【作者】 刘辙；

【导师】 崔广才；

【作者基本信息】 长春理工大学 ， 计算机应用技术， 2004， 硕士

【摘要】 分布式车间模式作为21世纪企业的先进制造模式，综合了JIT、并行工程、精良制造等多种先进制造模式的哲理，其目的是要以最低成本制造出顾客满意的产品。在这种模式下如何进行组织管理，包括如何组织动态联盟、如何重构车间和单元、如何安排生产计划、如何进行调度都是我们面临的问题。其中车间作业调度与控制技术是实现生产高效率、高柔性和高可靠性的关键，有效实用的调度方法和优化技术的研究与应用已成为先进制造技术实践的基础。本文结合分布式车间生产模式的实际情况，研究了自适应遗传算法和混合遗传算法-模拟退火遗传算法相混合)对该问题的解决策略和过程。详细地阐述了算法的基本结构、编码方式、解码规则、适值函数的选取、自适应变异和交叉算子的设计、染色体可行性的判断流程，最后以数据表和甘特图的方式给出了计算结果。从结果中可以看出遗传算法是解决该问题的行之有效的方法，而混合遗传算法则是解决该问题的更为优良的方法。更多还原

【Abstract】 As advanced manufacturing mode for the 21th business enterprise, distribution shop mode synthesizes many exellent philosophies of manufacturing pattern such as JIT, parallel engineering project, ecellent manufacturing and so on. Its purpose is making out customer satisfied products with the most low cost. Under distribution shop mode, we are faced with how to organize and manage production, which include how to organize dynamic alliance, how to restructure shop and unit, how to arrange producing planning, and how to proceed scheduling. Among them, job-shop scheduling and control technique is the key to achieve producing high-efficiencyly, high-flexibility, and high-dependability. To study and apply an effective scheduling method and optimizing technique have become practised basic of advanced manufacturing technique. This paper studies how to use self-adapted genetic algorithm and hybrid genetic algorithm (GASA) to solve this problem and its application. The author expatiated on the basic structure, coding manners, decoding rules, fitness function selection, self-adapted mutation and crossover operator, the judging flow of chromosome feasibility of the algorithm, finally , put forward the computing result with pattern of data table and GANTT graph. In this paper, the author come to a conclusion that genetic algorithm is an efficient solution to distribution job-shop problem, while GASA is a more superior method than it.更多还原