Research On Two-Sided Matching Model Design Of Enterprise Manufacturing Tasks Under The Cloud Manufacturing Platform

Manufacturing is the pillar of national economic development,and the development affects the country’s strategic layout.The acceleration of globalization and the impact of the Internet have restructured the global industrial chain.The development of traditional manufacturing has encountered bottlenecks;the manufacturing production model has changed from traditional production to service.In order to preempt intelligence manufacturing market,countries had introduced policies to promote manufacturing transformation.Cloud manufacturing,an emerging model and means of intelligent manufacturing,can share manufacturing services by different regions,and effectively solves conflicts of idle resources,a waste of capacity,and unmet needs.Because the manufacturing resources of the cloud manufacturing platform are regional and decentralized,the improvement of resource allocation efficiency has become the key to the development of the cloud manufacturing model.How to design a matching mechanism more in line with actual needs has become an urgent problem to be solved in the current research.Complex tasks usually need to be coordinated by the service side,so this paper studies the one-to-many bilateral matching problem of enterprises under the cloud manufacturing platform.In order to ensure the integrity of the information,intuitionistic fuzzy sets are used to evaluate the index information.Because the matching parties are individuals with independent learning ability,and the task often competes with resources on the task side,and the service side also has a synergistic effect on the task.At the same time,the subject often makes decisions based on himself and others in the social network.Therefore,based on psychological behavior,this article studies the matching mechanism from three perspectives: no reference,personal reference and social reference.First of all,this article enriches the measurement index system to measure satisfaction under the effect of cooperation and cooperation.Because the transaction of the cloud platform is repetitive and dynamic,the satisfaction of the two parties under the learning effect is measured for the learning behavior of the agent when participating in the task multiple times.Secondly,the satisfaction under the cooperation effect and learning effect is added to the model,and the satisfaction is modeled with three perspectives:(1)No reference point: the subject is rational at this time,and the axiom design is used to measure the satisfaction of both parties Degree;(2)Personal reference point: Take personal expectations as reference point and use prospect theory to measure mutual satisfaction;(3)Social reference point: Take social expectation as reference point and use prospect theory to measure mutual satisfaction.Third,based on the satisfaction of both parties,design and improve the particle swarm algorithm to solve the model in different scenarios,and use the method of independent decision-making perspective to comprehensively evaluate the most satisfactory solution.Finally,this paper takes the actual demand of an automobile manufacturing project as an example,solves and analyzes the target model in different scenarios,and then verifies the feasibility of the matching mechanism.