Research On Emergency Material Allocation Scheduling Problem Based On Improved Differential Evolution Algorithm

With the development of social economy and the intensification of industrialization process,large-scale emergencies have occurred at home and abroad,and the level of disaster is becoming more and more uncontrollable,which has caused inestimable losses to all countries in the world.After the disaster,it is often necessary to mobilize,and select the best emergency material distribution point through the emergency material allocation and dispatch strategy,so as to minimize the loss of people's life and property,stabilize the social order and control the development of affairs in timely.For this reason,based on the characteristics and inherent strategies of emergency material allocation scheduling,this paper aims at efficiently deploying emergency supplies and maximizing the demand for material demand,studies the emergency material allocation and scheduling which based on the intelligent optimization algorithm-improved differential evolution algorithm.Firstly,starting from the theory of emergency material allocation and scheduling,this paper analyzes the characteristics and differences of emergency material scheduling and common material scheduling,combs the process of emergency material scheduling,and constructs a network topology map of emergency material allocation and scheduling.Secondly,the problem of emergency material allocation and scheduling is a high-dimensional multi-modal optimization problem.To improve the efficiency of this solution,this paper applies the differential evolution algorithm with high optimization performance to solve the problem.For this reason,an improved differential evolution algorithm-adaptive weighted dynamic differential evolution algorithm(AWDDE)is proposed.The improvement points are as follows: firstly,the chaotic mapping theory is adopted to initialize the population,which improves the quality of the initial population.Secondly,a new weighted mutation operator is proposed by weighted combination of standard DE mutation strategies,the scaling factor and crossover probability are adaptively adjusted,which dynamically balances the global exploration ability and local development ability of the algorithm.Finally,the Gauss perturbation operator is introduced to generate random disturbance and accelerate premature individuals to jump out of the local optimum.To verify the performance of the algorithm,the AWDDE algorithm is compared with the standard DE algorithm,SADE algorithm,and CAPSO algorithm.Using MATLAB tools,each algorithm is run independently for 30 times and 50 times on the 5 Benchmark functions,finally,the optimal solution and simulation curves of each algorithm are obtained.The results show that compared with other optimization algorithms,AWDDE algorithm has the characteristics of strong global optimization,fast convergence,high accuracy and stability.Finally,the emergency material allocation and scheduling is a multi-objective and real-time dynamic process.From the actual emergency activities,this paper takes into account the effects of changing strategies on the original scheduling,and integrating various emergency rescue times,rescue costs,and rescue satisfaction levels,constructing a two stage emergency material scheduling model: the emergency rescue center selection model before disasters and the emergency rescue dispatch-rescheduling model during disaster occurrence.The adaptive weighted dynamic differential evolution algorithm proposed in this paper is applied to the solution of the two-stage scheduling model: the first stage of the choice of scheduling material distribution point;the second stage of the re-scheduling option,that is,when the demand point changes,how to set up the supply of the reserve and the scattered points,so that the satisfaction of transportation costs and demand points is optimal.Through the solution and simulation of specific problems,the feasibility and efficiency of the AWDDE algorithm in solving the emergency material dispatch problem are verified.