Stochastic optimal control methodologies in risk-informed community resilience planning

Abolghasemi, 2008, Revisiting blood transfusion preparedness: experience from the Bam earthquake response, Prehospital Disaster Med, 23, 391, 10.1017/S1049023X00006117 Woods, 2015, Four concepts for resilience and the implications for the future of resilience engineering, Reliab Eng Syst Saf, 141, 5, 10.1016/j.ress.2015.03.018 Koliou M, van de Lindt J, McAllister TP, Ellingwood BR, Dillard M, Cutler H. A Critical Appraisal of Community Resilience Studies: Progress and Challenges. Sustainable and Resilient Infrastructure 2018; 3(1). doi.org/10.1080/23789689.2017.1418547. Bruneau, 2003, A framework to quantitatively assess and enhance the seismic resilience of communities, Earthquake Spectra, 19, 733, 10.1193/1.1623497 Sharma, 2018, Resilience analysis: a mathematical formulation to model resilience of engineering systems, Sustain Resili Infrastruct, 3, 49, 10.1080/23789689.2017.1345257 Guidotti, 2019, Integration of physical infrastructure and social systems in communities’ reliability and resilience analysis, Reliab Eng Syst Saf, 185, 476, 10.1016/j.ress.2019.01.008 Lin, 2017, Stochastic post-disaster functionality recovery of community building portfolios I: Modeling, Struct Saf, 69, 96, 10.1016/j.strusafe.2017.05.002 Masoomi, 2018, Quantifying socioeconomic impact of a Tornado by estimating population outmigration as a resilience metric at the community level, J Struct Eng, 144, 04018034, 10.1061/(ASCE)ST.1943-541X.0002019 Masoomi, 2018, Restoration and functionality assessment of a community subjected to tornado hazard, Struct Infrastruct Eng, 14, 275, 10.1080/15732479.2017.1354030 Masoomi H. A resilience-based decision framework to determine performance targets for the built environment (Doctoral dissertation, Colorado State University); 2018. Zhang, 2016, A multi-objective optimization model for retrofit strategies to mitigate direct economic loss and population dislocation, Sustain Resili Infrastruct, 1, 123, 10.1080/23789689.2016.1254995 Gomez, 2019, An optimization-based decision support framework for coupled pre-and post-earthquake infrastructure risk management, Struct Saf, 77, 1, 10.1016/j.strusafe.2018.10.002 Xu, 2019, Improving repair sequence scheduling methods for postdisaster critical infrastructure systems, Comput-Aided Civ Infrastruct Eng, 34, 506, 10.1111/mice.12435 Nozhati, 2019, Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management, Reliab Eng Syst Saf, 18, 116, 10.1016/j.ress.2018.09.011 Gosavi, 2015 Bertsekas, D. P. Dynamic programming and optimal control (Vol. 1, No. 2). Belmont, MA: Athena scientific; 1995. Sutton RS, Barto AG. Introduction to reinforcement learning (Vol. 2, No. 4). Cambridge: MIT press; 1998. Cimellaro GP, Solari D, Arcidiacono V, Renschler CS, Reinhorn, AM, Bruneau M. Community resilience assessment integrating network interdependencies. InProc. Tenth US National Conf. on Earthquake Engineering (10NCEE); 2014. Busoniu, 2017 Zhang, 2018, Resilience-based network design under uncertainty, Reliab Eng Syst Saf, 169, 364, 10.1016/j.ress.2017.09.009 Hillier, 2001 Nozhati, 2019, Probabilistic framework for evaluating food security of households in the aftermath of a disaster, Struct Infrastruct Eng, 15, 1060, 10.1080/15732479.2019.1584824 Nozhati S, Ellingwood BR, Mahmoud H, van de Lindt JW. Identifying and analyzing interdependent critical infrastructure in post-earthquake urban reconstruction. In: 11th US National Conference on Earthquake Engineering: Integrating Science Engineering and Policy; 2018. Ouyang, 2014, Multi-dimensional hurricane resilience assessment of electric power systems, Struct Saf, 48, 15, 10.1016/j.strusafe.2014.01.001 Ouyang, 2012, A three-stage resilience analysis framework for urban infrastructure systems, Struct Saf, 36, 23, 10.1016/j.strusafe.2011.12.004 Fereshtehnejad, 2017, A randomized point-based value iteration POMDP enhanced with a counting process technique for optimal management of multi-state multi-element systems, Struct Saf, 65, 113, 10.1016/j.strusafe.2017.01.003 Meidani, 2015, Random Markov decision processes for sustainable infrastructure systems, Struct Infrastruct Eng, 11, 655, 10.1080/15732479.2014.893445 Bellman, 1966, Dynamic programming, Science, 153, 34, 10.1126/science.153.3731.34 Sarkale Y, Nozhati, S, Chong, EKP, Ellingwood B. R., Mahmoud H. Solving Markov decision processes for network-level post-hazard recovery via simulation optimization and rollout. In: 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) 2018; 906-912. Memarzadeh, 2019, Model-free reinforcement learning with model-based safe exploration: Optimizing adaptive recovery process of infrastructure systems, Struct Saf, 80, 46, 10.1016/j.strusafe.2019.04.003 Powell, 2009, What you should know about approximate dynamic programming, Naval Res Logist (NRL), 56, 239, 10.1002/nav.20347 Bertsekas, 1996 Birge, 2011 Katanyukul, 2011, Approximate dynamic programming for an inventory problem: Empirical comparison, Comput Ind Eng, 60, 719, 10.1016/j.cie.2011.01.007 Andriotis, 2019, Managing engineering systems with large state and action spaces through deep reinforcement learning, Reliab Eng Syst Saf, 191, 10.1016/j.ress.2019.04.036 Medury, 2013, Incorporating network considerations into pavement management systems: a case for approximate dynamic programming, Transport Res Part C: Emerg Technol, 33, 134, 10.1016/j.trc.2013.03.003 Bertsekas DP. Rollout algorithms for discrete optimization: A survey.Handbook of combinatorial optimization 2013; 2989-3013. Nozhati, 2019, Understanding community resilience from a PRA perspective using binary decision diagrams, Risk Anal, 10.1111/risa.13321 Nozhati, 2019, Optimal stochastic dynamic scheduling for managing community recovery from natural hazards, Reliab Eng Syst Saf He, 2018, Modeling the damage and recovery of interdependent critical infrastructure systems from natural hazards, Reliab Eng Syst Saf, 177, 162, 10.1016/j.ress.2018.04.029 Aslett LJ. MCMC for inference on phase-type and masked system lifetime models (Doctoral dissertation, Trinity College Dublin); 2012. Ibe, 2013 Department of Homeland Security Emergency Preparedness and Response Directorate, FEMA, Mitigation Division. Multi-hazard loss estimation methodology, earthquake model: HAZUS-MH MR1, advanced engineering building module. Washington DC; 2003. Limnios, 2013 Shapiro, 2011, Analysis of stochastic dual dynamic programming method, Eur J Oper Res, 209, 63, 10.1016/j.ejor.2010.08.007 Nozhati S. Optimal Stochastic Scheduling of Restoration of Infrastructure Systems from Hazards: An Approximate Dynamic Programming Approach (Doctoral dissertation, Colorado State University. Libraries); 2019. Naeini M. Rahnamay Stochastic dynamics of cascading failures in electric-cyber infrastructures (Doctoral dissertation Available from https://digitalrepository.unm.edu/ece_etds/213. 2014 University of New Mexico). Council, 2016