Multi-objective decision-making optimization method for deviation interval preference guidance based on MOEAD

A multi-objective decision-making and optimization method technology, applied in the field of aircraft structure maintenance decision-making, can solve problems such as the small number of effective solutions

Active Publication Date: 2018-03-30
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] The purpose of the present invention is to solve the shortcoming that the number of effective solutions that satisfy the decision maker's deviation interval preference obtained by solving the prior art is small, and propose a multi-objective decision-making optimization method based on MOEAD deviation interval preference guidance

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  • Multi-objective decision-making optimization method for deviation interval preference guidance based on MOEAD
  • Multi-objective decision-making optimization method for deviation interval preference guidance based on MOEAD
  • Multi-objective decision-making optimization method for deviation interval preference guidance based on MOEAD

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specific Embodiment approach 1

[0036] Specific implementation mode one: a kind of deviation interval preference guidance multi-objective decision-making optimization method based on MOEAD comprises the following steps:

[0037] When the remaining life of the two fleets (the average remaining life of all aircraft in the fleet is used as the remaining life of the fleet) needs to be repaired, due to the limitation of maintenance resources (the maintenance base cannot accommodate the aircraft of the two fleets at the same time overhaul), so it is necessary to reasonably arrange different task volumes for the two fleets so that the remaining life is separated by a certain distance. However, if the remaining service life of the two fleets is too large, it means that after the first fleet is repaired, the maintenance resources that have been prepared will be idle again, and the maintenance resources will not be fully utilized. Maintenance intervals should not be too large. Based on the preference for the remainin...

specific Embodiment approach 2

[0048] Specific embodiment two: the difference between this embodiment and specific embodiment one is that it is characterized in that: the mathematical model of the normal line boundary intersection method in the step two is:

[0049] Traditional BI methods are designed to obtain the non-inferior frontier (ParetoFront, PF) of multi-objective optimization problems with uniform distribution. Such as figure 1 As shown, the PF of the multi-objective optimization problem is the lower leftmost partial convex boundary in the feasible objective space.

[0050] Geometrically, BI finds the lower left boundary in the feasible target space and a series of reference points z*(z*=(minf 1 (x),minf 2 (x),...,minf z (x)), z represents the number of objective functions) to approach the PF by the intersection of uniformly divergent rays. Such as figure 1 , a ray L with a direction of λ is emitted from the reference point z*, where λ is the weight vector λ=(λ 1 ,...,λ z ) T , where λ i ...

specific Embodiment approach 3

[0056] Specific implementation mode 3: The difference between this implementation mode and specific implementation mode 1 or 2 is: the specific details of the reconstructed decomposition model obtained by reconstructing the multi-objective decision-making model with deviation relationship interval preference information established in step 1 The process is:

[0057] Since the decision maker's preference information is composed of l deviation relationship intervals, and the deviation relationship intervals can be discretized into deviation preference sets, one preference value is taken in each of the l deviation preference sets to form a preference vector. Therefore, the decision maker's preference information is expressed as a set of N preference vectors, specifically:

[0058]

[0059] Among them, PI represents the set of preference relations between the decision makers for the goals, l represents the number of preference relations (intervals) among the goals in the decisi...

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Abstract

Provided is a multi-objective decision-making optimization method for deviation interval preference guidance based on an MOEAD, and relates to the the field of multi-objective decision-making optimization methods for deviation interval preference guidance based on the MOEAD. The method aims to solve the problem that in the prior art, the number of efficient solutions which are obtained through solving and meet deviation interval interference of a decision maker is small. The method comprises the steps that 1, a multi-objective decision-making model with deviation relation interval interferenceinformation is built; 2, by means of a normal boundary crossing method, the multi-objective decision-making model with the deviation relation interval interference information built in the step 1 isreconstructed to obtain a decomposition module after reconstruction; 3, the decomposition model reconstructed in the step 2 is solved to obtain optimization solutions of the multi-objective decision-making model with the deviation relation interval interference information. By means of the method, high space complexity and time complexity caused by a traditional optimization method in which efficient solutions which meet the interference are obtained through a posterior method; the method is used in the field of plane structure maintenance decision-making.

Description

technical field [0001] The invention relates to the field of aircraft structure maintenance decision-making, in particular to an MOEAD-based deviation interval preference guidance multi-objective decision-making optimization method. Background technique [0002] Multi-objective decision-making with preference information, as a branch of multi-objective problems, has been extensively studied in the last 30 years (Liu, P.; Teng, F. An extended TODIM method for multiple attribute group decision-making based on 2- dimension uncertain linguisticVariable.Complexity 2016,21,20-30.Liu,P.;Teng,F.Multiple criteria decisionmaking method based on normal interval-valued intuitionistic fuzzygeneralized aggregation operator.Complexity 2016,21,277-290.P.Xu,J. Y.G. Multi-objective decision making model under fuzzy random environment and its application to inventory problems. Information Sciences 2008, 178, 675-684.). Because decision makers often cannot accurately describe their own prefere...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/15
Inventor 林琳罗斌郭丰王晨钟诗胜
Owner HARBIN INST OF TECH
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