A General Model and Efficient Algorithms for Reliable Facility Location Problem Under Uncertain Disruptions

References

• Aboolian R, Cui T, Shen ZJM (2013) An efficient approach for solving reliable facility location models. INFORMS J. Comput. 25(4):720–729.Link, Google Scholar
• Ahmadi-Javid A, Seddighi AH (2013) A location-routing problem with disruption risk. Transportation Res. Part E Logist. Transportation Rev. 53:63–82.Crossref, Google Scholar
• An Y, Zeng B, Zhang Y, Zhao L (2014) Reliable p-median facility location problem: Two-stage robust models and algorithms. Transportation Res. Part B Methodological 64:54–72.Crossref, Google Scholar
• Azad N, Hassini E (2019) A Benders decomposition method for designing reliable supply chain networks accounting for multimitigation strategies and demand losses. Transportation Sci. 53(5):1287–1312.Link, Google Scholar
• Azad N, Saharidis GK, Davoudpour H, Malekly H, Yektamaram SA (2013) Strategies for protecting supply chain networks against facility and transportation disruptions: An improved Benders decomposition approach. Ann. Oper. Res. 210(1):125–163.Crossref, Google Scholar
• Bamberger RL, Kumins L (2005) Oil and gas: Supply issues after Katrina. Technical report, Congressional Research Service, Library of Congress, Washington, DC.Google Scholar
• Bayraksan G, Love DK (2015) Data-driven stochastic programming using phi-divergences. The Operations Research Revolution, INFORMS TutORials in Operations Research (INFORMS, Catonsville, MD), 1–19.Link, Google Scholar
• Ben-Tal A, Den Hertog D, De Waegenaere A, Melenberg B, Rennen G (2013) Robust solutions of optimization problems affected by uncertain probabilities. Management Sci. 59(2):341–357.Link, Google Scholar
• Berman O, Krass D (2011) On n-facility median problem with facilities subject to failure facing uniform demand. Discrete Appl. Math. 159(6):420–432.Crossref, Google Scholar
• Berman O, Krass D, Menezes MB (2013) Location and reliability problems on a line: Impact of objectives and correlated failures on optimal location patterns. Omega 41(4):766–779.Crossref, Google Scholar
• Bertsimas D, Doan XV, Natarajan K, Teo CP (2010) Models for minimax stochastic linear optimization problems with risk aversion. Math. Oper. Res. 35(3):580–602.Link, Google Scholar
• Bertsimas D, Popescu I (2005) Optimal inequalities in probability theory: A convex optimization approach. SIAM J. Optim. 15(3):780–804.Crossref, Google Scholar
• Cashell BW, Labonte M (2005) The macroeconomic effects of Hurricane Katrina. Technical report, Congressional Research Service, Library of Congress, Washington, DC.Google Scholar
• Chen X, Sim M, Sun P, Zhang J (2008) A linear decision-based approximation approach to stochastic programming. Oper. Res. 56(2):344–357.Link, Google Scholar
• Chongvilaivan A (2012) Thailand’s 2011 flooding: Its impact on direct exports and global supply chains. Technical report, ARTNeT working paper series, EconStor, Bangkok.Google Scholar
• Christopher M, Peck H (2004) Building the resilient supply chain. Internat. J. Logist. Management 15(2):1–14.Crossref, Google Scholar
• Church RL, Scaparra MP (2007) Protecting critical assets: The r-interdiction median problem with fortification. Geographical Anal. 39(2):129–146.Crossref, Google Scholar
• Cui T, Ouyang Y, Shen ZJM (2010) Reliable facility location design under the risk of disruptions. Oper. Res. 58(4):998–1011.Link, Google Scholar
• Delage E, Ye Y (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58(3):595–612.Link, Google Scholar
• El Ghaoui L, Oks M, Oustry F (2003) Worst-case value-at-risk and robust portfolio optimization: A conic programming approach. Oper. Res. 51(4):543–556.Link, Google Scholar
• Esfahani PM, Kuhn D (2018) Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Math. Programming 171(1–2):115–166.Crossref, Google Scholar
• Goh J, Sim M (2010) Distributionally robust optimization and its tractable approximations. Oper. Res. 58(4-part-1):902–917.Link, Google Scholar
• Golany B, Kaplan EH, Marmur A, Rothblum UG (2009) Nature plays with dice—Terrorists do not: Allocating resources to counter strategic vs. probabilistic risks. Eur. J. Oper. Res. 192(1):198–208.Crossref, Google Scholar
• Haraguchi M, Lall U (2015) Flood risks and impacts: A case study of Thailand’s floods in 2011 and research questions for supply chain decision making. Internat. J. Disaster Risk Reduction 14:256–272.Crossref, Google Scholar
• Kuhn D, Wiesemann W, Georghiou A (2011) Primal and dual linear decision rules in stochastic and robust optimization. Math. Programming 130(1):177–209.Crossref, Google Scholar
• Latour A (2001) A fire in Albuquerque sparks crisis for European cell-phone giants: Nokia handles shock with aplomb as Ericsson of Sweden gets burned. Wall Street Journal (January 29), https://www.wsj.com/articles/SB980720939804883010.Google Scholar
• Li X, Ouyang Y (2010) A continuum approximation approach to reliable facility location design under correlated probabilistic disruptions. Transportation Res. Part B Methodological 44(4):535–548.Crossref, Google Scholar
• Li X, Ouyang Y, Peng F (2013) A supporting station model for reliable infrastructure location design under interdependent disruptions. Transportation Res. Part E Logist. Transportation Rev. 60:80–93.Crossref, Google Scholar
• Li Y, Shu J, Song M, Zhang J, Zheng H (2017) Multisourcing supply network design: Two-stage chance-constrained model, tractable approximations, and computational results. INFORMS J. Comput. 29(2):287–300.Link, Google Scholar
• Liberatore F, Scaparra MP, Daskin MS (2011) Analysis of facility protection strategies against an uncertain number of attacks: The stochastic r-interdiction median problem with fortification. Comput. Oper. Res. 38(1):357–366.Crossref, Google Scholar
• Liberatore F, Scaparra MP, Daskin MS (2012) Hedging against disruptions with ripple effects in location analysis. Omega 40(1):21–30.Crossref, Google Scholar
• Lim MK, Bassamboo A, Chopra S, Daskin MS (2013) Facility location decisions with random disruptions and imperfect estimation. Manufacturing Service Oper. Management 15(2):239–249.Link, Google Scholar
• Liu C, Fan Y, Ordóñez F (2009) A two-stage stochastic programming model for transportation network protection. Comput. Oper. Res. 36(5):1582–1590.Crossref, Google Scholar
• Lu M, Ran L, Shen ZJM (2015) Reliable facility location design under uncertain correlated disruptions. Manufacturing Service Oper. Management 17(4):445–455.Link, Google Scholar
• Lutter P, Degel D, Büsing C, Koster AM, Werners B (2017) Improved handling of uncertainty and robustness in set covering problems. Eur. J. Oper. Res. 263(1):35–49.Crossref, Google Scholar
• O’Hanley JR, Church RL (2011) Designing robust coverage networks to hedge against worst-case facility losses. Eur. J. Oper. Res. 209(1):23–36.Crossref, Google Scholar
• Popescu I (2007) Robust mean-covariance solutions for stochastic optimization. Oper. Res. 55(1):98–112.Link, Google Scholar
• Qi L, Shen ZJM, Snyder LV (2010) The effect of supply disruptions on supply chain design decisions. Transportation Sci. 44(2):274–289.Link, Google Scholar
• Ruvo C (2018) Typhoon impacts some promo product-producing factories in China. Advertising Specialty Institute (September 18), https://www.asicentral.com/news/newsletters/ promogram/september-2018/typhoon-impacts-some-promo-product-producing-factories-in-china/.Google Scholar
• Santos MC, Luss H, Nace D, Poss M (2019) Proportional and maxmin fairness for the sensor location problem with chance constraints. Discrete Appl. Math. 261:316–331.Crossref, Google Scholar
• Scaparra MP, Church RL (2008a) A bilevel mixed-integer program for critical infrastructure protection planning. Comput. Oper. Res. 35(6):1905–1923.Crossref, Google Scholar
• Scaparra MP, Church RL (2008b) An exact solution approach for the interdiction median problem with fortification. Eur. J. Oper. Res. 189(1):76–92.Crossref, Google Scholar
• Scarf H (1958) A Min-Max Solution of an Inventory Problem (Stanford University Press, Stanford, CA), 201–209.Google Scholar
• See CT, Sim M (2010) Robust approximation to multiperiod inventory management. Oper. Res. 58(3):583–594.Link, Google Scholar
• Shapiro A (2006) Worst-case distribution analysis of stochastic programs. Math. Programming 107(1–2):91–96.Crossref, Google Scholar
• Sheffi Y (2001) Supply chain management under the threat of international terrorism. Internat. J. Logist. Management 12(2):1–11.Crossref, Google Scholar
• Shen ZJM, Zhan RL, Zhang J (2011) The reliable facility location problem: Formulations, heuristics, and approximation algorithms. INFORMS J. Comput. 23(3):470–482.Link, Google Scholar
• Snyder LV, Atan Z, Peng P, Rong Y, Schmitt AJ, Sinsoysal B (2016) OR/MS models for supply chain disruptions: A review. IIE Trans. 48(2):89–109.Crossref, Google Scholar
• Snyder LV, Daskin MS (2005) Reliability models for facility location: The expected failure cost case. Transportation Sci. 39(3):400–416.Link, Google Scholar
• Snyder LV, Scaparra MP, Daskin MS, Church RL (2006) Planning for disruptions in supply chain networks. Tutorials Oper. Res. 2:234–257.Google Scholar
• Tang CS (2006) Robust strategies for mitigating supply chain disruptions. Internat. J. Logist. Res. Appl. 9(1):33–45.Crossref, Google Scholar
• Wiesemann W, Kuhn D, Sim M (2014) Distributionally robust convex optimization. Oper. Res. 62(6):1358–1376.Link, Google Scholar
• Xie S, Li X, Ouyang Y (2015a) Decomposition of general facility disruption correlations via augmentation of virtual supporting stations. Transportation Res. Part B Methodological 80:64–81.Crossref, Google Scholar
• Xie W, Ouyang Y, Wong SC (2015b) Reliable location-routing design under probabilistic facility disruptions. Transportation Sci. 50(3):1128–1138.Link, Google Scholar
• Xu H, Liu Y, Sun H (2018) Distributionally robust optimization with matrix moment constraints: Lagrange duality and cutting plane methods. Math. Programming 169(2):489–529.Crossref, Google Scholar
• Zeng B, Zhao L (2013) Solving two-stage robust optimization problems using a column-and-constraint generation method. Oper. Res. Lett. 41(5):457–461.Crossref, Google Scholar