论文标题
生产计划和机器速度优化问题的两阶段方法
A Two-Phase Method for Production Planning and Machine Speed Optimization Problem
论文作者
论文摘要
随着对纺织品的需求增加，纺织业正在成为一个竞争激烈的领域。由于扩大生产能力并不总是可行的，因此优化现有系统更实用。特别是，我们考虑了在这项研究中在土耳其运营的纺织工厂的毛毡生产系统。我们的目标是通过优化机器运营速度以及在计划范围内建立有效的生产量规划计划来最大程度地降低生产成本。在这个方向上，我们提出了批次尺寸和机器速度（LSMS）非线性模型，以确定最佳的单元处理时间和生产量，同时根据需求将机器操作速度更改，从而最大程度地减少了在制品和最终项目库存中。由于LSMS非线性优化问题是NP-HARD，因此我们设计了一种两阶段的启发式词，该术语通过在每个阶段使用商业求解器来迭代处理线性编程模型。我们通过随机生成的需求，计划范围和机器时间的容量方案进行了深入测试我们的两阶段启发式方法。我们的计算实验表明，引入的两相启发式可以在可接受的时间内找到局部最佳结果。
Textile industry is becoming a highly competitive area with the increase in demand for textile products. Since expanding the production capacity is not always feasible, optimizing the existing system is more practical. In particular, we consider a felt production system of a textile factory operating in Turkey in this study. We aim to minimize the production costs by optimizing machine operating speeds as well as building an efficient production lot sizing plan within the planning horizon. In this direction, we propose the Lot Sizing and Machine Speed (LSMS) nonlinear model to determine the optimal unit processing times and production quantities while minimizing the work-in-process and end item inventories by changing the machine operating speeds dynamically according to the demands. Since LSMS nonlinear optimization problem is NP-hard, we design a Two-Phase heuristic which iteratively processes a linear programming model by utilizing a commercial solver at each phase. We intensively test our Two-Phase heuristic via randomly generated demand, planning horizon and machine-hour capacity scenarios. Our computational experiments show that the introduced Two-Phase heuristic can find the local-optimal results in acceptable amount of time.
