CN112785079A - Equipment system guarantee scheme optimization method based on mixed heuristic algorithm - Google Patents

Equipment system guarantee scheme optimization method based on mixed heuristic algorithm Download PDF

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CN112785079A
CN112785079A CN202110143685.5A CN202110143685A CN112785079A CN 112785079 A CN112785079 A CN 112785079A CN 202110143685 A CN202110143685 A CN 202110143685A CN 112785079 A CN112785079 A CN 112785079A
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樊冬明
冯强
任羿
孙博
杨德真
王自力
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Abstract

The invention provides an equipment system guarantee scheme optimization method based on a mixed heuristic algorithm, which comprises the following specific steps of: step (1): describing the requirements of the equipment guarantee model according to the state of the equipment; step (2): describing each guarantee site according to the guarantee resource condition required by the equipment; and (3): optimizing constraint modeling of a guarantee scheme; and (4): evaluating the cost of a complex equipment system guarantee scheme; and (5): generating a specific complex equipment system guarantee scheme according to the relevant constraint conditions; the invention provides an equipment system guarantee scheme optimization method based on a mixed heuristic algorithm, which aims at the mapping relation between an equipment system and a guarantee site, the geographical position of the equipment system and the layout form of the equipment system, and generates an equipment system guarantee scheme under the conditions of guaranteeing facility distribution constraint, service constraint, support personnel constraint and guarantee time constraint according to the guarantee requirements of each equipment. The method generates a feasible guarantee scheme for a complex equipment system through a hybrid heuristic algorithm.

Description

Equipment system guarantee scheme optimization method based on mixed heuristic algorithm
(I) in the field of technology
The invention provides an equipment system guarantee scheme optimization method based on a mixed heuristic algorithm, which aims at the geographical position and the layout form of an equipment system and generates an equipment system guarantee scheme under the condition of ensuring facility distribution constraint, service constraint, support personnel constraint and support time constraint according to the support requirements of each equipment. The method generates a feasible guarantee scheme for the complex equipment system through a hybrid heuristic algorithm so as to guarantee the reliable operation of the equipment system. The invention belongs to the technical field of reliability engineering.
(II) background of the invention
The modern war is a combined war integrating five places of land, sea, air, sky and electromagnetism, and is essentially the confrontation of an equipment system to the equipment system. Under the condition of combined operation, the problem of guaranteeing an equipment system is more complex and outstanding, and in order to fully exert the operation efficiency of the equipment system, related guarantee plans, transportation scheduling of spare parts, planning of guarantee schemes and the like become one of hot topics in recent years. According to the statistics of equipment system reports published in 2010, the operating and maintenance costs of an equipment system typically account for about 20% -35% of its life cycle. The high-efficiency guarantee scheme can reduce the operation cost of the complex equipment system, improve the availability and the operational efficiency of the complex equipment system and play a decisive role in modern war.
For a complex equipment system, due to the badness of the operating environment and the strong timeliness of guaranteeing the work, the following constraints are generally considered in the guarantee scheme: 1) and ensuring the working time window. In view of the performance and safety of the transport equipment, the security work can only be performed within a time window that meets the transport conditions. Meanwhile, due to the fact that the operation environment changes frequently, the working time window is guaranteed to be discrete; 2) and the resource availability is guaranteed. In consideration of various heterogeneous elements in a complex equipment system, the types of special/general guarantee equipment, spare parts, guarantee personnel and the like are more. Based on the guarantee scheme, if various guarantee resources can complete the guarantee task of the equipment, the guarantee operation can be carried out, otherwise, the waiting is needed; 3) resource transportation constraint is guaranteed. The size and weight limits of transportation equipment (such as vehicles, ships, airplanes and the like) cannot be exceeded by various security resources in the transportation process; 4) provisioning resource allocation constraints, taking into account that complex equipment architectures are typically distributed over multiple discrete areas, and provisioning is typically provided by multiple maintenance sites. There is usually a defined relationship between the constraint relationship between the security site and the system operating area. Therefore, in consideration of the above constraints, optimization of a guarantee scheme of a complex equipment system is a complex problem to be solved urgently.
Disclosure of the invention
The invention provides an effective global guarantee scheme optimization method under the condition of considering the constraint in the guarantee process of a complex equipment system, and the purpose and the problem to be solved are as follows: the equipment in a complex equipment architecture is usually distributed in w discrete areas, and it has b maintenance sites to provide support services for it at the same time, where w and b form a multi-mapping relationship. How to utilize the guaranteed resources in the b maintenance sites is a problem to be solved by the invention, and a complex equipment system guarantee scheme of w areas is generated under the conditions of meeting the requirements of guaranteeing a working time window, guaranteeing resource availability, guaranteeing resource transportation and guaranteeing resource allocation constraint.
The invention provides an equipment system guarantee scheme optimization method based on a mixed heuristic algorithm, which aims at the mapping relation between an equipment system and a guarantee site, the geographical position of the equipment system and the layout form of the equipment system, and generates an equipment system guarantee scheme under the conditions of guaranteeing facility distribution constraint, service constraint, support personnel constraint and guarantee time constraint according to the guarantee requirements of each equipment. The invention generates a feasible guarantee scheme for a complex equipment system by a hybrid heuristic algorithm, which comprises the following specific steps:
step (1): describing the requirements of the equipment guarantee model according to the state of the equipment;
step (2): describing each guarantee site according to the guarantee resource condition required by the equipment;
and (3): optimizing constraint modeling of a guarantee scheme;
and (4): evaluating the cost of a complex equipment system guarantee scheme;
and (5): generating a specific complex equipment system guarantee scheme according to the relevant constraint conditions;
in the step (1), according to the state of the equipment, the requirements of the equipment guarantee model are described in a specific implementation process as follows: determining the number u of the equipment to be maintained and the required guarantee time m in the maintenance period according to the specific state of each equipment in the complex equipment systemuAcceptable latest guaranteed time
Figure BDA00029291181000000217
Number and type of required security personnel
Figure BDA00029291181000000216
Number of spare parts required
Figure BDA0002929118100000024
And the like.
In the step (2), according to the guaranteed resource condition required by the equipment, the concrete implementation process of each guaranteed site is described as follows: in the process of guaranteeing a complex equipment system, a plurality of guarantee sites usually exist, and a fixed mapping relationship exists between each site and guarantee equipment:
Figure BDA0002929118100000021
for each guarantee station, the number of the transport equipment contained in the guarantee station is determined according to the position and the requirement of the guarantee station
Figure BDA0002929118100000025
(
Figure BDA0002929118100000026
For the type of transport equipment), the number and types of various security personnel
Figure BDA00029291181000000215
Maximum number of persons in transport facility
Figure BDA00029291181000000219
Maximum guaranteed resource quantity that the transport equipment can bear
Figure BDA00029291181000000218
Availability status of security personnel
Figure BDA00029291181000000210
(
Figure BDA00029291181000000211
The support personnel representing the category γ are in a usable state), the usable state of the transport equipment
Figure BDA00029291181000000212
(
Figure BDA00029291181000000213
Representative categories
Figure BDA00029291181000000214
Is in a usable state).
In the step (3), the concrete implementation process of the guarantee scheme optimization constraint modeling is as follows: according to the requirements of a complex equipment system, the constraints of guaranteeing a working time window, guaranteeing resource availability, guaranteeing resource transportation, guaranteeing resource allocation and the like need to be met in the process of guaranteeing. Meanwhile, if the guarantee process exceeds the guarantee time which can be accepted by the equipment at the latest, corresponding punishment cost is generated.
Figure BDA0002929118100000031
Figure BDA0002929118100000032
Figure BDA0002929118100000033
Figure BDA0002929118100000034
Figure BDA0002929118100000035
Figure BDA0002929118100000036
Figure BDA0002929118100000037
Figure BDA0002929118100000038
Figure BDA0002929118100000039
Figure BDA00029291181000000310
Figure BDA00029291181000000311
Figure BDA00029291181000000312
Figure BDA00029291181000000313
Constraint (1): the number of the transportation equipment providing the guarantee service for the equipment system does not exceed the sum of the number of the transportation equipment of all the guarantee sites;
constraint (2): in any guarantee period, the transport equipment does not exceed the maximum transport equipment number of the guarantee station;
constraint (3): in any guarantee period, the number of guarantee personnel on the transportation equipment does not exceed the maximum bearing capacity of the transportation equipment;
constraint (4): the same transportation equipment can not provide guarantee service for equipment in a plurality of areas at the same time;
constraint (5): the equipment only needs to carry out a guarantee task once in a guarantee period;
constraint (6): the route from equipment i to equipment i' can only pass once within the guarantee period;
constraint (7): all transport equipment can start from any security station and return to any security station after the maintenance task is completed. Meanwhile, during the guarantee period, the system can move to and fro to the guarantee station for many times;
constraint (8): if the transportation equipment needs to stay in place during the guarantee period, the starting point and the end point of the transportation equipment are the same equipment;
constraint (9): the system is used for scheduling the guarantee scheme and maintaining the guarantee working time window;
constraint (10): the arbitrary guarantee working time window must be greater than the guarantee time required by the equipment and the sum of the time for guaranteeing personnel and guarantee equipment to load and unload from the transportation equipment;
constraint (11): the time for the transport equipment to leave the guarantee site is set to 0;
constraint (12): the time difference between arrival at equipment i and equipment i' is related only to the transport time and loading and unloading time of the transport device;
constraint (13): after reaching the equipment i', the number of various security personnel on the transportation equipment changes.
In the step (4), the cost evaluation of the complex equipment system guarantee scheme is specifically implemented as follows: for the guarantee scheme of a complex equipment system, the cost mainly comprises three parts: the personnel cost, the punishment cost and the transportation cost are guaranteed. It should be noted that, since the guarantee requirement of each equipment in the guarantee period does not change, the required guarantee resource cost is also a fixed value, so it is not considered in the cost evaluation step. The cost evaluation correlation formula is as follows:
Figure BDA0002929118100000041
Figure BDA0002929118100000042
Figure BDA0002929118100000043
wherein, CtecTo guarantee personnel expenses; cpenPenalty cost; ctraFor transportation costs.
In the step (5), according to the relevant constraint conditions, a specific complex equipment system guarantee scheme is generated, and the specific implementation process is as follows: for the problem of optimization of the guarantee scheme of a complex equipment system, the problem can be divided into two subproblems for analysis. Firstly, because the equipment area and the guarantee site have a many-to-many mapping relation, the corresponding relation between each transport device and the guarantee area under the initial condition needs to be determined under the condition of satisfying the constraint; secondly, on the basis of guaranteeing the corresponding relation, feasible guarantee resources and guarantee personnel distribution paths are designed and generated, and finally a complete guarantee scheme is generated.
1. Mapping relation between transportation equipment and guarantee area
(1) Particle swarm initialization
Let state X of particle ii＝(xi1，xi2，…xiH) And x isid(d e H) is the number of the area where the equipment is located, and H is the number of transport equipment. Because of the discrete nature of the equipment area code and the transport equipment, we randomly generate the initial position and velocity of each particle and force the equipment area code represented by it to be set to an integer. The velocity and position updating formula is as follows:
Figure BDA0002929118100000051
Figure BDA0002929118100000052
Figure BDA0002929118100000053
wherein the content of the first and second substances,
Figure BDA0002929118100000055
is the speed in dimension d in particle i in the Iters cycle;
Figure BDA0002929118100000056
is the position in dimension d in particle i in the Iters cycle;
Figure BDA0002929118100000057
is the position change value of the d-th dimension in particle i in the (Iters +1) -th cycle;
Figure BDA0002929118100000058
is the historical optimal position of the d-th dimension in the particle i;
Figure BDA0002929118100000059
is the optimal position of the d-th dimension in the particle swarm; tau is an inertia coefficient; c. C1，c2Is an acceleration factor; rhoidAre empirical parameters. Wherein the content of the first and second substances,
Figure BDA0002929118100000054
(2) crossing of individuals
Taking a certain number of particles from the population of particles, using Indsum(Indsum∈[F/(α1+1)，F/α1]) This is shown as cross particles. Wherein alpha is1Is a ratio parameter of individual cross particles. Recording the fitness function value Fit of the current particlej(i.e., the cost function).
Arbitrarily take two random positions in the individually optimal particle, denoted as d1And d2. Traverse all the crossed particles and find [ d ] in the optimal particle1，d2]Sequence substitutions to the same position in each cross-particle and calculating the new fitness function Fit 'of the cross-particle'j. If Fin'j＞FitjUpdating the sequence of the cross particles; otherwise, it remains unchanged.
(3) Group crossing
Taking a certain number of particles from the population of particles, using Indsum(Indsum∈[F/(α2+1)，F/α2]) This is shown as cross particles. Wherein alpha is2Is a ratio parameter of individual cross particles. Recording the fitness function value Fit of the current particlej。
Arbitrarily taking two random positions in the particles with the optimal population, and marking as d3And d4. Traverse all the cross particles and optimize [ d ] in the particle globally3，d4]Sequence substitutions to the same position in each cross-particle and calculating the new fitness function Fit 'of the cross-particle'j. If Fin'j＞FitjUpdating the sequence of the cross particles; otherwise, it remains unchanged.
(4) Cross variation
Taking a certain number of particles from the population of particles, using Mutsum(Mutsum∈[F/(α3+1)，F/α3]) This is shown as cross-mutated particles. Wherein alpha is3Is a ratio parameter of individual cross particles. RecordingFitness function value Fit of current particlej。
Traverse MutsumAnd arbitrarily take two random positions therein, denoted as d5And d6. Exchange d5And d6Elements of positions and calculating a new fitness function Fit 'after cross variation'j. If Fin'j＞FitjUpdating the sequence of the cross particles; otherwise, it remains unchanged.
2. Securing resource delivery paths
(1) Wolf pack initialization
On the basis of ensuring the corresponding relation, the arrangement of the guaranteed resource distribution route of the complex equipment system is abstracted into a two-dimensional code which is recorded as
Figure BDA0002929118100000062
And
Figure BDA0002929118100000067
wherein the content of the first and second substances,
Figure BDA0002929118100000064
an equipment delivery sequence representing a manual wolf i;
Figure BDA0002929118100000065
representing a sequence of transport devices.
Assume a total Num of artificial wolfs and their initial state
Figure BDA0002929118100000066
Randomly generating a first equipment distribution serial number
Figure BDA0002929118100000068
And then generating a residual equipment distribution sequence according to the chaotic sequence, wherein the formula is as follows:
Figure BDA0002929118100000061
wherein μ ═ 4.
According to an equipment delivery sequence
Figure BDA0002929118100000069
Generating corresponding transportation equipment sequence under the condition of meeting the constraints of guaranteed resources and guaranteed personnel
Figure BDA00029291181000000610
Calculating the fitness function (namely the cost function) of the artificial wolf i, and recording the optimal fitness function as FitmAnd the corresponding artificial wolf is regarded as the wolf.
(2) Wolf detection wandering
The remaining artificial wolves, except the head wolves, are considered as exploratory wolves and are explored according to their equipment distribution sequence. Sequence in artificial wolf i
Figure BDA0002929118100000071
Arbitrarily take two positions, and mark as d7And d8. Will [ d ]7，d8]And the sequence of the equipment is inverted to generate a new equipment distribution sequence. Calculating a fitness function of the new equipment sequence if Fit'i≤FitiUpdating the sequence of the artificial wolf i; otherwise, it remains unchanged. Meanwhile, if Fit exists in the process of explorationi≤FitmIf yes, the head wolf information is updated and recorded as Fitm′。
(3) Wolf of great violence
The rest artificial wolves except the head wolf are regarded as the fierce wolf, and the rushing is carried out according to the experience shared by the head wolf so as to find a better equipment distribution sequence. Arbitrarily take two positions in the sequence of wolf head m', and mark d9And d10. Then, [ d ] is9，d10]The sequence in (1) is shared with the wolf, i.e. [ d ]9，d10]The sequence in (1) replaces the sequence at the same position in the wolf. Meanwhile, the equipment can be guaranteed only once in the guarantee period, and the wolf remaining sequence is adjusted to generate a new equipment distribution sequence. Calculating a fitness function of the new equipment sequence if Fit'i≤FitiUpdating the sequence of the artificial wolf i; whether or notThen, it remains unchanged. Meanwhile, if Fit exists in the process of explorationi≤Fitm′If yes, the head wolf information is updated and recorded as Fitm″。
(4) Enclosure attack
Randomly selecting a serial number of the equipment, finding the position of the equipment in the attacking wolf and the head wolf, and recording the position as d11And d12. Comparison d11And d12Position of (d)12≠d11Moving the position of the selected equipment in the attacking wolf to the head wolf stepsiegeAnd (4) a position. Calculating a fitness function of the new equipment sequence if Fit'i≤FitiUpdating the sequence of the artificial wolf i; otherwise, it remains unchanged. Meanwhile, if Fit exists in the process of explorationm′≤Fitm″If yes, the head wolf information is updated and recorded as Fitm″′。
The invention provides an equipment system guarantee scheme optimization method based on a mixed heuristic algorithm through the steps, which has the advantages that: 1. the guarantee process of multi-region distribution and multi-guarantee point supply of the equipment system is considered, so that the guarantee process can be described more truly; 2. under the condition of considering the distribution and goods taking processes, the circulation and open-loop scheduling of the distribution route of the guarantee scheme is supported; 3. a hybrid heuristic algorithm is provided to ensure the correctness and accuracy of the assurance scheme.
(IV) description of the drawings
FIG. 1 is a schematic diagram of a complex equipment system and a security site
FIG. 2 is a diagram of a complex equipment system support scheme optimization problem decomposition
FIG. 3 is a schematic diagram of individual cross-over operation of particle swarm
FIG. 4 is a schematic diagram of a population-crossing operation of a particle swarm
FIG. 5 is a schematic diagram of cross-mutation operations of particle swarm
FIG. 6 is a schematic diagram of equipment assurance sequence and transportation device encoding
FIG. 7 is a schematic diagram of the operation of the wolf pack wolf detection process
FIG. 8 is a schematic diagram of the operation of the rushing process of the wolf cluster
FIG. 9 is a schematic diagram of the operation of the wolf colony attack process
FIG. 10 is a scheme of guaranteed resource scheduling in a guaranteed period for a certain equipment area
(V) detailed description of the preferred embodiments
Exemplary implementations of the present invention are described in detail below with reference to the accompanying drawings. The following description includes specific details to aid understanding, but these specific details should be shown as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various examples described herein can be made without departing from the scope and spirit of the disclosure. Moreover, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
The terms and words used in the following description and claims are not limited to their literal meanings, but are merely used by the inventors to achieve a clear and consistent understanding of the invention. Accordingly, it will be apparent to those skilled in the art that the following description of various exemplary embodiments of the invention is provided for illustration only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents.
Step (1): and describing the requirements of the equipment guarantee model according to the state of the equipment. And determining parameters such as the number of the equipment to be maintained, required guarantee time, acceptable latest guarantee time and the like in the maintenance period according to the specific state of each equipment in the complex equipment system. In this example, the parameters required for the equipment are shown in table 1.
TABLE 1 Equipment assurance model parameters
Figure BDA0002929118100000081
Figure BDA0002929118100000091
Step (2): and describing each guarantee site according to the guarantee resource condition required by the equipment. In the process of guaranteeing a complex equipment system, a plurality of guarantee sites exist generally, and a solid exists between each site and guarantee equipmentAnd (4) determining the mapping relation. In this example, there are 3 guard sites (denoted b) coexisting1，b2And b3) And 3 equipment areas (denoted as w)1，w2And w3). Wherein:
1) guarantee station b1Can be w1，w2And w3Providing guarantee service for the area;
2) guarantee station b2Can be w1And w2Providing guarantee service for the area;
3) guarantee station b3Can be w2And w3Providing guarantee service for the area;
in addition, for each guarantee site, the number of transportation devices, the number and the types of various guarantee personnel, the maximum number of guarantee personnel transportable in the transportation device, the maximum number of guarantee resources bearable by the transportation device, the available state of the guarantee personnel, the available state of the transportation device and the like are determined according to the position and the requirement of the guarantee site. In this example, the parameters required for the guaranteed site are shown in tables 2 and 3.
TABLE 2 guaranteed time Window parameters
Figure BDA0002929118100000092
Table 3 transport equipment related parameters
Figure BDA0002929118100000093
And (3): the concrete implementation process of the guarantee scheme optimization constraint modeling is as follows: according to the requirements of a complex equipment system, the constraints of guaranteeing a working time window, guaranteeing resource availability, guaranteeing resource transportation, guaranteeing resource allocation and the like need to be met in the process of guaranteeing. Meanwhile, if the guarantee process exceeds the guarantee time which can be accepted by the equipment at the latest, corresponding punishment cost is generated.
And (4): for the guarantee scheme of a complex equipment system, the cost mainly comprises three parts: the personnel cost, the punishment cost and the transportation cost are guaranteed. It should be noted that, since the guarantee requirement of each equipment in the guarantee period does not change, the required guarantee resource cost is also a fixed value, so it is not considered in the cost evaluation step. The cost evaluation correlation formula is as follows:
Figure BDA0002929118100000101
Figure BDA0002929118100000102
Figure BDA0002929118100000103
wherein, CtecTo guarantee personnel expenses; cpenPenalty cost; ctraFor transportation costs.
In this example, the generated warranty scheme costs are shown in Table 4.
TABLE 4 Provisions for each item of cost
Figure BDA0002929118100000104
And (5): for the problem of optimization of the guarantee scheme of a complex equipment system, the problem can be divided into two subproblems for analysis. Firstly, because the equipment area and the guarantee site have a many-to-many mapping relation, the corresponding relation between each transport device and the guarantee area under the initial condition needs to be determined under the condition of satisfying the constraint; secondly, on the basis of guaranteeing the corresponding relation, feasible guarantee resources and guarantee personnel distribution paths are designed and generated, and finally a complete guarantee scheme is generated. In this example, the specific delivery schedule is shown in Table 5.
TABLE 5 Provisions for each item of cost
Figure BDA0002929118100000105
Figure BDA0002929118100000111
While the above examples illustrate the implementation of the various parts of the invention in detail, the specific form of implementation of the invention is not limited thereto, and it will be apparent to those skilled in the art that various changes can be made therein without departing from the spirit of the method and the scope of the claims.

Claims (2)

1. An equipment system guarantee scheme optimization method based on a mixed heuristic algorithm comprises the following steps:
step (1): describing the requirements of the equipment guarantee model according to the state of the equipment;
step (2): describing each guarantee site according to the guarantee resource condition required by the equipment;
and (3): optimizing constraint modeling of a guarantee scheme;
and (4): evaluating the cost of a complex equipment system guarantee scheme;
and (5): and generating a specific complex equipment system guarantee scheme according to the relevant constraint conditions.
2. The method of claim 1, wherein generating the specific complex equipment architecture assurance plan according to the relevant constraints comprises:
(1) mapping relation between transportation equipment and guarantee area
And establishing a mapping relation between the transportation equipment and the guarantee area through a particle swarm algorithm. Firstly, initializing a particle swarm according to constraint conditions such as equipment area codes and the like, and randomly establishing a mapping relation between transportation equipment and a guarantee area; secondly, according to the cost evaluation method of the complex equipment system guarantee scheme in claim 1, the fitness function of each particle in the particle swarm is evaluated. Then, optimizing the mapping relation in a particle swarm individual crossing mode and a particle swarm crossing mode; and finally, in order to enhance the globality of the particle swarm algorithm, the mapping relation is explored to a greater extent in a cross variation mode, and the mapping relation between the transportation equipment and the guarantee area is finally generated.
(2) Securing resource delivery paths
And under the condition of the mapping relation between the transportation equipment and the guarantee area, generating a distribution path of the guarantee resources through a wolf pack algorithm. First, the guaranteed resource distribution routing of a complex equipment hierarchy is abstracted into a two-dimensional code representing the distribution sequence of equipment and the corresponding transport equipment sequence. Secondly, according to the cost evaluation method of the complex equipment system guarantee scheme in claim 1, the fitness function of the individuals in the wolf group is evaluated, and the wolf is determined. Then, three group behaviors of 'exploring wolf wandering', 'rushing' and 'attacking' are designed, and finally a guarantee resource distribution path is generated.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113326649A (en) * 2021-06-09 2021-08-31 北京航空航天大学 Equipment guarantee resource allocation strategy making method considering health state degradation

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080140597A1 (en) * 2006-07-04 2008-06-12 University Of New Brunswick System and method suitable for optimizing linehaul operations
CN108038581A (en) * 2018-01-10 2018-05-15 广东电网有限责任公司电力科学研究院 A kind of intellect economy dispatching method and device based on wolf pack algorithm
CN112001506A (en) * 2020-08-12 2020-11-27 中国人民解放军空军工程大学 Supply chain cost optimization method for equipment autonomous guarantee

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080140597A1 (en) * 2006-07-04 2008-06-12 University Of New Brunswick System and method suitable for optimizing linehaul operations
CN108038581A (en) * 2018-01-10 2018-05-15 广东电网有限责任公司电力科学研究院 A kind of intellect economy dispatching method and device based on wolf pack algorithm
CN112001506A (en) * 2020-08-12 2020-11-27 中国人民解放军空军工程大学 Supply chain cost optimization method for equipment autonomous guarantee

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
叶勇等: "求解带时间窗车辆路径问题的狼群算法", 《公路交通科技》 *
彭鹏菲等: "基于优先排序与粒子群优化的装备保障任务规划方法", 《兵工学报》 *
毕娅等: "基于约束的集合覆盖选址-分配问题", 《上海交通大学学报》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113326649A (en) * 2021-06-09 2021-08-31 北京航空航天大学 Equipment guarantee resource allocation strategy making method considering health state degradation

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