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Managing risk and disruption in production-inventory and supply chain systems: A review

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  • This paper presents a literature review on risk and disruption management in production-inventory and supply chain systems. The review is conducted on the basis of comparing various works published in this research domain, specifically the papers, which considered real-life risk factors, such as imperfect production processes, risk and disruption in production, supply, demand, and transportation, while developing models for production-inventory and supply chain systems. Emphasis is given on the assumptions and the types of problems considered in the published research. We also focus on reviewing the mathematical models and the solution approaches used in solving the models using both hypothetical and real-world problem scenarios. Finally, the literature review is summarized and future research directions are discussed.
    Mathematics Subject Classification: Primary: 90B05, 90B06; Secondary: 90C90.

    Citation:

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  • [1]

    N. E. Abboud, A discrete-time Markov production-inventory model with machine breakdowns, Computers and Industrial Engineering, 39 (2001), 95-107.doi: 10.1016/S0360-8352(00)00070-X.

    [2]

    M. H. Al-Rifai and M. D. Rossetti, An efficient heuristic optimization algorithm for a two-echelon (R, Q) inventory system, International Journal of Production Economics, 109 (2007), 195-213.doi: 10.1016/j.ijpe.2006.12.052.

    [3]

    F. B. Atoei, E. Teimory and A. B. Amiri, Designing reliable supply chain network with disruption risk, International Journal of Industrial Engineering Computations, 4 (2013), 111-126.

    [4]

    S. Bag, D. Chakraborty and A. R. Roy, A production inventory model with fuzzy random demand and with flexibility and reliability considerations, Computers and Industrial Engineering, 56 (2009), 411-416.doi: 10.1016/j.cie.2008.07.001.

    [5]

    A. Baghalian, S. Rezapour and R. Z. Farahani, Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case, European Journal of Operational Research, 227 (2013), 199-215.doi: 10.1016/j.ejor.2012.12.017.

    [6]

    J. Banks, J. Carson, B. Nelson and D. Nicol, Discrete-event system simulation, $5^{th}$ Edition, Prentice Hall, USA, 2014.

    [7]

    J. M. Betts, Calculating target inventory levels for constrained production: A fast simulation-based approximation, Computers and Operations Research, 49 (2014), 18-27.doi: 10.1016/j.cor.2014.03.014.

    [8]

    C. Blome and M. Henke, Single Versus Multiple Sourcing: A Supply Risk Management Perspective, in Supply Chain Risk (eds. G. A. Zsidisin and B. Ritchie), Springer, 124 (2009), 125-135.doi: 10.1007/978-0-387-79934-6_8.

    [9]

    C. Blome and T. Schoenherr, Supply chain risk management in financial crises-A multiple case-study approach, International Journal of Production Economics, 134 (2011), 43-57.doi: 10.1016/j.ijpe.2011.01.002.

    [10]

    J. R. Bradley, An improved method for managing catastrophic supply chain disruptions, Business Horizons, 57 (2014), 483-495.doi: 10.1016/j.bushor.2014.03.003.

    [11]

    Z. H. Che, A particle swarm optimization algorithm for solving unbalanced supply chain planning problems, Applied Soft Computing, 12 (2012), 1279-1287.doi: 10.1016/j.asoc.2011.12.006.

    [12]

    L.-M. Chen, Y. E. Liu and S.-J. S. Yang, Robust supply chain strategies for recovering from unanticipated disasters, Transportation Research Part E: Logistics and Transportation Review, 77 (2015), 198-214.doi: 10.1016/j.tre.2015.02.015.

    [13]

    T. C. E. Cheng, An economic production quantity model with flexibility and reliability considerations, European Journal of Operational Research, 39 (1989), 174-179.doi: 10.1016/0377-2217(89)90190-2.

    [14]

    S. W. Chiu, C. L. Chou and W. K. Wu, Optimizing replenishment policy in an EPQ-based inventory model with nonconforming items and breakdown, Economic Modelling, 35 (2013), 330-337.doi: 10.1016/j.econmod.2013.07.004.

    [15]

    S. W. Chiu, S. L. Wang and Y. S. P. Chiu, Determining the optimal run time for EPQ model with scrap, rework, and stochastic breakdowns, European Journal of Operational Research, 180 (2007), 664-676.doi: 10.1016/j.ejor.2006.05.005.

    [16]

    S. Chopra, G. Reinhardt and U. Mohan, The importance of decoupling recurrent and disruption risks in a supply chain, Naval Research Logistics, 54 (2007), 544-555.doi: 10.1002/nav.20228.

    [17]

    S. Chopra and M. S. Sodhi, Managing Risk To Avoid Supply-Chain Breakdown, MIT Sloan Management Review, 46 (2004), 53-61.

    [18]

    S. Chopra and M. S. Sodhi, Reducing the risk of supply chain disruptions, MIT Sloan Management Review, 55 (2014), 73-80.

    [19]

    A. Costa, G. Celano, S. Fichera and E. Trovato, A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms, Computers and Industrial Engineering, 59 (2010), 986-999.doi: 10.1016/j.cie.2010.09.011.

    [20]

    C. W. Craighead, J. Blackhurst, M. J. Rungtusanatham and R. B. Handfield, The severity of supply chain disruptions: Design characteristics and mitigation capabilities, Decision Sciences, 38 (2007), 131-156.doi: 10.1111/j.1540-5915.2007.00151.x.

    [21]

    P. Dadhich, A. Genovese, N. Kumar and A. Acquaye, Developing sustainable supply chains in the UK construction industry: A case study, International Journal of Production Economics, 164 (2015), 271-284.doi: 10.1016/j.ijpe.2014.12.012.

    [22]

    M. M. de Barros and A. Szklo, Petroleum refining flexibility and cost to address the risk of ethanol supply disruptions: The case of Brazil, Renewable Energy, 77 (2015), 20-31.doi: 10.1016/j.renene.2014.11.081.

    [23]

    L. A. Deleris and F. Erhun, Risk management in supply networks using monte-carlo simulation, Proceedings of the 2005 winter Simulation conference, (2005), 1643-1649.doi: 10.1109/WSC.2005.1574434.

    [24]

    A. Diabat, Hybrid algorithm for a vendor managed inventory system in a two-echelon supply chain, European Journal of Operational Research, 238 (2014), 114-121.doi: 10.1016/j.ejor.2014.02.061.

    [25]

    D. D. Eisenstein, Recovering Cyclic Schedules Using Dynamic Produce-Up-To Policies, Operations Research, 53 (2005), 675-688.doi: 10.1287/opre.1040.0201.

    [26]

    J. Fang, L. Zhao, J. C. Fransoo and T. Van Woensel, Sourcing strategies in supply risk management: An approximate dynamic programming approach, Computers and Operations Research, 40 (2013), 1371-1382.doi: 10.1016/j.cor.2012.08.016.

    [27]

    P. Finch, Supply chain risk management, Supply Chain Management: An International Journal, 9 (2004), 183-196.doi: 10.1108/13598540410527079.

    [28]

    T. L. Friesz, I. Lee and C. C. Lin, Competition and disruption in a dynamic urban supply chain, Transportation Research Part B: Methodological, 45 (2011), 1212-1231.doi: 10.1016/j.trb.2011.05.005.

    [29]

    G. Gallego, When is a base stock policy optimal in recovering disrupted cyclic schedules?, Naval Research Logistics, 41 (1994), 317-333.doi: 10.1002/1520-6750(199404)41:3<317::AID-NAV3220410303>3.0.CO;2-T.

    [30]

    L. C. Giunipero and R. A. Eltantawy, Securing the upstream supply chain: A risk management approach, International Journal of Physical Distribution and Logistics Management, 34 (2004), 698-713.doi: 10.1108/09600030410567478.

    [31]

    X. Gong, X. Chao and S. Zheng, Dynamic Pricing and Inventory Management with Dual Suppliers of Different Leadtimes and Disruption Risks, Production and Operations Management, 23 (2014), 2058-2074.

    [32]

    P. Guchhait, M. K. Maiti and M. Maiti, A production inventory model with fuzzy production and demand using fuzzy differential equation: An interval compared genetic algorithm approach, Engineering Applications of Artificial Intelligence, 26 (2013), 766-778.doi: 10.1016/j.engappai.2012.10.017.

    [33]

    R. K. Gupta, A. K. Bhunia and S. K. Goyal, An application of Genetic Algorithm in solving an inventory model with advance payment and interval valued inventory costs, Mathematical and Computer Modelling, 49 (2009), 893-905.doi: 10.1016/j.mcm.2008.09.015.

    [34]

    H. S. Heese, Single versus Multiple Sourcing and the Evolution of Bargaining Positions, Omega, 54 (2015), 125-133.doi: 10.1016/j.omega.2015.01.016.

    [35]

    J. Hill and M. Galbreth, A heuristic for single-warehouse multiretailer supply chains with all-unit transportation cost discounts, European Journal of Operational Research, 187 (2008), 473-482.doi: 10.1016/j.ejor.2007.03.015.

    [36]

    H. Hishamuddin, Optimal Inventory Policies for Multi-Echelon Supply Chain Systems with Disruption, Ph.D thesis, The University of New South Wales, Canberra, Australia, 2013.

    [37]

    H. Hishamuddin, R. A. Sarker and D. Essam, A disruption recovery model for a single stage production-inventory system, European Journal of Operational Research, 222 (2012), 464-473.doi: 10.1016/j.ejor.2012.05.033.

    [38]

    H. Hishamuddin, R. A. Sarker and D. Essam, A recovery model for a two-echelon serial supply chain with consideration of transportation disruption, Computers and Industrial Engineering, 64 (2013), 552-561.doi: 10.1016/j.cie.2012.11.012.

    [39]

    H. Hishamuddin, R. A. Sarker and D. Essam, A recovery mechanism for a two echelon supply chain system under supply disruption, Economic Modelling, 38 (2014), 555-563.doi: 10.1016/j.econmod.2014.02.004.

    [40]

    J. Hou, A. Z. Zeng and L. Zhao, Coordination with a backup supplier through buy-back contract under supply disruption, Transportation Research Part E: Logistics and Transportation Review, 46 (2010), 881-895.doi: 10.1016/j.tre.2010.03.004.

    [41]

    F. Hu, C. C. Lim, Z. Lu and X. Sun, Coordination in a Single-Retailer Two-Supplier Supply Chain under Random Demand and Random Supply with Disruption, Discrete Dynamics in Nature and Society, 2013 (2013), 1-12.

    [42]

    C. Huang, G. Yu, S. Wang and X. Wang, Disruption management for supply chain coordination with exponential demand function, Acta Mathematica Scientia, 26 (2006), 655-669.doi: 10.1016/S0252-9602(06)60092-1.

    [43]

    M. Y. Jaber, M. Bonney and I. Moualek, An economic order quantity model for an imperfect production process with entropy cost, International Journal of Production Economics, 118 (2009), 26-33.doi: 10.1016/j.ijpe.2008.08.007.

    [44]

    N. Jawahar and A. N. Balaji, A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge, European Journal of Operational Research, 194 (2009), 496-537.doi: 10.1016/j.ejor.2007.12.005.

    [45]

    E. J. L. Jr and S. Taskin, An insurance risk management framework for disaster relief and supply chain disruption inventory planning, Journal of Operational Research Society, 59 (2008), 674-684.

    [46]

    Ö. Kabak and F. Ülengin, Possibilistic linear-programming approach for supply chain networking decisions, European Journal of Operational Research, 209 (2011), 253-264.doi: 10.1016/j.ejor.2010.09.025.

    [47]

    B. B. Keskin, S. H. Melouk and I. L. Meyer, A simulation-optimization approach for integrated sourcing and inventory decisions, Computers and Operations Research, 37 (2010), 1648-1661.doi: 10.1016/j.cor.2009.12.012.

    [48]

    P. R. Kleindorfer and G. H. Saad, Managing disruption risks in supply chains, Production and Operations Management, 14 (2005), 53-68.doi: 10.1111/j.1937-5956.2005.tb00009.x.

    [49]

    R. Kuik and M. Salomon, Multi-level lot-sizing problem: Evaluation of a simulated-annealing heuristic, European Journal of Operational Research, 45 (1990), 25-37.doi: 10.1016/0377-2217(90)90153-3.

    [50]

    O. Lavastre, A. Gunasekaran and A. Spalanzani, Supply chain risk management in French companies, Decision Support Systems, 52 (2012), 828-838.doi: 10.1016/j.dss.2011.11.017.

    [51]

    K.-N. F. Leung, A generalized geometric-programming solution to "An economic production quantity model with flexibility and reliability considerations," European Journal of Operational Research, 176 (2007), 240-251.doi: 10.1016/j.ejor.2005.06.049.

    [52]

    X. Li and Y. Chen, Impacts of supply disruptions and customer differentiation on a partial-backordering inventory system, Simulation Modelling Practice and Theory, 18 (2010), 547-557.doi: 10.1016/j.simpat.2009.12.010.

    [53]

    J. Li, S. Wang and T. C. E. Cheng, Competition and cooperation in a single-retailer two-supplier supply chain with supply disruption, International Journal of Production Economics, 124 (2010), 137-150.doi: 10.1016/j.ijpe.2009.10.017.

    [54]

    Z. Li, S. H. Xu and J. Hayya, A Periodic-Review Inventory System With Supply Interruptions, Probability in the Engineering and Informational Sciences, 18 (2004), 33-53.doi: 10.1017/S0269964804181035.

    [55]

    C. J. Liao, C. C. Shyu and C. T. Tseng, A least flexibility first heuristic to coordinate setups in a two- or three-stage supply chain, International Journal of Production Economics, 117 (2009), 127-135.doi: 10.1016/j.ijpe.2008.10.002.

    [56]

    C. J. Liao, Y. L. Tsai and C. W. Chao, An ant colony optimization algorithm for setup coordination in a two-stage production system, Applied Soft Computing, 11 (2011), 4521-4529.doi: 10.1016/j.asoc.2011.08.014.

    [57]

    G. C. Lin and D. C. Gong, On a production-inventory system of deteriorating items subject to random machine breakdowns with a fixed repair time, Mathematical and Computer Modelling, 43 (2006), 920-932.doi: 10.1016/j.mcm.2005.12.013.

    [58]

    S. C. Liu and J. R. Chen, A heuristic method for the inventory routing and pricing problem in a supply chain, Expert Systems with Applications, 38 (2011), 1447-1456.doi: 10.1016/j.eswa.2010.07.051.

    [59]

    F. Longo and G. Mirabelli, An advanced supply chain management tool based on modeling and simulation, Computers and Industrial Engineering, 54 (2008), 570-588.doi: 10.1016/j.cie.2007.09.008.

    [60]

    M. Lu, S. Huang and Z. J. M. Shen, Product substitution and dual sourcing under random supply failures, Transportation Research Part B: Methodological, 45 (2011), 1251-1265.doi: 10.1016/j.trb.2010.09.005.

    [61]

    I. Manuj and J. T. Mentzer, Global supply chain risk management, Journal of Business Logistics, 29 (2008), 133-155.doi: 10.1002/j.2158-1592.2008.tb00072.x.

    [62]

    M. A. A. Masud, S. K. Paul and A. Azeem, Optimisation of a production inventory model with reliability considerations, International Journal of Logistics Systems and Management, 17 (2014), 22-45.doi: 10.1504/IJLSM.2014.057979.

    [63]

    M. Mobini, T. Sowlati and S. Sokhansanj, A simulation model for the design and analysis of wood pellet supply chains, Applied Energy, 111 (2013), 1239-1249.doi: 10.1016/j.apenergy.2013.06.026.

    [64]

    E. Mohebbi, A replenishment model for the supply-uncertainty problem, International Journal of Production Economics, 87 (2004), 25-37.doi: 10.1016/S0925-5273(03)00098-7.

    [65]

    E. Mohebbi and D. Hao, An inventory model with non-resuming randomly interruptible lead time, International Journal of Production Economics, 114 (2008), 755-768.doi: 10.1016/j.ijpe.2008.03.009.

    [66]

    K. Moinzadeh and P. Aggarwal, Analysis of a Production/Inventory System Subject to Random Disruptions, Management Science, 43 (1997), 1577-1588.doi: 10.1287/mnsc.43.11.1577.

    [67]

    A. R. Nia, M. H. Far and S. T. A. Niaki, A fuzzy vendor managed inventory of multi-item economic order quantity model under shortage: An ant colony optimization algorithm, International Journal of Production Economics, 155 (2014), 259-271.

    [68]

    A. Oke and M. Gopalakrishnan, Managing disruptions in supply chains: A case study of a retail supply chain, International Journal of Production Economics, 118 (2009), 168-174.doi: 10.1016/j.ijpe.2008.08.045.

    [69]

    S. Özekici and M. Parlar, Inventory models with unreliable suppliers in a random environment, Annals of Operations Research, 91 (1999), 123-136.doi: 10.1023/A:1018937420735.

    [70]

    B. Pal, S. S. Sana and K. Chaudhuri, Maximising profits for an EPQ model with unreliable machine and rework of random defective items, International Journal of System Science, 44 (2013), 582-594.doi: 10.1080/00207721.2011.617896.

    [71]

    B. Pal, S. S. Sana and K. Chaudhuri, Joint pricing and ordering policy for two echelon imperfect production inventory model with two cycles, International Journal of Production Economics, 155 (2013), 229-238.doi: 10.1016/j.ijpe.2013.11.027.

    [72]

    B. Pal, S. S. Sana and K. Chaudhuri, A multi-echelon supply chain model for reworkable items in multiple-markets with supply disruption, Economic Modelling, 29 (2012), 1891-1898.doi: 10.1016/j.econmod.2012.06.005.

    [73]

    B. Pal, S. S. Sana and K. Chaudhuri, A multi-echelon production-inventory system with supply disruption, Journal of Manufacturing Systems, 33 (2014), 262-276.doi: 10.1016/j.jmsy.2013.12.010.

    [74]

    D. Panda and M. Maiti, Multi-item inventory models with price dependent demand under flexibility and reliability consideration and imprecise space constraint: A geometric programming approach, Mathematical and Computer Modelling, 49 (2009), 1733-1749.doi: 10.1016/j.mcm.2008.10.019.

    [75]

    M. Parlar and D. Berkin, Future supply uncertainty in EOQ models, Naval Research Logistics, 38 (1991), 107-121.doi: 10.1002/1520-6750(199102)38:1<107::AID-NAV3220380110>3.0.CO;2-4.

    [76]

    M. Parlar and D. Perry, Inventory models of future supply uncertainty with single and multiple suppliers, Naval Research Logistics, 43 (1996), 191-210.

    [77]

    S. H. R. Pasandideh, S. T. A. Niaki and A. R. Nia, A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model, Expert Systems with Applications, 38 (2011), 2708-2716.

    [78]

    S. K. Paul, A. Azeem, R. Sarker and D. Essam, Development of a production inventory model with uncertainty and reliability considerations, Optimization and Engineering, 15 (2014), 697-720.doi: 10.1007/s11081-013-9218-6.

    [79]

    S. K. Paul, R. Sarker and D. Essam, Managing real-time demand fluctuation under a supplier-retailer coordinated system, International Journal of Production Economics, 158 (2014), 231-243.doi: 10.1016/j.ijpe.2014.08.007.

    [80]

    S. K. Paul, R. Sarker and D. Essam, A disruption recovery model in a production-inventory system with demand uncertainty and process reliability, Leture Notes in Computer Science, 8104 (2013), 511-522.doi: 10.1007/978-3-642-40925-7_47.

    [81]

    S. K. Paul, R. Sarker and D. Essam, Real time disruption management for a two-stage batch production-inventory system with reliability considerations, European Journal of Operational Research, 237 (2014), 113-128.doi: 10.1016/j.ejor.2014.02.005.

    [82]

    S. K. Paul, R. Sarker and D. Essam, Managing disruption in an imperfect production-inventory system, Computers and Industrial Engineering, 84 (2015), 101-112.doi: 10.1016/j.cie.2014.09.013.

    [83]

    S. K. Paul, R. Sarker and D. Essam, A disruption recovery plan in a three-stage production-inventory system, Computers and Operations Research, 57 (2015), 60-72.doi: 10.1016/j.cor.2014.12.003.

    [84]

    S. K. Paul, R. Sarker and D. Essam, Managing supply disruption in a three-tier supply chain with multiple suppliers and retailers, in 2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Malaysia, 2014, 194-198.doi: 10.1109/IEEM.2014.7058627.

    [85]

    S. Perron, P. Hansen, S. Le Digabel and N. Mladenovic, Exact and heuristic solutions of the global supply chain problem with transfer pricing, European Journal of Operational Research, 202 (2010), 864-879.doi: 10.1016/j.ejor.2009.06.018.

    [86]

    F. Persson, SCOR template-A simulation based dynamic supply chain analysis tool, International Journal of Production Economics, 131 (2011), 288-294.doi: 10.1016/j.ijpe.2010.09.029.

    [87]

    H. Pierreval, R. Bruniaux and C. Caux, A continuous simulation approach for supply chains in the automotive industry, Simulation Modelling Practice and Theory, 15 (2007), 185-198.doi: 10.1016/j.simpat.2006.09.019.

    [88]

    L. Qi, A continuous-review inventory model with random disruptions at the primary supplier, European Journal of Operational Research, 225 (2013), 59-74.doi: 10.1016/j.ejor.2012.09.035.

    [89]

    X. Qi, J. F. Bard and G. Yu, Supply chain coordination with demand disruptions, Omega, 32 (2004), 301-312.doi: 10.1016/j.omega.2003.12.002.

    [90]

    L. Qi, Z. J. M. Shen and L. V. Snyder, The effect of supply disruptions on supply chain design decisions, Transportation Science, 44 (2010), 274-289.doi: 10.1287/trsc.1100.0320.

    [91]

    U. Ramanathan, Performance of supply chain collaboration-A simulation study, Expert Systems with Applications, 41 (2014), 210-220.doi: 10.1016/j.eswa.2013.07.022.

    [92]

    A. M. Ross, Y. Rong and L. V. Snyder, Supply disruptions with time-dependent parameters, Computers and Operations Research, 35 (2008), 3504-3529.doi: 10.1016/j.cor.2007.01.018.

    [93]

    M. S. Sajadieh and A. Thorstenson, Comparing sourcing strategies in two-echelon supply chains, Computers and Operations Research, 45 (2014), 108-115.doi: 10.1016/j.cor.2013.12.006.

    [94]

    N. Salehi Sadghiani, S. a. Torabi and N. Sahebjamnia, Retail supply chain network design under operational and disruption risks, Transportation Research Part E: Logistics and Transportation Review, 75 (2015), 95-114.doi: 10.1016/j.tre.2014.12.015.

    [95]

    S. S. Sana, A production-inventory model in an imperfect production process, European Journal of Operational Research, 200 (2010), 451-464.doi: 10.1016/j.ejor.2009.01.041.

    [96]

    S. S. Sana, A production-inventory model of imperfect quality products in a three-layer supply chain, Decision Support Systems, 50 (2011), 539-547.doi: 10.1016/j.dss.2010.11.012.

    [97]

    B. Sarkar and I. Moon, An EPQ model with inflation in an imperfect production system, Applied Mathematics and Computation, 217 (2011), 6159-6167.doi: 10.1016/j.amc.2010.12.098.

    [98]

    T. Sawik, Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing, Computers and Operations Research, 51 (2014), 11-20.doi: 10.1016/j.cor.2014.04.006.

    [99]

    A. J. Schmitt and M. Singh, Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation, in Proceedings of the 2009 Winter Simulation conference, 2009, 1237-1248.doi: 10.1109/WSC.2009.5429561.

    [100]

    A. J. Schmitt and M. Singh, A quantitative analysis of disruption risk in a multi-echelon supply chain, International Journal of Production Economics, 139 (2012), 22-32.

    [101]

    A. J. Schmitt, L. V. Snyder and Z. J. M. Shen, Inventory systems with stochastic demand and supply: Properties and approximations, European Journal of Operational Research, 206 (2010), 313-328.doi: 10.1016/j.ejor.2010.02.029.

    [102]

    A. J. Schmitt and L. V. Snyder, Infinite-horizon models for inventory control under yield uncertainty and disruptions, Computers and Operations Research, 39 (2012), 850-862.doi: 10.1016/j.cor.2010.08.004.

    [103]

    D. A. Serel, Production and pricing policies in dual sourcing supply chains, Transportation Research Part E: Logistics and Transportation Review, 76 (2015), 1-12.doi: 10.1016/j.tre.2015.01.007.

    [104]

    X.-F. Shao and M. Dong, Supply disruption and reactive strategies in an assemble-to-order supply chain with time-sensitive demand, IEEE Transactions on Engineering Management, 59 (2012), 201-212.doi: 10.1109/TEM.2010.2066280.

    [105]

    L. Silbermayr and S. Minner, A multiple sourcing inventory model under disruption risk, International Journal of Production Economics, 149 (2014), 37-46.doi: 10.1016/j.ijpe.2013.03.025.

    [106]

    C. A. Silva, J. M. C. Sousa, T. A. Runkler and J. M. G. Sáda Costa, Distributed supply chain management using ant colony optimization, European Journal of Operational Research, 199 (2009), 349-358.doi: 10.1016/j.ejor.2008.11.021.

    [107]

    J. B. Skipper and J. B. Hanna, Minimizing supply chain disruption risk through enhanced flexibility, International Journal of Physical Distribution and Logistics Management, 39 (2009), 404-427.

    [108]

    L. V. Snyder, A tight approximation for an EOQ model with supply disruptions, International Journal of Production Economics, 155 (2014), 91-108.doi: 10.1016/j.ijpe.2014.01.025.

    [109]

    F. Talbot and J. Patterson, Optimal methods for scheduling project under resource constraints, Project Management Quarterly, 1979.

    [110]

    A. A. Taleizadeh, L. E. Cárdenas-Barrón and B. Mohammadi, A deterministic multi product single machine EPQ model with backordering, scraped products, rework and interruption in manufacturing process, International Journal of Production Economics, 150 (2014), 9-27.doi: 10.1016/j.ijpe.2013.11.023.

    [111]

    C. Tang, Robust strategies for mitigating supply chain disruptions, International Journal of Logistics Research and Applications, 9 (2006), 33-45.doi: 10.1080/13675560500405584.

    [112]

    O. Tang, Simulated annealing in lot sizing problems, International Journal of Production Economics, 88 (2004), 173-181.doi: 10.1016/j.ijpe.2003.11.006.

    [113]

    L. C. Tang and L. H. Lee, A simple recovery strategy for economic lot scheduling problem: A two-product case, International Journal of Production Economics, 98 (2005), 97-107.doi: 10.1016/j.ijpe.2004.10.003.

    [114]

    B. Tomlin, On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks, Management Science, 52 (2006), 639-657.doi: 10.1287/mnsc.1060.0515.

    [115]

    P. K. Tripathy, W. M. Wee and P. R. Majhi, An EOQ model with process reliability considerations, Journal of Operational Research Society, 54 (2003), 549-554.doi: 10.1057/palgrave.jors.2601540.

    [116]

    P. K. Tripathy and M. Pattnaik, Optimal inventory policy with reliability consideration and instantaneous receipt under imperfect production process, International Journal of Management Science and Engineering Management, 6 (2011), 413-420.

    [117]

    G. Tuncel and G. Alpan, Risk assessment and management for supply chain networks: A case study, Computers in Industry, 61 (2010), 250-259.doi: 10.1016/j.compind.2009.09.008.

    [118]

    A. Unnikrishnan and M. Figliozzi, Online Freight Network Assignment Model with Transportation Disruptions and Recourse, Transportation Research Record: Journal of the Transportation Research Board, 2224 (2011), 17-25.doi: 10.3141/2224-03.

    [119]

    H. J. Weiss and E. C. Rosenthal, Optimal ordering policies when anticipating a disruption in supply or demand, European Journal of Operational Research, 59 (1992), 370-382.doi: 10.1016/0377-2217(92)90194-E.

    [120]

    G. A. Widyadana and H. M. Wee, Optimal deteriorating items production inventory models with random machine breakdown and stochastic repair time, Applied Mathematical Modelling, 35 (2011), 3495-3508.doi: 10.1016/j.apm.2011.01.006.

    [121]

    A. Wieland and C. M. Wallenburg, Dealing with supply chain risks: Linking risk management practices and strategies to performance, International Journal of Physical Distribution and Logistics Management, 42 (2012), 887-905.

    [122]

    M. C. Wilson, The impact of transportation disruptions on supply chain performance, Transportation Research Part E: Logistics and Transportation Review, 43 (2007), 295-320.doi: 10.1016/j.tre.2005.09.008.

    [123]

    T. Wu, J. Blackhurst and P. O'grady, Methodology for supply chain disruption analysis, International Journal of Production Research, 45 (2007), 1665-1682.doi: 10.1080/00207540500362138.

    [124]

    T. Wu, S. Huang, J. Blackhurst, X. Zhang and S. Wang, Supply chain risk management: An agent-based simulation to study the impact of retail stockouts, IEEE Transactions on Engineering Management, 60 (2013), 676-686.doi: 10.1109/TEM.2012.2190986.

    [125]

    D. Wu and D. L. Olson, Supply chain risk, simulation, and vendor selection, International Journal of Production Economics, 114 (2008), 646-655.doi: 10.1016/j.ijpe.2008.02.013.

    [126]

    A. Xanthopoulos, D. Vlachos and E. Iakovou, Optimal newsvendor policies for dual-sourcing supply chains: A disruption risk management framework, Computers and Operations Research, 39 (2012), 350-357.doi: 10.1016/j.cor.2011.04.010.

    [127]

    Y. Xia, M. H. Yang, B. Golany, S. M. Gilbert and G. Yu, Real-time disruption management in a two-stage production and inventory system, IIE Transactions, 36 (2004), 111-125.doi: 10.1080/07408170490245379.

    [128]

    T. Xiao, X. Qi and G. Yu, Coordination of supply chain after demand disruptions when retailers compete, International Journal of Production Economics, 109 (2007), 162-179.doi: 10.1016/j.ijpe.2006.11.013.

    [129]

    T. Xiao and G. Yu, Supply chain disruption management and evolutionarily stable strategies of retailers in the quantity-setting duopoly situation with homogeneous goods, European Journal of Operational Research, 173 (2006), 648-668.doi: 10.1016/j.ejor.2005.02.076.

    [130]

    T. Xiao, G. Yu, Z. Sheng and Y. Xia, Coordination of a supply chain with one-manufacturer and two-retailers under demand, Annals of Operations Research, 135 (2005), 87-109.doi: 10.1007/s10479-005-6236-6.

    [131]

    X. Yan, M. Zhang, K. Liu and Y. Wang, Optimal ordering policies and sourcing strategies with supply disruption, Journal of Industrial and Management Optimization, 10 (2014), 1147-1168.doi: 10.3934/jimo.2014.10.1147.

    [132]

    Z. Yang, G. Aydin, V. Babich and D. R. Beil, Supply disruptions, asymmetric information, and a backup production option, Management Science, 55 (2009), 192-209.

    [133]

    M. F. Yang and Y. Lin, Applying the linear particle swarm optimization to a serial multi-echelon inventory model, Expert Systems with Applications, 37 (2010), 2599-2608.doi: 10.1016/j.eswa.2009.08.021.

    [134]

    J. Yang, X. Qi and G. Yu, Disruption management in production planning, Naval Research Logistics, 52 (2005), 420-442.doi: 10.1002/nav.20087.

    [135]

    G. Yu and X. Qi, Disruption Management, World Scientific, Singapore, 2004.doi: 10.1142/9789812561701.

    [136]

    H. Yu, A. Z. Zeng and L. Zhao, Single or dual sourcing: Decision-making in the presence of supply chain disruption risks, Omega, 37 (2009), 788-800.doi: 10.1016/j.omega.2008.05.006.

    [137]

    S. H. Zegordi and H. Davarzani, Developing a supply chain disruption analysis model: Application of colored Petri-nets, Expert Systems with Applications, 39 (2012), 2102-2111.doi: 10.1016/j.eswa.2011.07.137.

    [138]

    F. Zeynep Sargut and L. Qi, Analysis of a two-party supply chain with random disruptions, Operations Research Letter, 40 (2012), 114-122.doi: 10.1016/j.orl.2011.11.006.

    [139]

    Z. Zhang and M. A. Figliozzi, A survey of China's logistics industry and the impacts of transport delays on importers and exporters, Transport Reviews, 30 (2009), 179-194.doi: 10.1080/01441640902843232.

    [140]

    D. Zhang, Z. Sheng, J. Du and S. Jin, A study of emergency management of supply chain under supply disruption, Neural Computing and Applications, 24 (2013), 13-20.doi: 10.1007/s00521-013-1511-y.

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