Operations Research - 2019

Afrati, F., & Chirkova, R. (2019). Answering queries using views. San Rafael, CA: Morgan & Claypool Publishers.

Bansal, A., Uzsoy, R., & Kempf, K. G. (2019). Coordinating Capacity Negotiations in Semiconductor Manufacturing. Proceedings of the IEEE Conference on Automation Science and Engineering. Presented at the CASE 2019 - International Conference on Automation Science and Engineering, Vancouver, British Columbia, Canada.

Kapadia, M. S., Starly, B., Thomas, A., Uzsoy, R., & Warsing, D. (2019, August). Impact of Scheduling Policies on the Performance of an Additive Manufacturing System. Presented at the 25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, Chicago, IL.

Kapadia, M. S., Starly, B., Thomas, A., Uzsoy, R., & Warsing, D. (2019, May). Impact of Part Scheduling Policy on Additive Manufacturing Production System Performance. Presented at the Institute of Industrial and Systems Engineering Research Conference, Orlando, FL.

Manda, A. B., & Uzsoy, R. (2019, May). Simulation Optimization For Planning Product Transitions In Semiconductor Manufacturing Facilities. Presented at the Institute of Industrial and Systems Engineering Research Conference, Orlando, FL.

Gopalswamy, K., & Uzsoy, R. (2019, May). Conic Reformulation of Production Planning Models with Clearing Functions. Presented at the Institute of Industrial and Systems Engineering Research Conference, Orlando, FL.

Manda, A. B., & Uzsoy, R. (2019). Optimzing Engineering and Production Lots During Product Transitions in Semiconductor Manufacturing. Presented at the 2019 Winter Simulation Conference (WSC). https://doi.org/10.1109/wsc40007.2019.9004879

Bluemink, B., de Kok, A. G., Srinivasan, B., & Uzsoy, R. (2019). Evaluating Mixed Integer Programming Models for Solving Stochastic Inventory Problems. Presented at the 2019 Winter Simulation Conference (WSC). https://doi.org/10.1109/wsc40007.2019.9004920

Bansala, A., Uzsoy, R., & Kempf, K. (2019). Iterative combinatorial auctions for managing product transitions in semiconductor manufacturing. IISE TRANSACTIONS. https://doi.org/10.1080/24725854.2019.1651951

Albey, E., Yanikoglu, I., & Uzsoy, R. (2019). A Robust Optimization Approach for Production Planning Under Exogenous Planned Lead Times. Presented at the 2019 Winter Simulation Conference (WSC). https://doi.org/10.1109/wsc40007.2019.9004671

Mohan, M., Catts, G. P., Vaughan, B., Roise, J. P., Silva, C. A., McCarter, J. B., … Abdul Maulud, K. N. (2019). What Makes a Location into a “Favorable Habitat” under Changing Climate and Environmental Conditions? A Pilot Study Focused on Exploring the Differences between Natural and Non-natural Habitats using Airborne LiDAR. IOP Conference Series: Earth and Environmental Science, 228, 012018. https://doi.org/10.1088/1755-1315/228/1/012018

Lu, C., Deng, Z., Fang, S.-C., Jin, Q., & Xing, W. (2019). Fast computation of global solutions to the single-period unit commitment problem. Journal of Combinatorial Optimization, 11. https://doi.org/10.1007/s10878-019-00489-9

Zhang, Y., Wu, H., Denton, B. T., Wilson, J. R., & Lobo, J. M. (2019). Probabilistic sensitivity analysis on Markov models with uncertain transition probabilities: an application in evaluating treatment decisions for type 2 diabetes. Health Care Management Science, 22(1), 34–52. https://doi.org/10.1007/S10729-017-9420-8

Li, Z., & Norris, L. (2019). Introduction to Partial Differential Equations. https://doi.org/10.1142/12052

Kafalı, Ö., Ajmeri, N., & Singh, M. P. (2019). DESEN: Specification of Sociotechnical Systems via Patterns of Regulation and Control. ACM Transactions on Software Engineering and Methodology (TOSEM), 29(1), 1–50. https://doi.org/10.1145/3365664

Günay, A., Chopra, A. K., & Singh, M. P. (2019). Supple: Multiagent Communication Protocols with Causal Types. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 781–789. Retrieved from https://eprints.lancs.ac.uk/id/eprint/131277/

Campbell, S. L., & Campbell, M. A. (2019). A Problem from Genetics and the Singular Value Decomposition [Preprint]. https://doi.org/10.13140/RG.2.2.29979.92964

Kapadia, M. S., Starly, B., Thomas, A., Uzsoy, R., & Warsing, D. (2019). Impact of Scheduling Policies on the Performance of an Additive Manufacturing Production System. Procedia Manufacturing, 39, 447–456. https://doi.org/10.1016/j.promfg.2020.01.388

Nagulpelli, K. S., King, R. E., & Warsing, D. (2019). Integrated traditional and additive manufacturing production profitability model. 47TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 47), Vol. 34, pp. 619–630. https://doi.org/10.1016/j.promfg.2019.06.121

Jones, J. L., Broughton, R., Iamsasri, T., Fancher, C. M., Wilson, A. G., Reich, B., & Smith, R. C. (2019). The use of Bayesian inference in the characterization of materials and thin films. ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES, Vol. 75, pp. A211–A211. https://doi.org/10.1107/S0108767319097940

Cleaves, H. L., Alexanderian, A., Guy, H., Smith, R. C., & Yu, M. (2019). DERIVATIVE-BASED GLOBAL SENSITIVITY ANALYSIS FOR MODELS WITH HIGH-DIMENSIONAL INPUTS AND FUNCTIONAL OUTPUTS. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 41(6), A3524–A3551. https://doi.org/10.1137/19M1243518

Singh, S., Atrey, A., Sichitiu, M. L., & Viniotis, Y. (2019). Clearer than Mud: Extending Manufacturer Usage Description (MUD) for Securing IoT Systems. INTERNET OF THINGS - ICIOT 2019, Vol. 11519, pp. 43–57. https://doi.org/10.1007/978-3-030-23357-0_4

Vasheghani Farahani, I., Chien, A., King, R. E., Kay, M. G., & Klenz, B. (2019). Time series anomaly detection from a markov chain perspective. Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, 1000–1007. https://doi.org/10.1109/ICMLA.2019.00170

Li, Z., Dong, B., Tong, F., & Wang, W. (2019). An Augmented IB Method & Analysis for Elliptic BVP on Irregular Domains. CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 119(1), 63–72. https://doi.org/10.32604/cmes.2019.04635

Long, J., & Wu, F. (2019). Iterative-Learning-Control-Based Tracking for Asteroid Close-Proximity Operations. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 42(5), 1195–1203. https://doi.org/10.2514/1.G003884

Uzsoy, R. (2019, May). 2018 Best Paper Award. IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, Vol. 32, pp. 139–139. https://doi.org/10.1109/TSM.2019.2910984

Dyer, T., Altuntas, A., & Baugh, J. (2019). Bounded Verification of Sparse Matrix Computations. 2019 IEEE/ACM 3rd International Workshop on Software Correctness for HPC Applications (Correctness), 36–43. https://doi.org/10.1109/correctness49594.2019.00010

Akbulut, A., & Perros, H. G. (2019). Performance Analysis of Microservice Design Patterns. IEEE INTERNET COMPUTING, 23(6), 19–27. https://doi.org/10.1109/MIC.2019.2951094

Rezaei Arangdad, S., Thoney-Barletta, K., Joines, J., & Rothenberg, L. (2019). Influence of demographics and motivational factors on US consumer clothing and shoes disposal behavior. Research Journal of Textile and Apparel, 23(3), 170–188. https://doi.org/10.1108/rjta-08-2018-0051

Roscoe, S., Cousins, P. D., & Handfield, R. (2019). The microfoundations of an operational capability in digital manufacturing. JOURNAL OF OPERATIONS MANAGEMENT, 65(8), 774–793. https://doi.org/10.1002/joom.1044

Alexanderian, A., Reese, W., Smith, R. C., & Yu, M. (2019). Model Input and Output Dimension Reduction Using Karhunen-Loeve Expansions With Application to Biotransport. ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART B-MECHANICAL ENGINEERING, 5(4). https://doi.org/10.1115/1.4044317

Cook, J. A., Smith, R. C., Hite, J. M., Stefanescu, R., & Mattingly, J. (2019). Application and Evaluation of Surrogate Models for Radiation Source Search. ALGORITHMS, 12(12). https://doi.org/10.3390/a12120269

Cockayne, J., Oates, C. J., Ipsen, I. C. F., & Girolami, M. (2019). A Bayesian conjugate gradient method. Bayesian Anal., 14(3), 937–1012. https://doi.org/doi:10.1214/19-BA1145

Al-Shaer, E., Wei, J., Hamlen, K. W., & Wang, C. (2019). Modeling and Analysis of Deception Games Based on Hypergame Theory. In Autonomous Cyber Deception (pp. 49–74). https://doi.org/10.1007/978-3-030-02110-8_4

Singh, M. P., & Chopra, A. K. (2019). Requirements Engineering as Science in the Small. 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER). Presented at the 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER). https://doi.org/10.1109/icse-nier.2019.00020

Guo, H., Kafalı, Ö., Jeukeng, A.-L., Williams, L., & Singh, M. P. (2019). Çorba: crowdsourcing to obtain requirements from regulations and breaches. Empirical Software Engineering, 8. https://doi.org/10.1007/s10664-019-09753-2

Kalia, A. K., Ajmeri, N., Chan, K. S., Cho, J.-H., Adalı, S., & Singh, M. P. (2019). The Interplay of Emotions and Norms in Multiagent Systems. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Presented at the Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. https://doi.org/10.24963/ijcai.2019/53

Zhang, Z., & Singh, M. (2019). Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Presented at the Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). https://doi.org/10.18653/v1/d19-1555

Handfield, R. (2019). Shifts in buyer-seller relationships: A retrospective on Handfield and Bechtel (2002). INDUSTRIAL MARKETING MANAGEMENT, 83, 194–206. https://doi.org/10.1016/j.indmarman.2019.08.012

Uzsoy, R. (2019, November). Appreciation to the TSM Community. IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, Vol. 32, pp. 362–362. https://doi.org/10.1109/TSM.2019.2946034

Handfield, R., Jeong, S., & Choi, T. (2019). Emerging procurement technology: data analytics and cognitive analytics. INTERNATIONAL JOURNAL OF PHYSICAL DISTRIBUTION & LOGISTICS MANAGEMENT, 49(10), 972–1002. https://doi.org/10.1108/IJPDLM-11-2017-0348

Gordon, N., Gilkey, L., Smith, R. C., Michaud, I., Williams, B., Mousseau, V., … Jones, C. (2019, December 2). A Mutual Information-Based Experimental Design Framework to Use High-Fidelity Nuclear Reactor Codes to Calibrate Low-Fidelity Codes. NUCLEAR TECHNOLOGY, Vol. 205, pp. 1685–1696. https://doi.org/10.1080/00295450.2019.1590073

Kennedy, E., King, E., & Tran, H. (2019). Real-time implementation and analysis of a modified energy based controller for the swing-up of an inverted pendulum on a cart. EUROPEAN JOURNAL OF CONTROL, 50, 176–187. https://doi.org/10.1016/j.ejcon.2019.05.002

Hicklin, K. T., Ivy, J. S., Wilson, J. R., Cobb Payton, F., Viswanathan, M., & Myers, E. R. (2019). Simulation model of the relationship between cesarean section rates and labor duration. Health Care Management Science, 22(4), 635–657. https://doi.org/10.1007/s10729-018-9449-3

Heikkola, E., Ito, K., & Toivanen, J. (2019). A PARALLEL DOMAIN DECOMPOSITION METHOD FOR THE HELMHOLTZ EQUATION IN LAYERED MEDIA. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 41(5), C505–C521. https://doi.org/10.1137/18M1230906

Girolami, M., Ipsen, I. C. F., Oates, C. J., Owen, A. B., & Sullivan, T. J. (2019, November). Editorial: special edition on probabilistic numerics. STATISTICS AND COMPUTING, Vol. 29, pp. 1181–1183. https://doi.org/10.1007/s11222-019-09892-y

Bartels, S., Cockayne, J., Ipsen, I. C. F., & Hennig, P. (2019). Probabilistic linear solvers: a unifying view. STATISTICS AND COMPUTING, 29(6), 1249–1263. https://doi.org/10.1007/s11222-019-09897-7

Yao, Y., Zhang, Y., Tian, L., Zhou, N., Li, Z., & Wang, M. (2019). Analysis of Network Structure of Urban Bike-Sharing System: A Case Study Based on Real-Time Data of a Public Bicycle System. SUSTAINABILITY, 11(19). https://doi.org/10.3390/su11195425

Hao, Y., Duan, Z., Chen, G., & Wu, F. (2019). New Controllability Conditions for Networked, Identical LTI Systems. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 64(10), 4223–4228. https://doi.org/10.1109/TAC.2019.2893899

Roy, A., Ghosal, S., Prescott, J., & Choudhury, K. R. (2019). BAYESIAN MODELING OF THE STRUCTURAL CONNECTOME FOR STUDYING ALZHEIMER'S DISEASE. ANNALS OF APPLIED STATISTICS, 13(3), 1791–1816. https://doi.org/10.1214/19-AOAS1257

Sheshadri, K., & Singh, M. P. (2019). The public and legislative impact of hyperconcentrated topic news. SCIENCE ADVANCES, 5(8). https://doi.org/10.1126/sciadv.aat8296

Morrow, Z., Liu, C., Kelley, C. T., & Jakubikova, E. (2019). Approximating Periodic Potential Energy Surfaces with Sparse Trigonometric Interpolation. The Journal of Physical Chemistry B, 123(45), 9677–9684. https://doi.org/10.1021/acs.jpcb.9b08210

Warsing, D. P., Jr., Wangwatcharakul, W., & King, R. E. (2019). Computing base-stock levels for a two-stage supply chain with uncertain supply. Omega, 89, 92–109. https://doi.org/10.1016/j.omega.2018.10.001

Yao, Y., Jiang, X., & Li, Z. (2019). Spatiotemporal characteristics of green travel: A classification study on a public bicycle system. JOURNAL OF CLEANER PRODUCTION, 238. https://doi.org/10.1016/j.jclepro.2019.117892

Peng, H., & Pang, T. (2019). Optimal strategies for a three-level contract-farming supply chain with subsidy. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 216, 274–286. https://doi.org/10.1016/j.ijpe.2019.06.011

Scolforo, H. F., McTague, J. P., Burkhart, H., Roise, J., McCarter, J., Alvares, C. A., & Stape, J. L. (2019). Stand-level growth and yield model system for clonal eucalypt plantations in Brazil that accounts for water availability. FOREST ECOLOGY AND MANAGEMENT, 448, 22–33. https://doi.org/10.1016/j.foreco.2019.06.006

Peng, H., & Pang, T. (2019). A mutual-aid mechanism for supply chains with capital constraints. INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT, 14(4), 304–312. https://doi.org/10.1080/17509653.2019.1578275

Gopalswamy, K., & Uzsoy, R. (2019). A data-driven iterative refinement approach for estimating clearing functions from simulation models of production systems. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 57(19), 6013–6030. https://doi.org/10.1080/00207543.2018.1557351

Halabian, H., Lambadaris, I., & Viniotis, Y. (2019). Optimal Server Assignment in Multi-Server Queueing Systems with Random Connectivities. JOURNAL OF COMMUNICATIONS AND NETWORKS, 21(4), 405–415. https://doi.org/10.1109/JCN.2019.000023

Dayıoğlu, E. G., Karagül, K., Şahin, Y., & Kay, M. G. (2019). Route planning methods for a modular warehouse system. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 10(1), 17. https://doi.org/10.11121/ijocta.01.2020.00752

Telang, P. R., Singh, M. P., & Yorke-Smith, N. (2019). A Coupled Operational Semantics for Goals and Commitments. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 65, 31–85. https://doi.org/10.1613/jair.1.11494

Ellis, J. A., Evans, T. M., Hamilton, S. P., Kelley, C. T., & Pandya, T. M. (2019). Optimization of processor allocation for domain decomposed Monte Carlo calculations. Parallel Computing, 87, 77–86. https://doi.org/10.1016/j.parco.2019.06.001

Luo, J., Hong, T., & Fang, S.-C. (2019). Robust Regression Models for Load Forecasting. IEEE Transactions on Smart Grid, 10(5), 5397–5404. https://doi.org/10.1109/TSG.2018.2881562

Schwartz, B., McConnell, B. M., & Parlier, G. H. J. S. (2019). How Data Analytics Will Improve Logistics Planning. Army Sustainment, 51(3), 54–57. Retrieved from https://www.army.mil/article/223842/how_data_analytics_will_improve_logistics_planning

Miles, P. R., Pash, G. T., Smith, R. C., & Oates, W. S. (2019). Global Sensitivity Analysis of Fractional-Order Viscoelasticity Models. BEHAVIOR AND MECHANICS OF MULTIFUNCTIONAL MATERIALS XIII, Vol. 10968. https://doi.org/10.1117/12.2514160

Bravo, N., & Smith, R. C. (2019). Parameter-dependent Surrogate Model Development for PZT Bimorph Actuators Employed for Micro-air Vehicles. BEHAVIOR AND MECHANICS OF MULTIFUNCTIONAL MATERIALS XIII, Vol. 10968. https://doi.org/10.1117/12.2514246

Liu, C., Kelley, C. T., & Jakubikova, E. (2019). Molecular Dynamics Simulations on Relaxed Reduced-Dimensional Potential Energy Surfaces. The Journal of Physical Chemistry A, 123(21), 4543–4554. https://doi.org/10.1021/acs.jpca.9b02298

Lara, L. A. de C., Santos, M. F., Jank, L., Chiari, L., Vilela, M. de M., Amadeu, R. R., … Garcia, A. A. F. (2019). Genomic Selection with Allele Dosage in Panicum maximum Jacq. G3-GENES GENOMES GENETICS, 9(8), 2463–2475. https://doi.org/10.1534/g3.118.200986

Wang, J., Pambudi, S., Wang, W., & Song, M. (2019). Resilience of IoT Systems Against Edge-Induced Cascade-of-Failures: A Networking Perspective. IEEE INTERNET OF THINGS JOURNAL, 6(4), 6952–6963. https://doi.org/10.1109/JIOT.2019.2913140

Xiao, X., Feng, X., & Li, Z. (2019). A gradient recovery-based adaptive finite element method for convection-diffusion-reaction equations on surfaces. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 120(7), 901–917. https://doi.org/10.1002/nme.6163

Williams, N. D., Brady, R., Gilmore, S., Gremaud, P., Tran, H. T., Ottesen, J. T., … Olufsen, M. S. (2019). Cardiovascular dynamics during head-up tilt assessed via pulsatile and non-pulsatile models. Journal of Mathematical Biology, 79(3), 987–1014. https://doi.org/10.1007/s00285-019-01386-9

Leon, L. S., Miles, P. R., Smith, R. C., & Oates, W. S. (2019). Active subspace analysis and uncertainty quantification for a polydomain ferroelectric phase-field model. JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, 30(14), 2027–2051. https://doi.org/10.1177/1045389X19853636

Alexopoulos, C., Goldsman, D., Mokashi, A. C., Tien, K.-W., & Wilson, J. R. (2019). Sequest: A Sequential Procedure for Estimating Quantiles in Steady-State Simulations. OPERATIONS RESEARCH, 67(4), 1162–1183. https://doi.org/10.1287/opre.2018:1829

Schmidt, K., Smith, R. C., Hite, J., Mattingly, J., Azmy, Y., Rajan, D., & Goldhahn, R. (2019). Sequential optimal positioning of mobile sensors using mutual information. STATISTICAL ANALYSIS AND DATA MINING, 12(6), 465–478. https://doi.org/10.1002/sam.11431

Qian, X., Fang, S.-C., Huang, M., Nie, T., & Wang, X. (2019). Bidding Decisions with Nonequilibrium Strategic Thinking in Reverse Auctions. Group Decision and Negotiation, 28(4), 757–786. https://doi.org/10.1007/s10726-019-09624-7

Bruccoleri, M., Perrone, G., Mazzola, E., & Handfield, R. (2019). The magnitude of a product recall: offshore outsourcing vs. captive offshoring effects. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 57(13), 4211–4227. https://doi.org/10.1080/00207543.2018.1533652

Zhao, T., Zhang, J., Li, Z., & Zhang, Z. (2019). Numerical Validations of the Tangent Linear Model for the Lorenz Equations. CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 120(1), 83–104. https://doi.org/10.32604/cmes.2019.04483

Zhu, R., & Ghosal, S. (2019). Bayesian nonparametric estimation of ROC surface under verification bias. STATISTICS IN MEDICINE, 38(18), 3361–3377. https://doi.org/10.1002/sim.8181

McConnell, B. M., Hodgson, T. J., Kay, M. G., King, R. E., Liu, Y., Parlier, G. H., … Wilson, J. R. (2019). Assessing uncertainty and risk in an expeditionary military logistics network. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 7, 154851291986059. https://doi.org/10.1177/1548512919860595

Xue, X., Yuan, C., & Wu, F. (2019). Convexified H-infinity output-feedback consensus synthesis for linear multi-agent systems. IET CONTROL THEORY AND APPLICATIONS, 13(11), 1619–1628. https://doi.org/10.1049/iet-cta.2018.5337

Stefanescu, R., Hite, J., Cook, J., Smith, R. C., & Mattingly, J. (2019). Surrogate-Based Robust Design for a Non-Smooth Radiation Source Detection Problem. ALGORITHMS, 12(6). https://doi.org/10.3390/a12060113

Dai, J., Fang, S.-C., & Xing, W. (2019). Recovering optimal solutions via SOC-SDP relaxation of trust region subproblem with nonintersecting linear constraints. Journal of Industrial & Management Optimization, 15(4), 1677–1699. https://doi.org/10.3934/jimo.2018117

Liu, Y., Dinh, N. T., Smith, R. C., & Sun, X. (2019). Uncertainty quantification of two-phase flow and boiling heat transfer simulations through a data-driven modular Bayesian approach. INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 138, 1096–1116. https://doi.org/10.1016/j.ijheatmasstransfer.2019.04.075

Pang, T., & Varga, K. (2019). Portfolio Optimization for Assets with Stochastic Yields and Stochastic Volatility. JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 182(2), 691–729. https://doi.org/10.1007/s10957-019-01513-y

Qian, X., Fang, S.-C., Huang, M., & Wang, X. (2019). Winner determination of loss-averse buyers with incomplete information in multiattribute reverse auctions for clean energy device procurement. Energy, 177, 276–292. https://doi.org/10.1016/j.energy.2019.04.072

Yoo, W. W., & Ghosal, S. (2019). Bayesian mode and maximum estimation and accelerated rates of contraction. BERNOULLI, 25(3), 2330–2358. https://doi.org/10.3150/18-BEJ1056

Kacar, N. B., Moench, L., & Uzsoy, R. (2019, June). A problem reduction approach for production planning using clearing functions. AT-AUTOMATISIERUNGSTECHNIK, Vol. 67, pp. 455–467. https://doi.org/10.1515/auto-2018-0109

Liu, R., Kuhl, M. E., Liu, Y., & Wilson, J. R. (2019). Modeling and Simulation of Nonstationary Non-Poisson Arrival Processes. INFORMS JOURNAL ON COMPUTING, 31(2), 347–366. https://doi.org/10.1287/ijoc.2018.0828

Xiong, Y., Yang, Y., Ye, Y., & Rouskas, G. N. (2019). A machine learning approach to mitigating fragmentation and crosstalk in space division multiplexing elastic optical networks. OPTICAL FIBER TECHNOLOGY, 50, 99–107. https://doi.org/10.1016/j.yofte.2019.03.001

Scolforo, H. F., McTague, J. P., Burkhart, H., Roise, J., Carneiro, R. L., & Stape, J. L. (2019). Generalized stem taper and tree volume equations applied to eucalyptus of varying genetics in Brazil. CANADIAN JOURNAL OF FOREST RESEARCH, 49(5), 447–462. https://doi.org/10.1139/cjfr-2018-0276

Kwon, C., Hegarty, M., Oxenham, W., Thoney-Barletta, K., Grant, E., & Reid, L. (2019). An introduction to a new direct testing method for characterizing pressure in compression fabrics. JOURNAL OF THE TEXTILE INSTITUTE, 110(6), 822–831. https://doi.org/10.1080/00405000.2018.1527966

Jiang, S., Fang, S.-C., An, Q., & Lavery, J. E. (2019). A sub-one quasi-norm-based similarity measure for collaborative filtering in recommender systems. Information Sciences, 487, 142–155. https://doi.org/10.1016/j.ins.2019.03.011

Murad, N., Tran, H. T., Banks, H. T., Everett, R. A., & Rosenberg, E. S. (2019). IMMUNOSUPPRESSANT TREATMENT DYNAMICS IN RENAL TRANSPLANT RECIPIENTS: AN ITERATIVE MODELING APPROACH. DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B, 24(6), 2781–2797. https://doi.org/10.3934/dcdsb.2018274

Jalooli, A., Song, M., & Wang, W. (2019). Message coverage maximization in infrastructure-based urban vehicular networks. VEHICULAR COMMUNICATIONS, 16, 1–14. https://doi.org/10.1016/j.vehcom.2019.02.001

Coleman, K. D., Lewis, A., Smith, R. C., Williams, B., Morris, M., & Khuwaileh, B. (2019). Gradient-Free Construction of Active Subspaces for Dimension Reduction in Complex Models with Applications to Neutronics. SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 7(1), 117–142. https://doi.org/10.1137/16M1075119

Patankar, N., Queiroz, A. R., DeCarolis, J. F., Bazilian, M. D., & Chattopadhyay, D. (2019). Building conflict uncertainty into electricity planning: A South Sudan case study. ENERGY FOR SUSTAINABLE DEVELOPMENT, 49, 53–64. https://doi.org/10.1016/j.esd.2019.01.003

Drineas, P., & Ipsen, I. C. F. (2019). LOW-RANK MATRIX APPROXIMATIONS DO NOT NEED A SINGULAR VALUE GAP. SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 40(1), 299–319. https://doi.org/10.1137/18M1163658

Oliveira, M. P., & Handfield, R. (2019). Analytical foundations for development of real-time supply chain capabilities. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 57(5), 1571–1589. https://doi.org/10.1080/00207543.2018.1493240

Bouterse, B., & Perros, H. (2019). Performance analysis of the reserve capacity policy for dynamic VM allocation in a SaaS environment. SIMULATION MODELLING PRACTICE AND THEORY, 93, 293–304. https://doi.org/10.1016/j.simpat.2018.07.002

Williams, N. D., Mehlsen, J., Tran, H. T., & Olufsen, M. S. (2019, April). An optimal control approach for blood pressure regulation during head-up tilt. BIOLOGICAL CYBERNETICS, Vol. 113, pp. 149–159. https://doi.org/10.1007/s00422-018-0783-9

Ning, B., Ghosal, S., & Thomas, J. (2019). Bayesian Method for Causal Inference in Spatially-Correlated Multivariate Time Series. BAYESIAN ANALYSIS, 14(1), 1–28. https://doi.org/10.1214/18-BA1102

Olsen, C. H., Ottesen, J. T., Smith, R. C., & Olufsen, M. S. (2019, April). Parameter subset selection techniques for problems in mathematical biology. BIOLOGICAL CYBERNETICS, Vol. 113, pp. 121–138. https://doi.org/10.1007/s00422-018-0784-8

Chen, X., Feng, X., & Li, Z. (2019). A direct method for accurate solution and gradient computations for elliptic interface problems. NUMERICAL ALGORITHMS, 80(3), 709–740. https://doi.org/10.1007/s11075-018-0503-5

Zhu, R., & Ghosal, S. (2019). Bayesian Semiparametric ROC surface estimation under verification bias. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 133, 40–52. https://doi.org/10.1016/j.csda.2018.09.003

Moheb-Alizadeh, H., & Handfield, R. (2019). Sustainable supplier selection and order allocation: A novel multi-objective programming model with a hybrid solution approach. COMPUTERS & INDUSTRIAL ENGINEERING, 129, 192–209. https://doi.org/10.1016/j.cie.2019.01.011

Murad, N., Tran, H. T., & Banks, H. T. (2019). Optimal control of immunosuppressants in renal transplant recipients susceptible to BKV infection. OPTIMAL CONTROL APPLICATIONS & METHODS, 40(2), 292–309. https://doi.org/10.1002/oca.2478

Chen, X., & Kelley, C. T. (2019). Convergence of the EDIIS Algorithm for Nonlinear Equations. SIAM Journal on Scientific Computing, 41(1), A365–A379. https://doi.org/10.1137/18M1171084

Baron, B., Spathis, P., Amorim, M. D., Viniotis, Y., & Ammar, M. H. (2019). Mobility As an Alternative Communication Channel: A Survey. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 21(1), 289–314. https://doi.org/10.1109/COMST.2018.2841192

Gopalswamy, K., Fathi, Y., & Uzsoy, R. (2019). Valid inequalities for concave piecewise linear regression. OPERATIONS RESEARCH LETTERS, 47(1), 52–58. https://doi.org/10.1016/j.orl.2018.12.004

Strong, D., Kay, M., Conner, B., Wakefield, T., & Manogharan, G. (2019). Hybrid manufacturing-Locating AM hubs using a two-stage facility location approach. ADDITIVE MANUFACTURING, 25, 469–476. https://doi.org/10.1016/j.addma.2018.11.027

Scolforo, H. F., McTague, J. P., Burkhart, H., Roise, J., Alvares, C. A., & Stape, J. L. (2019). Modeling whole-stand survival in clonal eucalypt stands in Brazil as a function of water availability. FOREST ECOLOGY AND MANAGEMENT, 432, 1002–1012. https://doi.org/10.1016/j.foreco.2018.10.044

Zhu, Y., Wu, F., Karimi, H. R., & Lu, B. (2019, January). Special issue on advanced analysis and control design of switching linear parameter-varying systems and its applications. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, Vol. 233, pp. 3–4. https://doi.org/10.1177/0959651818819594

Li, W., Guo, B., Li, X., Zhou, Y., Huang, S., & Rouskas, G. N. (2019). A large-scale nesting ring multi-chip architecture for manycore processor systems. OPTICAL SWITCHING AND NETWORKING, 31, 183–192. https://doi.org/10.1016/j.osn.2018.10.004

Scolforo, McTague, J. P., Burkhart, H., Roise, J., Campoe, O., & Stape, J. (2019). Yield pattern of eucalypt clones across tropical Brazil: An approach to clonal grouping. Forest Ecology and Management, 432, 30–39. https://doi.org/10.1016/j.foreco.2018.08.051

Scolforo, H., McTauge, J. P., Burkhart, H., Roise, J., Campoe, O., & Stape, J. (2019). Eucalyptus growth and yield system: linking individual-tree and stand-level growth models in clonal eucalypt plantations in Brazil. Forest Ecology and Management, 432, 1–16. https://doi.org/10.1016/j.foreco.2018.08.045

Tutunchi, G. K., & Fathi, Y. (2019). Effective methods for solving the Bi-criteria p-Center and p-Dispersion problem. COMPUTERS & OPERATIONS RESEARCH, 101, 43–54. https://doi.org/10.1016/j.cor.2018.08.009

Du, X., & Ghosal, S. (2019). Multivariate Gaussian network structure learning. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 199, 327–342. https://doi.org/10.1016/j.jspi.2018.07.009