Multi-objective Optimization System For Emergency Material Center Location

In recent years,the global environment has undergone rapid changes,and a series of unexpected events have posed a certain threat to social security and stability.However,the current emergency logistics system in China still needs to be further strengthened.In practical rescue,facing large-scale and multi disaster emergency rescue tasks that occur randomly,whether emergency logistics can consider timeliness,economy,coverage area,rescue fairness,etc.,and quickly find the most ideal location plan for emergency material centers is a very important research topic.This article considers practical issues from multiple perspectives and adopts different methods for predicting material demand in different periods,making predictions more accurate and scientific.At the initial stage of the emergency,the relevant disaster data of the emergency event is less,the sample input is incomplete,and the number of people affected by the emergency event and other data are updated daily.Therefore,this paper completes the prediction of the initial material demand through the method based on fuzzy transfer learning;When the emergency situation enters the later stage and the material demand tends to flatten out,the accumulation of disaster data for the event is already relatively clear.At this time,dynamic data sequence update prediction algorithms are used for prediction,thus achieving complete dynamic prediction of material demand in emergency situations.On the basis of demand prediction,a multi-objective emergency material center location selection method based on improved predator optimization algorithm is used to achieve location selection: 1.Construct a priority evaluation system for demand point rescue.2.Construct a multi-objective emergency material center location model in a dynamic environment.In the early stages of emergency situations,focus on minimizing rescue response time and maximizing rescue coverage area.In the later stages of emergency situations,focus on minimizing rescue costs and maximizing fairness.3.Improve the predator optimization algorithm by using random perturbation factors,improving population initialization methods,and increasing population diversity.Utilize a global optimal position comprehensive guidance strategy to improve the predator position update formula,and use a multi-sample learning strategy to improve the prey position update formula,thereby improving the performance of the algorithm and solving the mathematical problem of emergency material center location selection.Finally,this article designs and implements a multi-objective optimization system for the location of emergency material centers,in order to provide an efficient and reasonable optimization plan for the location of emergency material centers.The main modules of the system include information management,demand forecasting,and location optimization.Through basic system testing,it has been further verified that the demand forecasting and location optimization models and algorithms proposed in this article have strong practical value.