Capacity Allocation in Flexible Production Networks: Theory and Applications

References

• Alptekindǧlu A, Banerjee A, Paul A, Jain N (2013) Inventory pooling to deliver differentiated service. Manufacturing Service Oper. Management 15(1):33–44.Link, Google Scholar
• Asadpour A, Wang X, Zhang J (2017) Online resource allocation with limited flexibility. Working paper, Leonard N. Stern School of Business, New York University, New York.Google Scholar
• Cesa-Bianchi N, Lugosi G (2006) Prediction, Learning, and Games (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Cesa-Bianchi N, Conconi A, Gentile C (2004) On the generalization ability of on-line learning algorithms. IEEE Trans. Inform. Theory 50(9):2050–2057.Crossref, Google Scholar
• Chen J, Lin DK, Thomas DJ (2003) On the item fill rate for a finite horizon. Oper. Res. Lett. 31(2):119–199.Crossref, Google Scholar
• Chen X, Zhang J, Zhou YT (2015) Optimal sparse designs for process flexibility via probabilistic expanders. Oper. Res. 63(5):1159–1176.Link, Google Scholar
• Chen X, Ma T, Zhang J, Zhou Y (2018) Optimal design of process flexibility for general production systems. Working paper, New York University, New York.Google Scholar
• Chou MC, Chua GA, Zheng H (2014) On the performance of sparse process structures in partial postponement production systems. Oper. Res. 62(2):348–365.Link, Google Scholar
• Chou MC, Chua GA, Teo CP, Zheng H (2010) Design for process flexibility: Efficiency of the long chain and sparse structure. Oper. Res. 58(1):43–58.Link, Google Scholar
• Chou MC, Chua GA, Teo CP, Zheng H (2011) Process flexibility revisited: The graph expander and its applications. Oper. Res. 59(5):1090–1105.Link, Google Scholar
• Désir A, Goyal V, Wei Y, Zhang J (2016) Sparse process flexibility designs: Is the long chain really optimal? Oper. Res. 64(2):416–431.Link, Google Scholar
• Goyal M, Netessine S (2011) Volume flexibility, product flexibility, or both: The role of demand correlation and product substitution. Manufacturing Service Oper. Management 13(2):180–193.Link, Google Scholar
• Hallgren M, Olhager J (2009) Flexibility configurations: Empirical analysis of volume and product mix flexibility. Omega 37(4):746–756.Crossref, Google Scholar
• Hopp WJ, Spearman ML (2008) Factory Physics: Foundations of Manufacturing Management (McGraw-Hill, New York).Google Scholar
• Jordan WC, Graves SC (1995) Principles on the benefits of manufacturing process flexibility. Management Sci. 41(4):577–594.Link, Google Scholar
• Kakade SM, Tewari A (2008) On the generalization ability of online strongly convex programming algorithms. Proc. 21st Conf. Neural Inform. Processing Systems, vol. 1 (Curran Associates, Inc., Red Hook, NY), 801–808.Google Scholar
• Lyu G, Chou MC, Teo CP, Zheng Z, Zhong Y (2017) Capacity allocation with stock-out probability targets: Theory and applications. Working paper, NUS Business School, National University of Singapore, Singapore.Google Scholar
• Mahdavi M, Jin R, Yang T (2012) Trading regret for efficiency: Online convex optimization with long term constraints. J. Machine Learn. Res. 13(September):2503–2528.Google Scholar
• Mahdavi M, Yang T, Jin R (2013) Stochastic convex optimization with multiple objectives. Proc. 26th Internat. Conf. Neural Inform. Processing Systems, vol. 1 (Curran Associates, Inc., Red Hook, NY), 1115–1123.Google Scholar
• Megiddo N (1977) A good algorithm for lexicographically optimal flows in multi-terminal networks. Bull. Amer. Math. Soc. 83(3):407–409.Crossref, Google Scholar
• Nocedal J, Wright SJ (2006) Numerical Optimization (Springer, New York).Google Scholar
• Porteus EL (1990) Stochastic inventory theory. Heyman DP, Sobel MJ, eds. Stochastic Models, Handbooks in Operations Research and Management Science, vol. 2 (North-Holland, Amsterdam), 605–652.Crossref, Google Scholar
• Shalev-Shwartz S (2011) Online learning and online convex optimization. Foundations Trends Machine Learn. 4(2):107–194.Crossref, Google Scholar
• Shalev-Shwartz S, Shamir O, Srebro N, Sridharan K (2009) Stochastic convex optimization. Proc. Conf. Learn. Theory (COLT), Montreal.Google Scholar
• Shapiro A, Dentcheva D, Ruszczyński A (2009) Lectures on Stochastic Programming: Modeling and Theory (Society for Industrial and Applied Mathematics, Philadelphia).Crossref, Google Scholar
• Shi C, Wei Y, Zhong Y (2015) Process flexibility for multi-period production systems. Working paper, University of Michigan, Ann Arbor.Google Scholar
• Simchi-Levi D, Wei Y (2012) Understanding the performance of the long chain and sparse designs in process flexibility. Oper. Res. 60(5):1125–1141.Link, Google Scholar
• Simchi-Levi D, Wei Y (2015) Worst-case analysis of process flexibility designs. Oper. Res. 63(1):166–185.Link, Google Scholar
• Suarez FF, Cusumano MA, Fine CH (1996) An empirical study of manufacturing flexibility in printed circuit board assembly. Oper. Res. 44(1):223–240.Link, Google Scholar
• Wang X, Zhang J (2015) Process flexibility: A distribution-free bound on the performance of k-chain. Oper. Res. 63(3):555–571.Link, Google Scholar
• Zinkevich M (2003) Online convex programming and generalized infinitesimal gradient ascent. Proc. 20th Internat. Conf. Machine Learn. (ICML) (AAAI Press, Menlo Park, CA), 928–936.Google Scholar
• Zhong Y, Zheng Z, Chou MC, Teo CP (2018) Resource pooling and allocation policies to deliver differentiated service. Management Sci. 64(4):1555–1573.Link, Google Scholar