Skip to main content
SpringerLink
Log in

OR/MS Methods for Structural Dynamics in Supply Chain Risk Management

  • Chapter
  • First Online:
Structural Dynamics and Resilience in Supply Chain Risk Management

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 265))

Abstract

In this Chapter, we analyze state-of-the-art research streams on managing operational and disruption risks in supply chain design and planning. It structures and classifies existing research and practical applications of different quantitative methods subject to recently derived empirical frameworks. We identify gaps in current research and delineate future research avenues. The results of this literature analysis are twofold. Supply chain managers can observe which quantitative tools are available for different applications. On the other hand, from the point of view of operational research, limitations and future research needs can be identified for decision-supporting methods in supply chain risk management domains.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Acar Y, Kadipasaoglu S, Schipperij P (2010) A decision support framework for global supply chain modeling: an assessment of the impact of demand, supply and lead-time uncertainties on performance. Int J Prod Res 48(11):3245–3268

    Article  Google Scholar 

  • Afanasiev VN, Kolmanovskii VB, Nosov VR (1996) Mathematical theory of control systems design. Kluwer, Dordrecht

    Book  Google Scholar 

  • Ahmadi-Javid A, Seddighi AH (2013) A location-routing problem with disruption risk. Transp Res E-Log 53:63–82

    Article  Google Scholar 

  • Aliev RA, Fazlollahi B, Guirimov BG, Aliev RR (2007) Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management. Inform Sci 177(20):4241–4255

    Article  Google Scholar 

  • Ambulkar S, Blackhurst J, Grawe S (2015) Firm’s resilience to supply chain disruptions: scale development and empirical examination. J Oper Manag 33(34:111–122

    Article  Google Scholar 

  • Amiri A (2006) Designing a distribution network in a supply chain system: formulation and efficient solution procedure. Eur J Oper Res 171(2):567–576

    Article  Google Scholar 

  • Archipoy A, Ivanov D (2011) An entropy-based approach to simultaneous analysis of supply chain structural complexity and adaptation potential. Int J Shipp Transp Logist 3(2):180–197

    Article  Google Scholar 

  • Askin RG, Baffo I, Mingjun X (2014) Multi-commodity warehouse location and distribution planning with inventory consideration. Int J Prod Res 52(7):1897–1910

    Article  Google Scholar 

  • Axsäter S (1985) Control theory concepts in production and inventory control. Int J Syst Sci 16(2):161–169

    Article  Google Scholar 

  • Axsäter S, Rosling K (1993) Installation vs. echelon stock policies for multi-level inventory control. Manag Sci 39:1274–1280

    Article  Google Scholar 

  • Azaron A, Brown KN, Tarim SA, Modarres M (2008) A multi-objective stochastic programming approach for supply chain design considering risk. Int J Prod Econ 116(1):129–138

    Article  Google Scholar 

  • Babich V, Burnetas AN, Ritchken PH (2007) Competition and diversification effects in supply chains with supplier default risk. M&SOM-Manuf Serv Op 9(2):123–146

    Article  Google Scholar 

  • Baghalian A, Rezapour S, Farahani RZ (2013) Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. Eur J Oper Res 227(1):199–215

    Article  Google Scholar 

  • Bakshi N, Kleindorfer PR (2009) Co-opetition and investment for supply-chain resilience. Prod Oper Manag 18(6):583–603

    Article  Google Scholar 

  • Baron O, Milner J, Naseraldin H (2011) Facility location: a robust optimization approach. Prod Oper Manag 20(5):772–785

    Article  Google Scholar 

  • Bartholdi JJ, Eisenstein DD, Lim YF (2010) Self-organizing logistics systems. Annu Rev Control 34(1):111–117

    Article  Google Scholar 

  • Bearzotti LA, Salomone E, Chiotti OJ (2012) An autonomous multi-agent approach to supply chain event management. Int J Prod Econ 135(1):468–478

    Article  Google Scholar 

  • Bellmann R (1972) Adaptive control processes: a guided tour. Princeton University Press, Princeton

    Google Scholar 

  • Bensoussan A, Çakanyildirim M, Sethi S (2007) Optimal ordering policies for inventory problems with dynamic information delays. Prod Oper Manag 16(2):241–256

    Article  Google Scholar 

  • Benyoucef L, Xie X, Tanonkou GA (2013) Supply chain network design with unreliable suppliers: a lagrangian relaxation-based approach. Int J Prod Res 51(21):6435–6454

    Article  Google Scholar 

  • Blackhurst J, Dunn KS, Craighead CW (2011) An empirically derived framework of global supply resiliency. J Bus Logist 32(4):374–391

    Article  Google Scholar 

  • Blos MF, Da Silva RM, Miyagi PE (2015) Application of an agent-based supply chain to mitigate supply chain disruptions. IFAC 48(3):640–645

    Google Scholar 

  • Bode C, Wagner SM, Petersen KJ, Ellram LM (2011) Understanding responses to supply chain disruptions: insights from information processing and resource dependence perspectives. Acad Manag J 54(4):833–856

    Article  Google Scholar 

  • Boltyanskiy B (1973) Optimal control of discrete systems. Nauka, Moscow

    Google Scholar 

  • Borisovsky P, Dolgui A, Eremeev A (2009) Genetic algorithms for a supply management problem: MIP-recombination vs greedy decoder. Eur J Oper Res 195(3):770–779

    Article  Google Scholar 

  • Bowersox DJ, Closs DJ, Copper MB (2013) Supply chain logistics management, 3rd edn. McGraw-Hill, Boston

    Google Scholar 

  • Braun MW, Rivera DE, Flores ME, Carlyle WM, Kempf KG (2003) A model predictive control framework for robust management of multi-product, multi-echelon demand networks. Annu Rev Control 27:229–245

    Article  Google Scholar 

  • Bryson AE, Ho YC (1975) Applied optimal control. Hemisphere, Washington, DC

    Google Scholar 

  • Bubnicki Z (2005) Modern control theory. Springer, Berlin/Heidelberg

    Google Scholar 

  • Bueno-Solano A, Cedillo-Campos MG (2014) Dynamic impact on global supply chain performance of disruptions propagation produced by terrorist acts. Transp Res E-Log 61:1–12

    Article  Google Scholar 

  • Camacho EF, Bordons C (2004) Model predictive control. Springer, London

    Google Scholar 

  • Carvalho H, Barroso AP, Machado VH, Azevedo S, Cruz-Machado V (2012) Supply chain redesign for resilience using simulation. Comput Ind Eng 62(1):329–341

    Article  Google Scholar 

  • Casti JL (1979) Connectivity, complexity and catastrophe in large-scale systems. Wiley-Interscience, New York/London

    Google Scholar 

  • Chaudhuri A, Mohanty BK, Singh KN (2013) Supply chain risk assessment during new product development: a group decision making approach using numeric and linguistic data. Int J Prod Res 51(10):2790–2804

    Article  Google Scholar 

  • Chauhan SS, Gordon V, Proth JM (2007) Scheduling in supply chain environment. Eur J Oper Res 183(3):961–970

    Article  Google Scholar 

  • Chauhan SS, Dolgui A, Proth JM (2009) A continuous model for supply planning of assembly systems with stochastic component procurement times. Int J Prod Econ 120(2):411–417

    Article  Google Scholar 

  • Chávez H, Castillo-Villar KK, Herrera L, Bustos A (2017) Simulation-based multi-objective model for supply chains with disruptions in transportation. Robot Cim Int Manuf 43:39–49

    Article  Google Scholar 

  • Chen L, Miller-Hooks E (2012) Resilience: an indicator of recovery capability in intermodal freight transport. Transp Sci 46(1):109–123

    Article  Google Scholar 

  • Chen F, Drezner Z, Ryan JK, Simchi-Levi D (2000) Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information. Manag Sci 46(3):436–443

    Article  Google Scholar 

  • Chen Q, Li X, Ouyang Y (2011) Joint inventory-location problem under the risk of probabilistic facility disruptions. Transp Res B- Methodol 45(7):991–1003

    Article  Google Scholar 

  • Chen J, Sohal AS, Prajogo DI (2013) Supply chain operational risk mitigation: a collaborative approach. Int J Prod Res 57(1):2186–2199

    Article  Google Scholar 

  • Chern CC, Lei ST, Huang KL (2014) Solving a multi-objective master planning problem with substitution and a recycling process for a capacitated multi-commodity supply chain network. J Intell Manuf 25(1):1–25

    Article  Google Scholar 

  • Chopra S, Meindl P (2012) Supply chain management: strategy planning and operation, 5th edn. Pearson, London

    Google Scholar 

  • Chopra S, Sodhi MS (2004) Managing risk to avoid supply-chain breakdown. MIT Sloan Manag Rev 46:52–61

    Google Scholar 

  • Chopra S, Sodhi MS (2014) Reducing the risk of supply chain disruptions. MIT Sloan Manag Rev 55(3):73–80

    Google Scholar 

  • Chopra S, Reinhardt G, Mohan U (2007) The importance of decoupling recurrent and disruption risks in a supply chain. Nav Res Log 54(5):544–555

    Article  Google Scholar 

  • Christopher M, Mena C, Khan O, Yurt O (2011) Approaches to managing global sourcing risk. Supply Chain Manag 16(2):67–81

    Article  Google Scholar 

  • Costantino A, Dotoli M, Falagario M, Fanti MP, Mangini AM, Sciancalepore F, Ukovich W (2011) A fuzzy programming approach for the strategic design of distribution networks. IEEE Int Aut Sci Eng:66–71

    Google Scholar 

  • Costantino A, Dotoli M, Falagario M, Fanti MP, Mangini AM (2012) A model for supply management of agile manufacturing supply chains. Int J Prod Econ 135:451–457

    Article  Google Scholar 

  • Craighead C, Blackhurst J, Rungtusanatham M, Handfield R (2007) The severity of supply chain disruptions: design characteristics and mitigation capabilities. Decis Sci 38(1):131–156

    Article  Google Scholar 

  • Cui T, Ouyang Y, Shen ZJM (2010) Reliable facility location design under the risk of disruptions. Oper Res 58(4–1):998–1011

    Article  Google Scholar 

  • Daganzo CF (2004) On the stability of supply chains. Oper Res 52(6):909–921

    Article  Google Scholar 

  • Dai H, Tseng MM, Zipkin PH (2015) Design of traceability systems for product recall. Int J Prod Res 53(2):511–531

    Article  Google Scholar 

  • Daskin MS (1995) Network and discrete location: models, algorithms, and applications. Wiley, New York

    Book  Google Scholar 

  • Dejonckheere J, Disney SM, Lambrecht MR, Towill DR (2004) The impact of information enrichment on the bullwhip effect in supply chains: a control engineering perspective. Eur J Oper Res 153(3):727–750

    Article  Google Scholar 

  • Deleris LA, Erhun F (2005) Risk management in supply networks using Monte-Carlo simulation. In: Kuhl ME, Steiger NM, Armstrong FB, Joines JA (eds) Planning production and inventories in the extended enterprise. P Wint Comp, New York

    Google Scholar 

  • Deleris LA, Erhun F (2011) Quantitative risk assessment in supply chains: a case study based on engineering risk analysis concepts. In: Kempf KG, Keskinocak P, Uzsoy R (eds) Planning production and inventories in the extended enterprise. Int Ser Oper Res Man 152:105–131. Springer, New York

    Google Scholar 

  • Dessouky M, Kijowski B, Verma S (1999) Simultaneous batching and scheduling for chemical processes with earliness and tardiness penalties. Prod Oper Manag 8(4):433–444

    Article  Google Scholar 

  • Disney SM, Towill DR (2002) A discrete linear control theory model to determine the dynamic stability of vendor managed inventory supply chains. Int J Prod Res 40(1):179–204

    Article  Google Scholar 

  • Disney SM, Towill DR, Warburton RDH (2006) On the equivalence of control theoretic, differential, and difference equation approaches to modeling supply chains. Int J Prod Econ 101:194–208

    Article  Google Scholar 

  • Dolgui A, Louly MA (2002) A model for supply planning under lead time uncertainty. Int J Prod Econ 78(2):145–152

    Article  Google Scholar 

  • Dolgui A, Pashkevich M (2008) On the performance of binomial and beta-binomial models of demand forecasting for multiple slow-moving inventory items. Comput Oper Res 35(3):893–905

    Article  Google Scholar 

  • Dolgui A, Prodhon C (2007) Supply planning under uncertainties in MRP environments: a state of the art. Annu Rev Control 31(2):269–279

    Article  Google Scholar 

  • Dolgui A, Proth JM (2008) RFID technology in supply chain management: state of the art and perspectives. In: Proceedings of the IFAC world congress, Seoul

    Google Scholar 

  • Dolgui A, Proth JM (2010) Supply chains engineering: useful methods and techniques. Springer, London

    Book  Google Scholar 

  • Dolgui A, Levin G, Louly MA (2005) Decomposition approach for a problem of lot-sizing and sequencing under uncertainties. Int J Comput Integ M 18(5):376–385

    Article  Google Scholar 

  • Dolgui A, Ivanov D, Sokolov B (2018) Ripple effect in the supply chain: an analysis and recent literature. Int J Prod Res. Published online

    Google Scholar 

  • Drezner Z (ed) (1995) Facility location: a survey of applications and methods. Springer, New York

    Google Scholar 

  • Fan LT, Wang CS (1964) The discrete maximum principle – a study of multistage systems optimization. Wiley, New York

    Google Scholar 

  • Federgruen A, Yang N (2011) Procurement strategies with unreliable suppliers. Oper Res 59(4):1033–1039

    Article  Google Scholar 

  • Feichtinger G, Hartl R (1985) Optimal pricing and production in an inventory model. Eur J Oper Res 19:45–56

    Article  Google Scholar 

  • Fleming WH, Rishel RW (1975) Deterministic and stochastic optimal control. Springer, Berlin/Heidelberg

    Book  Google Scholar 

  • Fu D, Ionescu CM, Aghezzaf EH (2015) Quantifying and mitigating the bullwhip effect in a benchmark supply chain system by an extended prediction self-adaptive control ordering policy. Comput Ind Eng 81:46–57

    Article  Google Scholar 

  • Gaimon C (1988) Simultaneous and dynamic price, production, inventory and capacity decisions. Eur J Oper Res 35:426–441

    Article  Google Scholar 

  • Garvey MD, Carnovale S, Yeniyurt S (2014) Analytical framework for supply network risk propagation: a Bayesian network approach. Eur J Oper Res 243:618–627

    Article  Google Scholar 

  • Gedik R, Medal H, Rainwater CE, Pohl EA, Mason SJ (2014) Vulnerability assessment and re-routing of freight trains under disruptions: a coal supply chain network application. Transp Res E-Log 71:45–57

    Article  Google Scholar 

  • Geoffrion A, Graves G (1974) Multicommodity distribution system design by benders decomposition. Manag Sci 29(5):822–844

    Article  Google Scholar 

  • Georgiadis MC, Tsiakis P, Longinidis P, Sofioglou MK (2011) Optimal design of supply chain networks under uncertain transient demand variations. Omega 39(3):254–272

    Article  Google Scholar 

  • Goetschalckx M, Vidal CJ, Dogan K (2002) Modeling and design of global logistics systems: a review of integrated strategic and tactical model and design algorithms. Eur J Oper Res 143(1):1–18

    Article  Google Scholar 

  • Goh M, Lim JYS, Meng F (2007) A stochastic model for risk management in global chain networks. Eur J Oper Res 182(1):164–173

    Article  Google Scholar 

  • Govindan K (2015) The optimal replenishment policy for time-varying stochastic demand under vendor managed inventory. Eur J Oper Res 242(2):402–423

    Article  Google Scholar 

  • Graves SC, Tomlin B (2003) Process flexibility in supply chains. Manag Sci 49:907–919

    Article  Google Scholar 

  • Graves SC, Willems SP (2005) Optimizing the supply chain configuration for new products. Manag Sci 51(8):1165–1180

    Article  Google Scholar 

  • Gulpinar N, Pachamanova D, Canakoglu E (2012) Robust strategies for facility location under uncertainty. Eur J Oper Res 225(1):21–35

    Article  Google Scholar 

  • Gümüs M, Ray S, Gurnani H (2012) Supply side story: risks, guarantees, competition, and information asymmetry. Manag Sci 58(9):1694–1714

    Article  Google Scholar 

  • Gupta V, He B, Sethi SP (2015) Contingent sourcing under supply disruption and competition. Int J Prod Res 53(10):3006–3027

    Article  Google Scholar 

  • Hahn GJ, Kuhn H (2012) Value-based performance and risk management in supply chains: a robust optimization approach. Int J Prod Econ 139(1):135–144

    Article  Google Scholar 

  • Hallikas J, Karvonen I, Pulkkinen U, Virolainen VM, Tuominen M (2004) Risk management processes in supplier networks. Int J Prod Econ 90(1):47–58

    Article  Google Scholar 

  • Han J, Shin KS (2016) Evaluation mechanism for structural robustness of supply chain considering disruption propagation. Int J Prod Res 54(1):135–151

    Article  Google Scholar 

  • Harjunkoski I, Maravelias CT, Bongers P, Castro PM, Engell S, Grossmann IE, Hooker J, Méndez C, Sand G, Wassick J (2014) Scope for industrial applications of production scheduling models and solution methods. Comput Chem Eng 62:161–193

    Article  Google Scholar 

  • Harrison TP (2005) Principles for the strategic design of supply chains. In: Harrison TP, Lee HL, Neale JJ (eds) The practice of supply chain management. Kluwer, Boston

    Google Scholar 

  • Hartl RF, Sethi SP, Vickson R (1995) A survey of the maximum principle for optimal control problems with state constraints. SIAM Rev 37(21):181–218

    Article  Google Scholar 

  • Hasani A, Khosrojerdi A (2016) Robust global supply chain network design under disruption and uncertainty considering resilience strategies: a parallel memetic algorithm for a real-life case study. Transp Res E-Log 87:20–52

    Article  Google Scholar 

  • He X, Prasad A, Sethi SP (2009) Cooperative advertising and pricing in a stochastic supply chain: feedback Stackelberg strategies. Prod Oper Manag 18(1):78–94

    Google Scholar 

  • Hendricks KB, Singhal VR (2005) Association between supply chain glitches and operating performance. Manag Sci 51(5):695–711

    Article  Google Scholar 

  • Hendricks KB, Singhal VR, Zhang R (2009) The effect of operational slack, diversification, and vertical relatedness on the stock market reaction to supply chain disruptions. J Oper Manag 27(3):233–246

    Article  Google Scholar 

  • Hennet JC, Arda Y (2008) Supply chain coordination: a game-theory approach. Eng Appl Artif Intell 21(3):399–405

    Article  Google Scholar 

  • Hishamuddin H, Sarker RA, Essam D (2013) A recovery model for a two-echelon serial supply chain with consideration of transportation disruption. Comput Ind Eng 64(2):55–561

    Article  Google Scholar 

  • Hishamuddin H, Sarker RA, Essam D (2015) A simulation model of a three-echelon supply chain system with multiple supplier subject to supply and transportation disruption. IFAC-PapersOnLine 48(3):2036–2040

    Article  Google Scholar 

  • Ho W, Zheng T, Yildiz H, Talluri S (2015) Supply chain risk management: a literature review. Int J Prod Res 53(16):5031–5069

    Article  Google Scholar 

  • Hoberg K, Bradley JR, Thonemann UW (2007) Analyzing the effect of the inventory policy on order and inventory variability with linear control theory. Eur J Oper Res 176:1620–1642

    Article  Google Scholar 

  • Holt CC, Modigliani F, Muth JF, Simon HA (1960) Planning production, inventories and work force. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Hou J, Zeng AZ, Zhao L (2010) Coordination with a backup supplier through buy-back contract under supply disruption. Transp Res E-Log 46(6):881–895

    Article  Google Scholar 

  • Hsu CI, Li HC (2011) Reliability evaluation and adjustment of supply chain network design with demand fluctuations. Int J Prod Econ 132(1):131–145

    Article  Google Scholar 

  • Hu X, Gurnani H, Wang L (2013) Managing risk of supply disruptions: incentives for capacity restoration. Prod Oper Manag 22(1):137–150

    Article  Google Scholar 

  • Hwang CL, Fan LT, Erikson LE (1967) Optimum production planning by the maximum principle. Manag Sci 13(9):751–755

    Article  Google Scholar 

  • Hwarng HB, Xie N (2008) Understanding supply chain dynamics: a chaos perspective. Eur J Oper Res 184(3):1163–1178

    Article  Google Scholar 

  • Iakovou E, Vlachos D, Xanthopoulos A (2010) A stochastic inventory management model for a dual sourcing supply chain with disruptions. Int J Syst Sci 41(3):315–324

    Article  Google Scholar 

  • Ivanov D (2009) DIMA – a research methodology for comprehensive multi-disciplinary modeling of production and logistics networks. Int J Prod Res 47(5):1133–1155

    Article  Google Scholar 

  • Ivanov D (2010) A framework for aligning (re)planning decisions on supply chains strategy, design, tactics, and operations. Int J Prod Res 48(13):3999–4017

    Article  Google Scholar 

  • Ivanov D (2017a) Simulation-based ripple effect modeling in the supply chain. Int J Prod Res 55(7):2083–2101

    Article  Google Scholar 

  • Ivanov D (2017b) Revealing interfaces of supply chain resilience and sustainability: a simulation study. Int J Prod Res. https://doi.org/10.1080/00207543.2017.1343507

  • Ivanov D (2017c) Simulation-based single vs dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. Int J Integr Supply Manage 11(1):24–43

    Article  Google Scholar 

  • Ivanov D, Sokolov B (2010) Adaptive supply chain management. Springer, London

    Book  Google Scholar 

  • Ivanov D, Sokolov B (2012) Dynamic supply chain scheduling. J Sched 15(2):201–216

    Google Scholar 

  • Ivanov D, Sokolov B (2013) Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis, and adaptation of performance under uncertainty. Eur J Oper Res 224(2):313–323

    Article  Google Scholar 

  • Ivanov D, Sokolov B, Kaeschel J (2010) A multi-structural framework for adaptive supply chain planning and operations with structure dynamics considerations. Eur J Oper Res 200:40–420

    Article  Google Scholar 

  • Ivanov D, Sokolov B, Dolgui A (2012) Applicability of optimal control theory to adaptive supply chain planning and scheduling. Annu Rev Control 36:73–84

    Article  Google Scholar 

  • Ivanov D, Sokolov B, Pavlov A (2013) Dual problem formulation and its application to optimal re-design of an integrated production–distribution network with structure dynamics and ripple effect considerations. Int J Prod Res 51(18):5386–5403

    Article  Google Scholar 

  • Ivanov D, Sokolov B, Dolgui A (2014a) The ripple effect in supply chains: trade-off ‘efficiency-flexibility-resilience’ in disruption management. Int J Prod Res 52(7):2154–2172

    Article  Google Scholar 

  • Ivanov D, Sokolov B, Pavlov A (2014b) Optimal distribution (re)planning in a centralized multi-stage network under conditions of ripple effect and structure dynamics. Eur J Oper Res 237(2):758–770

    Article  Google Scholar 

  • Ivanov D, Sokolov B, Dilou Raguinia EA (2014c) Integrated dynamic scheduling of material flows and distributed information services in collaborative cyber physical supply networks. Int J Syst Sci 1(1):18–26

    Google Scholar 

  • Ivanov D, Hartl R, Dolgui A, Pavlov A, Sokolov B (2015) Integration of aggregate distribution and dynamic transportation planning in a supply chain with capacity disruption and the ripple effect consideration. Int J Prod Res 53(23):6963–6979

    Article  Google Scholar 

  • Ivanov D, Sokolov B, Pavlov A, Dolgui A, Pavlov D (2016a) Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies. Transp Res E-Log 90:7–24

    Article  Google Scholar 

  • Ivanov D, Dolgui A, Sokolov B (2016b) Robust dynamic schedule coordination control in the supply chain. Comput Ind Eng 94:18–31

    Article  Google Scholar 

  • Ivanov D, Dolgui A, Sokolov B, Werner F (2016c) Schedule robustness analysis with the help of attainable sets in continuous flow problem under capacity disruptions. Int J Prod Res 54(1):3397–3413

    Article  Google Scholar 

  • Ivanov D, Dolgui A, Sokolov B, Ivanova M (2017) Literature review on disruption recovery in the supply chain. Int J Prod Res 55(20):6158–6174

    Article  Google Scholar 

  • Kamath NB, Roy R (2007) Capacity augmentation of a supply chain for a short lifecycle product: a system dynamics framework. Eur J Oper Res 179(2):334–351

    Article  Google Scholar 

  • Khakzad N (2015) Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliab Eng Syst Safe 138:263–272

    Article  Google Scholar 

  • Khmelnitsky E, Kogan K, Maimom O (1997) Maximum principle-based methods for production scheduling with partially sequence-dependent setups. Int J Prod Res 35(10):2701–2712

    Article  Google Scholar 

  • Khmelnitsky E, Presman E, Sethi SP (2011) Optimal production control of a failure-prone machine. Ann Oper Res 182:67–86

    Article  Google Scholar 

  • Kılıç YE, Tuzkaya UR (2015) A two-stage stochastic mixed-integer programming approach to physical distribution network design. Int J Prod Res 53(4):1291–1306

    Article  Google Scholar 

  • Kim SH, Tomlin B (2013) Guilt by association: strategic failure prevention and recovery capacity investments. Manag Sci 59(7):1631–1649

    Article  Google Scholar 

  • Kim Y, Chen YS, Linderman K (2015) Supply network disruption and resilience: a network structural perspective. J Oper Manag 33–34:43–59

    Article  Google Scholar 

  • Kinemia JG, Gershwin SB (1983) An algorithm for the computer control of a flexible manufacturing system. IIE Trans 15:353–362

    Article  Google Scholar 

  • Kleindorfer PR, Saad GH (2005) Managing disruption risks in supply chains. Prod Oper Manag 14(1):53–68

    Article  Google Scholar 

  • Klibi W, Martel A (2012) Modeling approaches for the design of resilient supply networks under disruptions. Int J Prod Econ 135(2):882–898

    Article  Google Scholar 

  • Klibi W, Martel A, Guitouni A (2010) The design of robust value-creating supply chain networks: a critical review. Eur J Oper Res 203(2):283–293

    Article  Google Scholar 

  • Knemeyer AM, Zinn W, Eroglu C (2009) Proactive planning for catastrophic events in supply chains. J Oper Manag 27(2):141–153

    Article  Google Scholar 

  • Kogan K, Khmelnitsky E (2000) Scheduling: control-based theory and polynomial-time algorithms. Kluwer, Dordrecht

    Book  Google Scholar 

  • Kouvelis P, Li J (2012) Contingency strategies in managing supply systems with uncertain lead-times. Prod Oper Manag 21(1):161–176

    Article  Google Scholar 

  • Kouvelis P, Chambers C, Wang H (2006) Supply chain management research and production and operations management: review, trends, and opportunities. Prod Oper Manag 15(3):449–469

    Article  Google Scholar 

  • Krajewski L, Wei JC, Tang LL (2005) Responding to schedule changes in build-torder supply chains. J Oper Manag 23:452–469

    Article  Google Scholar 

  • Kumar SK, Tiwari MK (2013) Supply chain system design integrated with risk pooling. Comput Ind Eng 64:580–588

    Article  Google Scholar 

  • Lee EB, Markus L (1967) Foundations of optimal control theory. Wiley, New York

    Google Scholar 

  • Lee HL, Padmanabhan P, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43:546–558

    Article  Google Scholar 

  • Lee AJL, Zhang AN, Goh M, Tan PS (2014) Disruption recovery modeling in supply chain risk management. In: Proceedings of the IEEE international conference on management of innovation and technology (ICMIT), Singapore, pp 279–283

    Google Scholar 

  • Lewis BM, Erera AL, Nowak MA, White CC (2013) Managing inventory in global supply chains facing port-of-entry disruption risks. Transp Sci 47(2):162–180

    Article  Google Scholar 

  • Li Y, Kramer MR, Beulens AJM, van der Vorst JGAJ (2010a) A framework for early warning and proactive control systems in food supply chain networks. Comput Ind 61(9):852–862

    Article  Google Scholar 

  • Li J, Wang S, Cheng TCE (2010b) Competition and cooperation in a single-retailer two-supplier supply chain with supply disruption. Int J Prod Econ 124:137–150

    Article  Google Scholar 

  • Li Q, Zeng B, Savachkin A (2013a) Reliable facility location design under disruptions. Comput Oper Res 40(4):901–909

    Article  Google Scholar 

  • Li T, Sethi SP, Zhang J (2013b) How does pricing power affect a firm’s sourcing decisions from unreliable suppliers. Int Prod Res 51(23):6990–7005

    Article  Google Scholar 

  • Liberatore F, Scaparra MP, Daskin MS (2012) Hedging against disruptions with ripple effects in location analysis. Omega 40:21–30

    Article  Google Scholar 

  • Lim M, Daskin MS, Bassamboo A, Chopra S (2010) A facility reliability problem: formulation, properties and algorithm. Nav Res Log 57(1):58–70

    Google Scholar 

  • Lim MK, Bassamboo A, Chopra S, Daskin MS (2013) Facility location decisions with random disruptions and imperfect estimation. M&SOM-Manuf Serv Op 15(2):239–249

    Article  Google Scholar 

  • Lin CC, Wang TH (2011) Build-to-order supply chain network design under supply and demand uncertainties. Transp Res B-Methodol 45(8):1–15

    Article  Google Scholar 

  • Lin YK, Huang CF, Yeh CT (2014) Network reliability with deteriorating product and production capacity through a multi-state delivery network. Int J Prod Res 52(22):6681–6694

    Article  Google Scholar 

  • Longo F, Mirabelli G (2008) An advanced supply chain management tool based on modeling and simulation. Comput Ind Eng 54:570–588

    Article  Google Scholar 

  • Losada C, Scaparra MP, O’Hanley JR (2012) Optimizing system resilience: a facility protection model with recovery time. Eur J Oper Res 217:519–530

    Article  Google Scholar 

  • Louly MA, Dolgui A (2002) Generalized newsboy model to compute the optimal planned lead times in assembly systems. Int J Prod Res 40(17):4401–4414

    Article  Google Scholar 

  • Louly MA, Dolgui A, Hnaien F (2008) Optimal supply planning in MRP environments for assembly systems with random component procurement times. Int J Prod Res 46(19):5441–5467

    Article  Google Scholar 

  • Lu M, Huang S, Shen ZM (2011) Product substitution and dual sourcing under random supply failures. Transp Res B-Meth 45:1251–1265

    Article  Google Scholar 

  • Madadi OA, Kurz MB, Mason SJ, Taaffe KM (2014) Supply network design under risk of supply disruptions and tainted materials delivery. Transp Res E-Log 67:105–123

    Article  Google Scholar 

  • Maimon O, Khmelnitsky E, Kogan K (1998) Optimal flow control in manufacturing systems. Springer, Berlin

    Google Scholar 

  • Manzini R, Bindi D (2009) Strategic design and operational management optimization of a multistage physical distribution system. Transp Res E-Log 45(6):915–936

    Article  Google Scholar 

  • Mastragostino R, Patel S, Swartz C (2014) Robust decision making for hybrid process supply chain systems via model predictive control. Comput Chem Eng 62:37–55

    Article  Google Scholar 

  • Matsuo H (2015) Implications of the Tohoku earthquake for Toyota’s coordination mechanism: supply chain disruption of automotive semiconductors. Int J Prod Econ 161:217–227

    Article  Google Scholar 

  • Mayne DQ, Rawlings JB, Rao CV, Scokaert POM (2000) Constrained model predictive control: stability and optimality. Automatica 36:789–814

    Article  Google Scholar 

  • Meepetchdee Y, Shah N (2007) Logistical network design with robustness and complexity considerations. Int J Oper Prod Manag 37(3):201–222

    Google Scholar 

  • Meisel F, Bierwirth C (2014) The design of make-to-order supply networks under uncertainties using simulation and optimization. Int J Prod Res 52(22):6590–6607

    Article  Google Scholar 

  • Melo MT, Nickel S, Saldanha-da-Gama F (2009) Facility location and supply chain management – a review. Eur J Oper Res 196(2):401–412

    Article  Google Scholar 

  • Mesarovic MD, Takahara Y (1975) General systems theory: mathematical foundations. Academic Press, New York/San Francisco/London

    Google Scholar 

  • Mohammadi BH, Yusuff RM, Megat Ahmad MMH, Abu Bakar MR (2009) Development of a new approach for deterministic supply chain network design. Eur J Oper Res 198(1):121–128

    Article  Google Scholar 

  • Mohebbi E (2003) Supply interruptions in a lost-sales inventory system with random lead time. Comput Oper Res 30:411–426

    Article  Google Scholar 

  • Moiseev NN (1974) Element of the optimal systems theory. Nauka, Moskow

    Google Scholar 

  • Mula J, Peidro D, Díaz-Madroñero M, Vicens E (2010) Mathematical programming models for supply chain production and transport planning. Eur J Oper Res 204(3):377–390

    Article  Google Scholar 

  • Nair A, Vidal JM (2010) Supply network topology and robustness against disruptions – an investigation using a multi-agent model. Int J Prod Res 49(5):1391–1404

    Article  Google Scholar 

  • Narendra KS (2005) Stable adaptive systems, 2nd edn. Dover, Mineola

    Google Scholar 

  • Oliveira JB, Lima RS, Montevechi JAB (2016) Perspectives and relationships in supply chain simulation: a systematic literature review. Simul Model Pract Theory 62:166–191

    Article  Google Scholar 

  • Ortega M, Lin L (2004) Control theory applications to the production-inventory problem: a review. Int J Prod Res 42:2303–2322

    Article  Google Scholar 

  • Ouyang Y, Li X (2010) The bullwhip effect in supply chain networks. Eur J Oper Res 201(3):799–810

    Article  Google Scholar 

  • Pan F, Nagi R (2013) Multi-echelon supply chain network design in agile manufacturing. Omega 41:969–983

    Article  Google Scholar 

  • Paul SK, Sarker R, Essam D (2014) Real time disruption management for a two-stage batch production-inventory system with reliability considerations. Eur J Oper Res 237:113–128

    Article  Google Scholar 

  • Peng P, Snyder LV, Lim A, Liu Z (2011) Reliable logistics networks design with facility disruptions. Transp Res B-Methodol 45(8):1190–1211

    Article  Google Scholar 

  • Perea E, Grossmann I, Ydstie E, Tahmassebi T (2000) Dynamic modeling and classical control theory for supply chain management. Comput Chem Eng 24:1143–1149

    Article  Google Scholar 

  • Petrovic D, Roy R, Petrovic R (1998) Modelling and simulation of a supply chain in an uncertain environment. Eur J Oper Res 109(2):299–309

    Article  Google Scholar 

  • Pettit TJ, Croxton KL, Fiksel J (2013) Ensuring supply chain resilience: development and implementation of an assessment tool. J Bus Logist 34(1):46–76

    Article  Google Scholar 

  • Pishvaee MS, Razmi J, Torabi SA (2012) Robust possibilistic programming for socially responsible supply chain network design: a new approach. Fuzzy Sets Syst 206:1–20

    Article  Google Scholar 

  • Pontryagin LS, Boltyanskiy VG, Gamkrelidze RV, Mishchenko EF (1964) The mathematical theory of optimal processes. Pergamon Press, Oxford

    Google Scholar 

  • Proth JM (2007) Scheduling: new trends in industrial environment. Annu Rev Control 31(1):157–166

    Article  Google Scholar 

  • Puigjaner L, Lainez JM (2008) Capturing dynamics in integrated supply chain management. Comput Chem Eng 32:2582–2605

    Article  Google Scholar 

  • Pyke D, Tang CS (2010) How to mitigate product safety risks proactively – process, challenges and opportunities. Int J Log Res Appl 13(4):243–256

    Article  Google Scholar 

  • Qi L (2013) A continuous-review inventory model with random disruptions at the primary supplier. Eur J Oper Res 225(1):59–74

    Article  Google Scholar 

  • Qi L, Shen ZJ, Snyder LV (2009) A continuous-review inventory model with disruptions at both supplier and retailer. Prod Oper Manag 18(5):516–532

    Article  Google Scholar 

  • Qi L, Shen ZJM, Snyder LV (2010) The effect of supply disruptions on supply chain design decisions. Transp Sci 44(2):274–289

    Article  Google Scholar 

  • Qiang Q, Nagurney A, Dong J (2009) Modelling of supply chain risk under disruptions with performance measurement and robustness analysis. In: Wu T, Blackhurst JV (eds) Managing supply chain risk and vulnerability: tools and methods for supply chain decision makers, 1st edn. Springer, New York, pp 91–111

    Chapter  Google Scholar 

  • Qu WW, Bookbinder JH, Iyogun P (1999) Integrated inventory-transportation system with modified periodic policy for multiple products. Eur J Oper Res 115(2):254–269

    Article  Google Scholar 

  • Rafiei M, Mohammadi M, Torabi S (2013) Reliable multi period multi product supply chain design with facility disruption. Decis Sci Lett 2(2):81–94

    Article  Google Scholar 

  • Ravindran A, Bilsel RU, Wadhwa V, Yang T (2010) Risk adjusted multi-criteria supplier selection models with applications. Int J Prod Res 48(2):405–424

    Article  Google Scholar 

  • Riddalls CE, Bennett S, Tipi NS (2000) Modelling the dynamics of supply chains. Int J Syst Sci 31(8):969–976

    Article  Google Scholar 

  • Ritchie B, Brindley C (2007) Supply chain risk management and performance: a guiding framework for future development. Int J Oper Prod Manag 27(3):303–322

    Article  Google Scholar 

  • Sadjady H, Davoudpour H (2012) Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs. Comput Oper Res 39:1345–1354

    Article  Google Scholar 

  • Sagawa JK, Nagano MS (2015) Modeling the dynamics of a multi-product manufacturing system: a real case application. Eur J Oper Res 244(2):624–636

    Article  Google Scholar 

  • Santoso T, Ahmed S, Goetschalckx G, Shapiro A (2005) A stochastic programming approach for supply chain network design under uncertainty. Eur J Oper Res 167:96–115

    Article  Google Scholar 

  • Sarimveis H, Patrinos P, Tarantilis CD, Kiranoudis CT (2008) Dynamic modeling and control of supply chain systems: a review. Comput Oper Res 35:3530–3561

    Article  Google Scholar 

  • Sawik T (2013) Integrated selection of suppliers and scheduling of customer orders in the presence of supply chain disruption risks. Int J Prod Res 51(23–24):7006–7022

    Article  Google Scholar 

  • Sawik T (2014) On the robust decision-making in a supply chain under disruption risks. Int J Prod Res 52(22):6760–6781

    Article  Google Scholar 

  • Sawik T (2015) On the fair optimization of cost and customer service level in a supply chain under disruption risks. Omega 53:58–66

    Article  Google Scholar 

  • Sawik T (2016) On the risk-averse optimization of service level in a supply chain under disruption risks. Int J Prod Res 54(1):98–113

    Article  Google Scholar 

  • Sawik T (2017) Supply chain disruption management using stochastic mixed integer programming. Springer Nature, Cham, New York

    Google Scholar 

  • Schmidt W, Simchi-Levi D (2013) Nissan Motor Company Ltd.: building operational resiliency. MIT Sloan Manag Rev:1–12

    Google Scholar 

  • Schmitt AJ, Singh M (2012) A quantitative analysis of disruption risk in a multi-echelon supply chain. Int J Prod Econ 139(1):23–32

    Article  Google Scholar 

  • Schmitt AJ, Sun SA, Snyder LV, Shen ZJM (2015) Centralization versus decentralization: risk pooling, risk diversification, and supply chain disruptions. Omega 52:201–212

    Article  Google Scholar 

  • Schoenlein M, Makuschewitz T, Wirth F, Scholz-Reiter B (2013) Measurement and optimization of robust stability of multiclass queuing networks: applications in dynamic supply chains. Eur J Oper Res 229:179–189

    Article  Google Scholar 

  • Scholz-Reiter B, Dashkovskiy S, Görges M, Naujok L (2010) Stability analysis of autonomously controlled production networks. Int J Prod Res 49(16):4857–4877

    Article  Google Scholar 

  • Schütz P, Tomasgard A, Ahmed S (2009) Supply chain design under uncertainty using sample average approximation and dual decomposition. Eur J Oper Res 199(2):409–419

    Article  Google Scholar 

  • Schwartz JD, Rivera DE (2010) A process control approach to tactical inventory management in production-inventory systems. Int J Prod Econ 125(1):111–124

    Article  Google Scholar 

  • Seirstad A, Sydsaeter K (1985) Optimal control theory with economic applications. North-Holland, Amsterdam

    Google Scholar 

  • Serel DA (2008) Inventory and pricing decisions in a single-period problem involving risky supply. Int J Prod Econ 116(1):115–128

    Article  Google Scholar 

  • Sethi SP (1978) A survey of management science applications of the deterministic maximum principle. TIMS Stud Manag Sci 9:33–68

    Google Scholar 

  • Sethi SP (1984) Applications of the maximum principle to production and inventory problems. In: Proceedings third international symposium on inventories, Budapest, 27–31 August. pp 753–756

    Google Scholar 

  • Sethi SP, Thompson GL (2000) Optimal control theory: applications to management science and economics, 2nd edn. Springer, Berlin

    Google Scholar 

  • Sethi SP, Yan H, Zhang H, Zhang Q (2002) Optimal and hierarchical controls in dynamic stochastic manufacturing systems: a survey. Manuf Serv Oper Mant 4(2):133–170

    Article  Google Scholar 

  • Shah N (2005) Process industry supply chains: advances and challenges. Comput Chem Eng 29:1225–1235

    Article  Google Scholar 

  • Shao XF, Dong M (2012) Supply disruption and reactive strategies in an assemble-to-order supply chain with time-sensitive demand. IEEE T Eng Manag 59(2):201–212

    Article  Google Scholar 

  • Sheffi Y, Rice JB (2005) A supply chain view of the resilient enterprise. MIT Sloan Manag Rev 47(1):41–48

    Google Scholar 

  • Shu J, Ma Q, Li S (2010) Integrated location and two-echelon inventory network design under uncertainty. Ann Oper Res 181:233–247

    Article  Google Scholar 

  • Siliak DD (1990) Decentralized control of complex systems. Academic Press, New York

    Google Scholar 

  • Simangunsong E, Hendry LC, Stevenson M (2012) Supply-chain uncertainty: a review and theoretical foundation for future research. Int J Prod Res 50(16):4493–4523

    Article  Google Scholar 

  • Simchi-Levi D, Wei Y (2015) Worst-case analysis of process flexibility designs. Oper Res 63:166–185

    Article  Google Scholar 

  • Simchi-Levi D, Wu SD, Zuo-Yun S (2004) Handbook of quantitative supply chain analysis. Springer, New York

    Book  Google Scholar 

  • Simchi-Levi D et al (eds) (2010) Designing and managing supply chains. McGraw-Hill, New York

    Google Scholar 

  • Simchi-Levi D, Schmidt W, Wei Y (2014) From superstorms to factory fires: managing unpredictable supply chain disruptions. Harvard Business Review, February

    Google Scholar 

  • Singh AR, Mishra PK, Jain R, Khurana MK (2012) Design of global supply chain network with operational risks. Int J Adv Manuf Tech 60(1–4):273–290

    Article  Google Scholar 

  • Skipper JB, Hanna JB (2009) Minimizing supply chain disruption risk through enhanced flexibility. Int J Phys Distrib 39(5):404–427

    Article  Google Scholar 

  • Skurihin VI, Zabrodsky VA, Kopeychenko YV (1989) Adaptive control systems in machine-building industry. Mashinostroenie, Moscow. (in Russian)

    Google Scholar 

  • Snyder LV, Daskin MS (2005) Reliability models for facility location: the expected failure cost case. Transp Sci 39:400–416

    Article  Google Scholar 

  • Snyder LV, Zümbül A, Peng P, Ying R, Schmitt AJ, Sinsoysal B (2016) OR/MS models for supply chain disruptions: a review. IIE Trans 48(2):89–109

    Article  Google Scholar 

  • Sodhi MS, Lee S (2007) An analysis of sources of risk in the consumer electronics industry. J Oper Res Soc 58(11):1430–1439

    Article  Google Scholar 

  • Sodhi MS, Son BG, Tang C (2012) Researchers’ perspectives on supply chain risk management. Prod Oper Manag 21(1):1–13

    Article  Google Scholar 

  • Sokolov B, Ivanov D, Dolgui A, Pavlov A (2016) Structural analysis of the ripple effect in the supply chain. Int J Prod Res 54(1):152–169

    Article  Google Scholar 

  • Son YJ, Venkateswaran J (2007) Hierarchical supply chain planning architecture for integrated analysis of stability and performance. Int J Simul Process Model 3(3):153–169

    Article  Google Scholar 

  • Soni U, Jain V, Kumar S (2014) Measuring supply chain resilience using a deterministic modeling approach. Comput Ind Eng 74:11–25

    Article  Google Scholar 

  • Soroor J, Tarokh MJ, Keshtgary M (2009) Preventing failure in IT-enabled systems for supply chain management. Int J Prod Res 47(23):6543–6557

    Article  Google Scholar 

  • Sourirajan K, Ozsen L, Uzsoy R (2009) A genetic algorithm for a single product network design model with lead time and safety stock considerations. Eur J Oper Res 197:599–608

    Article  Google Scholar 

  • Spiegler VLM, Naim MM, Wikner J (2012) A control engineering approach to the assessment of supply chain resilience. Int J Prod Res 50(21):6162–6187

    Article  Google Scholar 

  • Spiegler VLM, Potter AT, Naim MM, Towill DR (2016a) The value of nonlinear control theory in investigating the underlying dynamics and resilience of a grocery supply chain. Int J Prod Res 54(1):265–286

    Article  Google Scholar 

  • Spiegler VLM, Naim MM, Towill DR, Wikner J (2016b) A technique to develop simplified and linearised models of complex dynamic supply chain systems. Eur J Oper Res 251(3):888–903

    Article  Google Scholar 

  • Stadtler H, Kilger C (2008) Supply chain management and advanced planning. Springer, Berlin

    Book  Google Scholar 

  • Stecke KE, Kumar S (2009) Sources of supply chain disruptions, factors that breed vulnerability, and mitigating strategies. J Mark Channels 16(3):193–226

    Article  Google Scholar 

  • Steven AB, Dong Y, Corsi T (2014) Global sourcing and quality recalls: an empirical study of outsourcing-supplier concentration-product recalls linkages. J Oper Manag 32(5):241–253

    Article  Google Scholar 

  • Stevenson M, Spring M (2007) Flexibility from a supply chain perspective: definition and review. Int J Oper Prod Man 27(7):685–713

    Google Scholar 

  • Surana A, Kumara S, Greaves M, Raghavan UN (2005) Supply-chain networks: a complex adaptive systems perspective. Int J Prod Res 43(20):4235–4265

    Article  Google Scholar 

  • Swaminathan JM, Smith SF, Sadeh NM (1998) Modeling supply chain dynamics: a multiagent approach. Decis Sci 29(3):607–632

    Article  Google Scholar 

  • Tabak D, Kuo BC (1971) Optimal control by mathematical programming. Prentice Hall, New York

    Google Scholar 

  • Tako AA, Robinson S (2012) The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decis Support Syst 52:802–815

    Article  Google Scholar 

  • Tang O, Musa SN (2011) Identifying risk issues and research advancements in supply chain risk management. Int J Prod Econ 133(1):25–34

    Article  Google Scholar 

  • Tang SY, Gurnani H, Gupta D (2014) Managing disruptions with endogenous supply reliability. Prod Oper Manag 23(7):1198–1211

    Article  Google Scholar 

  • Tang L, Jing K, He J, Stanley HE (2016) Complex interdependent supply chain networks: cascading failure and robustness. Physica A 443:58–69

    Article  Google Scholar 

  • Teimoury E, Fathi M (2013) An integrated operations-marketing perspective for making decisions about order penetration point in multi-product supply chain: a queuing approach. Int J Prod Res 51(18):5776–5796

    Article  Google Scholar 

  • Thompson GL, Seth SP, Teng J (1984) Strong planning and forecast horizons for a model with simultaneous price and production decisions. Eur J Oper Res 16(3):378–388

    Article  Google Scholar 

  • Tomlin B (2006) On the value of mitigation and contingency strategies for managing supply chain disruption risks. Manag Sci 52:639–657

    Article  Google Scholar 

  • Torabi SA, Baghersad M, Mansouri SA (2015) Resilient supplier selection and order allocation under operational and disruption risks. Transp Res E-Log 79:22–48

    Article  Google Scholar 

  • Tsiakis P, Shah N, Pantelides CC (2001) Design of multi-echelon supply chain networks under demand uncertainty. Ind Eng Chem Res 40:3585–3604

    Article  Google Scholar 

  • Tuncel G, Alpan G (2010) Risk assessment and management for supply chain networks – a case study. Comput Ind 61(3):250–259

    Article  Google Scholar 

  • Unnikrishnan A, Figliozzi M (2011) Online freight network assignment model with transportation disruptions and recourse. Transp Res Rec 2224(1):17–25

    Article  Google Scholar 

  • Vahdani B, Zandieh M, Roshanaei V (2011) A hybrid multi-stage predictive model for supply chain network collapse recovery analysis: a practical framework for effective supply chain network continuity management. Int J Prod Res 49(7):2035–2060

    Article  Google Scholar 

  • Validi S, Bhattacharya A, Byrne PJ (2014) A case analysis of a sustainable food supply chain distribution system-a multi-objective approach. Int J Prod Econ 152:71–87

    Article  Google Scholar 

  • van Delft C, Vial JP (2004) A practical implementation of stochastic programming: an application to the evaluation of option contracts in supply chains. Automatica 40:743–756

    Article  Google Scholar 

  • Vidal C, Goetschalckx M (1997) Strategic production–distribution models: a critical review with emphasis on global supply chain models. Eur J Oper Res 98:1–18

    Article  Google Scholar 

  • Villegas FA, Smith NR (2006) Supply chain dynamics: analysis of inventory vs. order oscillations trade off. Int J Prod Res 44(6):1037–1054

    Article  Google Scholar 

  • Volkswagnis (2012) Wirtschaftswoche, 45

    Google Scholar 

  • Wadhwa S, Saxena A, Chan FTS (2008) Framework for flexibility in dynamic supply chain management. Int J Prod Res 46(6):1373–1404

    Google Scholar 

  • Wagner SM, Neshat N (2010) Assessing the vulnerability of supply chains using graph theory. Int J Prod Econ 126(1):121–129

    Article  Google Scholar 

  • Wakolbinger T, Cruz JM (2011) Supply chain disruption risk management through strategic information acquisition and sharing and risk-sharing contracts. Int J Prod Res 49(13):4063–4084

    Article  Google Scholar 

  • Wang D, Ip WH (2009) Evaluation and analysis of logistic network resilience with application to aircraft servicing. IEEE Syst J 3:166–173

    Article  Google Scholar 

  • Wang W, Rivera D, Kempf KG (2007) Model predictive control strategies for supply chain management in semiconductor industry. Int J Prod Econ 107:56–77

    Article  Google Scholar 

  • Wang X, Disney SM, Wang J (2012) Stability analysis of constrained inventory systems with transportation delay. Eur J Oper Res 223(1):86–95

    Article  Google Scholar 

  • Warburton RDH, Disney SM, Towill DR, Hodgson JPE (2004) Further insights into the stability of supply chains. Int J Prod Res 42(3):639–648

    Article  Google Scholar 

  • Wierczek A (2014) The impact of supply chain integration on the “snowball effect” in the trans-mission of disruptions: an empirical evaluation of the model. Int J Prod Econ 157(1):89–104

    Article  Google Scholar 

  • Wikner J, Towill DR, Naim MM (1991) Smoothing supply chain dynamics. Int J Prod Econ 22(3):231–248

    Article  Google Scholar 

  • Wilson MC (2007) The impact of transportation disruptions on supply chain performance. Transp Res E-Log 43:295–320

    Article  Google Scholar 

  • Wu T, Blackhurst J, O’Grady P (2007) Methodology for supply chain disruption analysis. Int J Prod Res 45(7):1665–1682

    Article  Google Scholar 

  • Xia Y, Yang MH, Golany B, Gilbert SM, Yu G (2004) Real-time disruption management in a two-stage production and inventory system. IIE Trans 36(2):111–125

    Article  Google Scholar 

  • Xiao T, Yu G (2006) Supply chain disruption management and evolutionarily stable strategies of retailers in the quantity-setting duopoly situation with homogeneous goods. Eur J Oper Res 173(2):648–668

    Article  Google Scholar 

  • Xu M, Wang X, Zhao L (2014) Predicted supply chain resilience based on structural evolution against random supply disruptions. Int J Syst Sci 1(2):105–117

    Google Scholar 

  • Xu K, Dong Y, Xia Y (2015) ‘Too little’ or ‘too late’: the timing of supply chain demand collaboration. Eur J Oper Res 241(2):370–380

    Article  Google Scholar 

  • Yang T, Fan W (2016) Information management strategies and supply chain performance under demand disruptions. Int J Prod Res 54(1):8–27

    Article  Google Scholar 

  • Yang Z, Aydin G, Babich V, Beil DR (2012) Using a dual-sourcing option in the presence of asymmetric information about supplier reliability: competition vs diversification. M&SOM-Manuf Serv Op 14(2):202–217

    Article  Google Scholar 

  • Yu H, Zeng AZ, Zhao L (2009) Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega 37(4):788–800

    Article  Google Scholar 

  • Zegordi SH, Davarzani H (2012) Developing a supply chain disruption analysis model: application of coloured petri-nets. Expert Syst Appl 39(2):2102–2111

    Article  Google Scholar 

  • Zhou W, Zhang R, Zhou Y (2013) A queuing model on supply chain with the form postponement strategy. Comput Ind Eng 66(4):643–652

    Article  Google Scholar 

  • Zobel CW, Khansa L (2014) Characterizing multi-event disaster resilience. Comput Oper Res 42:83–94

    Article  Google Scholar 

  • Zsidisin G, Ellram L, Carter J, Cavinato J (2004) An analysis of supply risk assessment techniques. Int J Phys Distrib 34(5):397–413

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Professor of Supply Chain Management, Department of Business and Economics, Berlin School of Economics and Law, Berlin, Germany

    Dmitry Ivanov

Authors
  1. Dmitry Ivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ivanov, D. (2018). OR/MS Methods for Structural Dynamics in Supply Chain Risk Management. In: Structural Dynamics and Resilience in Supply Chain Risk Management. International Series in Operations Research & Management Science, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-69305-7_5

Download citation

Publish with us

Policies and ethics

Search

Navigation