CN110570011A - Complex system resource optimization scheduling method and system under multi-constraint condition - Google Patents

Complex system resource optimization scheduling method and system under multi-constraint condition Download PDF

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CN110570011A
CN110570011A CN201910712457.8A CN201910712457A CN110570011A CN 110570011 A CN110570011 A CN 110570011A CN 201910712457 A CN201910712457 A CN 201910712457A CN 110570011 A CN110570011 A CN 110570011A
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张宏军
罗永亮
谭玮
李昊华
黄百乔
朱梦妮
巩彦明
赵海丹
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CSSC Systems Engineering Research Institute
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Abstract

the invention relates to a complex system resource optimization scheduling method under a multi-constraint condition, which comprises the following steps: injecting a fault model of a complex system guarantee resource into a multi-main-element multi-dimensional association network of the complex system to form a multi-main-element fault association network; constructing a complex system resource optimization multi-objective planning model according to the multi-subject element fault correlation network and the complex system resource multi-scale model; receiving task requirements of a complex system, and optimizing a multi-objective planning model according to the complex system resources to obtain an optimal complex system resource scheduling solution; the method can obtain good optimization effect, obtain an optimal solution, greatly improve the operation speed and improve the scheduling efficiency.

Description

Complex system resource optimization scheduling method and system under multi-constraint condition
Technical Field
The invention relates to a complex system optimal scheduling method and a complex system optimal scheduling system under a multi-constraint condition, and belongs to the technical field of information.
Background
the traditional complex system design and development mainly adopts the idea of reduction theory, the complex system is decomposed into a plurality of simple systems, and the method is suitable for system structure fixation and cause and effect relationship determination, and the result is repeatable, predictable and stable. The most adaptable feature of the complex system is that the environment can cause the system structure to change continuously. In order to adapt to the environment, the structure of the system can be continuously adjusted, and a new system structure is evolved, so that new properties and functions are generated, which is determined by the principle that the systematic structure determines the functions and the properties of the system. Therefore, the traditional design method for system dimension reduction decomposition is difficult to realize the real depiction and analysis of a human-computer loop complex large system similar to aviation guarantee, the system structure is variable, the result is unpredictable and unrepeatable, the system is mechanical, and a plurality of independent elements in the system interact with each other, so that the complex system as a whole generates spontaneous self-organization.
the complex system scheduling problem is a large-scale multilayer multi-stage multi-factor coupled constraint decision problem, a linear programming method commonly used at home and abroad can generate dimension disaster when the dimension of the problem is increased, and the complexity of modeling and the difficulty of solving are both increased rapidly. Therefore, the optimization method integrating the intelligent algorithm and the rule scheduling can simultaneously ensure the solution excellence and the calculation efficiency.
Taking an aviation support system as an example, the existing resource scheduling research of the aviation support system mainly comprises the steps of designing, simulating, configuring and establishing an abstract mathematical model for local or simplified support operation flows, and further optimizing the abstract mathematical model. The optimization of the whole flow of aviation guarantee operation, the guarantee resource distribution and the guarantee part distribution of each operation are rarely involved. In addition, a universal modeling theory and a scheduling method are lacked, so that the generation efficiency of the aviation guarantee large-scale limited resource scheduling scheme under the multi-target and multi-task scene is low, and the intelligent level is poor.
disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a complex system optimal scheduling method under a multi-constraint condition, which comprises the steps of firstly, injecting a fault model of a complex system guarantee resource into a multi-main-element multi-dimensional association network of the complex system to form the multi-main-element fault association network; then, constructing a complex system resource optimization multi-objective planning model according to the multi-subject element fault association network and the complex system resource multi-scale model; and finally, receiving the task requirements of the complex system, and optimizing the multi-objective planning model according to the complex system resources to obtain an optimal complex system resource scheduling solution.
Another object of the present invention is to provide a complex system optimized scheduling system under multiple constraints.
The above purpose of the invention is mainly realized by the following technical scheme:
A complex system resource optimization scheduling method under a multi-constraint condition comprises the following steps:
Injecting a fault model of a complex system guarantee resource into a multi-main-element multi-dimensional association network of the complex system to form a multi-main-element fault association network;
constructing a complex system resource optimization multi-objective planning model according to the multi-subject element fault correlation network and the complex system resource multi-scale model;
And receiving the task requirement of the complex system, and optimizing the multi-objective planning model according to the complex system resources to obtain an optimal complex system resource scheduling solution.
In the above method for optimizing and scheduling complex system resources under multiple constraints, the multi-principal element multidimensional correlation network is a network set formed by superimposing correlation networks of different types, wherein elements of the same type of correlation are divided into the same correlation network.
In the method for optimizing and scheduling the resources of the complex system under the multi-constraint condition, the multi-scale model of the resources of the complex system comprises a human model or a guaranteed resource model.
In the above complex system resource optimization scheduling method under multiple constraint conditions, the construction method of the human model is as follows:
(1) Constructing a personnel individual model according to the personnel working type, wherein the personnel individual model comprises a cognitive fatigue model and a physical fatigue model;
(2) and constructing a personnel team model, wherein the personnel team model comprises the personnel individual model and team characteristic elements.
In the complex system resource optimization scheduling method under the multi-constraint condition, a cognitive fatigue model is built according to commanders, and a physical fatigue model is built according to guarantors; the team characteristic elements comprise team work targets and work environments.
in the complex system resource optimization scheduling method under the multi-constraint condition, the guaranteed resource model comprises static attributes and dynamic behaviors; the static attributes comprise the positions and the number of the security equipment, security positions and security objects, and the dynamic behaviors comprise security resource use time calculation formulas.
in the method for optimizing and scheduling the resources of the complex system under the multi-constraint condition, the fault model of the guaranteed resources of the complex system comprises fault types, fault occurrence probabilities of different types and fault occurrence times of different types.
In the complex system resource optimization scheduling method under the multi-constraint condition, a complex system resource optimization multi-target planning model is formed according to the association constraint relation among the elements in the multi-subject element fault association network and a complex system resource multi-scale model, and the multi-target planning model is a set of a guarantee operation time model, a guarantee part selection model, a guarantee part conflict occupation model and a guarantee equipment conflict occupation model.
In the method for optimizing and scheduling complex system resources under the multi-constraint condition, the guarantee operation time model is a guarantee operation working mode and a guarantee operation time, wherein the guarantee operation working mode is as follows: after each guarantee operation is started, the guarantee operation cannot be stopped until the guarantee operation is finished, and the working time of the guarantee operation is the sum of the preparation time and the execution time.
in the above method for optimizing and scheduling complex system resources under multiple constraint conditions, the guaranteed part selection model is: and corresponding guarantee equipment is arranged at a guarantee part where the guarantee operation is located, and the guarantee part is in an idle available state at present.
In the above method for optimizing and scheduling complex system resources under multiple constraint conditions, the guarantee location conflict occupation model is: and when more than one security object needs to perform security operation on the same security position, selecting the security object with the highest priority to perform the security operation.
In the above method for optimizing and scheduling complex system resources under multiple constraint conditions, the conflict occupation model of the provisioning device is: when more than one security object needs to use the same security equipment for security operation, the security object with the highest priority is selected to use the security equipment.
In the method for optimizing and scheduling the resources of the complex system under the multi-constraint condition, the complex system comprises an aviation support system or a combat system.
In the foregoing method for optimizing and scheduling complex system resources under multiple constraints, the specific method for receiving task requirements of a complex system, and obtaining an optimal complex system resource scheduling solution by calculation according to the complex system resource optimization multi-objective planning model is as follows:
(1) Receiving task requirements of a complex system, and generating an initial rough solution set by using a random sampling method, wherein the initial rough solution set comprises a guarantee object element, a guarantee part element, a guarantee equipment element, a guarantee operation element, a guarantee time element and an incidence relation among the elements, and the incidence relation meets an element incidence constraint relation in a multi-body element fault incidence network;
(2) Sorting the solutions in the initial rough solution set according to a complex system resource optimization multi-objective planning model, and selecting the first N solutions as an optimal solution set;
(3) and obtaining an optimal scheduling solution by utilizing an improved genetic algorithm according to the optimal solution set.
In the foregoing method for optimizing and scheduling resources of a complex system under multiple constraints, in the step (2), the solutions in the initial rough solution set are sorted, and a sorting rule is as follows: the shortest guarantee time and the fewest movement times of the guarantee objects are taken as targets, the scores of the solutions are comprehensively calculated according to the set weight, the smaller the score is, the better the solution is, and the ranking is forward.
In the above complex system resource optimization scheduling method under multiple constraints, the value calculation formula of the solution is as follows:
wherein: scoreiIs the score of the ith solution, WSTFor time-preserving weighting, STiGuaranteed time for ith solution, max (ST)N) For the maximum value of the guarantee time in N solutions, WSMfor the weight of the number of movements, SMiNumber of moves, max (ST), for the ith solutionM) Is the maximum of the number of moves in N solutions, where WST+WSMEach solution value interval is [0,1 ═ 1]。
In the complex system resource optimization scheduling method under the multi-constraint condition, the value of N in the step (2) is 30-100.
In the foregoing method for optimizing and scheduling resources of a complex system under multiple constraints, the task requirements of the complex system are as follows: providing guarantee resources and guarantee positions, performing Y guarantee operations on N guarantee objects of different types, and ensuring the required guarantee operations only by adopting the required guarantee resources at the required guarantee positions; the guarantee resource comprises M guarantee devices, Z guarantee positions are provided, and the time of each guarantee operation is TYWherein N, M, Y, Z are all positive integers.
In the above complex system resource optimization scheduling method under multiple constraints, a specific method for obtaining an optimal scheduling solution by using an improved genetic algorithm according to the optimal solution set is as follows:
(1) Performing cross operation on the N initial solutions in the optimal solution set to obtain N crossed solutions;
(2) Carrying out mutation operation on the N solutions formed after the crossing to obtain N solutions after the mutation;
(3) calculating the scheduling time of each solution by using the original N solutions, the crossed N solutions and the mutated N solutions to total 3N solutions, selecting the N solutions with the shortest scheduling time as new initial solutions, and entering the step (4);
(4) if the set iteration times are reached, the step (5) is carried out, and if the set iteration times are not reached, the step (1) is returned;
(5) And selecting the solution with the shortest scheduling time from the N new initial solutions as the optimal scheduling scheme.
in the above complex system resource optimization scheduling method under multiple constraints, the step (1) performs cross operation on N initial solutions in the optimal solution set by using the following four methods:
the method comprises the steps of (A) randomly selecting a security object j, and crossing the security object j in the ith initial solution and the (i + 1) th initial solution;
The method (II) randomly selects a security object j, and correspondingly crosses the 1 st to the jth security object in the ith initial solution and the (i + 1) th initial solution respectively;
randomly selecting a guarantee object j, randomly selecting a position node t with a guarantee position changed in the ith and (i + 1) th initial solutions of the guarantee object j, and crossing guarantee operations behind the position node t in the ith and (i + 1) th initial solutions;
And (IV) randomly selecting a security object j, finding out all the same positions of the security object j at the ith and (i + 1) th initial solutions, randomly selecting a same position m, and crossing security equipment used by the ith and (i + 1) th initial solutions at the position m.
in the foregoing method for optimizing and scheduling resources of a complex system under multiple constraints, in the step (2), the N solutions formed after the intersection are subjected to mutation operations by using the following three methods:
The method comprises the steps that (A) for the ith solution, a guarantee object j is randomly selected, all position nodes of the guarantee object j with changed positions are found out, a position node t is randomly selected from the position nodes, the position node t is subjected to variation, and the variation operation is that one guarantee position is randomly selected from all guarantee positions meeting the operation requirements of the position node t, and guarantee equipment meeting the operation requirements in the position replaces the position node t and guarantee equipment of the position node t;
The method (II) randomly selects a guarantee object j for the ith solution, finds out all position nodes of the guarantee object j with changed positions, then randomly selects a position node t from the position nodes, performs variation on the position node t and all position nodes behind the position node t, the variation operation is traversing the position node t to the last node, firstly randomly selects a guarantee position from all guarantee positions meeting the operation requirement of the node t and guarantee equipment meeting the operation requirement in the position to replace the guarantee equipment of the position node t and the node t, then randomly selects a guarantee position from all guarantee positions meeting the operation requirement of the node t +1 and guarantee equipment meeting the operation requirement in the position to replace the guarantee equipment of the position node t +1 and the node t +1, … …, and the like in sequence until randomly selects a guarantee position from all guarantee positions meeting the operation requirement of the last node and guarantee position and guarantee equipment meeting the operation requirement in the position The barrier equipment replaces the last position node and the guarantee equipment of the last position node;
and (III) for the ith solution, randomly selecting a guarantee object j, randomly selecting a guarantee operation, and mutating guarantee positions and guarantee equipment used by the guarantee operation.
a complex system resource optimization scheduling system under multi-constraint conditions comprises a fault association network building module, a multi-objective planning model building module and a complex system resource scheduling solution calculating module, wherein:
a fault correlation network construction module: injecting a fault model of a complex system guarantee resource into a multi-main-element multi-dimensional association network of the complex system to form the multi-main-element fault association network, and sending the multi-main-element fault association network to a multi-objective planning model construction module;
The multi-target planning model building module: constructing a complex system resource optimization multi-objective planning model according to the multi-main-element fault correlation network and the complex system resource multi-scale model, and sending the complex system resource optimization multi-objective planning model to a complex system resource scheduling solution calculation module;
The complex system resource scheduling solution calculation module: and receiving the task requirement of the complex system, and calculating to obtain an optimal complex system resource scheduling solution according to the complex system resource optimization multi-objective planning model.
Compared with the prior art, the invention has the following beneficial effects:
(1) Firstly, injecting a fault model of a complex system guarantee resource into a multi-main-element multi-dimensional association network of the complex system to form a multi-main-element fault association network; then, constructing a complex system resource optimization multi-objective planning model according to the multi-subject element fault association network and the complex system resource multi-scale model; and finally, receiving the task requirements of the complex system, and obtaining an optimal complex system resource scheduling solution according to the complex system resource optimization multi-objective planning model.
(2) The invention firstly provides a complex system large-scale limited resource scheduling model with space constraint, takes into account various complex constraints of space, personnel, station position guarantee and equipment guarantee, introduces and improves an intelligent algorithm, and obtains good optimization effect and higher operation efficiency;
(3) The invention establishes a flexible and variable complex system multi-target planning model for the first time, and the model is expandable, reconfigurable, diversified and modularized, so that the resource configuration of almost all types of complex systems with variable resources can be supported under the condition of complete data, and the application range is wide;
(4) The invention firstly provides a complete complex system optimization scheduling solving method, uses a method of rapidly obtaining feasible solutions such as an intelligent algorithm, a constitutive algorithm and the like, and breaks through the limitation and one-sidedness in the traditional complex system modeling scheduling;
(5) The method provided by the invention is applied to a complex system, such as an aviation guarantee system, can solve the problem of multi-objective optimization of aviation guarantee resource elements under large-scale constraint conditions, can calculate a better scheduling scheme aiming at a specific guarantee task, enables the system to finish all guarantee operations of all guarantee objects by utilizing the existing guarantee resources in the shortest time, improves the balance rate of the use of the aviation guarantee resources, makes the guarantee flow more reasonable, realizes the improvement of the comprehensive efficiency of the aviation guarantee system, and improves the comprehensive capability of the aviation guarantee system to the maximum extent.
drawings
FIG. 1 is a flowchart of a complex system resource optimization scheduling method of the present invention;
FIG. 2 is a flow chart of an aviation safeguard system optimization scheduling method in the embodiment of the invention;
FIG. 3 is a modified genetic algorithm convergence curve in an embodiment of the present invention.
Detailed Description
the invention is described in further detail below with reference to the following figures and specific examples:
as shown in fig. 1, a flowchart of the method for optimizing and scheduling complex system resources according to the present invention is shown, and the method for optimizing and scheduling complex system resources according to the present invention includes the following steps:
injecting a fault model of the complex system guarantee resource into a multi-subject-element multi-dimensional association network of the complex system to form the multi-subject-element fault association network.
The multi-main-element multi-dimensional incidence network is a network set formed by overlapping incidence relation networks of different types, wherein elements of incidence relations of the same type are divided into the same incidence relation network. The incidence relation comprises a control constraint relation or a data incidence relation, and the control constraint relation comprises a guarantee equipment constraint relation, a guarantee process constraint relation or a guarantee part constraint relation; the data incidence relation comprises a data output incidence relation or a data input incidence relation. The constraint relation of the equipment is guaranteed, for example, the refueling equipment of an aviation guarantee system is taken as an example, the equipment can only refuel one airplane at a designated stand at a certain moment, and when a plurality of airplanes need to refuel the equipment at the same time, the equipment is queued in sequence according to the priority level. The guarantee flow constraint relationship, for example, in the case of an aviation guarantee system, mainly refers to a logic sequence of related guarantee operations, such as the fueling operation and the maintenance operation of a certain aircraft cannot be performed in parallel, and can be performed sequentially. The constraint relation of the guarantee positions, such as taking an aviation guarantee system as an example, in the guarantee process, each operation event of each airplane can be completed at only one guarantee position; each guarantee location can be used by a plurality of airplanes to complete a plurality of operation events, but each guarantee location can be used by only one airplane at the same time.
The fault model of the complex system guarantee resource comprises fault types, fault occurrence probabilities of different types and fault occurrence times of different types. The types of faults include faults that can be repaired on the spot, faults that need to be reworked, and faults that cannot be repaired.
Taking the fault injection of an aviation safeguard resource model as an example, aiming at aviation safeguard system resources, such as refueling equipment, the probability of faults occurring in the operation process of the aviation safeguard system needs to be considered, so that the faults are described by adopting a homogeneous poisson process { N (t), t is more than or equal to 0}, and three types of faults are considered here:
(1) Minor faults that can be repaired on the spot;
(2) a fault needing to be returned to the equipment factory for repair;
(3) A failure that cannot be repaired.
The probability of occurrence of the three types of faults is p1,p2,1-p1-p2At time intervals (0, t)]Number of occurrences of internal and external three faults N1(t)、N2(t)、N3(t)。
And the occurrence of the three fault events are independent of each other, and the interval time compliance parameter of each fault i (i is 1,2 and 3) is piNegative exponential distribution of λ (i ═ 1,2, 3).
And (II) constructing a complex system resource optimization multi-target planning model according to the multi-subject element fault association network and the complex system resource multi-scale model, namely forming the complex system resource optimization multi-target planning model according to an association constraint relation among elements in the multi-subject element fault association network and the complex system resource multi-scale model, wherein the multi-target planning model is a set of a guarantee operation time model, a guarantee part selection model, a guarantee part conflict occupation model and a guarantee equipment conflict occupation model, and further comprises a conflict occupation model of a tractor for the aviation guarantee system. The association constraint relation comprises a control constraint relation or a data association relation, and the control constraint relation comprises a guarantee equipment constraint relation, a guarantee process constraint relation or a guarantee part constraint relation; the data incidence relation comprises a data output incidence relation or a data input incidence relation.
the operation time guaranteeing model comprises a working mode of guaranteeing operation and working time of guaranteeing operation, wherein the working mode of guaranteeing operation is as follows: after each guarantee operation is started, the guarantee operation cannot be stopped until the guarantee operation is finished, and the working time of the guarantee operation is the sum of the preparation time and the execution time.
The model for ensuring the part selection is as follows: and corresponding guarantee equipment is arranged at a guarantee part where the guarantee operation is located, and the guarantee part is in an idle available state at present.
the guarantee part conflict occupation model is as follows: and when more than one security object needs to perform security operation on the same security position, selecting the security object with the highest priority to perform the security operation. The priority rules may be set by themselves as desired.
The conflict occupation model of the security device is as follows: when more than one security object needs to use the same security equipment for security operation, the security object with the highest priority is selected to use the security equipment. The priority rules may be set by themselves as desired.
The complex system resource multi-scale model comprises a human model or a guarantee resource model.
The construction method of the human model comprises the following steps:
(1) And constructing a personnel individual model according to the working type of the personnel, wherein the personnel individual model comprises a cognitive fatigue model and a physical fatigue model, for example, constructing the cognitive fatigue model according to a commander, and constructing the physical fatigue model according to support personnel (a mechanical support personnel and a service support personnel).
(2) And constructing a personnel team model according to the personnel individual model, wherein the personnel team model comprises the personnel individual model and team characteristic elements, and the team characteristic elements comprise team work targets, work environments and the like.
the resource guarantee model comprises static attributes and dynamic behaviors, wherein the static attributes comprise the positions and the number of the guarantee devices, guarantee positions and guarantee objects, and the dynamic behaviors comprise the calculation of the service time of the guarantee resources. And calculating the service time of the guaranteed resources by adopting a mathematical statistical method according to the collected service data of the guaranteed resources.
Taking the resource optimization scheduling multi-objective planning model of the aviation safeguard system as an example, based on the above constraints, the scheduling of the aviation safeguard operation needs to solve the following problems: for any aircraft, operational event i:
(1) Finishing an operation event i at a certain position of the airplane;
(2) And what security equipment is used by the airplane to finish the operation event i;
(3) When the airplane starts to operate an event i and when the airplane finishes the operation event i;
(4) And when the aircraft enters the guarantee location required for completion of the operational event i and when it leaves the guarantee location for completion of the operational event i.
Thus, according to the above rules, the following solving method is generated:
(1) Machining time rule model
It is specified that after each job event is started, the work process for that job event cannot be stopped, and the completion time for that job event is the start time plus (+) the execution time for that job event.
For any aircraft j, any operational event i, there are:
Ci,j＝Si,j+pij
Wherein: si,jis the start time of the job; p is a radical ofijFor the execution time of the job event, Ci,jIs the completion time of the job.
(2) selection rule model for guarantee part
if the operation event i requires security equipment, for example, a refueling operation event requires jet fuel, the security location can only be selected to be performed at a nearby security location with this type of security equipment.
if the operation event i does not need security equipment, the operation event can be stopped at any security position for completion. If such an operation event i belongs to a parallel set pariIf pariAll the work in the group does not need to guarantee equipment, and all the work events in the group are finished in parallel on one guarantee part; if pariIf the operation event needing to guarantee the equipment exists, the operation event i and the operation event which can be executed at the earliest time in the group, uses the same guarantee part and needs to guarantee the equipment are executed in parallel:
i∈pariIf ii e par for all job eventsi，nMiiIf 0, then for all job events ii there are
Si,j＝Sii,j
Bi,j＝Bii,j
SPi,j＝SPii,j
i∈pariIf there is a job event ii ∈ pari，Bi,j＝Bii,j，nMiiNot equal to 0, then
Si,j＝min(Sii,j)(ii∈pari，nMii≠0)
SPi,j＝SPii,j(Si,j＝min(Sii,j))
Wherein: nMiinumber of secured devices required for working time ii, Si,jStart time of operation i, S for aircraft jii,jStart time of operation ii for aircraft j, Bi,jposition of operation i for aircraft j, Bii,jPosition, SP, at which operation ii is to be carried out for aircraft ji,jFor the start time of the arrival of the aircraft j at the location of the operation i, SPii,jIs the starting time for aircraft j to reach the location where operation ii is to be performed.
(3) Conflict occupation rule model of guarantee part
Because the guarantee position and the guarantee equipment are limited, the situation that a plurality of airplanes use the same guarantee position for guarantee can occur in the warship surface guarantee process, and under the situation, the airplane with high airplane takeoff priority uses the conflict guarantee position in advance:
Namely: if rankj>rankj',Bi,j＝Bi',j'Then
SPi',j'≥CPi,j
wherein: rankj、rankj’Priority levels for the j-th and j' th aircraft, respectively, Bi,j、Bi',j'The working positions SP of the j-th frame and the j 'th frame of the airplane required for the i-th operation and the i' th operation respectivelyi',j'starting time, CP, for the jth aircraft to arrive at the location where the ith' operation was performedi,jThe end time of the j-th aircraft at the position where the i-th operation is performed.
Under the condition that the take-off priorities of a plurality of airplanes are the same, the airplane with the station for processing and working for less time preferentially uses the guarantee part. When the two airplane use guarantee parts conflict, the airplane with shorter processing operation time is selected to preferentially use the guarantee parts, so that the effect of optimizing and finishing the total time of the warship surface guarantee can be achieved.
If it is not
SPi',j'≥CPi,j
Wherein: i is addedworking hours; SPi',j'for the time of the ith' operation start of the jth aircraft, CPi,jthe time of the ith operation of the jth aircraft is the time of the ith operation; b isi,j、Bi',j'The guarantee positions required by the ith operation and the ith' operation of the jth and jth airplanes, respectively, Bii,j、Bii,j'The guarantee positions required by the j & ltth & gt aircraft and the j & ltth & gt aircraft in the ii & ltth & gt operation are respectively, and p is the guarantee position.
(4) Conflict occupancy rule model for provisioning devices
Because the guarantee equipment is limited, the situation that the same operation event of a plurality of airplanes is guaranteed by using the same guarantee equipment can occur in the surface guarantee, and under the situation, the airplane with high airplane takeoff priority uses the conflict guarantee equipment in advance:
Namely: if rankj>rankj',Mi,j＝Mi,j'then
Si,j'≥Ci,j
wherein: mi,j、Mi,j'guarantee equipment adopted by the jth and jth airplanes in the ith operation respectively; rankj、rankj’Priority levels, S, for the j-th and j' th aircraft, respectivelyi,j'starting time of i-th job for j' th rack, Ci,jand (5) carrying out the end time of the ith job for the jth machine.
If the take-off priorities of the airplanes are the same, the airplane with the guaranteed part with the stronger processing capacity is firstly allowed to use the guarantee equipment, so that the guaranteed part with the stronger processing capacity is vacated more quickly to complete the guarantee tasks of the rest airplanes:
Namely: if it is not
Si,j'≥Ci,j
wherein: mi,j、Mi,j'Guarantee equipment adopted by the jth and jth airplanes in the ith operation respectively; si,j'initiation of i-th job for j' -th rackTime, Ci,jand (5) carrying out the end time of the ith job for the jth machine.
(5) Conflict occupancy rule model for tractor
In the ship surface guarantee process of the aviation guarantee system, due to the fact that the number of the tractors is limited, the situation that a plurality of airplanes use the same tractor to move can occur, and the aircraft can be selected to use the tractor to finish moving work at the earliest time in a priority mode.
j1,j2,...,jcaircraft in use, respectively, tractor 1,2, …, c, i1,i2,...,icRespectively for the operation events finished when the airplanes go to the next guarantee position:
For an aircraft and its work event i to be completed by changing the security location, there are
wherein: CP (CP)i-1,jthe time for finishing the i-1 operation of the jth airplane; SPi',j'Starting time of i 'th work for j' th aircraft, SPi”,j”The start time of the i-th operation for the j-th aircraft, carga is the time interval between two uses of the tractor, cari,jTractor id, car used for ith work for jth framei”,j”Is the tractor id used by the jth "frame machine to perform the ith" job.
And (III) receiving the task requirements of the complex system, and calculating to obtain an optimal complex system resource scheduling solution according to the complex system resource optimization multi-objective planning model. The specific method comprises the following steps:
(1) Receiving task requirements of a complex system, and generating an initial rough solution set by using a random sampling method, wherein the initial rough solution set comprises guarantee objects, guarantee parts, guarantee equipment, guarantee operation and guarantee time elements and incidence relations among the elements, and the incidence relations meet element incidence constraint relations in a multi-subject element fault incidence network. Taking an aviation guarantee system as an example, a guarantee object is an airplane, a guarantee part is an airplane parking space, guarantee equipment comprises refueling equipment, power supply equipment, oxygen supply equipment and the like, guarantee operations comprise refueling operations, power supply operations, dispatching operations and the like, guarantee time refers to time consumed by a certain airplane to carry out certain guarantee operations at a certain airplane parking space, and the time for refueling operations is 20 minutes.
The task requirements of the complex system are as follows: providing guarantee resources and guarantee positions, performing Y guarantee operations on N guarantee objects, and ensuring the required guarantee operations only by adopting the required guarantee resources at the required guarantee positions; the guarantee resource comprises M guarantee devices, Z guarantee positions are provided, and the time of each guarantee operation is TYwherein N, M, Y, Z are all positive integers, T in the embodiment of the inventionYThe value range of (1) to (30) min, taking a positive integer.
for example, in an alternative embodiment of the present invention, N is 5, Y is 6, M is 7, Z is 11, T1 is 3, T2 is 3, T3 is 9, T4 is 4, T5 is 4, and T6 is 10.
(2) And according to the complex system resource optimization multi-objective planning model, sorting the solutions in the initial rough solution set according to the shortest time and the least moving times, and selecting the first N solutions as an optimal solution set. The sequencing rule is as follows: the shortest guarantee time and the fewest movement times of the guaranteed objects are taken as targets, the scores of the solutions are comprehensively calculated according to set weights, the solutions are better when the scores are smaller, the ordering is more advanced, and the value of N is preferably 30-100.
The solution score calculation formula is as follows:
Wherein: scoreiis the score of the ith solution, WSTFor time-preserving weighting, STiguaranteed time for ith solution, max (ST)N) For the maximum value of the guarantee time in N solutions, WSMIs the weight of the number of movementsHeavy, SMinumber of moves, max (ST), for the ith solutionM) Is the maximum of the number of moves in N solutions, where WST+WSM1, the integrated score interval of each solution is [0,1]。
assuming that the value of N is 30, assuming that the guaranteed time of the solution a with the longest guarantee time among the 30 solutions is 60min, the number of times of movement is 2, the guaranteed time of the solution b with the longest guarantee time is 50min, the number of times of movement is 3, and the guaranteed time of one solution c is 40min and the number of times of movement is 1. The weights of the moving time and the moving times are respectively 0.7 and 0.3 according to the expert experience, the score of a solution a is 0.9, the score of a solution b is 0.8833, the score of a solution c is 0.5667 according to the formula, and the like, the score of the 30 solutions is calculated, the solution with the minimum score represents the optimal solution, and the solution with the score in the first 30 digits is selected as the optimal solution set.
(3) And obtaining an optimal scheduling solution by utilizing an improved genetic algorithm according to the optimal solution set, wherein the specific method comprises the following steps:
(3.1) performing cross operation on the N initial solutions in the optimal solution set to obtain N crossed solutions, wherein the cross operation can be performed by adopting four methods as follows:
The method (3.1.1) randomly selects a guarantee object j, and crosses the guarantee object j in the ith initial solution and the (i + 1) th initial solution, namely, only one guarantee object j is crossed.
the method (3.1.2) randomly selects a guarantee object j, and correspondingly crosses the 1 st to the jth guarantee object (namely the previous j guarantee objects) in the ith initial solution and the (i + 1) th initial solution respectively; that is, the positions, devices and jobs of the first j safeguard objects are correspondingly crossed, for example, in the ith solution, which job is performed at which position by the 1 st to the jth safeguard objects, which devices are already arranged, in the ith solution, the arrangement of the first j safeguard objects of the ith solution and the ith +1 th solution is exchanged, that is, in the two solutions, the 1 st and the 1 st are exchanged, the 2 nd and the 2 nd are exchanged … …, and the j th are exchanged.
The method (3.1.3) randomly selects a guarantee object j, randomly selects a position node t of the guarantee object j, of which the guarantee position changes in the ith and (i + 1) th initial solutions, and respectively and correspondingly intersects guarantee operations behind the position node t in the ith and (i + 1) th initial solutions, namely interchanges.
The method (3.1.4) randomly selects a security object j, finds out all the same positions of the security object j arranged at the ith and (i + 1) th initial solutions, randomly selects a same position m, and crosses the security devices used at the position m by the ith and (i + 1) th initial solutions.
(3.2) carrying out mutation operation on the N solutions formed after the crossing by adopting the following three methods to obtain N solutions after the mutation;
The method (3.2.1) randomly selects a guarantee object j for the ith solution, finds out all position nodes of the guarantee object j with changed positions, randomly selects a position node t from the position nodes, and performs mutation on the position node t, wherein the mutation operation is to randomly select a guarantee position from all guarantee positions meeting the operation requirements of the position node t and guarantee equipment meeting the operation requirements in the position to replace the position node t and the guarantee equipment of the position node t.
The method (3.2.2) for the ith solution, randomly selecting a guarantee object j, finding out all position nodes of the guarantee object j with changed positions, then randomly selecting a position node t from the position nodes, carrying out mutation on the position node t and all position nodes behind the position node t, wherein the mutation operation is traversing the position node t to the last node, firstly randomly selecting a guarantee position from all guarantee positions meeting the operation requirement of the node t and guarantee equipment meeting the operation requirement in the position to replace the guarantee equipment of the position node t and the node t, then randomly selecting a guarantee position from all guarantee positions meeting the operation requirement of the node t +1 and guarantee equipment meeting the operation requirement in the position to replace the guarantee equipment of the position node t +1 and the node t +1, … …, and so on until randomly selecting a guarantee position from all guarantee positions meeting the operation requirement of the last node and ensuring position and ensuring that the operation is met in the position And the required guarantee equipment replaces the guarantee equipment of the last position node and the guarantee equipment of the last position node.
and (3.2.3) randomly selecting a guarantee object j for the ith solution, randomly selecting a guarantee operation, and mutating guarantee positions and guarantee equipment used by the guarantee operation.
(3.3) calculating the scheduling time of each solution according to various constraint rule conditions in the complex system resource optimization multi-objective planning model for the original N solutions, the crossed N solutions and the mutated N solutions to obtain 3N solutions in total, selecting the N solutions with the shortest scheduling time as new initial solutions, and entering step (3.4);
(3.4) if the set iteration times are reached, entering the step (3.5), and if the set iteration times are not reached, returning to the step (3.1);
and (3.5) selecting the solution with the shortest scheduling time from the N new initial solutions with the shortest time obtained finally as the optimal scheduling scheme.
Taking an aviation support system as an example:
According to the constraint condition and the multi-objective optimization rule in the aviation guarantee operation, the guarantee position and the guarantee equipment of each operation event of a group of aircrafts for common ship surface guarantee are determined, then the scheduling condition of the operation is calculated according to the constraint relation among related elements in the multi-dimensional association network, and then the size of the objective function is calculated. Therefore, the complex warship surface guarantee process can be disassembled into the determination process of the guarantee part and the related guarantee equipment thereof.
How to give a reasonable airplane guarantee sequence on each guarantee position to prevent the situation that the airplane has no guarantee, and simultaneously and reasonably arranging corresponding personnel and equipment, thereby efficiently finishing all guarantee operations of the airplane contained in the whole flight plan.
Because the search range is too large and exceeds the effective working range of a general intelligent algorithm, the genetic algorithm added with the search rule can be considered for solving, and finally, in order to prevent the jump-in local optimal solution from being unable to jump out, a proper variation rule needs to be added for solving, and the specific solving process is as follows:
(1) generating an initial rough solution set by using a random sampling method, wherein the selection of the initial rough solution is required to accord with the availability constraints of security objects, security parts, security equipment, security operation and security time elements, the constraints are obtained according to the element association constraint relation in the main element fault association network, and the expression mode of the initial rough solution is shown in table 1:
TABLE 1 rough solution representation
(2) and according to the initial rough solution set, various rule sets in the multi-target planning model and various operation times of the aviation guarantee operation flow, aiming at the solutions in the initial rough solution set according to the shortest guarantee time and the minimum guarantee object moving times, calculating the scores of the solutions according to set weights, sequencing the solutions, and selecting the first 30 solutions as the optimal solution set.
(3) And according to the initial solution set, reserving the first 30 better solutions in the initial solution set, and generating and outputting an optimal scheduling result according to an improved genetic algorithm. The main steps for improving the genetic algorithm are as follows:
and (3.1) crossing. And traversing 30 solutions, wherein the interval is 2 each time, and taking an odd solution i and an even solution i + 1. And (3) initially de-interleaving, and randomly selecting one of the following 4 interleaving modes:
(3.1.1) one workpiece crossover: randomly selecting an airplane j, and crossing the guarantee position and the guarantee equipment of the j airplane in the ith initial solution and the (i + 1) th initial solution.
(3.1.2) multiple workpiece intersections: randomly selecting an airplane j, and correspondingly crossing the 1 st airplane to the jth airplane in the ith initial solution and the (i + 1) th initial solution respectively.
(3.1.3) crossing the position of a certain airplane under the condition of position change and the corresponding machine: randomly selecting an airplane j, randomly selecting a node t of the airplane with guarantee position change in the ith and (i + 1) th initial solutions, and correspondingly crossing guarantee operations behind the node t in the ith and (i + 1) th initial solutions respectively.
(3.1.4) machine crossing over the places with the same position: randomly selecting an airplane j, finding all the same positions of the airplane in the ith and (i + 1) th initial solutions, randomly selecting a same position m, and crossing the machines used by the ith and (i + 1) th initial solutions at the position m.
After interleaving, 30 solutions are generated.
(3.2) mutation. The cross offspring may have variation, and the variation operation is performed on 30 solutions formed after the cross, and one of the following 3 variation modes is randomly selected:
(3.2.1) random position of a random aircraft where position changes occur and the corresponding machine variation: randomly selecting an airplane i, randomly selecting a node t with a changed position of the airplane, and randomly selecting a guarantee position and guarantee equipment capable of meeting the operation requirement in the position from all guarantee positions meeting the operation requirement of the position node t to replace the guarantee equipment of the position node t and the guarantee equipment of the t node.
(3.2.2) a series of positions and corresponding machine variations following the random position of a random aircraft where the change in position occurred: randomly selecting an airplane j, randomly selecting a node t with the position changed of the airplane, traversing the position node t to the last node in the mutation operation, randomly selecting a guarantee position and guarantee equipment in the position from all guarantee positions meeting the operation requirement of the node t to replace the guarantee equipment of the position node t and the node t, randomly selecting a guarantee position and guarantee equipment in the position from all guarantee positions meeting the operation requirement of the node t +1 to replace the guarantee equipment of the position node t +1 and the node t +1, … …, and so on until randomly selecting a guarantee position and guarantee equipment in the position from all guarantee positions meeting the operation requirement of the last node to replace the guarantee equipment of the last position node and the node.
(3.2.3) a machine variation corresponding to a random position: randomly selecting an airplane i, randomly selecting an operation j, and mutating the position and the machine used by the operation.
After the mutation, 30 solutions are generated.
(3) And (6) evaluating. The 30 solutions (the original 30 solutions, the 30 solutions after intersection and the 30 solutions after variation) of the new and old different generations are evaluated in total by 90 solutions, and the 30 solutions with the shortest time are selected as the new initial solutions according to the total scheduling time of each solution.
(4) And (6) iteration. And if the set iteration times are not reached, based on the 30 new initial solutions, executing the step 1) until the iteration is finished, and taking the optimal solution with the shortest scheduling time from the last 30 better solutions as the optimal scheduling scheme.
specific examples are as follows:
For example, the first solution A is of the form
For example, the second solution B is of the form
if the cross-over mode is performed for one workpiece,
Randomly selecting an airplane, for example, a first airplane, and exchanging all the positions of all the operations of the first airplane in the solution a and the solution B and all the machines used at the positions, wherein the changes are as follows:
the first solution after A crossing is of the form
The second solution after B crossing is of the form
The evaluation was based on the time taken for each solution. Assuming a refueling time of t1Oxygen supply time is t2The power supply time is t3The aircraft moving speed is v1The distance between the No. 1 position and the No. 2 position is d1,2And the other distances are analogized in turn.
Time T for first solution AAcomprises the following steps:
max((t1+d1,3/v1+t2),(t1+d2,4/v1+t3))
Time T for first solution BBComprises the following steps:
max((t2+d3,5/v1+t3),(t1+d4,6/v1+t3))
Comparison TAand TBThe size of (2) is better and the time is shorter.
FIG. 2 is a flowchart of an aviation support system optimization scheduling method according to an embodiment of the present invention.
FIG. 3 shows the convergence curve of the improved genetic algorithm in the embodiment of the present invention.
By means of the optimal scheduling scheme, the maximum guarantee capacity of the aviation guarantee system under the specific task requirement can be found, guarantee time is further shortened, the problem of multi-objective optimization decision making under the condition of large-scale limited resources is solved, efficiency and quality are improved, the balance rate of aviation guarantee resource use is improved, guarantee processes are more reasonable, comprehensive efficiency of the aviation guarantee system is improved, and the comprehensive capacity of the aviation guarantee system is improved to the maximum extent.
the invention also provides a complex system resource optimization scheduling system under the multi-constraint condition, which comprises a fault associated network construction module, a multi-objective planning model construction module and a complex system resource scheduling solution calculation module, wherein:
And the fault associated network building module is used for injecting a fault model of the complex system guarantee resource into the multi-main-element multi-dimensional associated network of the complex system to form the multi-main-element fault associated network and sending the multi-main-element fault associated network to the multi-objective planning model building module.
And the multi-objective planning model building module builds a complex system resource optimization multi-objective planning model according to the multi-subject element fault association network and the complex system resource multi-scale model and sends the complex system resource optimization multi-objective planning model to the complex system resource scheduling solution calculation module.
And the complex system resource scheduling solution calculating module is used for receiving the task requirements of the complex system, optimizing the multi-objective planning model according to the complex system resources and calculating to obtain the optimal complex system resource scheduling solution.
The functions of the modules are similar to those described above for the method, and are not described again here.
in the application of the invention in an aviation guarantee system, aiming at typical guarantee tasks, based on the scheduling model and algorithm of the invention, a better scheduling scheme can be provided in a shorter time, so that the use of guarantee resources is more balanced, the guarantee flow is more reasonable, and the comprehensive capability of the aviation guarantee system is improved to the greatest extent.
The above description is only for the best mode of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.

Claims (22)

1. A complex system resource optimization scheduling method under the condition of multiple constraints is characterized in that: the method comprises the following steps:
Injecting a fault model of a complex system guarantee resource into a multi-main-element multi-dimensional association network of the complex system to form a multi-main-element fault association network;
Constructing a complex system resource optimization multi-objective planning model according to the multi-subject element fault correlation network and the complex system resource multi-scale model;
and receiving the task requirement of the complex system, and optimizing the multi-objective planning model according to the complex system resources to obtain an optimal complex system resource scheduling solution.
2. the method according to claim 1, wherein the method comprises: the multi-main-element multi-dimensional incidence network is a network set formed by overlapping incidence relation networks of different types, wherein elements of incidence relations of the same type are divided into the same incidence relation network.
3. The method according to claim 1, wherein the method comprises: the complex system resource multi-scale model comprises a human model or a guarantee resource model.
4. The method according to claim 3, wherein the method comprises: the construction method of the human model comprises the following steps:
(1) Constructing a personnel individual model according to the personnel working type, wherein the personnel individual model comprises a cognitive fatigue model and a physical fatigue model;
(2) and constructing a personnel team model, wherein the personnel team model comprises the personnel individual model and team characteristic elements.
5. The method according to claim 4, wherein the method comprises: constructing a cognitive fatigue model according to commanders, and constructing a physical fatigue model according to security personnel; the team characteristic elements comprise team work targets and work environments.
6. The method according to claim 3, wherein the method comprises: the guaranteed resource model comprises static attributes and dynamic behaviors; the static attributes comprise the positions and the number of the security equipment, security positions and security objects, and the dynamic behaviors comprise security resource use time calculation formulas.
7. The method according to claim 1, wherein the method comprises: the fault model of the complex system guarantee resource comprises fault types, fault occurrence probabilities of different types and fault occurrence times of different types.
8. the method according to claim 1, wherein the method comprises: and forming a complex system resource optimization multi-target planning model according to the association constraint relation among the elements in the multi-main-element fault association network and the complex system resource multi-scale model, wherein the multi-target planning model is a set of a guarantee operation time model, a guarantee part selection model, a guarantee part conflict occupation model and a guarantee equipment conflict occupation model.
9. The method according to claim 8, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: the operation time guaranteeing model comprises a working mode and operation time guaranteeing, wherein the working mode is as follows: after each guarantee operation is started, the guarantee operation cannot be stopped until the guarantee operation is finished, and the working time of the guarantee operation is the sum of the preparation time and the execution time.
10. The method according to claim 8, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: the model for selecting the guarantee part is as follows: and corresponding guarantee equipment is arranged at a guarantee part where the guarantee operation is located, and the guarantee part is in an idle available state at present.
11. The method according to claim 8, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: the guarantee part conflict occupation model is as follows: and when more than one security object needs to perform security operation on the same security position, selecting the security object with the highest priority to perform the security operation.
12. The method according to claim 8, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: the conflict occupation model of the safeguard equipment is as follows: when more than one security object needs to use the same security equipment for security operation, the security object with the highest priority is selected to use the security equipment.
13. The method according to claim 8, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: the complex system comprises an aviation support system or a combat system.
14. The method according to claim 1, wherein the method comprises: the specific method for receiving the task requirement of the complex system and calculating to obtain the optimal complex system resource scheduling solution according to the complex system resource optimization multi-objective planning model is as follows:
(1) Receiving task requirements of a complex system, and generating an initial rough solution set by using a random sampling method, wherein the initial rough solution set comprises a guarantee object element, a guarantee part element, a guarantee equipment element, a guarantee operation element, a guarantee time element and an incidence relation among the elements, and the incidence relation meets an element incidence constraint relation in a multi-body element fault incidence network;
(2) sorting the solutions in the initial rough solution set according to a complex system resource optimization multi-objective planning model, and selecting the first N solutions as an optimal solution set;
(3) and obtaining an optimal scheduling solution by utilizing an improved genetic algorithm according to the optimal solution set.
15. The method according to claim 14, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: in the step (2), the solutions in the initial rough solution set are sorted, and the sorting rule is as follows: the shortest guarantee time and the fewest movement times of the guarantee objects are taken as targets, the scores of the solutions are comprehensively calculated according to the set weight, the smaller the score is, the better the solution is, and the ranking is forward.
16. The method according to claim 15, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: the solution score calculation formula is as follows:
Wherein: scoreiIs the score of the ith solution, WSTfor time-preserving weighting, STiGuaranteed time for ith solution, max (ST)N) For the maximum value of the guarantee time in N solutions, WSMFor the weight of the number of movements, SMinumber of moves, max (ST), for the ith solutionM) Is the maximum of the number of moves in N solutions, where WST+WSMeach solution value interval is [0,1 ═ 1]。
17. The method according to claim 14, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: and (3) the value of N in the step (2) is 30-100.
18. The method according to claim 14, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: the task requirements of the complex system are as follows: providing guarantee resources and guarantee positions, performing Y guarantee operations on N guarantee objects of different types, and ensuring the required guarantee operations only by adopting the required guarantee resources at the required guarantee positions; the guarantee resource comprises M guarantee devices, Z guarantee positions are provided, and the time of each guarantee operation is TYWherein N, M, Y, Z are all positive integers.
19. The method according to claim 14, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: according to the optimal solution set, a specific method for obtaining an optimal scheduling solution by utilizing an improved genetic algorithm is as follows:
(1) Performing cross operation on the N initial solutions in the optimal solution set to obtain N crossed solutions;
(2) carrying out mutation operation on the N solutions formed after the crossing to obtain N solutions after the mutation;
(3) Calculating the scheduling time of each solution by using the original N solutions, the crossed N solutions and the mutated N solutions to total 3N solutions, selecting the N solutions with the shortest scheduling time as new initial solutions, and entering the step (4);
(4) If the set iteration times are reached, the step (5) is carried out, and if the set iteration times are not reached, the step (1) is returned;
(5) And selecting the solution with the shortest scheduling time from the N new initial solutions as the optimal scheduling scheme.
20. the method according to claim 19, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: in the step (1), the N initial solutions in the optimal solution set are subjected to cross operation by adopting the following four methods:
The method comprises the steps of (A) randomly selecting a security object j, and crossing the security object j in the ith initial solution and the (i + 1) th initial solution;
The method (II) randomly selects a security object j, and correspondingly crosses the 1 st to the jth security object in the ith initial solution and the (i + 1) th initial solution respectively;
randomly selecting a guarantee object j, randomly selecting a position node t with a guarantee position changed in the ith and (i + 1) th initial solutions of the guarantee object j, and crossing guarantee operations behind the position node t in the ith and (i + 1) th initial solutions;
And (IV) randomly selecting a security object j, finding out all the same positions of the security object j at the ith and (i + 1) th initial solutions, randomly selecting a same position m, and crossing security equipment used by the ith and (i + 1) th initial solutions at the position m.
21. The method according to claim 19, wherein the complex system resource optimal scheduling method under multiple constraints is as follows: in the step (2), the N solutions formed after the crossing are subjected to mutation operation by adopting the following three methods:
The method comprises the steps that (A) for the ith solution, a guarantee object j is randomly selected, all position nodes of the guarantee object j with changed positions are found out, a position node t is randomly selected from the position nodes, the position node t is subjected to variation, and the variation operation is that one guarantee position is randomly selected from all guarantee positions meeting the operation requirements of the position node t, and guarantee equipment meeting the operation requirements in the position replaces the position node t and guarantee equipment of the position node t;
The method (II) randomly selects a guarantee object j for the ith solution, finds out all position nodes of the guarantee object j with changed positions, then randomly selects a position node t from the position nodes, performs variation on the position node t and all position nodes behind the position node t, the variation operation is traversing the position node t to the last node, firstly randomly selects a guarantee position from all guarantee positions meeting the operation requirement of the node t and guarantee equipment meeting the operation requirement in the position to replace the guarantee equipment of the position node t and the node t, then randomly selects a guarantee position from all guarantee positions meeting the operation requirement of the node t +1 and guarantee equipment meeting the operation requirement in the position to replace the guarantee equipment of the position node t +1 and the node t +1, … …, and the like in sequence until randomly selects a guarantee position from all guarantee positions meeting the operation requirement of the last node and guarantee position and guarantee equipment meeting the operation requirement in the position The barrier equipment replaces the last position node and the guarantee equipment of the last position node;
And (III) for the ith solution, randomly selecting a guarantee object j, randomly selecting a guarantee operation, and mutating guarantee positions and guarantee equipment used by the guarantee operation.
22. A complex system resource optimization scheduling system under the condition of multiple constraints is characterized in that: the method comprises a fault associated network construction module, a multi-target planning model construction module and a complex system resource scheduling solution calculation module, wherein:
a fault correlation network construction module: injecting a fault model of a complex system guarantee resource into a multi-main-element multi-dimensional association network of the complex system to form the multi-main-element fault association network, and sending the multi-main-element fault association network to a multi-objective planning model construction module;
The multi-target planning model building module: constructing a complex system resource optimization multi-objective planning model according to the multi-main-element fault correlation network and the complex system resource multi-scale model, and sending the complex system resource optimization multi-objective planning model to a complex system resource scheduling solution calculation module;
the complex system resource scheduling solution calculation module: and receiving the task requirement of the complex system, and calculating to obtain an optimal complex system resource scheduling solution according to the complex system resource optimization multi-objective planning model.
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冯强等: "基于多主体的舰载机综合保障过程建模方法", 《系统工程与电子技术》 *
彭鹏菲等: "基于改进粒子群优化的多目标装备保障任务规划方法", 《系统工程与电子技术》 *
田丰等: "场站飞行后勤保障车辆调度仿真优化", 《计算机工程与设计》 *

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CN111882145A (en) * 2020-06-10 2020-11-03 中国人民解放军海军航空大学 Airplane service support serial scheduling method considering resource transfer time
CN111882145B (en) * 2020-06-10 2022-03-11 中国人民解放军海军航空大学 Airplane service support serial scheduling method considering resource transfer time
CN111506413A (en) * 2020-07-02 2020-08-07 上海有孚智数云创数字科技有限公司 Intelligent task scheduling method and system based on business efficiency optimization
CN111506413B (en) * 2020-07-02 2020-09-18 上海有孚智数云创数字科技有限公司 Intelligent task scheduling method and system based on business efficiency optimization
CN113848821A (en) * 2020-10-27 2021-12-28 上海飞机制造有限公司 Quality detection method, device, equipment and medium based on industrial neural network
CN113269432A (en) * 2021-05-20 2021-08-17 北京航空航天大学 Service bearing capacity evaluation method based on element aggregation network

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