A resilient supply portfolio considering political and disruption risks

被引：0
作者：
Hosnavi R. [1 ]
Nekooie M.A. [1 ]
Khalili S.M. [2 ]
Tavakoli A. [2 ]
机构：
[1] Department of Emergency Management, Malek Ashtar University of Technology (MUT), Tehran
[2] Department of Management, Ferdowsi University of Mashhad, Mashhad
来源：
International Journal of Industrial and Systems Engineering | 2019年 / 31卷 / 02期
关键词：
Mixed possibilistic two-stage stochastic programming; Resilient supply portfolio; Supply chain risk/disruption management;
D O I：
10.1504/IJISE.2019.097738
中图分类号：
学科分类号：
摘要：
The need of accounting for resilience in global supply chains has been growing from practical and academic points of view. However, there is still the need for developing quantitative decision support models on this issue. In the present work, we propose a novel multi-objective mixed possibilistic, two-stage scenario-based stochastic programming model to handle supplier selection and order allocation problem in a global supply chain under operational and disruption risks. The model minimises cost and political risk, while, maximising resilience of the supply portfolio. Various risk mitigation approaches including: contracting with backup suppliers, fortification of suppliers and procurement of emergency inventory, are considered in the model. In addition, the proposed model determines recovery plans. Reservation level driven Tchebycheff procedure is incorporated in the solution procedure to find Pareto-optimal solutions. The validation of the model via computational experiments demonstrates the applicability of the proposed model and solution method in building a resilient supply portfolio under consideration of operational and disruption risks. Copyright © 2019 Inderscience Enterprises Ltd.
引用
收藏
页码：209 / 249
页数：40
相关论文
共 94 条
[1]  
Abbasi M., Hosnavi R., Babazadeh R., Agile and flexible supply chain network design under uncertainty, International Journal of Industrial Engineering: Theory, Applications and Practice, 21, 4, pp. 190-208, (2014)
[2]  
Aggarwal R., Singh S., Chance constraint-based multi-objective stochastic model for supplier selection, The International Journal of Advanced Manufacturing Technology, 2, 2, pp. 7-12, (2015)
[3]  
Bank W., Worldwide Governance Indicators, (2015)
[4]  
Bansal K., A supply chain model with shortages under inflationary environment, Uncertain Supply Chain Management, 4, 4, pp. 331-340, (2016)
[5]  
Berger P., Zeng A., Single versus multiple sourcing in the presence of risks, Journal of The Operational Research Society, 57, 3, pp. 250-261, (2006)
[6]  
Berger P.D., Gerstenfeld A., Zeng A.Z., How many suppliers are best? A decision-analysis approach, Omega, 32, 1, pp. 9-15, (2004)
[7]  
Bhamra R., Dani S., Burnard K., Resilience: The concept, a literature review and future directions, International Journal of Production Research, 49, 18, pp. 5375-5393, (2011)
[8]  
Birge J.R., Louveaux F., Introduction to Stochastic Programming, (1997)
[9]  
Blackhurst J., Craighead C.W., Elkins D., Handfield R.B., An empirically derived agenda of critical research issues for managing supply-chain disruptions, International Journal of Production Research, 43, 19, pp. 4067-4081, (2005)
[10]  
Bruneau M., Chang S.E., Eguchi R.T., Lee G.C., O'Rourke T.D., Reinhorn A.M., Shinozuka M., Tierney K., Wallace W.A., Von Winterfeldt D., A framework to quantitatively assess and enhance the seismic resilience of communities, Earthquake Spectra, 19, 4, pp. 733-752, (2003)
12345678910