Workshop Based On Particle Swarm Optimization Scheduling Problem

With the fast development of social economy,competition in the market becomes increasingly intense.The diversification and customization of the customers' need increase the uncertainty and dynamism in the production plan of the enterprise,making the modern enterprise face severe challenges and making greater demands of the supply chain management.After entering the 1990s,the supply chain management already becomes one of the focuses in the study and practice of the enterprise management theory and practice in the international society.In the circumstances,in order to improve the profit level and the core competitive ability,the enterprise begins to focus on rationally allocating and efficiently utilizing its inner and outer resources.The scheduling model study based on the supply chain combines the problem of the supply chain management and the problem of the production scheduling to study how to more effectively solve the job-shop scheduling and coordination problem in the distributed environment,and ultimately realize the double optimization of the node enterprise's supply chain management and the job-shop scheduling,boasting certain theoretical and practical significance.Particle Swarm Optimization,a new swarm intelligence algorithm,originates from the investigation of the bird swarm preying behavior.It is an optimization technology based on iteration.System is initialized into a group of random solutions and optimization value is searched by iteration.Now,particle swarm optimization is applied into function optimization,neural network training,data mining and other application field.The paper focuses on the algorithm and application of the particle swarm and makes deep study on improving the property of the traditional particle swarm algorithm and on the application of the algorithm in the fields of the job shop scheduling and the supply chain scheduling.The background,aim and significance of the paper are introduced first, followed by the classification and characteristics of the job shop scheduling,as well as the major ways of studying the job shop scheduling problem.Then,the paper describes the genetic algorithm's application in the job shop scheduling problem.The orthogonal experiment is introduced to identify the operators,and the immune genetic algorithm is put forward on the basis of the orthogonal experiment to solve the job shop scheduling problem with the algorithm,achieving a satisfactory simulation result by comparison. After it,the paper describes the present status and the future trend for study of the particle swarm optimization algorithm.The application of the particle swarm algorithm is given, using the coding method based on particle position.Through the comparison with the genetic algorithm,the simulation results show the superiority and effectiveness of the particle swarm algorithm in solving the job shop scheduling problem,followed by the introduction of the definition and characteristics of the supply chain and the supply chain management.Coordinated control and shop scheduling problem in supply chain are given. At last,the on-line no-wait scheduling in the supply chain is studied.The coding method of the smallest position value is used in this thesis.The production plan will be made to the customer's urgent order without changing the scheduled jobs' processing orders.In the background of flow shop and job shop scheduling,the paper gives the algorithm to on-line schedule the urgent order in a certain time interval when resources are available. Making use of this algorithm will obtain the completion time of the order in no time,and propose a delivery time on the phone or on the Internet.It is of certain guiding significance to the manufacturer's practical production supply chain management.