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University of Nebraska–Lincoln


Esma Gel Photo

Esma Gel

Cynthia Hardin Milligan Chair of Business and Professor of Supply Chain Management and Analytics
Supply Chain Management and Analytics
HLH 511 K
P.O. Box 884114
Lincoln, NE 68588-4114
Esma Gel Photo
Ph.D. in Industrial Engineering and Management Sciences, Northwestern University
M.S. in Industrial Engineering and Management Sciences, Northwestern University
B.S. in Industrial Engineering, Middle East Technical University, Ankara, Turkey
Areas of Expertise
  • decision making under uncertainty
  • stochastic modeling
  • stochastic control models
  • inventory control theory
  • multiple criteria decision making
Research Interests
  • supply chain management
  • healthcare delivery systems
  • logistics operations
  • pandemic response
  • project portfolio management
  • Cynthia Hardin Milligan Chair of Business, COB, UNL
  • Professor of Supply Chain Management and Analytics, COB, UNL
  • Associate Professor of Industrial Engineering, College of Engineering, Arizona State University, 2000-2022
Esma S. Gel Vita (Aug 2023)
CliftonStrengths ®
  • Achiever
  • Woo
  • Strategic
  • Learner
  • Responsibility

Esma S. Gel is Cynthia Hardin Milligan Chair of Business and Professor at the Department of Supply Chain Management and Analytics of the University of Nebraska-Lincoln. Gel was a faculty member of Industrial Engineering at Arizona State University between 2000-2022.

Prof. Gel’s research focuses on the use of stochastic modeling and control techniques for the design, control and management of operations in various settings, with emphasis on manufacturing and service systems, business and logistics processes, health care systems and pandemic operations and reource management. Her work has been published in leading journals of her field as well as medical journals such as the British Medical Journal. Her work has been supported by funding from the National Institutes of Health (current R01 project) and National Science Foundation as well as her industrial partners (e.g., Intel, Mayo Clinic).

Prof. Gel holds a B.S. degree in Industrial Engineering from Middle East Technical University, Turkey, and M.S. and Ph.D. degrees in Industrial Engineering from Northwestern University, obtained in 1995 and 1999, respectively.

Selected Publications:

  • Kilinc, D., E. S. Gel, M. Y. Sir and K. S. Pasupathy, 2022, “Statistical characterization of patient response to offered access delays using healthcare transactional data,” Naval Research Logistics, 69(7), pp, 974-995,
  • Gel, E. S. and F. S. Salman, 2022, “Dynamic Ordering Decisions with Approximate Learning of Supply Yield Uncertainty,” International Journal of Production Economics, 243, 108252,
  • Romero-Brufau, S., A. Chopra, A. Ryu, E. S. Gel, R. Raskar, W. Kremers, K. Anderson, J. Subramanian, B. Krishnamurthy, A. Singh, K. Pasupathy, Y. Dong, J. C. O’Horo, W. R. Wilson, O. Mitchell, T. C. Kingsley, 2021 “Public health impact of delaying second dose of BNT162b2 or mRNA-1273 COVID-19 vaccine: simulation agent based modeling study,” , The British Journal of Medicine (BMJ), 373:n1087,
  • Kilinc, D., E. S. Gel, and A. Demirtas, 2021, “Intelligent Teletriage and Personalized Routing to Manage Patient Access in a Neurosurgery Clinic, IISE Transactions on Healthcare Systems Engineering, 11(3), pp. 224-239,
  • Sampath, S., E. S. Gel, K. Kempf, and J. W. Fowler, 2021, “A Generalized Decision Support Framework for Large-Scale Project Portfolio Decisions,” Decision Sciences, pp. 1-24,
  • Gel, E. S., M. L. Jehn, T. Lant, A. R. K. Muldoon, T. Nelson, H. M. Ross, 2020, “COVID-19 healthcare demand projections: Arizona,” PLoS ONE, 15(12): e0196556,
  • Gel E. S., J. W. Fowler, K. Khowala, 2020, “Queuing approximations for capacity planning under common setup rules,” IISE Transactions, 1-19,
  • Morris S., Subramanian J, E. S. Gel, G. Runger, E. Thompson, D. W. Mallery, G. J. Weiss, 2018, “Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform,” PLoS ONE, 13(4): e0196556.
  • Hafizoglu, A. B., E. S. Gel and P. Keskinocak, 2016, “Price and Lead Time Quotation for Contract and Spot Customers,” Operations Research, 64(2), pp. 406-415.
  • Sampath, S., E. S. Gel, J. W. Fowler, K. Kempf, 2015, “A Decision-Making Framework for Project Portfolio Planning at Intel Corporation,” Interfaces, 45(5), 391-408.
  • Clough, M. C., T. L. Jacobs, E. S. Gel, 2013, “A Choice-Based Mixed Integer Programming Formulation for Network Revenue Management,” Journal of Revenue and Pricing Management, 13(5), pp. 366-387
  • Marquis, J, E. S. Gel, J. W. Fowler, M. Koksalan, P. Korhonen, J. Wallenius, 2015, “Impact of Number of Interactions, Different Interaction Patterns and Human Inconsistencies on Some Hybrid Evolutionary Multi-Objective Optimization Algorithms,” Decision Sciences, 46(5), pp. 981-1006.
  • Ramirez-Nafarrate, A., A. B. Hafizoglu, E. S. Gel, and J. W. Fowler, 2014, “Optimal Ambulance Diversion Control Policies,” European Journal of Operational Research, 236(1), pp. 298-312.
  • Yucel, E., S. Salman, E. S. Gel, L. Ormeci, and A. Gel, 2013, “Optimizing specimen collection for processing in clinical testing laboratories,” European Journal of Operational Research, 227(3), pp. 503-514
  • Morris, S., E. S. Gel, J. V. Smith, J. D. Paulauskis, D. Van den Boom, P. Oeth, and R. Penny, 2013, “Two algorithms for biospecimen comparison and differentiation using SNP genotypes,” Pharmacogenomics, 14(4), pp. 379-390.
  • Yucel, E., F. S. Salman, L. Ormeci, and E. S. Gel, 2013, “A constant-factor approximation algorithm for multi-vehicle collection for processing problem,” Optimization Letters, 7(7), pp. 1627-1642.
  • Hafizoglu, A. B., E.S. Gel and P. Keskinocak, 2013, “Expected Tardiness Computations in Multi-class Priority M/M/c queues,” Journal of Computing, 25(2), 364-376.
  • Fowler, J. W., E. S. Gel, M. Koksalan, P. Korhonen, J. Marquis and J. Wallenius, 2010, “Interactive Evolutionary Multi-Objective Optimization for Quasi-Concave Preference Functions,” European Journal of Operational Research, 206(2), pp. 417-425.
  • Gel, E. S., N. Erkip, and A. Thulaseedas, 2010, “Analysis of simple inventory control systems with execution errors: Economic impact under correction opportunities,” International Journal of Production Economics, 125, pp. 153-166.
  • Bozkurt, B., J. W. Fowler, E. S. Gel, B. Kim, M. Koksalan, and J. Wallenius, 2010, “Quantitative Comparison of Approximate Solution Sets for Multi-Criteria Optimization Problems with Weighted Tchebycheff Preference Function,” Operations Research, 58(3), pp. 650-659.
  • Fowler, J. W. , P. Wirojanagud, and E. S. Gel, 2008, “Heuristics for workforce planning with worker differences,” European Journal of Operational Research, 190(3), pp. 724-740.
  • Gel, A., S. Pannala, M. Syamlal, T. J. O’Brien, and E. S. Gel, 2007, “Comparison of frameworks for next-generation multiphase flow solver, MFIX: A group decision-making exercise,” Concurrency and Computation: Practice and Experience, 19(5), pp.609-624.
  • Vardar, C., E. S. Gel, and J. W. Fowler, 2007, “A framework for evaluating remote diagnostics investment decisions for semiconductor equipment suppliers,” European Journal of Operational Research, 180(3), pp. 1411-142
  • Wirojanagud, P., E. S. Gel, J. W. Fowler, and R. Cardy, 2007, Modeling inherent worker differences for workforce planning,” International Journal of Production Research, 45(3), pp. 525-553.
  • Duarte, B., J. W. Fowler, K. Knutson, E. S. Gel, and D. Shunk, 2007, A Compact Abstraction of Manufacturing Nodes in a Supply Network, International Journal of Simulation and Process Modeling, (Special issue on Supply Chain Modeling and Simulation), 3(3), pp. 115-126.
  • Gel, E. S., W. J. Hopp, and M. P. Van Oyen, 2007, Hierarchical cross-training in WIP constrained environments, IIE Transactions, 39, pp. 125-143.
  • Armbruster, D., E. S. Gel, and J. Murakami, 2007, Bucket brigades with worker learning, European Journal of Operational Research, 176(1), pp. 264-274.
  • Berrado, A., N. F. Hubele, and E. S. Gel, 2006, An empirical investigation into the distribution of flatness measurements, Quality Engineering, 18(3), pp. 351-357.
  • Armbruster, D. and E. S. Gel, 2006, Bucket Brigades Revisited: Are they always effective?, European Journal of Operational Research, 172(1), pp. 213-229.
  • Hubele, N. F., A. Berrado, and E. S. Gel, 2005, A Wald Test for Comparing Multiple Capability Indices, Journal of Quality Technology, 37(4), pp. 304-307.
  • Karady, G. G. , G. T. Heydt, E. S. Gel, and N. F. Hubele, 2005, The utilization of Micromechanical Devices in a Power Circuit Breaker,” Electric Power Component and Systems, 33(10), pp. 1159-1174.
  • Kim, B., E. S. Gel, J. W. Fowler, W. M. Carlyle, and J. Wallenius, 2005, Evaluation of non-dominated solution sets for k-objective optimization problems: An exact method and approximations, European Journal of Operational Research, 173(2), 565-582.
  • Fowler, J. W., B. Kim, W. M. Carlyle, E. S. Gel, and S.-M. Horng, 2005, Evaluating A Posteriori Solution Techniques for Bi-Criteria Parallel Machine Scheduling Problems, Journal of Scheduling, 8(1), pp. 75-96.
  • Ye, N., E. S. Gel, X. Li, T. Farley, and Y.-C. Lai, 2005, Web server QoS models: applying scheduling rules from production planning, Computers & Operations Research, 32(5), pp. 1147-1164.
  • Carlyle, M. W., J. W. Fowler, E. S. Gel, B. Kim, 2003, Quantitative comparison of approximate solution sets for bi-criteria optimization problems, Decision Sciences, 34(1), pp. 63-82.
  • Hopp, W. J., M. L. Spearman, S. Chayet, K. L. Donohue, and E. S. Gel, 2002, Using an optimized queueing network model to support wafer fab design, IIE Transactions, 34(2), pp. 119-130.
  • Van Oyen, M. P., E. S. Gel, and W. J. Hopp, 2001, Performance opportunity for workforce agility in collaborative and noncollaborative work systems, IIE Transactions, 33(9), pp. 761-777.
  • Gel, E. S.,W. J. Hopp, and M. P. Van Oyen, 2002, Factors affecting opportunity of worksharing as a dynamic line balancing mechanism, IIE Transactions, 34(10), 847-863.