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Based hybrid algorithm for job shop scheduling problem

Author: LvTao
Tutor: LiDaLeiï¼›ZuoZhiHua
School: Zhengzhou University
Course: Mechanical Manufacturing and Automation
Keywords: Genetic Algorithms Ant Colony Algorithm Flexible Job Shop Scheduling Active Scheduler
CLC: TH186
Type: Master's thesis
Year: 2009
Downloads: 42
Quote: 0
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Abstract


With the global economy is gradually moving towards integration, product development and design, manufacturing cycles shorten and become a mainstream mode of production in the manufacturing sector, one-piece, more variety, small batch production mode. Co-ordinate scheduling the use of this mode of production of manufacturing resources demanding requirements of the factors of production to be able to respond quickly to market demand. Related statistics show that the parts in the production process 95% of the time in a non-value-added sectors like transport, waiting, and how to effectively develop production plans, a reasonable allocation of production resources, and reducing non-value-added aspects of time, shorten the production cycle, reduce costs, has the more and more and more companies and research institutions concerned. A market changing times, shortening the production cycle to respond quickly to market demand is particularly important this relationship to the survival of the enterprise. Therefore, how to scheduling production resources, and reduce the value of this part time to become a serious problem facing many productive enterprises. In response to this situation, this paper studied the hybrid genetic algorithm-based job-shop scheduling problem by hybrid genetic algorithm and ant colony algorithm, hybrid algorithm to solve the problem of the optimal solution. The hybrid algorithm combines the advantages of two intelligent algorithm, effectively avoiding a separate application for solving the defect, the characteristics of the algorithm is based on the step of the coding method, the introduction of variation manner based on the neighborhood searching; the junction of the two algorithms, using artificially increase The genetic algorithm initial value of the pheromone in the path of a better solution. Minimize process time the minimum tardiness optimization indicators, the application of hybrid algorithm to optimize shop scheduling. Decoding method designed to produce dynamic scheduling, and a number of textile machinery parts constitute an instance of the scheduling problem, solve different optimization indicators of the problem instance, and the simulation results show that, compared with the genetic algorithm or ant algorithm alone, mixed algorithm shop scheduling problem a faster solving and better global search capability. Hybrid genetic algorithm based on the development of a prototype system of job-shop scheduling, the system initially realized minimize the flow time and the smallest tardiness shop scheduling functions to optimize the indicators and scheduling result can be a table and Gantt graphical output.

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CLC: > Industrial Technology > Machinery and Instrument Industry > Machine shop (workshop ) > Production and technology management
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