Research On The Location Of Double Objective Reverse Logistics Based On Improved Simulated Plant Growth Algorithm

In the long history of human development,the development of material civilization and culture has reached an unprecedented height,but along with it is the shortage of resources and energy consumed,and most of the resources and energy is not renewable.In the background of this is at stake,how resources will be limited and the new energy recycling,to reduce the consumption of resources and energy is one of the focus of attention.The reverse logistics location in solving the consumption of resources and energy,achieve the goal of sustainable development in an increasingly significant role.Although human has created a highly developed material civilization,but it has consumed a large number of natural resources and ecological energy,resulting in irreversible waste of resources and property loss.In order to meet the needs of economic development and ecological crisis,reverse logistics network in the realization of the grand blueprint for sustainable development is becoming increasingly urgent.The paper introduced the plant growth simulation algorithm and analyzed detailed the plant growth simulation algorithm to the initial value problems,excessive dependence and the optimal solution can be obtained after the judgment to quickly terminate itself,aiming at these problems put forward corresponding improving method,namely in the process of optimization and effective judgment of outstanding merit the growth point of the fixed step search algorithm for intelligent clustering algorithm and the change point of the initial growth to alleviate the dependence,a detailed explanation of the three methods,and the improved algorithm simulation of plant growth steps and processes are defined and explained.Finally,the introduction of examples discussed the improvement method and verify the effectiveness of the case proves that the improved algorithm uses the selection method.The change of thesearch step and the clustering method has set the initial growth point.It is helpful to solve the limitation of the low efficiency of the simulation plant growth algorithm,and the algorithm can not effectively terminate.The paper built the dual objective of reverse logistics location model,that considerate comprehensively economic interests and social interests.It used Stanford model for estimating the waste production,and realized the construction of the waste reverse logistics location model which considered minimum economic cost and social cost,finally introduced the numerical example and verified the performance of the improved algorithm.Considering the uncertainty of percent recovery and model robustness,the paper restrained the optimized the model through the theory of robustness for solving uncertain factors,in order to simultaneously achieved the robustness and constraints of the model of objective function robustness,which made the results more practical.Through numerical example analysis,the paper verify the robustness of model and solution under the condition of uncertain recovery rate.Finally,through the analysis on the recovery rate sensitivity of the reverse logistics network model,it arrived at a conclusion that in the reverse logistics network location model,uncertain recovery rate caused some changes for the recovery cost of reverse logistics network location model,but did not affect the decision-making and allocation of reverse logistics facility location.