July  2016, 12(3): 1009-1029. doi: 10.3934/jimo.2016.12.1009

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

1. 

School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia, Australia, Australia

Received  October 2014 Revised  May 2015 Published  September 2015

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.
Citation: Sanjoy Kumar Paul, Ruhul Sarker, Daryl Essam. Managing risk and disruption in production-inventory and supply chain systems: A review. Journal of Industrial & Management Optimization, 2016, 12 (3) : 1009-1029. doi: 10.3934/jimo.2016.12.1009
References:
[1]

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

[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. doi: 10.1016/j.ijpe.2006.12.052. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/j.cie.2008.07.001. Google Scholar

[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. doi: 10.1016/j.ejor.2012.12.017. Google Scholar

[6]

J. Banks, J. Carson, B. Nelson and D. Nicol, Discrete-event system simulation,, $5^{th}$ Edition, (2014). Google Scholar

[7]

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

[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), 124 (2009), 125. doi: 10.1007/978-0-387-79934-6_8. Google Scholar

[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. doi: 10.1016/j.ijpe.2011.01.002. Google Scholar

[10]

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

[11]

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

[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. doi: 10.1016/j.tre.2015.02.015. Google Scholar

[13]

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

[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. doi: 10.1016/j.econmod.2013.07.004. Google Scholar

[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. doi: 10.1016/j.ejor.2006.05.005. Google Scholar

[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. doi: 10.1002/nav.20228. Google Scholar

[17]

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

[18]

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

[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. doi: 10.1016/j.cie.2010.09.011. Google Scholar

[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. doi: 10.1111/j.1540-5915.2007.00151.x. Google Scholar

[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. doi: 10.1016/j.ijpe.2014.12.012. Google Scholar

[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. doi: 10.1016/j.renene.2014.11.081. Google Scholar

[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. doi: 10.1109/WSC.2005.1574434. Google Scholar

[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. doi: 10.1016/j.ejor.2014.02.061. Google Scholar

[25]

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

[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. doi: 10.1016/j.cor.2012.08.016. Google Scholar

[27]

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

[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. doi: 10.1016/j.trb.2011.05.005. Google Scholar

[29]

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

[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. doi: 10.1108/09600030410567478. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/j.engappai.2012.10.017. Google Scholar

[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. doi: 10.1016/j.mcm.2008.09.015. Google Scholar

[34]

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

[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. doi: 10.1016/j.ejor.2007.03.015. Google Scholar

[36]

H. Hishamuddin, Optimal Inventory Policies for Multi-Echelon Supply Chain Systems with Disruption,, Ph.D thesis, (2013). Google Scholar

[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. doi: 10.1016/j.ejor.2012.05.033. Google Scholar

[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. doi: 10.1016/j.cie.2012.11.012. Google Scholar

[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. doi: 10.1016/j.econmod.2014.02.004. Google Scholar

[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. doi: 10.1016/j.tre.2010.03.004. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/S0252-9602(06)60092-1. Google Scholar

[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. doi: 10.1016/j.ijpe.2008.08.007. Google Scholar

[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. doi: 10.1016/j.ejor.2007.12.005. Google Scholar

[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. Google Scholar

[46]

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

[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. doi: 10.1016/j.cor.2009.12.012. Google Scholar

[48]

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

[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. doi: 10.1016/0377-2217(90)90153-3. Google Scholar

[50]

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

[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. doi: 10.1016/j.ejor.2005.06.049. Google Scholar

[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. doi: 10.1016/j.simpat.2009.12.010. Google Scholar

[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. doi: 10.1016/j.ijpe.2009.10.017. Google Scholar

[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. doi: 10.1017/S0269964804181035. Google Scholar

[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. doi: 10.1016/j.ijpe.2008.10.002. Google Scholar

[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. doi: 10.1016/j.asoc.2011.08.014. Google Scholar

[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. doi: 10.1016/j.mcm.2005.12.013. Google Scholar

[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. doi: 10.1016/j.eswa.2010.07.051. Google Scholar

[59]

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

[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. doi: 10.1016/j.trb.2010.09.005. Google Scholar

[61]

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

[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. doi: 10.1504/IJLSM.2014.057979. Google Scholar

[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. doi: 10.1016/j.apenergy.2013.06.026. Google Scholar

[64]

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

[65]

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

[66]

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

[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. Google Scholar

[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. doi: 10.1016/j.ijpe.2008.08.045. Google Scholar

[69]

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

[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. doi: 10.1080/00207721.2011.617896. Google Scholar

[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. doi: 10.1016/j.ijpe.2013.11.027. Google Scholar

[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. doi: 10.1016/j.econmod.2012.06.005. Google Scholar

[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. doi: 10.1016/j.jmsy.2013.12.010. Google Scholar

[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. doi: 10.1016/j.mcm.2008.10.019. Google Scholar

[75]

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

[76]

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

[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. Google Scholar

[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. doi: 10.1007/s11081-013-9218-6. Google Scholar

[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. doi: 10.1016/j.ijpe.2014.08.007. Google Scholar

[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. doi: 10.1007/978-3-642-40925-7_47. Google Scholar

[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. doi: 10.1016/j.ejor.2014.02.005. Google Scholar

[82]

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

[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. doi: 10.1016/j.cor.2014.12.003. Google Scholar

[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), (2014), 194. doi: 10.1109/IEEM.2014.7058627. Google Scholar

[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. doi: 10.1016/j.ejor.2009.06.018. Google Scholar

[86]

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

[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. doi: 10.1016/j.simpat.2006.09.019. Google Scholar

[88]

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

[89]

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

[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. doi: 10.1287/trsc.1100.0320. Google Scholar

[91]

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

[92]

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

[93]

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

[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. doi: 10.1016/j.tre.2014.12.015. Google Scholar

[95]

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

[96]

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

[97]

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

[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. doi: 10.1016/j.cor.2014.04.006. Google Scholar

[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. doi: 10.1109/WSC.2009.5429561. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/j.ejor.2010.02.029. Google Scholar

[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. doi: 10.1016/j.cor.2010.08.004. Google Scholar

[103]

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

[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. doi: 10.1109/TEM.2010.2066280. Google Scholar

[105]

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

[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. doi: 10.1016/j.ejor.2008.11.021. Google Scholar

[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. Google Scholar

[108]

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

[109]

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

[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. doi: 10.1016/j.ijpe.2013.11.023. Google Scholar

[111]

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

[112]

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

[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. doi: 10.1016/j.ijpe.2004.10.003. Google Scholar

[114]

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

[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. doi: 10.1057/palgrave.jors.2601540. Google Scholar

[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. Google Scholar

[117]

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

[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. doi: 10.3141/2224-03. Google Scholar

[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. doi: 10.1016/0377-2217(92)90194-E. Google Scholar

[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. doi: 10.1016/j.apm.2011.01.006. Google Scholar

[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. Google Scholar

[122]

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

[123]

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

[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. doi: 10.1109/TEM.2012.2190986. Google Scholar

[125]

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

[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. doi: 10.1016/j.cor.2011.04.010. Google Scholar

[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. doi: 10.1080/07408170490245379. Google Scholar

[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. doi: 10.1016/j.ijpe.2006.11.013. Google Scholar

[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. doi: 10.1016/j.ejor.2005.02.076. Google Scholar

[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. doi: 10.1007/s10479-005-6236-6. Google Scholar

[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. doi: 10.3934/jimo.2014.10.1147. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/j.eswa.2009.08.021. Google Scholar

[134]

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

[135]

G. Yu and X. Qi, Disruption Management,, World Scientific, (2004). doi: 10.1142/9789812561701. Google Scholar

[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. doi: 10.1016/j.omega.2008.05.006. Google Scholar

[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. doi: 10.1016/j.eswa.2011.07.137. Google Scholar

[138]

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

[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. doi: 10.1080/01441640902843232. Google Scholar

[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. doi: 10.1007/s00521-013-1511-y. Google Scholar

show all references

References:
[1]

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

[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. doi: 10.1016/j.ijpe.2006.12.052. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/j.cie.2008.07.001. Google Scholar

[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. doi: 10.1016/j.ejor.2012.12.017. Google Scholar

[6]

J. Banks, J. Carson, B. Nelson and D. Nicol, Discrete-event system simulation,, $5^{th}$ Edition, (2014). Google Scholar

[7]

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

[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), 124 (2009), 125. doi: 10.1007/978-0-387-79934-6_8. Google Scholar

[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. doi: 10.1016/j.ijpe.2011.01.002. Google Scholar

[10]

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

[11]

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

[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. doi: 10.1016/j.tre.2015.02.015. Google Scholar

[13]

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

[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. doi: 10.1016/j.econmod.2013.07.004. Google Scholar

[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. doi: 10.1016/j.ejor.2006.05.005. Google Scholar

[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. doi: 10.1002/nav.20228. Google Scholar

[17]

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

[18]

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

[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. doi: 10.1016/j.cie.2010.09.011. Google Scholar

[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. doi: 10.1111/j.1540-5915.2007.00151.x. Google Scholar

[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. doi: 10.1016/j.ijpe.2014.12.012. Google Scholar

[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. doi: 10.1016/j.renene.2014.11.081. Google Scholar

[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. doi: 10.1109/WSC.2005.1574434. Google Scholar

[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. doi: 10.1016/j.ejor.2014.02.061. Google Scholar

[25]

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

[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. doi: 10.1016/j.cor.2012.08.016. Google Scholar

[27]

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

[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. doi: 10.1016/j.trb.2011.05.005. Google Scholar

[29]

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

[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. doi: 10.1108/09600030410567478. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/j.engappai.2012.10.017. Google Scholar

[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. doi: 10.1016/j.mcm.2008.09.015. Google Scholar

[34]

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

[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. doi: 10.1016/j.ejor.2007.03.015. Google Scholar

[36]

H. Hishamuddin, Optimal Inventory Policies for Multi-Echelon Supply Chain Systems with Disruption,, Ph.D thesis, (2013). Google Scholar

[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. doi: 10.1016/j.ejor.2012.05.033. Google Scholar

[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. doi: 10.1016/j.cie.2012.11.012. Google Scholar

[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. doi: 10.1016/j.econmod.2014.02.004. Google Scholar

[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. doi: 10.1016/j.tre.2010.03.004. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/S0252-9602(06)60092-1. Google Scholar

[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. doi: 10.1016/j.ijpe.2008.08.007. Google Scholar

[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. doi: 10.1016/j.ejor.2007.12.005. Google Scholar

[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. Google Scholar

[46]

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

[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. doi: 10.1016/j.cor.2009.12.012. Google Scholar

[48]

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

[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. doi: 10.1016/0377-2217(90)90153-3. Google Scholar

[50]

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

[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. doi: 10.1016/j.ejor.2005.06.049. Google Scholar

[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. doi: 10.1016/j.simpat.2009.12.010. Google Scholar

[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. doi: 10.1016/j.ijpe.2009.10.017. Google Scholar

[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. doi: 10.1017/S0269964804181035. Google Scholar

[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. doi: 10.1016/j.ijpe.2008.10.002. Google Scholar

[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. doi: 10.1016/j.asoc.2011.08.014. Google Scholar

[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. doi: 10.1016/j.mcm.2005.12.013. Google Scholar

[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. doi: 10.1016/j.eswa.2010.07.051. Google Scholar

[59]

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

[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. doi: 10.1016/j.trb.2010.09.005. Google Scholar

[61]

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

[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. doi: 10.1504/IJLSM.2014.057979. Google Scholar

[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. doi: 10.1016/j.apenergy.2013.06.026. Google Scholar

[64]

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

[65]

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

[66]

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

[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. Google Scholar

[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. doi: 10.1016/j.ijpe.2008.08.045. Google Scholar

[69]

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

[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. doi: 10.1080/00207721.2011.617896. Google Scholar

[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. doi: 10.1016/j.ijpe.2013.11.027. Google Scholar

[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. doi: 10.1016/j.econmod.2012.06.005. Google Scholar

[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. doi: 10.1016/j.jmsy.2013.12.010. Google Scholar

[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. doi: 10.1016/j.mcm.2008.10.019. Google Scholar

[75]

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

[76]

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

[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. Google Scholar

[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. doi: 10.1007/s11081-013-9218-6. Google Scholar

[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. doi: 10.1016/j.ijpe.2014.08.007. Google Scholar

[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. doi: 10.1007/978-3-642-40925-7_47. Google Scholar

[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. doi: 10.1016/j.ejor.2014.02.005. Google Scholar

[82]

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

[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. doi: 10.1016/j.cor.2014.12.003. Google Scholar

[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), (2014), 194. doi: 10.1109/IEEM.2014.7058627. Google Scholar

[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. doi: 10.1016/j.ejor.2009.06.018. Google Scholar

[86]

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

[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. doi: 10.1016/j.simpat.2006.09.019. Google Scholar

[88]

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

[89]

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

[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. doi: 10.1287/trsc.1100.0320. Google Scholar

[91]

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

[92]

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

[93]

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

[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. doi: 10.1016/j.tre.2014.12.015. Google Scholar

[95]

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

[96]

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

[97]

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

[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. doi: 10.1016/j.cor.2014.04.006. Google Scholar

[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. doi: 10.1109/WSC.2009.5429561. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/j.ejor.2010.02.029. Google Scholar

[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. doi: 10.1016/j.cor.2010.08.004. Google Scholar

[103]

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

[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. doi: 10.1109/TEM.2010.2066280. Google Scholar

[105]

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

[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. doi: 10.1016/j.ejor.2008.11.021. Google Scholar

[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. Google Scholar

[108]

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

[109]

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

[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. doi: 10.1016/j.ijpe.2013.11.023. Google Scholar

[111]

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

[112]

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

[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. doi: 10.1016/j.ijpe.2004.10.003. Google Scholar

[114]

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

[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. doi: 10.1057/palgrave.jors.2601540. Google Scholar

[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. Google Scholar

[117]

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

[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. doi: 10.3141/2224-03. Google Scholar

[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. doi: 10.1016/0377-2217(92)90194-E. Google Scholar

[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. doi: 10.1016/j.apm.2011.01.006. Google Scholar

[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. Google Scholar

[122]

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

[123]

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

[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. doi: 10.1109/TEM.2012.2190986. Google Scholar

[125]

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

[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. doi: 10.1016/j.cor.2011.04.010. Google Scholar

[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. doi: 10.1080/07408170490245379. Google Scholar

[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. doi: 10.1016/j.ijpe.2006.11.013. Google Scholar

[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. doi: 10.1016/j.ejor.2005.02.076. Google Scholar

[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. doi: 10.1007/s10479-005-6236-6. Google Scholar

[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. doi: 10.3934/jimo.2014.10.1147. Google Scholar

[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. Google Scholar

[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. doi: 10.1016/j.eswa.2009.08.021. Google Scholar

[134]

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

[135]

G. Yu and X. Qi, Disruption Management,, World Scientific, (2004). doi: 10.1142/9789812561701. Google Scholar

[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. doi: 10.1016/j.omega.2008.05.006. Google Scholar

[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. doi: 10.1016/j.eswa.2011.07.137. Google Scholar

[138]

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

[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. doi: 10.1080/01441640902843232. Google Scholar

[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. doi: 10.1007/s00521-013-1511-y. Google Scholar

[1]

Yeong-Cheng Liou, Siegfried Schaible, Jen-Chih Yao. Supply chain inventory management via a Stackelberg equilibrium. Journal of Industrial & Management Optimization, 2006, 2 (1) : 81-94. doi: 10.3934/jimo.2006.2.81

[2]

Andrew P. Sage. Risk in system of systems engineering and management. Journal of Industrial & Management Optimization, 2008, 4 (3) : 477-487. doi: 10.3934/jimo.2008.4.477

[3]

Min Li, Jiahua Zhang, Yifan Xu, Wei Wang. Effect of disruption risk on a supply chain with price-dependent demand. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-21. doi: 10.3934/jimo.2019095

[4]

Jiuping Xu, Pei Wei. Production-distribution planning of construction supply chain management under fuzzy random environment for large-scale construction projects. Journal of Industrial & Management Optimization, 2013, 9 (1) : 31-56. doi: 10.3934/jimo.2013.9.31

[5]

Bibhas C. Giri, Bhaba R. Sarker. Coordinating a multi-echelon supply chain under production disruption and price-sensitive stochastic demand. Journal of Industrial & Management Optimization, 2019, 15 (4) : 1631-1651. doi: 10.3934/jimo.2018115

[6]

Amin Aalaei, Hamid Davoudpour. Two bounds for integrating the virtual dynamic cellular manufacturing problem into supply chain management. Journal of Industrial & Management Optimization, 2016, 12 (3) : 907-930. doi: 10.3934/jimo.2016.12.907

[7]

Jun Li, Hairong Feng, Kun-Jen Chung. Using the algebraic approach to determine the replenishment optimal policy with defective products, backlog and delay of payments in the supply chain management. Journal of Industrial & Management Optimization, 2012, 8 (1) : 263-269. doi: 10.3934/jimo.2012.8.263

[8]

Prasenjit Pramanik, Sarama Malik Das, Manas Kumar Maiti. Note on : Supply chain inventory model for deteriorating items with maximum lifetime and partial trade credit to credit risk customers. Journal of Industrial & Management Optimization, 2019, 15 (3) : 1289-1315. doi: 10.3934/jimo.2018096

[9]

Lili Ding, Xinmin Liu, Yinfeng Xu. Competitive risk management for online Bahncard problem. Journal of Industrial & Management Optimization, 2010, 6 (1) : 1-14. doi: 10.3934/jimo.2010.6.1

[10]

Ximin Huang, Na Song, Wai-Ki Ching, Tak-Kuen Siu, Ka-Fai Cedric Yiu. A real option approach to optimal inventory management of retail products. Journal of Industrial & Management Optimization, 2012, 8 (2) : 379-389. doi: 10.3934/jimo.2012.8.379

[11]

Jun Pei, Panos M. Pardalos, Xinbao Liu, Wenjuan Fan, Shanlin Yang, Ling Wang. Coordination of production and transportation in supply chain scheduling. Journal of Industrial & Management Optimization, 2015, 11 (2) : 399-419. doi: 10.3934/jimo.2015.11.399

[12]

Eungab Kim. On the admission control and demand management in a two-station tandem production system. Journal of Industrial & Management Optimization, 2011, 7 (1) : 1-18. doi: 10.3934/jimo.2011.7.1

[13]

Qingguo Bai, Fanwen Meng. Impact of risk aversion on two-echelon supply chain systems with carbon emission reduction constraints. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-23. doi: 10.3934/jimo.2019037

[14]

Jonas C. P. Yu, H. M. Wee, K. J. Wang. Supply chain partnership for Three-Echelon deteriorating inventory model. Journal of Industrial & Management Optimization, 2008, 4 (4) : 827-842. doi: 10.3934/jimo.2008.4.827

[15]

K.H. Wong, Chi Kin Chan, H. W.J. Lee. Optimal feedback production for a single-echelon supply chain. Discrete & Continuous Dynamical Systems - B, 2006, 6 (6) : 1431-1444. doi: 10.3934/dcdsb.2006.6.1431

[16]

Gang Xie, Wuyi Yue, Shouyang Wang. Optimal selection of cleaner products in a green supply chain with risk aversion. Journal of Industrial & Management Optimization, 2015, 11 (2) : 515-528. doi: 10.3934/jimo.2015.11.515

[17]

Ruopeng Wang, Jinting Wang, Chang Sun. Optimal pricing and inventory management for a loss averse firm when facing strategic customers. Journal of Industrial & Management Optimization, 2018, 14 (4) : 1521-1544. doi: 10.3934/jimo.2018019

[18]

Kegui Chen, Xinyu Wang, Min Huang, Wai-Ki Ching. Compensation plan, pricing and production decisions with inventory-dependent salvage value, and asymmetric risk-averse sales agent. Journal of Industrial & Management Optimization, 2018, 14 (4) : 1397-1422. doi: 10.3934/jimo.2018013

[19]

K. F. Cedric Yiu, S. Y. Wang, K. L. Mak. Optimal portfolios under a value-at-risk constraint with applications to inventory control in supply chains. Journal of Industrial & Management Optimization, 2008, 4 (1) : 81-94. doi: 10.3934/jimo.2008.4.81

[20]

Ali Naimi Sadigh, S. Kamal Chaharsooghi, Majid Sheikhmohammady. A game theoretic approach to coordination of pricing, advertising, and inventory decisions in a competitive supply chain. Journal of Industrial & Management Optimization, 2016, 12 (1) : 337-355. doi: 10.3934/jimo.2016.12.337

2018 Impact Factor: 1.025

Metrics

  • PDF downloads (73)
  • HTML views (0)
  • Cited by (10)

Other articles
by authors

[Back to Top]