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Oklahoma State University


Papers published or accepted in 2018-2020

Z. Miao and B. Balasundaram. An ellipsoidal bounding scheme for the quasi-clique number of a graph. INFORMS Journal on Computing. To appear in 2020.


F. Nasirian, F. M. Pajouh, and B. Balasundaram. Detecting a most closeness-central clique in complex networks. European Journal of Operational Research. 283(2):461-475, June 2020. 
J. Ma and B. Balasundaram. On the chance-constrained minimum spanning k-core problem. Journal of Global Optimization, 74(4):783-801, 2019. 
S. Sun, Z. Miao, B. Ratcliffe, P. Campbell, B. Pasch, Y. A. El-Kassaby, B. Balasundaram, and C. Chen. SNP variable selection by generalized graph domination. PLOS ONE, 14(1):1–18, 2019. 
Y. Lu, E. Moradi, and B. Balasundaram. Correction to: Finding a maximum k-club using the k-clique formulation and canonical hypercube cuts. Optimization Letters, 12(8):1959–1969, 2018. 
E. Moradi and B. Balasundaram. Finding a maximum k-club using the k-clique formulation and canonical hypercube cuts. Optimization Letters, 12(8):1947–1957, 2018. 
J.S. Borrero, O.A. Prokopyev, P. Krokhmal, Optimization of cascading processes in arbitrary networks with stochastic interactions. IEEE Transactions on Network Science and Engineering. Accepted for Publication. 
J.S. Borrero, O.A. Prokopyev, D. Saure, Sequential interdiction with incomplete information and learning. Operations Research, 67(1): 72-89, 2019. 
H. Salemi and A. Buchanan. Parsimonius formulations for low-diameter clusters. Mathematical Programming ComputationAccepted for Publication
H. Validi, A. Buchanan. The optimal design of low-latency virtual backbones. INFORMS Journal on Computing. Accepted for Publication.
H. Validi, A. Buchanan. A Note on “A linear-size zero-one programming model for the minimum spanning tree problem in planar graphs". Networks, 73(1): 135-142, 2019. 
J.L. Walteros, A. Buchanan. Why is maximum clique often easy in practice? Operations Research. Accepted for Publication. Honorable Mention in the 2019 JFIG Paper Competition.
A. Buchanan, Y. Wang, S. Butenko. Algorithms for node-weighted Steiner tree and maximum-weight connected subgraph. Networks, 72(2): 238-248, 2018.
O. Battaia, A. Dolgui, S.S. Heragu, S.M. Meerkov, and M. K. Tiwari, Design for manufacturing and assembly/disassembly: joint design of products and production systems, International Journal of Production Research, 56(24): 7181-7189, 2018. 
Srivathsan, S. and M. Kamath, Understanding the value of upstream inventory information sharing in supply chain networks, Applied Mathematical Modelling, 54:393-412, 2018. 
Ma, J., Y.T. Leung, and M. Kamath, 2019, Service system design under uncertainty: Insights from an M/G/1 model, Service Science. 11(1):40-56, 2019. 
C. Liu, A. Law, D. Roberson, and Z, Kong, Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication. Journal of Manufacturing Systems. 51: 75-86, 2019 
J. Liu, C. Liu, Y. Bai, P. Rao, Z. Kong, and C. Williams, Layer-wise spatial modeling of porosity in additive manufacturing. IISE Transactions. 51(2):109-123, 2019. 
C. Liu, A. Kapoor, J. VanOsdol,K. Ektate, Z. Kong, and A. Ranjan, A spectral fiedler field-based contrast platform for Imaging of nanoparticles in colon tumor, Scientific Reports. 8(11390):1-8, 2018. 

Gupta, A., T. Liu, C. Crick. 2020.  Utilizing Time Series Data Embedded in Electronic Health Records to Develop Continuous Mortality Risk Prediction Models using Hidden Markov Models: A Sepsis Case Study. Accepted by Statistical Methods in Medical Research.


Gupta, A., T. Liu, S. Shepherd. 2019. Clinical Decision Support System to Assess the Risk of Sepsis Using Tree Augmented Bayesian Networks and Electronic Medical Record Data. Health Informatics Journal.


Hariharan S., T. Liu, M. Z. Shen. 2019. Role of Resource Flexibility and Responsive Pricing in Mitigating the Uncertainties in Production Systems. Accepted by European Journal of Operational Research

S. Piri, D. Delen, T. Liu, A synthetic informative minority over-sampling (SIMO) algorithm embedded into Support Vector Machine to learn from imbalanced datasets. Decision Support Systems. 106: 15-29, 2018. 
S. Piri, D. Delen, T. Liu, W. Paiva, Development of a new metric to identify rare patterns in association analysis: The case of analyzing diabetic comorbidities. Expert Systems with Applications, 94: 112-125, 2018. 
A. Gupta, T. Liu, S. Shepherd, W. Paiva. Using statistical and machine learning methods to evaluate the prognostic accuracy of SIRS and qSOFA. Healthcare Informatics Research. 24(2): 139-147, 2018. 
Y. Zhou, T. Liu, C. Zhao. Backup capacity coordination with renewable energy certificates in a regional electricity market. IISE Transactions, 50(8): 711– 719, 2018. 
Y. Zhou, T. Liu, G. Cai, Impact of in-store promotion and spillover effect on private label introduction. Service Science, 11(2), 96 – 112.
S. Babaei, C. Zhao, L. Fan, T. Liu. Incentive-based coordination mechanism for renewable and conventional energy suppliers. IEEE Transactions on Power Systems, 34(3): 1761-1770: 2018. 
A. Gupta, T. Liu, S. Shepherd. 2019. Clinical decision support system to assess the risk of sepsis using tree augmented Bayesian networks and electronic medical record data. Health Informatics Journal. Published Online 13 Jun 2019.
B. Yao, C. McLean, and H. Yang. Robust optimization of dynamic route planning in same-day delivery networks with one-time observation of new demand. Networks, 73(4): 434-452, 2019. 
F. Imani, B. Yao, R. Chen, P. Rao, and H. Yang, Joint multifractal and lacunarity analysis of image profiles for manufacturing quality control. ASME Journal of Manufacturing Science and Engineering, 141 (4): 044501, 2019. 
B. Yao and H. Yang, Constrained Markov Decision Process modeling for sequential optimization of additive manufacturing build quality. IEEE Access, 6 (1): 54786-54794, 2018. 
B. Yao, F. Imani, and H. Yang, Markov Decision Process for image-guided additive manufacturing. IEEE Robotics and Automation Letters, 3(4): 2792-2798, 2018. 
B. Yao, F. Imani, A. Sakpal, E. W. Reutzel and H. Yang, Multifractal analysis of image profiles for the characterization and detection of defects in additive manufacturing, ASME Journal of Manufacturing Science and Engineering, 140 (3): 031014, 2018. 
R. Zhu, B. Yao, F. Leonelli, and H. Yang, Optimal sensor placement for space-time potential mapping and data fusion, IEEE Sensors Letters, 3 (1), 2018. 
F. Yousefian, A. Nedich, and U.V. Shanbhag, On stochastic and deterministic quasi-Newton methods for non-strongly convex optimization: Asymptotic convergence and rate analysis, SIAM Journal on Optimization, to appear, 2021
F. Yousefian, A. Nedich, and U.V. Shanbhag, On stochastic mirror-prox algorithms for stochastic Cartesian variational inequalities: Randomized block coordinate and optimal averaging schemes, Set-Valued and Variational Analysis, 26 (4), 789-819, 2018. 
D. Newton, F. Yousefian, R. Pasupathy, Stochastic gradient descent: recent trends, INFORMS Tutorials in Operations Research. Published Online: 19 Oct 2018; 193-220. 
N. Majlesinasab, F. Yousefian, A. Pourhabib, Self-tuned stochastic mirror descent methods for smooth and nonsmooth high-dimensional stochastic optimization. IEEE Transactions on Automatic Control, 64 (10), 4377-4384, 2019.