Font Size: a A A

Research And Application Of Improved Particle Swarm Optimization In Job Shop Scheduling

Posted on:2019-05-13Degree:MasterType:Thesis
Country:ChinaCandidate:X Q YangFull Text:PDF
GTID:2428330548977659Subject:Software engineering
Abstract/Summary:PDF Full Text Request
In an increasingly competitive global economy,how to increase manufacturing efficiency and reduce business costs has become an important way to increase the competitiveness of enterprises.With the large scale of production and the continuous production scale,workshop scheduling has become One of the key elements of production efficiency.Job-shop Scheduling Problem(JSP)is a research hotspot in the manufacturing industry.Effective scheduling plans and optimization methods are the basic and key factors for improving production efficiency.However,the job-shop scheduling problem is NP-hard.The difficulty is that it is impossible.Realizing its optimal solution in a polynomial time,so a good optimization algorithm is the key to solve the problem of workshop scheduling.In recent decades,scholars have been trying to find better optimization algorithms.Among them,particle swarm optimization(PSO)and tabu search(TS)are two prominent optimization algorithms.Particle Swarm Optimization(PSO)is a swarm intelligence algorithm.Because it has the advantages of simple,easy to implement,fast convergence,and less parameters,it has attracted attention at home and abroad since it was proposed.Particle swarm optimization algorithm is diffusely used in scheduling optimization,function optimization,Chemical engineering,data mining,biological engineering,environmental engineering,fuzzy control and many other neighborhoods.The tabu search algorithm is a heuristic algorithm which is gradually optimized globally.It avoids trapping into local optimal solutions in the form of tabu list,so as to jump out of the local optimum and has good search performance.At present,the tabu search algorithm has been popularized.Machine learning,investment analysis,circuit design,function optimization and combinatorial optimization are many neighborhoods with wide application prospects.I have read a large number of documents that the traditional particle swarm optimization algorithm is not good for discrete optimization problems,and it is prone to lost in local optimal solutions.The tabu search algorithm has higher requirements for the initial solution of the problem.The corresponding improvement,based on the previous two algorithms,proposes a hybrid particle swarm optimization algorithm that uses the global optimal solution of particle swarm optimization as the initial solution of the tabu search algorithm,and applies it to the problem of shop scheduling.Discretization processing,in the field of flexible job shop scheduling,has made corresponding improvements to the neighborhood structure of the tabu search algorithm.The improved algorithm can not only improve the optimization ability of the particle swarm algorithm,but also has obvious advantages for solving job-shop scheduling and flexible job-shop scheduling.Finally,this paper solves the job shop scheduling problem and flexible job shop scheduling problem through experimental simulation,and verifies the feasibility and superiority of the improved algorithm.
Keywords/Search Tags:job-shop scheduling, particle swarm optimization, tabu search algorithm, hybrid particle swarm optimization
PDF Full Text Request
Related items