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The Intelligent Approaches for Job-Shop Scheduling Problems

Author: QuXiaoWei
Tutor: LiangYanChun
School: Jilin University
Course: Applied Computer Technology
Keywords: Job Shop Scheduling Combinatorial optimization Swarm Intelligence Ant Colony Algorithm Meet algorithm Particle Swarm Optimization Artificial Immune System
CLC: TP18
Type: Master's thesis
Year: 2005
Downloads: 364
Quote: 2
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Abstract


Of two based on swarm intelligence algorithm - ant colony algorithm and particle swarm optimization algorithm - improved to solve the job shop scheduling problem . Encounter algorithm is an improved ant colony algorithm , it inherits the advantages of the ant colony algorithm itself - self - adaptive , distributed , parallel , robustness , and simple , while two ants common the search process , the ant colony algorithm to calculate the length of time the shortcomings to be improved ; immune particle swarm algorithm is an improved discrete particle swarm algorithm , this algorithm introduces the mechanism of immune algorithm . Due to particle swarm algorithm solving easy to fall into local optimal solution when solving into a local optimum immune algorithm mechanism to be optimized , to jump out of the local optimal solution . The numerical results show that these two methods have a certain degree of feasibility , provide a more alternative approach to solve the job shop scheduling problem . The second chapter introduces some of the basics of the job shop scheduling problem . A brief description shop scheduling problem , the classification of the encoding and scheduling algorithm is introduced . Chapter met algorithms in order to introduce the first ant colony algorithm system ; then introduced particle swarm algorithm and immune algorithm theory . The fourth chapter describes the encounter algorithm for job shop scheduling problem solving process , first introduced the main parameters set , then the algorithm flow . The use the FT class benchmark data verified the feasibility of the application of this algorithm , experimental results show that such algorithms are compared with basic ant colony algorithm to improve the performance , there is a certain application value . The fifth chapter introduces a key theoretical and introduced the improved algorithm - immune particle swarm algorithm , based on discrete particle swarm optimization . The experimental results show that the method has certain application prospects of some benchmark data .

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