Research On Optimal Matching Of Instant Delivery Orders Based On Algorithmic Economy

As an important tool in the era of rapid development of sc ience and technology,the algorithm has profoundly affected the economic market and promoted the optimization of the overall resource structure of society.Instant delivery is a combination of advanced al gorithms and logistics under the Internet.Therefore,it is of great theoretical significance and practical value to study the matching problem of real-time distribution orders in conjunction with algorithms and to explore the internal mechanism of matching of order resources by algorithms.This paper takes order matching in the instant delivery industry as the research object,constructs an intelligent order matching optimization model,and proposes different solving algorithms to demonstrate the impact of the algorithm on resource allocation.By establishi ng a model for instant delivery order matching,a reinforcement learning algorithm based on Markov decision process is designed and compared with greedy algorithm and genetic algorithm.The results show that as an advanced intelligent algorithm,the reinforcement learning algorithm can learn with the changes of the environment,which fully reflects the intelligent characteristics of the machine learning algorithm,but requires a lot of time and data for tr aining;the greedy algorithm has the fastest calculation speed in a random environment The total benefit obtained under the maximum is the largest;the calculation speed of the genetic algorithm cannot meet the requirements of timeliness of instant delivery.Enterprises of instant delivery platforms need to reasonably choose the best order matching solution based on their own situation.Research shows that the progress of algorithms is not only in the updating of computer systems,but also in the acquisitio n and processing of information.Reinforcement learning algorithms can train the dynamic environment of order matching problems,transform data into additional information and learn the rules,and ultimately provide a basis for decision-making.In essence,the main reason why the algorithm can make decision s is that it can use the data to obtain more information and learn,and finally achieve the optimal allocation of resources,which has become a new mechanism for resource allocation.The innovations of this paper are as follows: First,a mathematical model of the order matching problem in instant delivery is constructed,and the order matching process is transformed into a Markov decision model,which quantifies the dynamic process and future utility expec tations.Secondly,the customized reinforcement learning algorithm is innovatively designed according to the dynamic model of order matching,which reduces the complexity of the algorithm model,solves the problem of low learning efficiency of the algorithm,and explains the inherent decision making of the algorithm by comparing different algorithms mechanism.