Process Flexibility for Multiperiod Production Systems

References

• Ahuja RK, Magnanti TL, Orlin JB (1993) Network Flows (Prentice Hall, Englewood Cliffs, NJ).Google Scholar
• Andradóttir S, Ayhan H, Down DG (2003) Dynamic server allocation for queueing networks with flexible servers. Oper. Res. 51(6):952–968.Link, Google Scholar
• Andradóttir S, Ayhan H, Down DG (2007) Compensating for failures with flexible servers. Oper. Res. 55(4):753–768.Link, Google Scholar
• Andradóttir S, Ayhan H, Down DG (2013) Design principles for flexible systems. Production Oper. Management 22(5):1144–1156.Google Scholar
• Asadpour A, Wang X, Zhang J (2018) Online resource allocation with limited flexibility. Working paper, New York University, New York.Google Scholar
• Ata B, Kumar S (2005) Heavy traffic analysis of open processing networks with complete resource pooling: Asymptotic optimality of discrete review policies. Ann. Appl. Probab. 15(1A):331–391.Crossref, Google Scholar
• Bertsekas DP, Shreve SE (2007) Stochastic Optimal Control: The Discrete-Time Case (Athena Scientific, Cambridge, MA).Google Scholar
• Bertsimas D, Gamarnik D, Tsitsiklis JN (2001) Performance of multiclass Markovian queueing networks via piecewise linear. Ann. Appl. Probab. 11(4):1384–1428.Crossref, Google Scholar
• Cachon G, Terwiesch C (2011) Matching Supply with Demand: An Introduction to Operations Management, 3rd ed. (McGraw-Hill, Ashland, OH).Google Scholar
• Chen X, Zhang J, Zhou Y (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 (2019) Optimal design of process flexibility for general production systems. Oper. Res. 67(2):516–531.Google Scholar
• Chou MC, Chua GA, Teo C-P, 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 C-P, Zheng H (2011) Process flexibility revisited: The graph expander and its applications. Oper. Res. 59(5):1090–1105.Link, Google Scholar
• Dai JG, Lin W (2005) Maximum pressure policies in stochastic processing networks. Oper. Res. 53(2):197–218.Link, Google Scholar
• Deng T, Shen Z-JM (2013) Process flexibility design in unbalanced networks. Manufacturing Service Oper. Management 15(1):24–32.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
• Eryilmaz A, Srikant R (2012) Asymptotically tight steady-state queue length bounds implied by drift conditions. Queueing Systems 72(3/4):311–359.Crossref, Google Scholar
• Gurvich I, Whitt W (2009) Scheduling flexible servers with convex delay costs in many-server service systems. Manufacturing Service Oper. Management 11(2):237–253.Link, Google Scholar
• Harrison JM, López MJ (1999) Heavy traffic resource pooling in parallel-server systems. Queueing Systems 33(4):339–368.Crossref, Google Scholar
• Hopp WJ, Tekin E, Van Oyen MP (2004) Benefits of skill chaining in serial production lines with cross-trained workers. Management Sci. 50(1):83–98.Link, Google Scholar
• Iravani SM, Van Oyen MP, Sims KT (2005) Structural flexibility: A new perspective on the design of manufacturing and service operations. Management Sci. 51(2):151–166.Link, Google Scholar
• Janakiraman G, Nagarajan M, Veeraraghavan S (2014) Simple policies for managing flexible capacity. Manufacturing Service Oper. Management 20(2):333–346.Google Scholar
• Jordan W, Graves S (1995) Principles on the benefits of manufacturing process flexibility. Management Sci. 41(4):577–594.Link, Google Scholar
• Mandelbaum A, Stolyar AL (2004) Scheduling flexible servers with convex delay costs: Heavy-traffic optimality of the generalized cμ-rule. Oper. Res. 52(6):836–855.Link, Google Scholar
• McKeown N, Mekkittikul A, Anantharam V, Walrand J (1999) Achieving 100% throughput in an input-queued switch. IEEE Trans. Comm. 47(8):1260–1267.Crossref, Google Scholar
• Müller A, Stoyan D (2002) Comparison Methods for Stochastic Models and Risks (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Powell WB (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
• Shah D, Wischik D (2012) Switched networks with maximum weight policies: Fluid approximation and multiplicative state space collapse. Ann. Appl. Probab. 22(1):70–127.Crossref, Google Scholar
• Sheng L, Zheng H, Rong Y, Huh WT (2015) Flexible system design: A perspective from service levels. Oper. Res. Lett. 43(3):219–225.Crossref, Google Scholar
• Simchi-Levi D (2010) Operations Rules: Delivering Customer Value Through Flexible Operations (MIT Press, Cambridge, MA).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
• Stolyar AL (2004) Maxweight scheduling in a generalized switch: State space collapse and workload minimization in heavy traffic. Ann. Appl. Probab. 14(1):1–53.Crossref, Google Scholar
• Tanrisever F, Morrice D, Morton D (2012) Managing capacity flexibility in make-to-order production environments. Eur. J. Oper. Res. 216(2):334–345.Crossref, Google Scholar
• Tassiulas L, Ephremides A (1992) Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Trans. Automatic Control 37(12):1936–1948.Crossref, Google Scholar
• Tsitsiklis JN, Xu K (2017) Flexible queueing architectures. Oper. Res. 65(5):1398–1413.Link, Google Scholar
• Wallace RB, Whitt W (2005) A staffing algorithm for call centers with skill-based routing. Manufacturing Service Oper. Management 7(4):276–294.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