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.

翻译：随着纺织品需求的增加,纺织业正在成为一个高度竞争的领域。由于扩大生产能力并不总是可行的,优化现有系统更为实用。特别是,我们认为在本研究中土耳其一家纺织厂的有感觉的生产系统更为实用。我们的目标是通过优化机器操作速度和在规划范围内建立一个高效的生产批量计划来尽量减少生产成本。在这方面,我们提议采用 " 托运规模和机器速度(LSMS)非线性模式 " 非线性化模式来确定最佳单位处理时间和生产数量,同时根据需求动态地修改机器操作速度,从而最大限度地减少流程中的工作和最终项目库存。由于LSMS非线性优化问题非常严重,我们设计了两阶段的超标准,通过在每一个阶段使用商业求解器来交互处理线性编程模型。我们通过随机生成的需求、规划地平线和机器时速能力设想来深入测试我们的两阶段超标准。我们的计算实验显示,引入的二阶段超标准能在可接受的时间范围内找到当地最佳结果。