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

这笔赠款为数学模型和数值工具的开发提供资金，这些模型和数值工具可用于确定钢坯的最佳设计（尺寸、等级和重量），以及钢坯库存的最佳补充政策。将采用两步走的方法。在第一步中，将使用启发式组合来确定最佳设计配置，以生成良好的初始解决方案和对初始解决方案的改进。一个涉及二元变量的随机整数规划公式将用于提供这些启发式解决方案的性能界限。对于给定数量的板坯设计，这些方法将确定最大覆盖范围的配置，以所选配置可以制造的总成品吨来衡量。在第二步中，将开发一个多周期随机线性程序，以确定每个板坯设计配置的补货批量大小，以最小化库存持有和生产效率低下成本的总和。如果成功，这项研究的结果将为综合钢厂（ISMs）面临的一个长期问题提供基于科学的解决方案。通过专注于减少板坯设计的数量，ISMs可以追求特种钢市场，同时降低生产过程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更广泛地应用于具有类似流程架构的行业，例如造纸和纸浆，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许多应用程序中。为确定最佳补充政策而提出的模型，特别是当与推迟产品差异化的可能性结合在一起时，将对试图应付产品种类增加的制造公司有用。

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.