Research On Multi-depot Vehicle Dispatching Disruption Management Considering The Change Of Customer Delivery Address

With the development of logistics industry,irregular distribution of demand network and continuous expansion of enterprise business,a single distribution center has been unable to meet the increasingly diversified distribution needs of customers.In order to better serve customers,large-scale logistics service enterprises equipped with multiple depots are developing rapidly.At the same time,in the process of logistics distribution,customers often change the delivery address temporarily,which will cause interference to the transportation scheme being implemented.How to quickly get a new distribution scheme with less disturbance is a difficult problem for logistics operators.The main research work of this paper is as follows:Firstly,according to the characteristics of multi-depot vehicle scheduling problem,the initial distribution model is established.When the delivery address of a customer changes,the disturbance analysis is carried out,and the disturbance measurement is carried out from the interests of customers,logistics operators and distribution personnel.The multi-depot vehicle scheduling disruption management model is established with the goal of minimum customer dissatisfaction,minimum logistics transportation cost and minimum route deviation.Secondly,uncertain multi-attribute decision-making method is used to determine the weight of each objective,and then each objective function is standardized,and the multi-objective problem is simplified into a single objective problem by using the weighted method.Aiming at the premature defect of genetic algorithm,an improved genetic algorithm is proposed to solve the interference disruption model by introducing external memory bank,iterative exchange process and adaptive crossover mutation operator.Finally,an example is given to verify the model and algorithm in MATLAB.Compared with the existing rescheduling method,the disruption management method proposed in this paper can effectively balance the interests of all parties and get the adjustment scheme with less disturbance.Moreover,compared with the basic genetic algorithm and the basic ant colony algorithm,the algorithm designed in this paper has better convergence effect and stronger optimization performance.