About

Rakesh Nagi is the Donald Biggar Willett Professor of Engineering at the University of Illinois, Urbana-Champaign, serving in the Department of Industrial and Enterprise Systems Engineering. He has previously served as the Department Head of Industrial and Enterprise Systems Engineering at Illinois from 2013 to 2019 and as the Interim Director of the Illinois Applied Research Institute from 2016 to 2018. Prior to his current position, he was a Professor and Chair of the Department of Industrial and Systems Engineering at the University at Buffalo (SUNY) from 2006 to 2012, and held various faculty roles there since 1993. His academic background includes a Ph.D. and M.S. in Mechanical Engineering from the University of Maryland at College Park, and a B.E. in Mechanical Engineering from the University of Roorkee (now IIT-R), India. His research focuses on production systems and applied/military operations research, with interests in location theoretic approaches to facilities design, agile enterprises, information-based manufacturing, just-in-time production, and information fusion. He has published extensively in reputable journals and has received numerous awards, including the IIE Fellow Award, NSF CAREER Award, and others, recognizing his scholarly contributions and leadership in engineering.

Research topics

• Computer Science
• Economics
• Business
• Engineering
• Manufacturing engineering
• Environmental economics
• Marketing
• Industrial organization
• Algorithm
• Mathematics
• Theoretical computer science
• Management science
• Risk analysis (engineering)
• Combinatorics
• Parallel computing

Selected publications

• Semi-Automated Knowledge Engineering and Process Mapping for Total Airport Management

  ArXiv.org · 2026-03-27

  articleOpen access

  Documentation of airport operations is inherently complex due to extensive technical terminology, rigorous regulations, proprietary regional information, and fragmented communication across multiple stakeholders. The resulting data silos and semantic inconsistencies present a significant impediment to the Total Airport Management (TAM) initiative. This paper presents a methodological framework for constructing a domain-grounded, machine-readable Knowledge Graph (KG) through a dual-stage fusion of symbolic Knowledge Engineering (KE) and generative Large Language Models (LLMs). The framework employs a scaffolded fusion strategy in which expert-curated KE structures guide LLM prompts to facilitate the discovery of semantically aligned knowledge triples. We evaluate this methodology on the Google LangExtract library and investigate the impact of context window utilization by comparing localized segment-based inference with document-level processing. Contrary to prior empirical observations of long-context degradation in LLMs, document-level processing improves the recovery of non-linear procedural dependencies. To ensure the high-fidelity provenance required in airport operations, the proposed framework fuses a probabilistic model for discovery and a deterministic algorithm for anchoring every extraction to its ground source. This ensures absolute traceability and verifiability, bridging the gap between "black-box" generative outputs and the transparency required for operational tooling. Finally, we introduce an automated framework that operationalizes this pipeline to synthesize complex operational workflows from unstructured textual corpora.

  PublisherOA PDF

• Integrated assembly variant design framework in agile manufacturing

  2026-02-11

  article

  The distributed and horizontally integrated manufacturing environment in Agile Manufacturing (AM) paradigm demands for new product development methods that posses favorable advantages over conventional manufacturing approaches developed for vertically integrated environments. This paper proposes an integrated framework for the variant design of complex assemblies. First, the complementary assembly modelling concept is introduced. To cater for the needs for assembly variant design, the concept is materialized as two kinds of assembly models: assembly variants model (AVM) and assembly mating graphs (AMG). The former explicitly captures the hierarchical and functional relationships between constituent components while the latter explicitly captures the mating relationships at the manufacturing feature level. Then the assembly variant design methodology, which is based on these assembly models, is developed based on the concept of Constraint Group (CG). The matching components are searched and retrieved from the AVM and then the CGs are identified by manipulating the AMGs. Finally, the assembly variant design process is formulated as a Mixed Integer Linear Programming (MILP) problem which is solved using a standard solver. This framework provides a systematic approach to facilitate the variant design of complex assembly products in the agile manufacturing environment.

  PublisherDOI

• Preventing overdose deaths with effective naloxone inventory distribution

  IISE Transactions on Healthcare Systems Engineering · 2026-05-11

  article
  PublisherDOI

• Semi-Automated Knowledge Engineering and Process Mapping for Total Airport Management

  arXiv (Cornell University) · 2026-03-27

  preprintOpen access

  Documentation of airport operations is inherently complex due to extensive technical terminology, rigorous regulations, proprietary regional information, and fragmented communication across multiple stakeholders. The resulting data silos and semantic inconsistencies present a significant impediment to the Total Airport Management (TAM) initiative. This paper presents a methodological framework for constructing a domain-grounded, machine-readable Knowledge Graph (KG) through a dual-stage fusion of symbolic Knowledge Engineering (KE) and generative Large Language Models (LLMs). The framework employs a scaffolded fusion strategy in which expert-curated KE structures guide LLM prompts to facilitate the discovery of semantically aligned knowledge triples. We evaluate this methodology on the Google LangExtract library and investigate the impact of context window utilization by comparing localized segment-based inference with document-level processing. Contrary to prior empirical observations of long-context degradation in LLMs, document-level processing improves the recovery of non-linear procedural dependencies. To ensure the high-fidelity provenance required in airport operations, the proposed framework fuses a probabilistic model for discovery and a deterministic algorithm for anchoring every extraction to its ground source. This ensures absolute traceability and verifiability, bridging the gap between "black-box" generative outputs and the transparency required for operational tooling. Finally, we introduce an automated framework that operationalizes this pipeline to synthesize complex operational workflows from unstructured textual corpora.

  PublisherDOI

• Expansive Space Trees with Reality Warping Actions for Simultaneous Design and Kinodyanmic Motion Planning

  2025-08-28

  articleSenior author

  Peg-in-hole is an extensively studied robotics task as it is representative of assembly-type tasks, which are highly common in practical applications (connector insertion, general assembly). These tasks incorporate highly discontinuous dynamics that result in high requirements for manipulator precision unless compliant strategies can be found. Despite the canonical nature of this problem, consideration of use of explicitly designed geometric structures to provide caging and compliance is uncommon, and frameworks that automatically design such structures while planning the assembly action are nonexistent. In this paper, we shed some light on this underexplored approach and introduce a belief-space kinodynamic planning framework that incorporates object design into state-space while providing actions that allow the motion planner to perform edits to the object’s design as part of the planning process. We use this framework to plan open-loop force-control trajectories while designing connector geometry to match, and evaluate them under significant variance in dynamics parameters against hand-designed caging geometry as well as a bare peg.

  PublisherDOI

• Cutaway View Learning for Visually-Guided Assembly with Crane

  2025-08-17

  articleSenior author

  Object manipulation for construction assembly using a crane is a control problem with highly challenging dynamics, merging contact-rich manipulation, high dynamics uncertainty, and an underactuated system. Learning a vision-guided controller for such a system using reinforcement learning is a promising but challenging approach, as mating surfaces are occluded during the last stage of assembly, making feedback indirect. We present a novel form of cutaway-view privileged information for assembly tasks that is used within a student-teacher framework, making alignment information readily available during the initial stage of training. This is paired with a pretrained encoder and embedding buffer that leverages nonphysical manipulation within the simulator to collect its training data. We evaluate our method on four different assembly-type placement tasks, and find that our system significantly outperforms both kinodynamic planning and standard reinforcement-learning baselines. We also evaluate the ability of our trained controllers to transfer to a realistic simulation environment with different underlying dynamics, demonstrating continued superior performance under deployment with a significant dynamics gap.

  PublisherDOI

• Towards Automation of Apron Services: A Theoretical Framework for Determining Equipment Requirements in Manipulator Design

  2025-11-04

  articleSenior author

  Aircraft turnaround efficiency and reliability is crucial for maximizing airport capacity and airline profitability, driving significant interest in automation of turnaround tasks. While systems are close to deployment in some areas (such as autonomous baggage transport tugs), the majority of aircraft turnaround tasks consist of manipulation-intensive hose/cable connection and disconnection, and hatch/panel opening and closing. Due to the expense of such systems, a design for automated ground servicing ideally shares mobile manipulators between tasks, while remaining optimal in a time sense (does not incur any delays). However, the task mix of aircraft turnaround is relatively heterogeneous, and fully generalpurpose platforms designed to be capable of completing all turnaround tasks are likely to be extremely expensive. This paper addresses this challenge of setting design requirements for mobile manipulators to perform a selected set of turnaround tasks by proposing a mixed integer model formulation that sets mobile manipulator platform requirements, and simultaneously assigns them to specific tasks, optimizing for cost via platform sharing while avoiding delays. We run this model using a set of estimated task requirement and time figures, and show that across different turnaround time targets, the ideal configuration for shared manipulator platforms consists of one platform type handling lightweight tasks at lower heights, one handling highpayload tasks at lower heights, and one handing taller heights.

  PublisherDOI

• Occupancy-belief Planning of Plant Manipulation for Staking

  2025-10-19

  articleSenior author

  While agricultural robotics has made great strides in recent years, manipulation of plants for tasks such as staking and harvesting remains highly challenging due to the high variability in dynamics and deformable nature of plants. To address the challenges created by dynamics uncertainty, we develop a system applying an occupancy-belief planning concept to plant manipulation for staking. We first train a dynamics model that predicts a per-pixel probability that the plant occupies the corresponding slice in space after a drag action using a large set of simulators. This model is then used to plan a manipulation action that maximizes the probability areas swept by the stake tying tool’s operating region are occupied by the plant, and minimize the probability areas swept by the non-operating side regions of the tool are occupied. We demonstrate our method both in simulation and with zero-shot sim-to-real transfer to a physical implementation. We show that adding consideration of belief through use of occupancy-belief allows our method to outperform both the visual foresight type approaches it is based on and other baselines and ablations, especially in the real-world case.

  PublisherDOI

• Horizontal Beam Placement With Occupancy-Belief Space Planning

  2024-06-24

  preprintOpen accessSenior author

  Object manipulation for construction assembly using a crane is a control problem with highly challenging dynamics, merging contact-rich manipulation, high dynamics uncertainty from the outdoors environment, coarse actuators, and an underactuated system. The inevitable contact from assembly presents both a potential to exacerbate small errors as well as an opportunity to limit uncertainty by restricting the possible configuration space. For a controller to take advantage of this beyond simple lift-up and lay-down operations, a system that is capable of considering how uncertainty evolves under contact dynamics is required. We approach this problem by learning the dynamics of the payload’s possible occupancy distribution using a visual foresight inspired model, then performing learning-based model predictive control with this learned model as the dynamics model, with the objective of creating possible occupancy that corresponds to one payload instance in its target position. We evaluate this system in simulation on the problem of I-beam assembly, specifically, aligning and inserting a horizontal box member between flanges of two opposing vertical I-beams.

  PublisherDOI

• Retraction notice to “A Markov model examining intervention effects on the HIV prevalence/incidence amongst the overall population” [Soc. Econ. Plann. Sci. 79C (2022) 101123]

  Socio-Economic Planning Sciences · 2024-04-10

  articleSenior author
  PublisherDOI

Recent grants

• Congestion in Facilities Location and Layout: Deterministic and Stochastic Models

  NSF · $212k · 2003–2007

Frequent coauthors

• Rajan Batta

  University at Buffalo, State University of New York

  43 shared

• Geoff Gross

  32 shared

• Ann M. Bisantz

  University at Buffalo, State University of New York

  24 shared

• Moises Sudit

  University at Buffalo, State University of New York

  23 shared

• Wen‐mei Hwu

  University of Illinois Urbana-Champaign

  22 shared

• Jinjun Xiong

  20 shared

• Ketan Date

  University of Illinois Urbana-Champaign

  18 shared

• G. Harhalakis

  University of Maryland, College Park

  17 shared

Education

• Ph.D., Computer Science

  University of Illinois at Urbana-Champaign

  2005

• M.S., Computer Science

  University of Illinois at Urbana-Champaign

  2001

• B.S., Computer Science

  University of Illinois at Urbana-Champaign

  1998

Awards & honors

• IIE Fellow Award (2010)
• UB's "Sustained Achievement Award" in recognition of outstan…
• Business First of Buffalo's "40 under Forty" award (2004)
• SME's Milton C. Shaw Outstanding Young Manufacturing Enginee…
• IIE's Outstanding Young Industrial Engineer Award in Academi…