改进的蚁群算法在硫化车间调度问题中的应用

发布时间：2018-08-06 13:50

【摘要】：轮胎制造行业是一个生产规模较大，资源和劳动力密集的行业，良好的生产计划的制定对企业的生产过程和实际收益具有重大的意义。在轮胎生产中，硫化工序作为瓶颈工序，其调度计划制定好坏直接影响整个轮胎生产流程的效率，，因此本文主要研究硫化车间中的生产调度问题。首先，本文介绍了车间调度的研究现状，包括研究的车间调度的分类、特点、研究方法和发展趋势。其次，本文根据硫化车间真实的生产情况，如各种约束条件和企业目标，提出并建立了硫化车间的数学模型。再次，本文针对针对基于最小化最大完成时间的硫化车间调度问题的特点，同时为了克服蚁群算法易陷入局部最优的缺点，提出了一种以硫化车间调度问题为背景的改进的蚁群算法。算法将遗传算法融入到了蚁群算法的每次迭代过程中以加强算法的局部搜索能力，同时保持搜索解的多样性；并利用蚁群算法正反馈的特性，加强整个算法的收敛速度，提高其求解效率。以硫化车间某生产小组为例，利用改进后的蚁群算法进行系统仿真，仿真结果结果与ACS算法、GAAA算法进行比较，证明本文提出的算法在求解质量和收敛速度方面都更加有效。然后，本文针对多目标硫化车间调度问题进行分析，并根据其特点对改进的蚁群算法的函数进行设计和改进，使之能够满足多目标的求解需求，通过系统仿真实验验证，本算法在求解质量和收敛速度上都较MACS算法、MOGA算法更优。最后，本文针对动态不确定条件下的硫化车间生产调度问题，本文以硫化机故障为例，采用改进的蚁群算法和滚动再调度技术相结合的方式，成功解决了这类调度问题，仿真结果十分有效。
[Abstract]:The tire manufacturing industry is an industry with large production scale and intensive resources and labor force. The formulation of a good production plan is of great significance to the production process and actual income of the enterprise. In tire production, the vulcanization process is used as a bottleneck process, and its scheduling plan has a good effect on the efficiency of the whole tire production process. This paper mainly studies the production scheduling problem in vulcanizing workshop.
Firstly, this paper introduces the research status of job shop scheduling, including the classification, characteristics, research methods and development trend of job shop scheduling.
Secondly, according to the actual production situation of vulcanization workshop, such as various constraints and enterprise objectives, the mathematical model of vulcanization workshop is proposed and established.
Thirdly, aiming at the characteristics of the vulcanization shop scheduling problem based on minimizing the maximum completion time, and in order to overcome the disadvantage that the ant colony algorithm is easy to fall into the local optimum, an improved ant colony algorithm based on the scheduling problem of vulcanization workshop is proposed. The algorithm integrates the genetic algorithm into the iterative process of the ant colony algorithm. In order to strengthen the local search ability of the algorithm, and maintain the diversity of the search solution, and use the characteristics of the positive feedback of the ant colony algorithm, the convergence speed of the whole algorithm is strengthened and its efficiency is improved. The system simulation is carried out by the improved ant colony algorithm, the result of simulation results and the ACS algorithm, the GAAA algorithm. The comparison shows that the algorithm proposed in this paper is more effective in solving the quality and convergence speed.
Then, this paper analyzes the scheduling problem of multi-objective vulcanization shop, and designs and improves the function of the improved ant colony algorithm according to its characteristics so that it can meet the needs of multi target solution. Through the system simulation experiment, it is proved that the algorithm is better than the MACS algorithm and the MOGA algorithm in the solution quality and convergence speed.
Finally, this paper, aiming at the production scheduling problem of the vulcanization workshop under the dynamic uncertainty, uses the improved ant colony algorithm and the rolling re scheduling technique to solve the scheduling problem successfully, and the simulation results are very effective.
【学位授予单位】：青岛科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP18;TB497

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