Research And Implementation Of Order Scheduling Strategy Based On Intelligent Optimization Algorithm

As the market environment becomes more complicated and competition among enterprises becomes more intense,production and operation strategies have gradually shifted from inventory production to order-to-order production.Make-to-order production can adjust production and operation activities in time according to customer needs,improve production efficiency,reduce inventory costs,and create higher corporate profits.Order selection and acceptance and production scheduling are important links in the production activities of enterprises using the production-to-order method.The traditional order selection and acceptance problem is the decision made by the sales department of the enterprise based on the production capacity of the enterprise,without considering the actual production.Condition.This often results in orders not being delivered on time,requiring companies to pay customers a certain penalty for breach of contract,and even customers giving up the next opportunity for cooperation due to the company's untrustworthy behavior.Therefore,studying the issue of order acceptance and scheduling has important theoretical significance and practical application value.This paper firstly combs the relevant theoretical knowledge and literature research on the order acceptance decision problem,and determines the decision model of the single machine order acceptance and scheduling problem with the goal of maximizing the profit of the enterprise.In order to ensure that the order is completed before the final delivery date,the processing sequence of accepting the order is reasonably arranged,so as to maximize the order income of the enterprise.The main contents of this article are as follows:Considering the production preparation time for switching between different orders,the single machine order acceptance and processing scheduling problem with the goal of maximizing enterprise revenue is studied.The seagull optimization algorithm has a good effect on solving high-dimensional constraint problems.In view of the strong NP-hard problem of the research problem,an improved seagull optimization algorithm is used to solve the problem.In order to solve the problem that the algorithm converges prematurely and falls into the local optimum and the population diversity is reduced,nonlinear descent control factors and reverse learning probability selection are adopted,and the algorithm's optimization ability is strengthened through the local search strategy.Finally,through several sets of single-peak and multi-peak test functions,the effect of the improved seagull algorithm is verified,and the improved seagull algorithm is compared with other algorithms,which proves that the improved seagull algorithm has obvious advantages over other algorithms in the single-machine scheduling problem.The single-machine order scheduling problem considering the outsourcing strategy with the goal of maximizing enterprise revenue is studied,and an improved migratory bird optimization algorithm is used to solve the problem.In order to strengthen the neighborhood search ability of the migratory bird optimization algorithm,three hybrid neighborhood search strategies for neighborhood operations are designed.Faced with the problem of premature convergence of the algorithm and falling into the local optimum,a reset mechanism was added,and the local search capability of the algorithm was strengthened by adding a local search.Finally,through a large number of example experiments,it is proved that the improved migratory bird optimization algorithm is better than the latest improved migratory bird optimization algorithm in the single-machine scheduling problem considering subcontracting decision-making.