基于恢复的批处理生产重调度及优化

发布时间：2018-01-22 01:33

本文关键词： 批处理生产重调度遗传算法约束规划算法　出处：《华南理工大学》2013年硕士论文　论文类型：学位论文

【摘要】：随着社会经济的快速发展，消费者对产品的需求逐渐呈现出多样化和个性化的趋势。各种产品更新换代的周期也越来越短，企业之间的竞争也愈演愈烈。在这种背景下，多品种小批量逐渐替代大批量或者单件大批量生产，成为现代企业的主要生产模式。多产品、多阶段的批处理生产方式以其高度的灵活性和柔性被广泛地应用于流程型企业，用以生产小批量、高附加值的产品，有效地缩短了产品生产周期、加速了市场响应速度。然而，批处理生产方式的灵活性对批处理生产管理提出了更高要求。在实际生产制造中，批处理生产过程总是面临诸多不确定因素的干扰，如紧急订单、设备故障、因质量问题返工等等，这些不确定因素的发生破坏了原计划的执行甚至可能引起整个生产的瘫痪。为了解决类似问题，重调度问题应运而生。然而，现有的批处理生产问题的研究仍然局限在经典调度理论算法的研究上，不仅难以解决大规模问题，还很难在实际生产过程中运用。另外，现在的大部分调度问题还在研究确定性调度，较少考虑不确定情况下的调度问题，重调度问题的研究更是尚在初始阶段。本文主要建立批处理生产的调度和重调度优化模型，考虑并行机器、储藏容量和储藏时间的约束。重调度优化模型在调度模型的基础上探索了机器故障和紧急订单等干扰对生产的影响，建立以恢复原计划为目标的批处理生产重调度模型。此重调度模型以原计划为指导，在尽量减少对原计划改动的基础上，重新制作新计划。这样的重调度方式可以优化利用现有资源，减少资源浪费，达到节约资源的目的，使现有的资源得到更好的重用。本文使用遗传算法和约束规划算法两个优化算法实现调度和重调度，同时比较和分析两者解决问题的表现。研究发现，约束规划算法在解决有复杂约束的调度和重调度问题表现较好。
[Abstract]:With the rapid development of social economy, consumer demand for products becomes diversified and personalized trend. All kinds of product replacement cycle is getting shorter, the competition between enterprises is fiercer. Under this background, many varieties of small batch production gradually replace large quantities or single batch production has become the main production mode of modern enterprise.
Multi product, multi stage batch production with its high flexibility and is widely used in process industry, for the production of small batch, high value-added products, to shorten the production cycle, accelerate the response speed of the market. However, the batch production flexibility put forward high requirements for batch production management in manufacturing. In actual production, the batch production process is facing many uncertain factors, such as emergency orders, equipment failure, rework due to quality issues and so on, these uncertain factors to the occurrence of the destruction of the original implementation of the plan and may even lead to paralysis of the entire production. In order to solve similar problems heavy, scheduling problems emerged.
However, the existing research on batch production problem is still limited in the study of classical scheduling theory algorithm, not only difficult to solve large-scale problems, it is difficult to use in the actual production process. In addition, the uncertainty is also studying the most scheduling problems scheduling now, seldom consider the scheduling problem in uncertain situations, the research of scheduling problem it is still in the initial stage.
This paper mainly set up batch production scheduling and re scheduling optimization model, parallel machine, storage capacity and storage time constraints. Re scheduling optimization model based on the scheduling model to explore the effects of machine breakdown and emergency order interference on the production, the establishment of batch production to restore the original plan for the target weight scheduling model this rescheduling model to the original plan as a guide, on the basis of the original plan to minimize changes, to make a new plan. Such rescheduling method can optimize the utilization of existing resources, reduce the waste of resources, to achieve the purpose of saving resources, the existing resources to obtain better reuse. We use genetic algorithm and constraint two optimization planning algorithm scheduling algorithm and scheduling, and the comparison and analysis of the two problems. The study found that constraint planning algorithm in solving complex constraints The problem of scheduling and rescheduling is better.

【学位授予单位】：华南理工大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP301.6;F406.2

【共引文献】