About

Shapour Azarm is a Professor of Mechanical Engineering at the University of Maryland, College Park, and also holds affiliate positions in Applied Mathematics & Statistics and Scientific Computation. He completed his Ph.D. in Mechanical Engineering from the University of Michigan, Ann Arbor. He is the Founding Director of the Design Decision Support Laboratory and has a research focus centered on engineering design optimization of multi-objective and multi-disciplinary systems, decision support systems, and design for market systems. His current research includes predictive maintenance optimization of unmanned systems, multi-objective robust and flexible design optimization under uncertainty, surrogate optimization with applications in quality part and additive manufacturing process design, and multi-vehicle routing optimization considering vehicle recharging time and failure incidents. His research program has been funded by various government agencies and industry partners, including AFOSR, AFRL, NASA, NAVAIR, NSF, NSWC, ONR, Stanley Black & Decker, Northrop Grumman, and Lockheed Martin. Dr. Azarm has mentored students who have gone on to positions in academia, government, and industry, including institutions such as Cornell University, Johns Hopkins University, Naval Postgraduate School, and companies like Amazon, Boeing, SpaceX, and Meta. He has held numerous editorial roles, including Editor-in-Chief of the Journal of Mechanical Design (ASME Transactions), and has served as Chair of the ASME Technical Committee on Publications and Communications. His professional service includes leadership roles within ASME and participation in international scientific committees. Dr. Azarm has received several awards recognizing his contributions to the field, including the ASME Design Automation Award, ASME Robert E. Abbott Award, ASME Machine Design Award, and the ASME Dedicated Service Award. He is a Fellow and Life Member of ASME, reflecting his significant contributions to engineering design, optimization, and decision support systems.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Machine Learning
• Medicine
• Mathematics
• Risk analysis (engineering)
• Mathematical optimization
• Algorithm
• Management science
• Operations research
• Data science
• Distributed computing

Selected publications

• A Two-Stage Reactive Auction Framework for the Multi-Depot Rural Postman Problem with Dynamic Vehicle Failures

  IEEE Access · 2026-01-01

  preprintOpen accessSenior author

  Although unmanned vehicle fleets offer efficiency in transportation, logistics and inspection, their susceptibility to failures poses a significant challenge to mission continuity. We study the Multi-Depot Rural Postman Problem with Rechargeable and Reusable Vehicles (MD-RPP-RRV) with vehicle failures, where unmanned rechargeable vehicles placed at multiple depots with capacity constraints may fail while serving arc-based demands. To address unexpected vehicle breakdowns during operation, we propose a two-stage real-time rescheduling framework. First, a centralized auction quickly generates a feasible rescheduling solution; for this stage, we derive a theoretical additive bound that establishes an analytical guarantee on the worst-case rescheduling penalty. Second, a peer auction refines this baseline through a problem-specific magnetic field router for local schedule repair, utilizing parameters calibrated via sensitivity analysis to ensure controlled computational growth. We benchmark this approach against a simulated annealing metaheuristic to evaluate solution quality and execution speed. Experimental results on 257 diverse failure scenarios demonstrate that the framework achieves an average runtime reduction of over 95% relative to the metaheuristic baseline, cutting rescheduling times from hours to seconds while maintaining high solution quality. The two-stage framework excels on large-scale instances, surpassing the centralized auction in nearly 80% of scenarios with an average solution improvement exceeding 12%. Moreover, it outperforms the simulated annealing mean and best solutions in 59% and 28% of scenarios, respectively, offering the robust speed-quality trade-off required for real-time mission continuity.

  PublisherDOI

• Probabilistic Deep Learning With Bayesian Networks for Predicting Complex Engineering Systems' Remaining Useful Life: A Case Study of Unmanned Surface Vessel

  ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · 2025-03-28 · 2 citations

  articleSenior author

  Abstract Remaining useful life (RUL) serves as a key indicator of system health, and its accurate and timely prediction supports informed decision-making for efficient operation and maintenance. This is essential for complex engineering systems (CESes) such as unmanned surface vessels (USVs), where the human operators have limited opportunity to intervene during the operation. This paper proposes a framework for predicting the RUL of the CESes. The proposed framework employs a probabilistic deep learning (PDL) approach to predict the component's RUL and an equation node-based Bayesian network (BN) to predict system RUL (SRUL) at any future time-step. The component-level RUL method is validated using the NASA's Commercial Modular Aero-Propulsion System Simulation (c-mapss) dataset, and then the proposed framework is demonstrated with a USV case study. The results are evaluated using a set of quality metrics. By making use of the condition-monitoring sensor data, component reliability data, and models that account for the complex causal relationships between components, the proposed framework can provide near real-time predictions of the RUL with uncertainty of a CES, thus supporting its informed decision-making during the operation.

  PublisherDOI

• Multi-Objective Optimization of Process Parameters for Part Quality With Laser Powder Bed Fusion: A Heat Exchanger Application

  Journal of Mechanical Design · 2025-12-09

  articleSenior author

  Abstract The widespread adoption of additive manufacturing (AM) is hindered by the challenges in achieving consistent part quality across AM processes and equipment. Typical quality metrics such as geometric accuracy and porosity are affected by the microstructure of the fabricated parts, which are controlled by the AM’s process parameters. This study presents an approach for optimizing the AM process parameters to achieve the desired part quality. The approach integrates design of experiments, AM process simulation, surrogate modeling, and multi-objective optimization. The applicability of the proposed approach is demonstrated through a laser powder bed fusion process. An application example for an air-to-water heat exchanger (HX) is used to demonstrate the effectiveness of the proposed approach. For this example, it is shown that with the optimized process parameters, the HX has about 19% better geometric accuracy and 0.2% porosity reduction when compared to baseline process parameters. This Technical Brief addresses a gap in the existing AM literature in which part quality, such as geometric accuracy and porosity, is multi-objectively optimized and demonstrated using a complex heat exchanger example.

  PublisherDOI

• On a Sampling-Based Method for Multi-Objective Robust Optimization

  Journal of Computing and Information Science in Engineering · 2025-01-23

  articleSenior author

  Abstract Many engineering design optimization problems are multi-objective, constrained, and contain uncertain parameters. For these problems, it is desirable to obtain solutions that are robustly optimum. A robust optimal solution is one which remains feasible and optimal despite perturbations in the uncertainty parameters. This article presents a scenario-generation-based method for solving multi-objective robust optimization (MORO) problems. The approach follows a sequential, single-level optimization problem workflow, with the uncertainty handled by a fixed sampling method. The proposed approach is applied to an unmanned surface vessel (USV) case study problem. The results obtained and subsequent performance analysis show that the proposed approach generally performs well.

  PublisherDOI

• Online Surrogate Sampling-Based Multi-Objective Robust Optimization With Interval Uncertainty

  2025-08-17

  articleSenior author

  Abstract Many engineering design optimization problems have multiple conflicting objectives and contain uncertainty. Multi-objective robust optimization (MORO) methods aim to solve these types of problems by finding robustly optimal solutions. A robustly optimal solution is one that satisfies all of the following: (i) is optimal, and (ii) will remain feasible, and limit the variation in any objective functions within an acceptable range, for any realizations of uncertainty. However, often times these problems are computationally intensive, thus requiring the need for developing surrogate models. This paper presents a new online surrogate sampling-based MORO (SSB-MORO) approach. It is an iterative sequential approach, where the optimization problem is solved in the first stage, then the uncertainty is handled in the second stage. In the approach, a surrogate model is developed for the problem, which is then iteratively updated as the MORO algorithm progresses. Thus, all the subsequent function evaluations are performed on the surrogate functions, reducing the computational cost. The robustness evaluation is performed by utilizing a scenario generation scheme to identify the worst-case scenario, which would then be added as a constraint to problem for the next MORO iteration. The proposed approach has been applied to several benchmark examples and compared to some previous methods reported in the literature. It has also been applied to an unmanned surface vessel (USV) case study to demonstrate its applicability to computationally expensive black-box problems. Overall, the proposed approach demonstrates substantial improvement in computational efficiency. The paper contributes to the existing literature by consistently matching existing MORO methods across several test cases and demonstrates potential for real-world engineering applications.

  PublisherDOI

• Online Surrogate Multi-Objective Design Optimization Using Generative Adversarial Networks With Constraint Assistance

  Journal of Mechanical Design · 2025-01-19

  article

  Abstract Solving multi-objective design optimization problems can be computationally expensive, particularly when the original objective and/or constraint functions of the problem are costly to compute. To reduce computational cost, a surrogate model can be constructed that is less costly than the original objective and/or constraint functions. The surrogate is then combined with an optimizer to solve the problem. The proposed approach is an online surrogate-based optimization method in which the surrogate is built and improved iteratively as the optimizer converges to the solution. The primary contribution of this article is a new approach based on generative adversarial networks, aided by a constraint boundary-informed support vector machine, to predict whether the solutions generated by the approach are feasible or not. The performance of the proposed method is compared with that of two other methods from the literature. The comparison is based on several quality metrics and uses a set of 30 numerical test problems. The approach is also demonstrated with a complex engineering case study for the operation of an unmanned surface vessel. The results indicate that the proposed approach outperforms the other approaches for most of the quality metrics and test problems and case study considered.

  PublisherDOI

• Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material

  ArXiv.org · 2025-06-12

  preprintOpen access

  An alternative data-driven modeling approach has been proposed and employed to gain fundamental insights into robot motion interaction with granular terrain at certain length scales. The approach is based on an integration of dimension reduction (Sequentially Truncated Higher-Order Singular Value Decomposition), surrogate modeling (Gaussian Process), and data assimilation techniques (Reduced Order Particle Filter). This approach can be used online and is based on offline data, obtained from the offline collection of high-fidelity simulation data and a set of sparse experimental data. The results have shown that orders of magnitude reduction in computational time can be obtained from the proposed data-driven modeling approach compared with physics-based high-fidelity simulations. With only simulation data as input, the data-driven prediction technique can generate predictions that have comparable accuracy as simulations. With both simulation data and sparse physical experimental measurement as input, the data-driven approach with its embedded data assimilation techniques has the potential in outperforming only high-fidelity simulations for the long-horizon predictions. In addition, it is demonstrated that the data-driven modeling approach can also reproduce the scaling relationship recovered by physics-based simulations for maximum resistive forces, which may indicate its general predictability beyond a case-by-case basis. The results are expected to help robot navigation and exploration in unknown and complex terrains during both online and offline phases.

  PublisherOA PDFDOI

• Prognostics and Health Management of Unmanned Surface Vessels: Past, Present, and Future

  Journal of Computing and Information Science in Engineering · 2024-05-08 · 2 citations

  articleSenior author

  Abstract With the increasing popularity and deployment of unmanned surface vessels (USVs) all over the world, prognostics and health management (PHM) has become an indispensable tool for health monitoring, fault diagnosis, health prognosis, and maintenance of marine equipment on USVs. USVs are designed to undertake critical and extended missions, often in extreme conditions, without human intervention. This makes the USVs susceptible to equipment malfunction, which increases the probability of system failure during mission execution. In fact, in the absence of any crew onboard, system failure during a mission can create a great inconvenience for the concerned stakeholders, which compels them to design highly reliable USVs that must have integrated intelligent PHM systems onboard. To improve mission reliability and health management of USVs, researchers have been investigating and proposing PHM-based tools or frameworks that are claimed to operate in real time. This paper presents a comprehensive review of the existing literature on recent developments in PHM-related studies in the context of USVs. It covers a broad perspective of PHM on USVs, including system simulation, sensor data, data assimilation, data fusion, advancements in diagnosis and prognosis studies, and health management. After reviewing the literature, this study summarizes the lessons learned, identifies current gaps, and proposes a new system-level framework for developing a hybrid (offline–online) optimization-based PHM system for USVs in order to overcome some of the existing challenges.

  PublisherDOI

• Cost-Benefit Analysis using Modular Dynamic Fault Tree Analysis and Monte Carlo Simulations for Condition-based Maintenance of Unmanned Systems

  arXiv (Cornell University) · 2024-05-15

  preprintOpen accessSenior author

  Recent developments in condition-based maintenance (CBM) have helped make it a promising approach to maintenance cost avoidance in engineering systems. By performing maintenance based on conditions of the component with regards to failure or time, there is potential to avoid the large costs of system shutdown and maintenance delays. However, CBM requires a large investment cost compared to other available maintenance strategies. The investment cost is required for research, development, and implementation. Despite the potential to avoid significant maintenance costs, the large investment cost of CBM makes decision makers hesitant to implement. This study is the first in the literature that attempts to address the problem of conducting a cost-benefit analysis (CBA) for implementing CBM concepts for unmanned systems. This paper proposes a method for conducting a CBA to determine the return on investment (ROI) of potential CBM strategies. The CBA seeks to compare different CBM strategies based on the differences in the various maintenance requirements associated with maintaining a multi-component, unmanned system. The proposed method uses modular dynamic fault tree analysis (MDFTA) with Monte Carlo simulations (MCS) to assess the various maintenance requirements. The proposed method is demonstrated on an unmanned surface vessel (USV) example taken from the literature that consists of 5 subsystems and 71 components. Following this USV example, it is found that selecting different combinations of components for a CBM strategy can have a significant impact on maintenance requirements and ROI by impacting cost avoidances and investment costs.

  PublisherOA PDFDOI

• Cost-Benefit Analysis using Modular Dynamic Fault Tree Analysis and Monte Carlo Simulations for Condition-based Maintenance of Unmanned Systems

  International Journal of Prognostics and Health Management · 2024-10-12 · 4 citations

  articleOpen accessSenior author

  Recent developments in condition-based maintenance (CBM) have helped make it a promising approach to maintenance cost avoidance in engineering systems. By performing maintenance based on conditions of the component with regards to failure or time, there is potential to avoid the large costs of system shutdown and maintenance delays. However, CBM requires a large investment cost compared to other available maintenance strategies. The investment cost is required for research, development, and implementation. Despite the potential to avoid significant maintenance costs, the large investment cost of CBM makes decision makers hesitant to implement. This study is the first in the literature that attempts to address the problem of conducting a cost-benefit analysis (CBA) for implementing CBM concepts for unmanned systems. This paper proposes a method for conducting a CBA to determine the return on investment (ROI) of potential CBM strategies. The CBA seeks to compare different CBM strategies based on the differences in the various maintenance requirements associated with maintaining a multi-component, unmanned system. The proposed method uses modular dynamic fault tree analysis (MDFTA) with Monte Carlo simulations (MCS) to assess the various maintenance requirements. The proposed method is demonstrated on an unmanned surface vessel (USV) example taken from the literature that consists of 5 subsystems and 71 components. Following this USV example, it is found that selecting different combinations of components for a CBM strategy can have a significant impact on maintenance requirements and ROI by impacting cost avoidances and investment costs.

  PublisherOA PDFDOI

Recent grants

• EAGER: Design for Bundling Decisions with Marketing and Public Policy Considerations

  NSF · $100k · 2011–2012

• Strategic Product Design for Retail Channel Acceptance Under Uncertainty and Competition

  NSF · $320k · 2007–2011

Frequent coauthors

• Jeffrey W. Herrmann

  University of Maryland, College Park

  20 shared

• P.K. Kannan

  19 shared

• A. Farhang‐Mehr

  Ames Research Center

  17 shared

• Vikrant Aute

  13 shared

• Balakumar Balachandran

  12 shared

• Lung‐Wen Tsai

  Taipei Medical University Hospital

  11 shared

• Nikhil Chopra

  10 shared

• Eliot Rudnick-Cohen

  10 shared

Awards & honors

• ASME Dedicated Service Award (2024)
• ASME Machine Design Award (2023)
• ASME Robert E. Abbott Award (2016)
• ASME/Ford Best Paper Award (2009)
• ASME Design Automation Award (2007)