Iterative combinatorial auctions for managing product transitions in semiconductor manufacturing

Abstract

Successful management of product transitions in the semiconductor industry requires effective coordination of manufacturing and product development activities. Manufacturing units must meet demand for current products while also allocating capacity to product development units for prototype fabrication that will support timely introduction of new products into high-volume manufacturing. Knowledge of detailed operational constraints and capabilities is only available within each unit, precluding the use of a centralized planning model with complete information of all units. However, the decision support tools used by the individual units offer the possibility of a decentralized decision framework that uses these local models as components to rapidly obtain mutually acceptable, implementable solutions. We develop Iterative Combinatorial Auctions (ICAs) that achieve coordinated decisions for all units to maximize the firm’s profit while motivating all units to share information truthfully. Computational results show that the ICA that uses column generation to update prices outperforms that using subgradient search, obtaining near-optimal corporate profit in low CPU times.

Keywords:

• Product transitions
• Lagrangian relaxation
• column generation
• iterative combinatorial auctions
• semiconductor manufacturing

Additional information

Funding

This research was supported by the National Science Foundation (NSF) under Grant Nos. CMMI-1744109 and CMMI-1826125. Any opinions stated are those of the authors, and do not necessarily reflect the position of NSF.

Notes on contributors

Ankit Bansal

Ankit Bansal received his Ph.D. degree in industrial engineering from North Carolina State University. His research interests are in the theory and applications of optimization and mechanism design. He will be joining the University of Minnesota as Postdoctoral fellow in Fall 2019.

Reha Uzsoy

Reha Uzsoy is Clifton A. Anderson Distinguished Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. He holds B.S. degrees in industrial engineering and mathematics and an MS in industrial engineering from Bogazici University, Istanbul, Turkey. He received his Ph.D. in industrial and systems engineering in 1990 from the University of Florida, and held faculty positions in industrial engineering at Purdue University prior to joining North Carolina State University in 2007. His teaching and research interests are in production planning and supply chain management. Before coming to the US he worked as a production engineer with Arcelik AS, a major appliance manufacturer in Istanbul, Turkey. He has also been a visiting researcher at Intel Corporation and IC Delco. He was named a Fellow of the Institute of Industrial Engineers in 2005, Outstanding Young Industrial Engineer in Education in 1997, and has received awards for both undergraduate and graduate teaching.

Karl Kempf

Karl G. Kempf is a Senior Fellow and Director of Decision Engineering at Intel Corporation. Since joining Intel in 1987 he has led a team of decision scientists charged with building decision-support processes and tools focused on faster better decision making across the corporation from factory design to manufacturing and supply chain execution, from product design to forecasting and portfolio management. He has been a research adjunct at the University of Missouri, Arizona State, North Carolina State and Stanford University. He is a member of the National Academy of Engineering (NAE), a Fellow of the IEEE, and a Fellow of INFORMS.