate that two decision variables are all non-zero i.e. 1 variables；
S2, parameter initialization；Firefly initial population quantity M, the number of iterations D are setmax, maximum Attraction Degree β0, fluorescein adjusting
Rate γ；Initial temperature Wmax, minimum temperature Wmin, Mapkob chain length L；
The position of S3, random initializtion firefly individual take its derivative by calculating the target function value of each firefly individual
As the corresponding self brightness of each firefly individual；
S4, the opposite Attraction Degree and brightness that firefly is calculated according to the opposite Attraction Degree of glowworm swarm algorithm and brightness-formula；
S5, firefly position is updated according to firefly location update formula is improved, the specific steps are as follows:
S5-1, a kind of improvement inertia weight strategy is proposed；
S5-2, propose a kind of adaptive step because of substrategy；
S6, the brightness for updating each firefly individual again according to brightness-formula, the maximum individual of brightness is optimal solution；
S7, local search is carried out using chaos neighborhood solution of the modified-immune algorithm to optimal solution, the specific steps are as follows:
S7-1, chaos neighborhood solution strategy is used；
S7-2, it is jumped using transition probability progress new explanation；
S7-3, heated up annealing strategy again using improvement；
S8, judge Dt< DmaxIt is whether true, the D if setting upt=Dt+ 1, S4 is jumped, if invalid reach maximum number of iterations,
Jump S9；
S9, terminating algorithm, export optimal solution, the optimal solution corresponds to the derivative of target function value, and performance target function value is smaller,
Vehicle running path is shorter, and cost is lower, and corresponding optimal solution is bigger, and fitness value is higher, as firefly individual from
Body brighter display.
2. service role dispatching method under a kind of remote health monitoring line with hard time window as described in claim 1, special
Sign is that the step S5-1 proposes a kind of improvement inertia weight strategy, further comprises:
Standard firefly location update formula is improved such as formula (14) by way of a kind of automatic adjusument；Wherein
w0For initial value；wtTo improve inertia weight value；DmaxRepresent maximum number of iterations；DtRepresent the t times iteration；Random number rand
The random number of () between [0,0.5]；
In formula, inertia weight value wtRepresent the influence mobile to current firefly position of previous generation firefly individual；At the beginning of iteration
When the phase, inertia weight value range is chosen for [0.7-1], so that firefly individual has stronger ability of searching optimum；And with
The increase of the number of iterations needs to be gradually reduced inertia weight value based on formula (14), until being reduced to wmin, make its local search
Ability enhancing, to avoid falling into locally optimal solution.
3. service role dispatching method under a kind of remote health monitoring line with hard time window as claimed in claim 2, special
Sign is, the step S5-2 proposes that a kind of adaptive step because of substrategy, further comprises:
By a kind of dynamic mode such as formula (15) come the value of adjustment parameter α；Wherein α0For the initial step length factor；ImaxIt is current bright
Spend maximum firefly individual brightness value, IiIt (t) is the brightness value of current firefly individual；DmaxRepresent maximum number of iterations；Dt
Represent the t times iteration；
In formula, step factor α is influenced by firefly individual brightness value and the number of iterations simultaneously；At iteration initial stage, current firefly
Fireworm individual brightness value differs greatly with the maximum firefly individual brightness value of present intensity, and by formula (15), dynamic regulation is allowed
Step factor α value is [0.3,0.5], and in the iteration later period, current firefly individual is slowly individual close to most bright firefly,
Self brightness value differs small with the maximum firefly individual brightness value of brightness, and step factor α needs smaller at this time, avoids because of step-length
It is too big, miss optimal solution；Meanwhile step factor α is influenced by the number of iterations, with DtIncrease, step factor α is gradually reduced.
4. service role dispatching method under a kind of remote health monitoring line with hard time window as claimed in claim 3, special
Sign is that S7-1 uses a kind of chaos neighborhood solution strategy, selects classics Logistic chaotic maps as the production of Chaos Variable
Life system, specific formula are as follows:
In formula, ZiChaos Variable is represented, u represents control parameter, and d represents population serial number, under normal circumstances, and when u takes 4,0≤Z0
When≤1, system represented by formula (16) is complete chaos state, it can generate an arbitrary size between 0 to 1
Output quantity, corresponding chaos system just has the ergodic of complete meaning at this time；
Corresponding Chaos Variable Z is generated using complete state under this statei, to carry out the initialization of chaos neighborhood solution.
5. service role dispatching method under a kind of remote health monitoring line with hard time window as claimed in claim 3, special
Sign is that step S7-2 carries out new explanation using a kind of transition probability and jumps, and is shifted using formula (17) Metropolis algorithm general
Rate P determines whether from the position a to b position transfer；When the adaptive value Y (a) of the position a is less than the adaptive value Y (b) of the position b, generation
The position table a is more excellent, but still mobile to the position b with certain probability, is laid out optimal predicament to jump out to fall into；When the position a
Adaptive value Y (a) when being greater than the adaptive value Y (b) of the position b, it is more excellent to represent the position b, and a is bound to mobile to the position b；
6. service role dispatching method under a kind of remote health monitoring line with hard time window as claimed in claim 3, special
Sign is, step S7-3 proposes that a kind of improvement heats up annealing strategy again, further comprises:
When simulated annealing stops search falling into local optimum, and its external temperature is increased, increases the probability for receiving new explanation,
To jump out locally optimal solution；Steps are as follows for specific improvement:
S7-3-1, record do not occur the frequency n of more excellent solution, and the tactful threshold of heating is that number reaches Mapkob/t again for setting1It is secondary,
Mapkob is interior cycle-index, t1General random takes the number between [4,6]；
S7-3-2, as n >=Mapkob/t1When, illustrate that algorithm the case where not searching more excellent solution repeatedly occurs and falls into part
It is optimal, at this moment using the mechanism that heats up again, by ambient temperature W according to W/t2It is heated up again, t2General random take [0.9,0.95] it
Between number, activate transition probability P, search the probability of optimal solution to increase.
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| CN111768851A (en) * | 2020-06-22 | 2020-10-13 | 杭州电子科技大学 | Multi-level home care scheduling method and system under dynamic demand |
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| CN113222096A (en) * | 2021-04-30 | 2021-08-06 | 桂林理工大学 | Improved particle swarm algorithm for cloud computing task scheduling |
| CN113313413A (en) * | 2021-06-17 | 2021-08-27 | 广东工业大学 | Method, equipment and carrier for identifying energy consumption mode of aluminum melting furnace by improving firefly algorithm |
| CN114863683A (en) * | 2022-05-11 | 2022-08-05 | 湖南大学 | Heterogeneous Internet of vehicles edge calculation unloading scheduling method based on multi-objective optimization |
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