Optimization and Operations Research in Mitigation of a Pandemic

doi:10.1007/s40305-022-00391-y

Journal of the Operations Research Society of China ›› 2022, Vol. 10 ›› Issue (2): 289-304.doi: 10.1007/s40305-022-00391-y

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Optimization and Operations Research in Mitigation of a Pandemic

Cai-Hua Chen¹, Yu-Hang Du², Dong-Dong Ge², Lin Lei², Yin-Yu Ye³

1 School of Management and Engineering, Nanjing University, Nanjing 210008, Jiangsu, China;
2 School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China;
3 Department of Management Science and Engineering, Stanford University, Stanford, CA 9430, USA

Received:2020-10-24 Revised:2021-12-22 Online:2022-06-30 Published:2022-06-13
Contact: Dong-Dong Ge, Cai-Hua Chen, Yu-Hang Du, Lin Lei, Yin-Yu Ye E-mail:ge.dongdong@mail.shufe.edu.cn;chchen@nju.edu.cn;yuhang_du@163.sufe.edu.cn;linleish@foxmail.com;yyye@stanford.edu
Supported by:
The first author was supported by the Natural Science Foundation of Jiangsu Province (No. BK20181259) and the National Natural Science Foundation of China (No. 11871269). The third author was supported by the National Natural Science Foundation of China (Nos. 11831002 and 11471205).

Abstract

Abstract: The pandemic of COVID-19 initiated in 2019 and spread all over the world in 2020 has caused significant damages to the human society, making troubles to all aspects of our daily life. Facing the serious outbreak of the virus, we consider possible solutions from the perspectives of both governments and enterprises. Particularly, this paper discusses several applications of supply chain management, public resource allocation, and pandemic prevention using optimization and machine learning methods. Some useful insights in mitigating the pandemic and economy reopening are provided at the end of this paper. These insights might help governments to reduce the severity of the current pandemic and prevent the next round of outbreak. They may also improve companies' reactions to the increasing uncertainties appearing in the business operations. Although the coronavirus imposes challenges to the entire society at the moment, we are confident to develop new techniques to prevent and eradicate the disease.

Key words: Pandemic, Operation research, Optimization, Machine learning

CLC Number:

90B50
90C90

Cai-Hua Chen, Yu-Hang Du, Dong-Dong Ge, Lin Lei, Yin-Yu Ye. Optimization and Operations Research in Mitigation of a Pandemic[J]. Journal of the Operations Research Society of China, 2022, 10(2): 289-304.

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References

[1] Delage, E., Ye, Y.:Distributionally robust optimization under moment uncertainty with application to data-driven problems. Op. Res. 58(3), 595-612(2010)
[2] Bertsimas, D., Gupta, V., Kallus, N.:Data-driven robust optimization. Math. Program. 167(2), 235-292(2017)
[3] Esfahani, P.M.:Data-driven distributionally robust optimization using the Wasserstein metric:performance guarantees and tractable reformulations. Math. Program. 171, 115-166(2018)
[4] Martínez-Jaramillo, S., Pérez, O.P., Embriz, F.A.D., Lopez, G.:Systemic risk, financial contagion and financial fragility. J. Econ. Dyn. Control 34(11), 2358-2374(2010)
[5] Musin, O.R.:The kissing number in four dimensions. Annal. Math. 35, 1-32(2008)
[6] Bachoc, C., Vallentin, F.:New upper bounds for kissing numbers from semidefinite programming. J. Am. Math. Soc. 21(3), 909-924(2008)
[7] Lee, J., Liberti, L.:On an SDP relaxation for kissing number. Optim. Lett. 14(2), 417-422(2020)
[8] Chiba, N., Nishizeki, T., Saito, N.:An approximation algorithm for the maximum independent set problem on planar graphs. SIAM J. Comput. 11(4), 663-675(1982)
[9] Kucherenko, S., Belotti, P., Liberti, L., Maculan, N.:New formulations for the kissing number problem. Discr. Appl. Math. 155(14), 1837-1841(2007)
[10] Nesterov, Y.:Semidefinite relaxation and nonconvex quadratic optimization. Optim. Methods Softw. 9(1-3), 141-160(1998)
[11] Luo, Z.-Q., Ma, W.-K., So, A.-C.:Ye, Y, Zhang, S:Semidefinite relaxation of quadratic optimization problems. IEEE Signal Process. Magaz 27(3), 20-34(2010)
[12] Zheng, X.J., Sun, X., Ling, L.D.:Convex relaxations for nonconvex quadratically constrained quadratic programming:matrix cone decomposition and polyhedral approximation. Math. Program. 129(2), 301-329(2011)
[13] Lovasz, L.:On the Shannon capacity of a graph. IEEE Trans. Inf. Theory 25(1), 1-7(1979)
[14] Chlamtac,E.,Singh,G.:ImprovedapproximationguaranteesthroughhigherlevelsofSDPhierarchies:A pproximation, randomization and combinatorial optimization. Algorithms and Techniques, pp. 49-62. Springer, Cham (2008)
[15] Wilson,Aaron T.:Applying the boundary point method to an SDP relaxation of the maximum independent set problem for a branch and bound algorithm. PhD thesis, New Mexico Institute of Mining and Technology (2009)
[16] Biswas,P, Ye, Y.:Semidefinite programming for ad hoc wireless sensor network localization. In:Proceedings of the 3rd international symposium on Information processing in sensor networks, pages 46-54(2004)
[17] Wang, Z., Zheng, S., Ye, Y., Boyd, S.:Further relaxations of the semidefinite programming approach to sensor network localization. SIAM J. Optim. 19(2), 655-673(2008)
[18] Wang,Z,Ding,Y.:Real-timetrackingforsensornetworksviasdpandgradientmethod.InProceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments, pages 109-112(2008)
[19] Carlsson, J.G., Armbruster, B., Ye, Y.:Finding equitable convex partitions of points in a polygon efficiently. ACM Trans. Algorithms 6(4), 1-19(2010)
[20] Devanur, N.R., Papadimitriou, C.H., Saberi, A., Vazirani, V.V.:Market equilibrium via a primal-dual algorithm for a convex program. J. ACM 55(5), 2541(2008)
[21] Jalota, D, Pavone, M, Ye Y.:Markets for efficient public good allocation (2020). http://arxiv.org/abs/2005.10765
[22] Feng, Z., Glasser, J.W., Hill, A.N.:On the benefits of flattening the curve:a perspective. Math. Biosci. 364, 108389(2020)
[23] Jordan, Rachel E., Adab, P, Cheng KK.:Covid-19:risk factors for severe disease and death, BMJ (2020). https://doi.org/10.1136/bmj.m1198

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