About

Jonathan Bard is a professor of Operations Research & Industrial Engineering in the Walker Department of Mechanical Engineering at the University of Texas. He holds the Industrial Properties Corporation Endowed Faculty Fellowship and serves as the Associate Director for the Center for the Management of Operations Logistics, as well as the Assistant Graduate Advisor for the Manufacturing Systems Engineering Program. His academic background includes a D.Sc. in Operations Research from George Washington University, an M.S. in Aeronautics and Astronautics from Stanford University, and a B.S. in Aeronautical Engineering from Rensselaer Polytechnic Institute. At the University of Texas, Dr. Bard teaches courses in mathematical modeling, production planning and control, optimization theory, and project management. His research centers on developing efficient algorithms for problems related to airline operations, vehicle routing, and machine scheduling; designing and analyzing manufacturing systems; utilizing decomposition techniques to solve large-scale hierarchical planning problems; and applying multicriteria decision making to socio-economic systems. He is an internationally recognized expert on bilevel programming and postal operations, and actively consults for government agencies and U.S. corporations. Dr. Bard is also the founding editor of IIE Transactions on Operations Engineering and serves on the editorial boards of several prominent journals. He is a fellow of INFORMS and IIE, a senior member of IEEE, and has held multiple offices within these organizations. His research has been published extensively in leading technical journals, earning numerous honors and awards.

Research topics

• Computer Science
• Mathematical optimization
• Mathematics
• Operations research
• Engineering
• Algorithm
• Statistics
• Operations management
• Economics

Selected publications

• Long-term workforce planning for home healthcare

  Socio-Economic Planning Sciences · 2026-02-03

  articleSenior author
  PublisherDOI

• Optimal Investment Planning for Multi-Period Productionnetworks with Adjustable Production Profiles

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• An Algorithm Based on Connectivity Properties for Finding Cycles and Paths on Kidney Exchange Compatibility Graphs

  Computation · 2025-05-06

  articleOpen accessSenior author

  Kidney-paired donation programs assist patients in need of a kidney to swap their incompatible donor with another incompatible patient–donor pair for a suitable kidney in return. The kidney exchange problem (KEP) is a mathematical optimization problem that consists of finding the maximum set of matches in a directed graph representing the pool of incompatible pairs. Depending on the specific framework, these matches can come in the form of (bounded) directed cycles or directed paths. This gives rise to a family of KEP models that have been studied over the past few years. Several of these models require an exponential number of constraints to eliminate cycles and chains that exceed a given length. In this paper, we present enhancements to a subset of existing models that exploit the connectivity properties of the underlying graphs, thereby rendering more compact and tractable models in both cycle-only and cycle-and-chain versions. In addition, an efficient algorithm is developed for detecting violated constraints and solving the problem. To assess the value of our enhanced models and algorithm, an extensive computational study was carried out comparing with existing formulations. The results demonstrated the effectiveness of the proposed approach. For example, among the main findings for edge-based cycle-only models, the proposed (*PRE(i)) model uses a new set of constraints and a small subset of the full set of length-k paths that are included in the edge formulation. The proposed model was observed to achieve a more than 98% reduction in the number of such paths among all tested instances. With respect to cycle-and-chain formulations, the proposed (*ReSPLIT) model outperformed Anderson’s arc-based (AA) formulation and the path constrained-TSP formulation on all instances that we tested. In particular, when tested on a difficult sets of instances from the literature, the proposed (*ReSPLIT) model provided the best results compared to the AA and PC-based models.

  PublisherOA PDFDOI

• Weekly crew scheduling for freight rail engineers: A network approach

  Journal of Rail Transport Planning & Management · 2025-04-04

  articleSenior authorCorresponding
  PublisherDOI

• Weekly home healthcare routing and scheduling with overlapping patient clusters

  Health Systems · 2024-11-22 · 4 citations

  articleOpen accessSenior authorCorresponding

  This paper presents a two-stage approach for efficiently solving a weekly home healthcare scheduling and routing problem. Two new mixed-integer linear programming (MILP) models are proposed, where the first is used for making patient-therapist assignments over the week, and the second for deriving daily routes. In both MILPs, the objective function contains a hierarchically weighted set of goals. The major components of the full problem are continuity of care, downgrading, workload balance, time windows, overtime, and mileage costs. A new preprocessing procedure is developed to limit the service area of each therapist to a single group of overlapping patients. Once the groups are formed, weekly schedules are constructed with the MILPs. The overall objective is to minimize the number of unscheduled visits and total travel and service costs subject to the operational constraints mentioned above. Computational experiments are conducted with real data sets provided by a national home health agency. The results show that optimal solutions can be obtained quickly at both the assignment and routing stages and that they are comparable to the results obtained with a proposed integrated model. In either case, the corresponding schedules were better on all metrics when compared to the schedules used in practice.

  PublisherOA PDFDOI

• Air traffic controller scheduling

  Computers & Industrial Engineering · 2024-04-12 · 4 citations

  articleSenior authorCorresponding
  PublisherDOI

• Solving the waste bin location problem with uncertain waste generation rate: A bi-objective robust optimization approach

  Waste Management & Research The Journal for a Sustainable Circular Economy · 2024-05-09 · 9 citations

  preprintOpen accessSenior author

  An efficient municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and liveability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, for example, the waste generation rate in an urban area. With this in mind, this article contributes with a robust optimization model to design the network of collection points (i.e. location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.

  PublisherOA PDFDOI

• A bilevel approach to multi-period natural gas pricing and investment in gas-consuming infrastructure

  Energy · 2024-05-29 · 1 citations

  articleCorresponding
  PublisherDOI

• Robust Routing and Scheduling of Home Healthcare Workers: A Nested Branch-and-Price Approach

  arXiv (Cornell University) · 2024-07-04

  preprintOpen access

  The global home healthcare market is growing rapidly due to aging populations, advancements in healthcare technology, and patient preference for home-based care. In this paper, we study the multi-day planning problem of simultaneously deciding patient acceptance, assignment, routing, and scheduling under uncertain travel and service times. Our approach ensures cardinality-constrained robustness with respect to timely patient care and the prevention of overtime. We take into account a wide range of criteria including patient time windows, caregiver availability and compatibility, a minimum time interval between two visits of a patient, the total number of required visits, continuity of care, and profit. We use a novel systematic modeling scheme that prioritizes health-related criteria as hard constraints and optimizes cost and preference-related criteria as part of the objective function. We present a mixed-integer linear program formulation, along with a nested branch-and-price technique. Results from a case study in Austin, Texas demonstrate that instances of realistic size can be solved to optimality within reasonable runtimes. The price of robustness primarily results from reduced patient load per caregiver. Interestingly, the criterion of geographical proximity appears to be of secondary priority when selecting new patients and assigning them to caregivers.

  PublisherOA PDFDOI

• Weekly scheduling for freight rail engineers & trainmen

  Transportation Research Part B Methodological · 2024-04-15 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

Frequent coauthors

• Douglas J. Morrice

  The University of Texas at Austin

  23 shared

• Luci K. Leykum

  South Texas Veterans Health Care System

  19 shared

• Maria Tsompana

  18 shared

• Ashley Orillion

  Janssen (United States)

  18 shared

• Ahmad I. Jarrah

  George Washington University

  18 shared

• Kiersten Marie Miles

  18 shared

• Georg A. Bjarnason

  Sunnybrook Health Science Centre

  18 shared

• Paula Sotomayor

  Pontificia Universidad Católica de Chile

  18 shared

Labs

• Advanced Power Systems and Control Laboratory (APSCL)PI

Education

• Other, Operations Research

  George Washington University

• M.S., Aeronautics and Astronautics

  Stanford University

• B.S., Aeronautical Engineering

  Rensselaer Polytechnic Institute

Awards & honors

• Industrial Properties Corporation Endowed Faculty Fellowship