About

Reha Uzsoy is a professor in the Edward P. Fitts Department of Industrial & Systems Engineering at North Carolina State University. He holds the title of C. A. Anderson Distinguished Professor. His research interests include production planning, scheduling, and supply chain management. Uzsoy has authored one book, three edited books, and numerous refereed journal publications. His professional background includes work as a production engineer with Arcelik AS, a major appliance manufacturer in Istanbul, Turkey, and as a visiting researcher at Intel Corporation and IC Delco. His research has been supported by organizations such as the National Science Foundation, Intel Corporation, Hitachi Semiconductor, Harris Corporation, Kimberly Clark, Union Pacific, Ascension Health, and General Motors. Uzsoy has been recognized as a Fellow of the Institute of Industrial Engineers since 2005, received the Outstanding Young Industrial Engineer in Education award in 1997, and was named a University Faculty Fellow by Purdue University in 2001. He has also received awards for both undergraduate and graduate teaching.

Research topics

• Computer Science
• Engineering
• Algorithm
• Mathematics
• Process management
• Operations management
• Economics
• Operations research
• Industrial engineering
• Geography
• Mathematical optimization

Selected publications

• Release date optimisation in MRP using clearing functions

  International Journal of Production Research · 2026-02-05 · 1 citations

  articleOpen accessSenior author

  This paper integrates a clearing function (CF)-based release planning approach into Material Requirements Planning (MRP) to address its limitations in modeling capacity constraints and dynamic lead times. The proposed optimization model replaces MRP's backward scheduling step while preserving its overall structure. Performance is evaluated through simulation experiments on two flow shop systems that explore a range of demand uncertainties and utilization levels. Computational results show that the proposed approach is capable of yielding significant improvements over the conventional backward scheduling approach, due to its ability to compute planned lead times for individual production orders as opposed to BOM items.

  PublisherDOI

• Editorial

  IEEE Transactions on Semiconductor Manufacturing · 2025-02-01

  editorialSenior author
  PublisherDOI

• Using Multi-Period Optimization to Parameterize a Short-Term Order Release Mechanism

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior authorCorresponding

  We design a novel order release system by combining a multi-period optimization model for order release planning (allocated clearing function model) with a short-term order release mechanism (LUMS). The former performs mid-term release/production planning while the latter controls order releases in the short term. The LUMS parameters are derived from the planning model in a consistent manner that provides the coupling of medium- and short-term level. Preliminary numerical experiments indicate that this approach substantially improves due date performance and cycle times at the cost of a moderate increase in FGI compared to a stand-alone allocated clearing function model.

  PublisherDOI

• Shaping Tomorrow's Factories: A Panel on Simulation-Driven Manufacturing

  2025-12-07

  article
  PublisherDOI

• Editorial

  IEEE Transactions on Semiconductor Manufacturing · 2025-10-31

  article1st authorCorresponding
  PublisherDOI

• Editorial

  IEEE Transactions on Semiconductor Manufacturing · 2024-02-01

  editorialOpen access1st authorCorresponding

  As we enter a New Year, we can look back on another year of solid accomplishment at IEEE Transactions on Semiconductor Manufacturing. I am happy to report that our impact factor remains steady at 2.70, and our mean time to first decision remains competitive at 8.3 weeks. Our Editorial Board remains as strong as ever, with the addition of Dr. Jun-Haeng Lee in the area of machine learning and data science applications in 2023, and we are actively seeking new board members. Our submissions remain strong, as do the special sections from conferences (ASMC, ISSM and CS-MANTECH). The Special Issue on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing appeared in the November issue, and two additional special issues are in preparation. Prof. Duane Boning of MIT and Dr. Bill Nehrer of Technology Consultancy are co-editing a special issue on “Semiconductor Design for Manufacturing,” which will be a collaborative effort with the IEEE Transactions on Electron Devices. Drs. Oliver Patterson of Intel and Tomasz Brozek of PDF Solutions are also co-editing a special issue on sustainable semiconductor manufacturing. We are also happy to announce the Best paper Award for 2023, in the companion editorial appearing in this issue. Congratulations to all the honorees, and we hope we will continue to see their submissions in the future. Our thanks go to Drs. Jeanne Bickford, Dragan Djurdjanovic and Mahadeva Iyer Natarajan for their work on this committee.

  PublisherOA PDFDOI

• Coordinating Resource Allocation during Product Transitions Using a Multifollower Bilevel Programming Model

  arXiv (Cornell University) · 2024-01-30 · 1 citations

  preprintOpen access

  We study the management of product transitions in a semiconductor manufacturing firm that requires the coordination of resource allocation decisions by multiple, autonomous Product Divisions using a multi-follower bilevel model to capture the hierarchical and decentralized nature of this decision process. Corporate management, acting as the leader, seeks to maximize the firm's total profit over a finite horizon. The followers consist of multiple Product Divisions that must share manufacturing and engineering resources to develop, produce and sell products in the market. Each Product Division needs engineering capacity to develop new products, and factory capacity to produce products for sale while also producing the prototypes and samples needed for the product development process. We model this interdependency between Product Divisions as a generalized Nash equilibrium problem at the lower level and propose a reformulation where Corporate Management acts as the leader to coordinate the resource allocation decisions. We then derive an equivalent single-level reformulation and develop a cut-and-column generation algorithm. Extensive computational experiments evaluate the performance of the algorithm and provide managerial insights on how key parameters and the distribution of decision authority affect system performance.

  PublisherOA PDFDOI

• All You Need is an Improving Column: Enhancing Column Generation for Parallel Machine Scheduling via Transformers

  arXiv (Cornell University) · 2024-10-21

  preprintOpen accessSenior author

  We present a neural network-enhanced column generation (CG) approach for a parallel machine scheduling problem. The proposed approach utilizes an encoder-decoder attention model, namely the transformer and pointer architectures, to develop job sequences with negative reduced cost and thus generate columns to add to the master problem. By training the neural network offline and using it in inference mode to predict negative reduced costs columns, we achieve significant computational time savings compared to dynamic programming (DP). Since the exact DP procedure is used to verify that no further columns with negative reduced cost can be identified at termination, the optimality guarantee of the original CG procedure is preserved. For small to medium-sized instances, our approach achieves an average 45% reduction in computation time compared to solving the subproblems with DP. Furthermore, the model generalizes not only to unseen, larger problem instances from the same probability distribution but also to instances from different probability distributions than those presented at training time. For large-sized instances, the proposed approach achieves an 80% improvement in the objective value in under 500 seconds, demonstrating both its scalability and efficiency.

  PublisherOA PDFDOI

• Editorial

  IEEE Transactions on Semiconductor Manufacturing · 2024-11-01

  editorial1st authorCorresponding
  PublisherDOI

• Decentralized Decision-Making Framework for Managing Product Rollovers in the Semiconductor Manufacturing

  2023-12-10

  article

  Competitiveness in the semiconductor industry requires continuous management of product rollovers, the process of introducing new products and retiring older ones to maintain market share. This paper presents a decentralized decision-making framework to coordinate product rollover decisions using Lagrangian decomposition of a centralized model using quadratic coordination errors in the subproblem objectives, and a decentralized heuristic that recovers the feasible solutions from the relaxed ones obtained from the Lagrangian procedure. Experimental results show that this decentralized framework delivers promising results, obtaining near-optimal solutions in modest CPU times.

  PublisherDOI

Recent grants

• EAGER: Negotiated Resource Sharing Strategies to Facilitate New Product Introductions

  NSF · $107k · 2017–2018

• Next Generation Algorithms for Planning Production and Inventories with Uncertain Demand and Congestion

  NSF · $480k · 2010–2014

Frequent coauthors

• Karl G. Kempf

  Intel (United States)

  29 shared

• Irfan M. Ovacik

  Intel (United States)

  24 shared

• Lars Mönch

  23 shared

• Necip Baris Kacar

  North Carolina State University

  17 shared

• Hubert Missbauer

  Universität Innsbruck

  14 shared

• Erinç Albey

  12 shared

• Edward Fitts

  North Carolina State University

  11 shared

• Chung‐Yee Lee

  10 shared

Awards & honors

• Fellow, Institute of Industrial Engineers (2005)
• Outstanding Young Industrial Engineer in Education, Institut…
• University Faculty Fellow, Purdue University (2001)
• C.A. Anderson Outstanding Faculty Award, ISE Department at N…
• Editorial Board Member, International Journal of Production…