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Research On Flexible Job Shop Scheduling Problem Using The Improved Quantum-behaved Particle Swarm Optimization Algorithm

Posted on:2017-01-25Degree:MasterType:Thesis
Country:ChinaCandidate:K ZhouFull Text:PDF
GTID:2308330488482686Subject:Control Science and Engineering
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The Flexible Job-shop Scheduling Problem(FJSP) is complex compared with Job-shop Scheduling Problem(JSP), many algorithms has the problem of getting trapped into local optimum with premature convergence because of the actual production and the calculation difficulty in sloving the problem, therefore lots of researchers pay more attention to improve algorithms for sloving the complex scheduling problem effectively. In this paper, the improved Quantum-behaved Particle Swarm Optimization(QPSO) algorithms are proposed to slove the single objective and multi-objective FJSP, meanwhile FJSP is researched in a mould job shop. The specific work and innovation in the following four points:(1)This paper lists and explains the ideal FJSP model and summarize the arrangement of equipment, work flow, processing schedule, constraint condition and evaluation objectives in a mould job shop.(2)The QPSO based on Opposition-Based Learning(OBL) and bounded mutation is proposed to slove the single objective FJSP. According to the problem of suffering from premature convergence, the improved algorithm introduced OBL strategy and bounded mutation into QPSO in accordance for expanding populations and avoiding algorithm into the optimum of boundary. Then applying the improved algorithm to solve three benchmark functions, two usual FJSP cases and a scheduling optimization。(3)The QPSO mixed bat algorithm(BA) is proposed to slove the multi-objective FJSP. The hybrid algorithm learns the speed changing of bats’ sound to transform the factor of QPSO and use the random walk strategy of bat algorithm to avoid getting into local optimum. The hybrid algorithm was tested on continuous functions and a multi-objective scheduling example of a mould job shop with three objectives.(4)Energy consumption optimization scheduling problem is studied. As the enterprise pay the attention to the energy consumption problem in the workshop, the optimization of energy consumption problems not only improves the enterprise economic benefits, but also has great significance to environment protection. The hybrid QPSO was solved the makespan and energy consumption in the mould shop scheduling problem.Through multiple simulation experiments, the improved QPSO are effective and superior in the continuous functions and flexible job-shop scheduling problems.
Keywords/Search Tags:opposition-based learning, bounded mutation, quantum-behaved particle swarm optimization, bat algorithm, flexible job shop scheduling
PDF Full Text Request
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