Research On Capacity Scheduling Of Real-time Delivery Platform Considering Order Consolidation

In recent years,the instant delivery industry has developed rapidly,and the demand for transport capacity has been expanding,and customers have higher and higher requirements for the timeliness of delivery.Due to the spatial and temporal difference of order quantity with the difference of region and time,it often happens that the order cannot be delivered on time in the peak period of order.Therefore,on the premise of ensuring service quality and platform cost,it has important practical application value to solve the problems of low distribution efficiency and unreasonable capacity matching under different order densities in the instant delivery industry.In this context,the study researches the capacity scheduling problem of the instant distribution platform,analyzes the order allocation mode of the instant distribution platform,and carries out specific research on the capacity allocation problem and the idle capacity scheduling,order allocation and path planning problems in the distribution process.The study mainly completed the following work:Firstly,the study summarizes the development status of the instant delivery industry and the business scenarios,business models and order distribution models of the main services of the instant delivery platform.On this basis,the study analyzes the capacity organization form and salary calculation method of the instant delivery platform,and then puts forward the relevant indicators that should be considered in the capacity allocation.It is beneficial for the platform to carry out targeted capacity arrangement and reduce the cost of idle time capacity scheduling.Secondly,the study deals with idle time capacity scheduling in the process of instant distribution.The study conducts pre-allocation of transport capacity for hot spot areas with high order density that are short of transport capacity during peak periods of orders.In order to minimize the total scheduling cost,a capacity scheduling model is established and solved by immune algorithm.The adaptive expected propagation probability operator is introduced into the traditional immune algorithm,which improves the stability and convergence speed of the algorithm.Example analysis is carried out with randomly generated data.The experimental results show that the proposed scheduling method can quickly realize the capacity scheduling between different regions.It can not only meet the capacity demand of the current order to be delivered,but also cope with the potential demand of the future order,so that the capacity of the subsequent order distribution process is sufficient.Finally,the study deals with the problem of order allocation and route planning in the process of instant delivery.The study presents an order consolidation strategy which considers the proximity of pickup points and the rule of the shortest new distance between orders and a two-stage algorithm is used to calculate it.Considering the constraints of time window and distribution capacity,the order distribution model is established to maximize the average income of deliverers.In the first stage,hierarchical clustering is used to merge orders according to the order consolidation strategy.The second stage is solved by genetic algorithm.The case data are introduced to verify the effectiveness of the order consolidation strategy and algorithm proposed in the study.The research work of the study enriches the theory of transport capacity allocation and immune algorithm,which has reference value and significance for improving the distribution efficiency of instant delivery orders and saving the cost of instant delivery platform.