Order Evaluation And Scheduling Research For Y Company

With the continuous development of market economy and social life,people are increasingly pursuing diversified products.The competition among enterprises is not only reflected in products and quality,but also meets the individual needs of customers.As a result,the order-oriented pull production model has gradually replaced traditional push production.This paper takes Y company,which is oriented to order production,as an example to optimize and study its existing scheduling method.In the pre-stage of the order scheduling,the order priority is determined.Firstly,through reviewing the data and analyzing the main factors affecting the production plan of Y Company,four primary indicator and eight secondary indicators were established.Then,six experts were selected to determine the weight of the index by using the G1 method,and the order was evaluated by the grey correlation analysis method.Finally,the preliminary of the order and the penalty coefficient of advance and delay were obtained according to the correlation coefficient.In the later stage of the order scheduling,Orders are divided into batches according to the actual production status of the enterprise,and establishes an objective function that minimizes the advance/delay cost.The order delay penalty cost includes the delay time and the number of deferred orders.Then,using particle swarmsimulated annealing algorithm to solve the problem,the Metroplics mechanism of the simulated degradation algorithm is introduced into the particle swarm optimization algorithm,so that it can jump out quickly when it falls into local optimum.At the same time,considering that the particle swarm optimization algorithm is generally applied to solve the discrete problem,a coding method based on particle position order and particle position blending is adopted,so that each particle represents a solution.Finally,taking the order information of Y Company in September 2018 as an example,verified the proposed scheduling method.The results show that the scheduling results of the proposed particle swarm-simulated annealing algorithm are significantly better than the scheduling results of the company's ERP system and particle swarm optimization algorithm,and the advance/delay cost is saved by 6549.23 yuan,4313.99 yuan respectively.The method proposed in this paper can effectively solve the production planning problems,The entire production process is closer to the ideal production of just-in-time production,reducing operating costs,improving production efficiency and the ability to respond quickly to the market.