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Study On Methods And Technologies Of Multiple Criteria Decision Making For Equipment Supporting

Posted on:2012-10-29Degree:DoctorType:Dissertation
Country:ChinaCandidate:H F LingFull Text:PDF
GTID:1112330335463580Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
Many equipment supporting decision-making problems have features of large scale, multiple objectives, and multiple constraints and so on. The traditional empirical decision making methods which depend on policymakers' experience, knowledge and preferences cannot satisfy the demands of making equipment supporting achieving the aim of "supporting at suitable time, on right place, and with appropriate manners". Therefore, it's necessary to learn how to make scientific quantitative decisions for complex, large scale and multi-objective equipment supporting decision problems. According to the ideas of making decisions after optimization, the methods and techniques of multiple criteria decision making for equipment supporting are studied in this paper, and the main research contents and innovation points are as follows:(1)MOP test functions and performance evaluation methods are reviewed and analyzed, several groups of typical constrained and unconstrained MOP test functions are given, and quantitative performance evaluation methods for measuring the diversity and convergence of the algorithm are introduced, which provides a basis for the performance analysis of the proposed algorithm in this paper and its comparative performance analysis to other similar algorithms.(2)Some important operators such as external archive, density measuring method, global guide and personal guide selecting strategies are surveyed, which provide guides for the improvement of multi-objective particle swarm algorithm.(3)Constrained multi-objective processing methods are summarized. On the basis of the analysis of the characteristics of the multi-objective search space and the difficulties of the constrained multi-objective processing, an improved constrained multi-objective particle swarm algorithm (CMOPSO) is put forward. A dynamicεunfeasible degree allowable constraint dominance relation as the main constraint processing method is brought forward in this algorithm, which improves the algorithm's edge search ability and its ability of crossing unconnected feasible regions; A simple density measuring method is put forward for external archive maintenance, which improves the efficiency of the algorithm; On the analysis of the relation of the particles in the swarm and the solutions of the external archive, a new global guide selection strategy is put forward, which brings a better convergence and diversity to the algorithm. The computer simulation and measurement comparison results verified the feasibility and the rationality of the algorithm.(4)Based on the analysis of the search mechanism of PSO and the characteristics of MOPSO, a fuzzy gathered multi-objective particle swarm algorithm called fuzzy learning subswarm multi-objective particle swarm optimization (FLSMOPSO) is put forward in this paper. The method of fuzzy learning is used to improve the CMOPSO. In the evolutionary process, each particle in the swarm can have linear regressive p particles to form a subwarm rather than a single particle. Then, a fuzzy satisfied solution particle should be selected as the new position of the particle. The improved fuzzy multi-objective particle swarm algorithm put forward in this paper can rapidly narrow the search area and speed up the convergence rate. At the same time, it can maintain the diversity of evolution population. Comparative analysis to the typical FMOPSO algorithm and the CMOPSO algorithm above show that the FLSMOPSO algorithm proposed in this paper can find a sufficient number of widely and evenly distributed Pareto optimal solutions, which are both good in uniformity and the approximation of the Pareto front.(5)Based on the survey of the decision methods based on Vague set, a new Vague set based multi-stage fuzzy multiple attribute decision making method is proposed. Through the two stages of non-inferior solution set shrinkage and optimum choice, it can automatically fetch the most satisfied solution from numerous non-inferior solution set for policymakers, effectively solve the problems existing in Vague set based multiple attribute decision making methods that multiple different Vague set have the same Vague value so they cannot be compared with each other.(6)Two typical complicated equip supporting problems of multi-objective maintenance tasks distribution and disaster emergency rescue positioning and transportation are analyzed and modeled. The former problem has the characteristics of multiple constraints, multiple objectives and a large number of optimal decision-making plans, while the latter problem is a multi-constraint/multi-objective/multi-stage mathematical programming problem, which can not be solved by traditional methods. Simulation experiments are done using the improved multi-objective particle swarm algorithms proposed in this paper respectively, Pareto non-inferior solution set can be soon obtainted. Under certain conditions where there are a large number of non-inferior solutions, a Vague set based multi-stage fuzzy multiple attribute decision making method is used to get the most satisfied solution for policymakers. Simulation experiments show that the proposed optimization and decision-making algorithms proposed in this paper can effectively solve the problem of making scientifically quantitative decision for equipment supporting, which provides a good way to transform complex equipment supporting decision-making problems from experimentally qualitative to scientifically quantitative.
Keywords/Search Tags:Equipment supporting decision making, Multi-objective particle swarm, Constrained multi-objective optimization, Multiple criteria decision making, Multiple objective decision making, Vague set, Fuzzy multiple attribute decision making
PDF Full Text Request
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