Method Of Integrating Variable Reduction Strategy With Intelligent Optimization Algorithms For Emergency Material Scheduling

Developing a reasonable and efficient emergency material scheduling plan to deliver emergency materials to disaster sites in a timely,accurate and effective manner is of great significance to decrease casualties and property losses.Large scales,complex constraints,and multiple objectives are the typical characteristics for practical emergency material scheduling problems.Intelligent optimization algorithms are competitive methods for solving a substantial number of emergency material scheduling problems.However,concerning handling optimization problems with large scales or equality constraints,the existing intelligent optimization algorithms still face considerable difficulties.To address the abovementioned difficulties,the thesis presents an automatic variable reduction algorithm to reduce the dimension of solution space and deal with partial equality constraints by harnessing a fairly general problem domain knowledge.Thereafter,we integrate the automatic variable reduction algorithm with intelligent optimization algorithms for effectively solving emergency material scheduling problems to improve the performance of algorithms.The main research work and contributions of the thesis are as follows.(1)To enable a lower difficulty for solving complex optimization problems like emergency material scheduling problems,we define an optimal variable reduction problem to represent a decision space with the smallest sets of variables.Thereafter,a simulated annealing-based automatic variable reduction algorithm is designed to address the problem,which realizes the automatic reduction.Compared with realizing the variable reduction in low efficiency and strong subjectivity of trial-anderror manner,the automatic variable reduction algorithm enables higher efficiency and a more extensive application.The experiments study,which is carried out on 22 constrained optimization problems and 35 nonlinear equations systems,substantiates that the proposed automatic variable reduction algorithm can effectively reduce the dimension of solution space and handle partial equality constraints.(2)Put attention to the effective integration of variable reduction strategy and intelligent optimization algorithms,according to the characteristics of optimization problems and the corresponding solution algorithms,we design the integration frameworks of the automatic variable reduction algorithm and intelligent optimization algorithms to improve the performance of the intelligent optimization algorithms for solving complex constrained optimization problems and nonlinear equations systems.Constrained optimization problems and nonlinear equations systems are both widespread and challenging optimization problems in the traffic field.Moreover,the emergency material scheduling problems also belong to constrained optimization problems in essence.We integrate the automatic variable reduction with some special intelligent optimization algorithms for the experimental study.Extensive experiments regarding constrained optimization problems and nonlinear equations systems verify that the effective integration of the automatic variable reduction algorithm enables the significant improvement of the performance of intelligent optimization algorithms.(3)Model a multi-objective emergency material parallel allocation and route planning problem and design a VNS-NSGAII hybrid algorithm to solve the problem.Thereafter,based on the research of the above automatic variable reduction and integrated algorithm,considering the hallmarks of the proposed problem,we apply the automatic variable reduction algorithm to the problem,thereby,reducing the dimension of the emergency material problem and dealing with partial equality constraints.Then,we integrate the VNS-NSGAII with the variable reduction strategy to improve the performance of the VNS-NSGAII.We construct a test case based on a multi-depot vehicle routing problem and a practical case combined with the initial 5?12 Wenchuan earthquake emergency material support situation.Experimental results on the test case and practical case verify that with the assistance of variable reduction strategy,VNS-NSGAII enables a better optimization efficiency and a higher quality solution.The research results of this thesis boost the efficiency of variable reduction strategy,enable a more extensive application,and improve the performance of intelligent optimization algorithms for solving emergency material scheduling problems.Moreover,our study provides support for the further applications of variable reduction strategy in practical complex optimization problems.There are 42 figures,28 tables,and 103 references.