Resilient supplier selection and order allocation under uncertainty

Document Type : Article


Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, P.O. Box 4851878195, Mazandaran, Iran.


Increasing the number of disasters around the world will decrease the performance of the supply chain. The decision makers should design resilience supply chain network which could encounter with disruptions. This paper develops an integrated resilience model of supplier selection and order allocation. Resiliency measures including quality, delivery, technology, continuity, environmental competences are explored for determining the Resilience Weight of suppliers. Fuzzy DEMATEL and ANP methods are applied to find overall performance of each supplier. Then, the developed mathematical model maximizes overall performance of suppliers while minimizes total cost of network. The proposed mathematical model helps the decision makers to select supplier and allocate the optimum order quantities by considering shortage. Since the disruptive incidents are inevitable events in real world problems, the impact of disruptions on suppliers, manufactures and retailers has been considered in the proposed model. Inherent uncertainties of parameters are taken into account to increase the compatibility of the approach with realistic environments. To tackle the uncertainty and multi-objectiveness of the proposed model, interval Method and TH aggregation function is adapted. The proposed model is validated through application to a real case study in a furniture company. Results demonstrate the usefulness and applicability of the proposed model.


Main Subjects

1. Amirtaheri, O., Zandieh, M., and Dorri, B. A bi-level  programming model for decentralized manufacturerdistributer  supply chain considering cooperative advertising",  Scientia Iranica, 25(2), pp. 891{910 (2018).  2. Golp^Ira, H., Zandieh, M., Naja_, E., and Sadi-Nezhad,  S. A multi-objective multi-echelon green supply chain  network design problem with risk-averse retailers in an  uncertain environment", Scientia Iranica, Transaction  E, Industrial Engineering, 24(1), pp. 413{423 (2017).  3. Sahebjamnia, N., Tavakkoli-Moghaddam, R., and  Ghorbani, N. Designing a fuzzy Q-learning multiagent  quality control system for a continuous chemical  production line-A case study", Computers & Industrial  Engineering, 93, pp. 215{226 (2016).  4. Jafari, H.R., Seifbarghy, M., and Omidvari, M. Sustainable  supply chain design with water environmental  impacts and justice-oriented employment considerations:  A case study in textile industry", Scientia  Iranica, 24(4). pp. 2119{2137 (2017).  5. Rao, C.J., Zheng, J.J., Hu, Z., and Goh, M., Compound  mechanism design on multi-attribute and multisource  procurement of electricity coal", Scientia Iranica,  23(3), pp. 1384{1398 (2016).  6. Torkaman, S., Ghomi, S.F., and Karimi, B. Multistage  multi-product multi-period production planning  with sequence-dependent setups in closed-loop supply  chain", Computers & Industrial Engineering, 113, pp.  602{613 (2017).  7. Bashiri, M. and Hasanzadeh, H. Modeling of locationdistribution  considering customers with di_erent priorities  by a lexicographic approach", Scientia Iranica,  23(2), pp. 701{710 (2016).  8. Hlioui, R., Gharbi, A., and Hajji, A. Joint supplier selection,  production and replenishment of an unreliable  manufacturing-oriented supply chain", International  Journal of Production Economics, 187, pp. 53{67  (2017).  9. Mahdavi, I., Shirazi, B., and Sahebjamnia, N. Development  of a simulation-based optimisation for controlling  operation allocation and material handling  equipment selection in FMS", International Journal of  Production Research, 49(23), pp. 6981{7005 (2011).  10. Gharaei, A., Pasandideh, S.H.R., and Akhavan Niaki,  S.T. An optimal integrated lot sizing policy of inventory  in a bi-objective multi-level supply chain with  stochastic constraints and imperfect products", Journal  of Industrial and Production Engineering, 35(1),  pp. 6{20 (2018).  11. Alfares, H.K. and Turnadi, R. Lot sizing and supplier  selection with multiple items, multiple periods,  quantity discounts, and backordering", Computers &  Industrial Engineering, 116, pp. 59{71 (2018).  12. Sahebjamnia, N., Torabi, S.A., and Mansouri, S.A.  Building organizational resilience in the face of multiple  disruptions", International Journal of Production  Economics, 197, pp. 63{83 (2018).  13. Scheibe, K.P. and Blackhurst, J. Supply chain disruption  propagation: a systemic risk and normal  accident theory perspective", International Journal of  Production Research, 56(1{2), pp. 43{59 (2018).  14. Aissaoui, N., Haouari, M., and Hassini, E. Supplier  selection and order lot sizing modeling: A review",  Computers & Operations Research, 34(12), pp. 3516{  3540 (2007).  15. Hajikhani, A., Khalilzadeh, M., and Sadjadi, S.J. A  fuzzy multi-objective multi-product supplier selection  and order allocation problem in supply chain under  coverage and price considerations: An urban agricultural  case study", Scientia Iranica, 25(1), pp. 431{449  (2018).  16. Govindan, K., Rajendran, S., Sarkis, J., and Murugesan,  P. Multi criteria decision making approaches for  green supplier evaluation and selection: a literature  review", Journal of Cleaner Production, 98, pp. 66{83  (2015).  17. Babbar, C. and Amin, S.H. A multi-objective mathematical  model integrating environmental concerns for  supplier selection and order allocation based on fuzzy  QFD in beverages industry", Expert Systems with  Applications, 92, pp. 27{38 (2018).  18. Fallahpour, A., Olugu, E.U., and Musa, S.N. A hybrid  model for supplier selection: integration of AHP and  N. Sahebjamnia/Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 411{426 423  multi expression programming (MEP)", Neural Computing  and Applications, 28(3), pp. 499{504 (2017).  19. Hammami, R., Temponi, C., and Frein, Y. A  scenario-based stochastic model for supplier selection  in global context with multiple buyers, currency uctuation  uncertainties, and price discounts", European  Journal of Operational Research, 233(1), pp. 159{170  (2014).  20. Goren, H.G. A decision framework for sustainable  supplier selection and order allocation with lost sales",  Journal of Cleaner Production, 183, pp. 1156{1169  (2018).  21. Chai, J., Liu, J.N., and Ngai, E.W. Application of  decision-making techniques in supplier selection: A  systematic review of literature", Expert Systems with  Applications, 40(10), pp. 3872{3885 (2013).  22. Mahmoudi, A., Sadi-Nezhad, S., and Makui, A. An  extended fuzzy VIKOR for group decision making  based on fuzzy distance to supplier selection", Scientia  Iranica, 23(4), pp. 1879{1892 (2016).  23. Keshavarz Ghorabaee, M., Amiri, M., Zavadskas,  E.K., and Antucheviciene, J. Supplier evaluation  and selection in fuzzy environments: a review of  MADM approaches", Economic Research-Ekonomska  Istra_zivanja, 30(1), pp. 1073{1118 (2017).  24. Rezaei, J. and Davoodi, M. Multi-objective models  for lot-sizing with supplier selection", International  Journal of Production Economics, 130(1), pp. 77{86  (2011).  25. Govindan, K., Fattahi, M., and Keyvanshokooh, E.  Supply chain network design under uncertainty: A  comprehensive review and future research directions",  European Journal of Operational Research, 263(1), pp.  108{141 (2017).  26. Tabibian, M. and Rezapour, M. Assessment of urban  resilience; a case study of Region 8 of Tehran city,  Iran", Scientia Iranica, 23(4), pp. 1699{1707 (2016).  27. Bidokhti, A.A., Shariepour, Z., and Sehatkashani,  S. Corrigendum to some resilient aspects of urban  areas to air pollution and climate change, case study:  Tehran, Iran", Scientia Iranica, 23(6), pp. 1994{2004  (2016).  28. Hsieh, C.Y., Wee, H.M., and Chen, A. Resilient  logistics to mitigate supply chain uncertainty: A case  study of an automotive company", Scientia Iranica,  23(5), pp. 2287{2296 (2016).  29. Khaloo, A.R. and Mobini, M.H. Towards resilient  structures", Scientia Iranica, 23(5), pp. 2077{2080  (2016).  30. Sawik, T., Selection of resilient supply portfolio under  disruption risks", Omega, 41(2), pp. 259{269 (2013).  31. Fattahi, M. Resilient procurement planning for supply  chains: A case study for sourcing a critical  mineral material", Resources Policy (In press) (2017).  32. Vahidi, F., Torabi, S.A., and Ramezankhani, M.J.  Sustainable supplier selection and order allocation  under operational and disruption risks", Journal of  Cleaner Production, 174, pp. 1351{1365 (2018).  33. Taleizadeh, A.A., Niaki, S.T.A., and Barzinpour, F.  Multiple-buyer multiple-vendor multi-product multiconstraint  supply chain problem with stochastic demand  and variable lead-time: A harmony search  algorithm", Applied Mathematics and Computation,  217(22), p. 9234{9253 (2011).  34. Sahebjamnia, N., Torabi, S.A., and Mansouri, S.A.  Integrated business continuity and disaster recovery  planning: Towards organizational resilience", European  Journal of Operational Research, 242(1), pp.  261{273 (2015).  35. Torabi, S., Baghersad, M., and Mansouri, S. Resilient  supplier selection and order allocation under operational  and disruption risks", Transportation Research  Part E: Logistics and Transportation Review, 79, pp.  22{48 (2015).  36. Gaur, J., Subramoniam, R., Govindan, K., and Huisingh,  D. Closed-loop supply chain management: From  conceptual to an action oriented framework on core  acquisition", Journal of Cleaner Production, 30, pp.  1{10 (2016).  37. Namdar, J., Tavakkoli-Moghaddam, R., Sahebjamnia,  N., and Sou_, H.R. Designing a reliable distribution  network with facility forti_cation and transshipment  under partial and complete disruptions", International  Journal of Engineering-Transactions C: Aspects C,  29(9), pp. 1273{1281 (2016).  38. Tukamuhabwa, B.R., Stevenson, M., Busby, J., and  Zorzini, M. Supply chain resilience: de_nition, review  and theoretical foundations for further study", International  Journal of Production Research, 53(18), pp.  5592{5623 (2015).  39. Altiparmak, F., Gen, M., Lin, L., and Paksoy, T.,  A genetic algorithm approach for multi-objective  optimization of supply chain networks", Computers &  Industrial Engineering, 51(1), pp. 196{215 (2006).  40. Vahidi, F., Torabi, S.A., and Ramezankhani, M.  Sustainable supplier selection and order allocation  under operational and disruption risks", Journal of  Cleaner Production, 174, pp. 1351{1365 (2018).  41. Awasthi, A., Govindan, K., and Gold, S. Multitier  sustainable global supplier selection using a fuzzy  AHP-VIKOR based approach", International Journal  of Production Economics, 195, pp. 106{117 (2018).  424 N. Sahebjamnia/Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 411{426  42. Soosay, C.A., Hyland, P.W., and Ferrer, M. Supply  chain collaboration: capabilities for continuous innovation",  Supply Chain Management: An International  Journal, 13(2), pp. 160{169 (2008).  43. Torabi, S.A., Rezaei Sou_, H., and Sahebjamnia, N. A  new framework for business impact analysis in business  continuity management (with a case study)", Safety  Science, 68, pp. 309{323 (2014).  44. Torabi, S.A., Giahi, R., and Sahebjamnia, N. An  enhanced risk assessment framework for business continuity  management systems", Safety Science, 89, pp.  201{218 (2016).  45. Jim_enez, M., Arenas, M., Bilbao, A., and Rodr_,  M.V. Linear programming with fuzzy parameters: an  interactive method resolution", European Journal of  Operational Research, 177(3), pp. 1599{1609 (2007).  46. Parra, M.A., Terol, A.B., Gladish, B.P., and Ur_a,  M.R. Solving a multiobjective possibilistic problem  through compromise programming", European Journal  of Operational Research, 164(3), pp. 748{759 (2005).  47. Pishvaee, M. and Torabi, S. A possibilistic programming  approach for closed-loop supply chain network  design under uncertainty", Fuzzy Sets and Systems,  161(20), pp. 2668{2683 (2010).  48. Torabi, S.A. and Hassini, E. An interactive possibilistic  programming approach for multiple objective supply  chain master planning", Fuzzy Sets and Systems,  159(2), pp. 193{214 (2008).  49. Liou, James J.H. Developing an integrated model for  the selection of strategic alliance partners in the airline  industry", Knowledge-Based Systems, 28, pp. 59{67  (2012).  50. Tzeng, G.-H., Chiang, C.-H., and Li, C.-W. Evaluating  intertwined e_ects in e-learning programs: A  novel hybrid MCDM model based on factor analysis  and DEMATEL", Expert Systems with Applications,  32(4), pp. 1028{1044 (2007).  51. Suo, W.-L., Feng, B., and Fan, Z.-P. Extension  of the DEMATEL method in an uncertain linguistic  environment", Soft Computing, 16(3), pp. 471{483  (2012).  52. Hiete, M., Merz, M., Comes, T., and Schultmann, F.  Trapezoidal fuzzy DEMATEL method to analyze and  correct for relations between variables in a composite  indicator for disaster resilience", OR Spectrum, 34(4),  pp. 971{995 (2012).  53. Li, C.-W. and Tzeng, G.-H. Identi_cation of a  threshold value for the DEMATEL method using the  maximum mean de-entropy algorithm to _nd critical  services provided by a semiconductor intellectual property  mall", Expert Systems with Applications, 36(6),  pp. 9891{9898 (2009).  54. Saaty, T.L., Decision Making with Dependence and  Feedback: The Analytic Network Process, Pittsburgh,  RWS Publications (1996).  55. Yang, J.L. and Tzeng, G.-H. An integrated MCDM  technique combined with DEMATEL for a novel  cluster-weighted with ANP method", Expert Systems  with Applications, 38(3), pp. 1417{1424 (2011).  56. Jim_enez, M. Ranking fuzzy numbers through the  comparison of its expected intervals", International  Journal of Uncertainty, Fuzziness and Knowledge-  Based Systems, 4(04), pp. 379{388 (1996).