多工艺路线作业车间批量调度问题研究

发布时间：2018-04-23 20:28

本文选题：作业车间调度 + 多工艺路线　；参考：《华中科技大学》2011年硕士论文

【摘要】：当今市场竞争日益激烈,消费个性化、需求多样化使得多品种小批量成为企业生产的新趋势,因此高效的车间调度对生产制造企业显得尤为重要,也是提高企业竞争力的关键因素。作业车间调度(Job Shop Scheduling, JSP)是实际调度问题的简化,现实制造系统的调度问题通常还具有多工艺和多批量的特点。由于作业车间调度是NP-hard问题,多工艺路线作业车间批量调度问题大大增加了问题的复杂性,对该问题的研究具有重要的理论意义和实用价值。首先,本文介绍了课题的来源、目的和意义,论述了作业车间调度问题、多工艺路线作业车间批量调度的研究现状,以及车间调度问题的发展趋势。然后,研究了多工艺路线作业车间调度问题,构建了多工艺路线JSP问题的数学集成模型。基于广义粒子群优化模型,构造了一种求解多工艺路线JSP问题的广义粒子群优化算法(GPSO)。在该算法中,利用遗传算法(GA)中的交叉操作作为粒子间的信息交换策略,遗传算法中的变异操作则作为粒子的随机搜索策略,而粒子的局部搜索策略则采用禁忌搜索(TS)来实现。实验结果表明,该算法可有效地求解多工艺路线JSP问题。接着,研究了多工艺路线作业车间批量调度问题。采用等量分批的策略,同时引入平行移动的方法,使其与等量分批结合起来,从而达到有效优化生产周期的目的。基于GPSO算法,采用一种有效初始化方法,使得粒子编码能产生较优的初始解。针对多工艺路线批量调度问题,设计了一种新的交叉方法。通过对具体实例的仿真测试,研究批次变化对多工艺批量调度生产周期的影响,验证了该算法的可行性和有效性。随后,通过研究多工艺路线批量调度问题,结合GPSO的优化思想,开发了相应的调度原型系统。通过测试结果,再次验证了算法的有效性。最后,对全文进行总结,并对多工艺路线作业车间批量调度问题的研究做了进一步展望。
[Abstract]:Nowadays, the market competition is increasingly fierce, the consumption individuation, the demand diversification causes the multi-variety small batch to become the enterprise production new tendency, therefore the high efficiency workshop scheduling appears to the production manufacture enterprise to be particularly important. It is also the key factor to improve the competitiveness of enterprises. Job shop Shop scheduling (JSP) is the simplification of practical scheduling problem. The scheduling problem of real manufacturing system usually has the characteristics of multi-process and multi-batch. Because job shop scheduling is a NP-hard problem, the multi-process route job shop batch scheduling problem greatly increases the complexity of the problem, so the study of this problem has important theoretical significance and practical value. First of all, this paper introduces the source, purpose and significance of the subject, discusses the job shop scheduling problem, the research status of the multi-process route job shop batch scheduling, and the development trend of the job shop scheduling problem. Then, the multi-process route job shop scheduling problem is studied, and the mathematical integration model of multi-process route JSP problem is constructed. Based on the generalized particle swarm optimization (GPSO) model, a generalized particle swarm optimization (GPSO) algorithm for solving multi-process route JSP problem is proposed. In this algorithm, the crossover operation in GA) is used as the information exchange strategy between particles, the mutation operation in genetic algorithm is used as the random searching strategy of particles, and the local search strategy of particles is implemented by Tabu search (TS). The experimental results show that the algorithm can effectively solve the multi-process route JSP problem. Then, the batch scheduling problem of multi-process route job shop is studied. The strategy of equal quantity batch and the method of parallel movement are introduced to combine it with equal quantity batch, so that the production cycle can be optimized effectively. Based on the GPSO algorithm, an effective initialization method is adopted to make the particle coding produce better initial solution. A new crossover method is designed for batch scheduling of multi-process routes. The effect of batch change on the production cycle of multi-process batch scheduling is studied through the simulation test of a concrete example, and the feasibility and effectiveness of the algorithm are verified. Then, by studying the multi-process route batch scheduling problem and combining the optimization idea of GPSO, the corresponding scheduling prototype system is developed. The test results show that the algorithm is effective again. Finally, the paper summarizes the whole paper and makes a further prospect on batch scheduling problem of multi-process route job shop.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH186

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