Collaborative Research: Scheduling Multiple Product Types on Parallel Resources

这项合作研究补助金提供资金，用于开发一种新的调度模型，该模型通过假设一个作业由多个可以同时并行处理的操作组成，从而概括了各种经典调度模型。有许多机器并行，但每台机器只能生产特定的产品类型子集。如果两种不同类型的产品在一台机器上一个接一个地加工，那么可能需要设置。在生产设施中的订单通常要求各种不同产品类型的特定数量，并期望所有订购的物品在同一到期日交付。该项目有三个目标。第一个目标是研究这类问题的理论性质。这包括为这些问题的简单版本搜索多项式时间算法，为更一般的版本建立NP-硬度结果，并建立优势结果或消除标准。第二个目标是发展和评估化学品。算法将基于分解、局部搜索以及混合方法。第三个目标是与几家对该项目感兴趣的公司合作，以开发和评估化学品。这些公司将提供真实的数据来验证自动化系统的性能。 这些公司包括软件开发公司，它们与大型制造公司以及大型制造公司本身都有正在进行的项目。这项研究的结果预计将导致许多制造环境中生产调度的改进，从而降低相关公司的成本并提高盈利能力。

This collaborative research grant provides funding for the development of a new scheduling model that generalizes various classical scheduling models by assuming that a job consists of multiple operations which may be processed simultaneously in parallel. There are a number of machines in parallel but each machine is only capable of producing a specific subset of the product types. If two products of different types are processed on a machine one after another, then a setup may be required. An order coming in at a production facility usually asks for specific quantities of various different product types with the expectation that all the items ordered are delivered at the same due date. The objectives of this project are three-fold. The first goal is to study the theoretical properties of this class of problems. This includes searching for polynomial time algorithms for the easier versions of these problems, establishing NP-hardness results for the more general versions, and establishing dominance results or elimination criteria. A second goal involves the development and evaluation of heuristics. The heuristics will be based on decomposition, local search, as well as hybrid approaches. A third goal is to collaborate with several companies that have expressed interest in the project regarding the development and evaluation of heuristics. These companies will provide real-life data to validate the performance of the heuristics. These companies include software development companies that have ongoing projects with larger manufacturing companies as well as large manufacturing companies themselves.The results of this research are expected to lead to improvements in production scheduling in a number of manufacturing settings, leading to lower cost and higher profitability for the companies involved.