Product Design and Inventory Deployment for Improving Delivery Time Performance in the Steel Industry

该补助金为数学模型和数值工具的开发提供资金，这些模型和工具可用于确定钢板的最佳设计（尺寸，等级和重量）以及板坯库存的最佳补充政策。 将采用分两步走的办法。 在第一步中，最优的设计配置将被确定使用的组合，以生成一个良好的初始解和初始解的细化。 一个随机整数规划制定涉及二进制变量将被用来提供这些启发式解决方案的性能上的界限。 对于给定数量的板设计，这些方法将确定最大化覆盖的那些配置，根据可以从所选配置制造的总成品吨来测量。 在第二步中，将开发一个多周期随机线性规划，以确定每个板坯设计配置的补货批量，以最小化库存和生产效率低下的成本之和。如果成功的话，这项研究的结果将为综合钢铁米尔斯（ISM）面临的长期问题提供一个基于科学的解决方案。 通过专注于减少板坯设计的数量，ISM可以追求特殊钢市场，同时降低生产工艺的复杂性和库存成本。为解决潜在的随机优化问题而开发的求解技术将在具有类似过程架构的行业中具有更广泛的应用，例如，纸和纸浆，以及涉及大量场景的其他随机优化问题。后者出现在从能源模型、容量规划到金融资产管理的一系列应用中。 提出的模型，以确定最佳的补货政策，特别是当再加上延迟产品差异化的可能性，将是有用的制造企业试图科普增加产品品种。

This grant provides funding for the development of mathematical models and numerical tools which can be used to determine the optimal design (dimensions, grade, and weight) of steel slabs, and optimal replenishment policies for slab inventories. A two-step approach will be used. In the first step, optimal design configurations will be determined using a combination of heuristics to generate both a good initial solution and refinements to the initial solution. A stochastic integer programming formulation involving binary variables will be used to provide bounds on the performance of these heuristic solutions. For a given number of slab designs, these methods will determine those configurations that maximize coverage, measured in terms of total finished tons that can be manufactured from the chosen configurations. In the second step, a multi period stochastic linear program will be developed to determine replenishment batch sizes for each slab design configuration in order to minimize the sum of inventory holding and production inefficiency costs. If successful, the results of this research will provide a science-based solution to a chronic problem faced by integrated steel mills (ISMs). By focusing on reducing the number of slab designs, ISMs can pursue specialty steel markets while simultaneously reducing their production process complexity and inventory costs. Solution techniques developed to solve the underlying stochastic optimization problems, will have wider applications to industries with similar process architecture, e.g., paper and pulp, and to other stochastic optimization problems involving a large number of scenarios. The latter occur in a host of applications ranging from energy models, capacity planning to financial asset management. Models proposed for determining optimal replenishment policies, especially when coupled with the possibility of delaying product differentiation, will be useful for manufacturing firms trying to cope with increased product variety.