About

Michael L. Pinedo is the Julius Schlesinger Professor of Operations Management in the Department of Technology, Operations, and Statistics at New York University Leonard N. Stern School of Business. He joined Stern in 1996 and has a distinguished academic background, having previously taught at Columbia University in the Industrial Engineering and Operations Research department from 1982 to 1997, as well as at the Instituto Venezolano de Investigaciones Cientificas in Caracas from 1978 to 1980 and at the Georgia Institute of Technology from 1980 to 1982. Professor Pinedo's research focuses on the modeling of production and service systems, with particular emphasis on the planning and scheduling of these systems. Recently, his work has concentrated on operational risk in financial services. He has authored and co-authored numerous technical papers on these topics and is the author of several books, including 'Scheduling: Theory, Algorithms, and Systems' and 'Planning and Scheduling in Manufacturing and Services,' as well as co-authoring 'Queueing Networks: Customers, Signals and Product Form Solutions.' Additionally, he is involved in editing and supervising scholarly work, serving as editor of the Journal of Scheduling and holding associate editor positions at several prominent journals. Over the past two decades, Professor Pinedo has been actively involved in industrial systems development, supervising the design and implementation of planning and scheduling systems for major corporations such as the International Paper Company, Goldman Sachs, Philips Electronics, Siemens, and Merck. His educational background includes an Ir. degree in Mechanical Engineering from Delft University of Technology and both an M.Sc. and Ph.D. in Operations Research from the University of California at Berkeley.

Research topics

• Computer Science
• Political Science
• Sociology
• Economics
• Knowledge management
• Computer network
• Mathematical optimization
• Engineering
• Distributed computing
• Statistics
• Operations management
• Mathematics
• Operations research
• Operating system
• Business

Selected publications

• Minimizing the sojourn time of open shop scheduling problems with unit-time operations

  European Journal of Operational Research · 2026-04-01

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• Fifty years of research in scheduling — Theory and applications

  European Journal of Operational Research · 2025-02-07 · 25 citations

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• Iterated greedy algorithm with constraint programming for scheduling steelmaking-continuous casting process

  European Journal of Operational Research · 2025-11-23

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• A flow shop scheduling problem with machine-dependent speeds: An ensemble approach with worst-case analysis

  European Journal of Operational Research · 2025-09-17

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• Fairness in repetitive scheduling

  European Journal of Operational Research · 2025-01-06 · 7 citations

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• The circular balancing problem

  European Journal of Operational Research · 2024-08-25

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• Minimizing the Number of Tardy Jobs and Maximal Tardiness on a Single Machine is NP-hard

  arXiv (Cornell University) · 2024-04-03 · 1 citations

  preprintOpen access

  This paper resolves a long-standing open question in bicriteria scheduling regarding the complexity of a single machine scheduling problem which combines the number of tardy jobs and the maximal tardiness criteria. We use the lexicographic approach with the maximal tardiness being the primary criterion. Accordingly, the objective is to find, among all solutions minimizing the maximal tardiness, the one which has the minimum number of tardy jobs. The complexity of this problem has been open for over thirty years, and has been known since then to be one of the most challenging open questions in multicriteria scheduling. We resolve this question by proving that the problem is strongly NP-hard. We also prove that the problem is at least weakly NP-hard when we switch roles between the two criteria (i.e., when the number of tardy jobs is the primary criterion). Finally, we provide hardness results for two other approaches (constraint and a priori approaches) to deal with these two criteria.

  PublisherOA PDFDOI

• Code and Data Repository for An Improved Combinatorial Benders Decomposition Algorithm for the Human-Robot Collaborative Assembly Line Balancing Problem

  INFORMS journal on computing · 2024-11-26 · 5 citations

  articleSenior author

  This software implements an improved combinatorial Benders decomposition algorithm for the human-Robot collaborative assembly line balancing problem.

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• An Improved Combinatorial Benders Decomposition Algorithm for the Human-Robot Collaborative Assembly Line Balancing Problem

  INFORMS journal on computing · 2024-11-26 · 9 citations

  articleSenior author

  As an emerging technology, human-robot collaboration (HRC) has been implemented to enhance the performance of assembly lines and improve the safety of human workers. By integrating the advantages of human workers and collaborative robots (cobots), HRC enables production systems to process tasks consecutively, concurrently, or collaboratively. However, the introduction of cobots also makes the corresponding human-robot collaborative assembly line balancing problem more complex and difficult to solve. To solve this problem, we first propose an enhanced mixed integer program (EMIP) with various enhancement techniques and tighter bounds, and then, we develop an improved combinatorial Benders decomposition algorithm (Algorithm ICBD) with new local search strategies, Benders cuts, and acceleration procedures. To verify the effectiveness of our proposed model and algorithms, we conduct extensive computational experiments, and the results show that our proposed EMIP model is significantly better than the existing mixed integer program model; the percentages of instances that can obtain feasible and optimal solutions are increased from 82.42% to 100% and from 29.17% to 43.5%, respectively, whereas the average gap is decreased from 19.81% to 5.64%. In addition, our proposed Algorithm ICBD can get 100% of feasible solutions and 65.92% of optimal solutions for all of the test instances, and the average gap is only 1.49%. Moreover, compared with existing Benders decomposition methods for this problem, our approach yields comparatively better solutions in notably shorter average computational time when run in the same computational environment. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research was supported by the National Natural Science Foundation Council of China [Grants 72401214, 92167206, 7221101377, 72471169, and 72231005], the Ministry of Education of China [Grant 24YJC630078], and Computation and Analytics of Complex Management Systems (Tianjin University). This research was also supported by the Tianjin Natural Science Foundation Project [Grant 23JCQNJC01900] and the Tianjin Philosophy and Social Science Planning Project [Grant TJGL21-016]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0279 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0279 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

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• Station-based, free-float, or hybrid: An operating mode analysis of a bike-sharing system

  Transportation Research Part B Methodological · 2024-11-07 · 3 citations

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Recent grants

• Collaborative Research: Container Scheduling - Complexity, Algorithms and Heuristics

  NSF · $134k · 2010–2014

• Collaborative Proposal: A Framework For Integrating Production Scheduling With Inventory Control

  NSF · $96k · 2006–2010

Frequent coauthors

• Kangbok Lee

  46 shared

• Joseph Y.‐T. Leung

  Hefei University of Technology

  34 shared

• Dirk Briskorn

  21 shared

• Xiuli Chao

  University of Michigan–Ann Arbor

  18 shared

• Byung‐Cheon Choi

  16 shared

• Chelliah Sriskandarajah

  12 shared

• Cheng‐Shang Chang

  11 shared

• H. Neil Geismar

  May Institute

  10 shared

Awards & honors

• Fellow of the POMS society (2010)
• Fellow of the INFORMS society (2009)