Optimization Of Dangerous Goods Recovery Path Based On Ant Colony Algorithm

The optimization of dangerous goods recovery path is essentially a special reverse logistics problem.With the rapid development of logistics field and the increasing global awareness of environmental protection,the research of reverse logistics has received more and more attention,especially in the field of polluting and destructive hazardous materials.Fruitful results has been achieved.In the existing researches on the path of hazardous materials recovery,the traditional Vehicle Routing Problem(VRP)model is usually used to solve the problem.The essence of this model is a service strategy of Last Come First Served(LCFS).For the transportation of dangerous goods,the first demand nodes are usually close to the processing center.However,under the LCFS strategy,the longest time needs to be saved in the transport vehicles and the transport risks in the recovery tasks are increased to some extent.The main work of this dissertation is as follows:First,the traditional method of circular transportation is changed,and the collection of dangerous goods by third-party logistics(3PL)can be used to recover the dangerous goods.The shorter distance from the recycling center is the shortest storage time,Then,taking into account both the length of the recovery route and the risk value of the recovery route,a multi-objective optimization model including dangerous goods and recycling center is established.Since the traditional ant colony algorithm can not solve the dangerous goods recovery path model using warehouse transport mode,the improved ant colony algorithm(ACO-nso)which is suitable for solving this model.Finally,the Pareto Optimal solution set is obtained.Through the comparative analysis of the example results,it is found that the transportation mode using warehousing stock reduces the recovery cost,reduces the risk of recovery and improves the safety of recovery.In the aspect of algorithm,compared with solving with genetic algorithm,the ant colony algorithm with adverse selection improves the search success rate and reduces the average computation time.At the same time Pareto Optimal solution set also provides a new reference direction for the selection of recovery routes.