GOALI: Intelligent Decomposition Heuristics for Scheduling Semiconductor Manufacturing Facilities

点击翻译按钮获取中文摘要

9613708 Uzsoy This GOALI award represents a combined industry-university research effort geared toward the development of optimization-based approximate scheduling procedures for complex manufacturing operations. While optimization-based scheduling approaches for complex manufacturing systems are not new, they have found relatively little use in practice, primarily because they have focused on exact solutions to computationally intractable problems. This research takes a different tack, employing a combination of heuristic decomposition methods and machine learning to develop effective approximate scheduling procedures for a general class of job-shops that encompass sequence dependent setup times, batching, and pipelining. The investigators include a senior researcher from Intel Corporation, and they will use semiconductor wafer fabrication facilities and assembly and test facilities as the testbeds for their research. The complexity of semiconductor manufacturing and the competitive pressures faced by the industry have made effective manufacturing management, in particular shop-floor control, essential to a company's survival. An important part of shop floor control is scheduling the movement of material through the equipment in the factory to obtain the best possible system performance. If successful, this work has the potential to reduce variability in lead times, thus allowing a company the ability to maintain lower floor inventories, respond rapidly to changing market conditions, and quote accurate and competitive lead times to their customers. The strong intellectual involvement of an industrial partner in this research will further ensure its relevance to practical manufacturing environments.