同类平行机批调度问题研究

发布时间：2019-01-09 09:55

【摘要】：生产调度问题是一类具有重要研究价值的组合优化问题,它广泛存在于现代化生产的各行各业。由经典调度问题拓展而来的批调度问题,由于其广泛的实用价值和较好的理论支撑,已成为当前生产调度领域研究的热点问题之一。新兴生产模式的兴起及其在企业中的广泛应用,使得企业的生产环境也由传统的单机生产环境扩展到了复杂的多机生产环境。信息技术的迅速发展,使得物联网技术开始广泛应用于企业的生产制造过程中,给企业的生产调度带来机遇的同时也带来了挑战。作为新一代信息技术的重要组成部分,物联网技术在生产调度领域的应用可以实现对生产调度对象的识别,获取生产调度对象及机器设备的状态信息和位置信息等。决策者能否在激烈的市场竞争中对这些信息加以充分利用从而制定高效可靠的调度策略,实现智能的优化调度过程,将成为企业能否在竞争激烈的市场中立于不败之地的关键。本文围绕半导体制造中的平行批处理机加工过程,以物联网技术提供的信息为基础,系统的研究了加工设备为同类平行机环境下的两种特殊情形的批调度问题。以最小化制造跨度为目标分别考虑了运输时间不同的情形以及加工设备具有不同容量的情形。论文的主要工作如下:(1)以最小化制造跨度为目标建立了考虑运输时间的同类平行机批调度问题的数学模型,在分析问题性质的基础上提出了一个启发式算法和一个局部搜索策略,并结合离散粒子群算法和遗传算法的优点,提出了混合DPSO-GA算法求解该问题。通过仿真实验将所提出的混合DPSO-GA算法与相关算法进行比较以验证所提算法的有效性。结果显示,所提出的混合DPSO-GA算法能够在合理的时间内高效的求解所研究的问题。(2)研究了不同容量的同类平行机加工环境下的批调度问题。以最小化制造跨度作为求解目标建立了该批调度问题的数学模型,并提出了一个启发式算法产生遗传算法的初始解,然后设计了带有局部搜索策略的改进遗传算法对该调度问题进行求解。实验结果显示,本文所提出的改进遗传算法能够有效求解不同容量的同类平行机批调度问题。
[Abstract]:Production scheduling problem is a kind of combinatorial optimization problem with important research value. It widely exists in various industries of modern production. The batch scheduling problem, which is extended from the classical scheduling problem, has become one of the hot issues in the field of production scheduling because of its extensive practical value and better theoretical support. With the rise of new production mode and its wide application in enterprises, the production environment of enterprises is extended from traditional single-machine production environment to complex multi-machine production environment. With the rapid development of information technology, the Internet of things (IoT) technology has been widely used in the manufacturing process of enterprises, which brings both opportunities and challenges to the production scheduling of enterprises. As an important part of the new generation of information technology, the application of Internet of things technology in the field of production scheduling can realize the identification of production scheduling objects and obtain the state information and location information of production scheduling objects and machines. Whether the decision-makers can make full use of this information in the fierce market competition to make efficient and reliable scheduling strategy and realize the intelligent optimal scheduling process will become the key to the enterprises to be invincible in the fierce competition market. Based on the information provided by the Internet of things (IoT), the batch scheduling problem of two special cases in which the processing equipment is in the same parallel machine environment is studied systematically in this paper around the process of parallel batch machining in semiconductor manufacturing. In order to minimize the manufacturing span, the conditions of different transportation time and different capacity of processing equipment are considered respectively. The main work of the thesis is as follows: (1) the mathematical model of the same parallel machine batch scheduling problem considering the transportation time is established with the goal of minimizing the manufacturing span. Based on the analysis of the properties of the problem, a heuristic algorithm and a local search strategy are proposed. Combined with the advantages of discrete particle swarm optimization and genetic algorithm, a hybrid DPSO-GA algorithm is proposed to solve the problem. The proposed hybrid DPSO-GA algorithm is compared with the related algorithms through simulation experiments to verify the effectiveness of the proposed algorithm. The results show that the proposed hybrid DPSO-GA algorithm can efficiently solve the problem in a reasonable time. (2) the batch scheduling problem in the same parallel machining environment with different capacity is studied. The mathematical model of the batch scheduling problem is established by minimizing the manufacturing span, and a heuristic algorithm is proposed to generate the initial solution of the genetic algorithm. Then an improved genetic algorithm with local search strategy is designed to solve the scheduling problem. Experimental results show that the improved genetic algorithm proposed in this paper can effectively solve the similar parallel machine batch scheduling problem with different capacity.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB497

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