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Interval-Parameter Conditional Value-at-Risk Two-Stage Stochastic Programming Model for Management of End-of-Life Vehicles

  • Vladimir SimicEmail author
Article
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Abstract

The management of end-of-life vehicles conserves natural resources, provides economic benefits, and reduces water, air, and soil pollution. Sound management of end-of-life vehicles is vitally important worldwide thus requiring sophisticated decision-making tools for optimizing its efficiency and reducing system risk. This paper proposes an interval-parameter conditional value-at-risk two-stage stochastic programming model for management of end-of-life vehicles. A case study is conducted in order to demonstrate the usefulness of the developed model. The model is able to provide the trade-offs between the expected profit and system risk. It can effectively control risk at extremely disadvantageous availability levels of end-of-life vehicles. The formulated model can produce optimal solutions under predetermined decision-making risk preferences and confidence levels. It can simultaneously determine the optimal long-term allocation targets of end-of-life vehicles and reusable parts as well as capital investment, production planning, and logistics management decisions within a multi-period planning horizon. The proposed model can efficiently handle uncertainties expressed as interval values and probability distributions. It is able to provide valuable insights into the effects of uncertainties. Compared to the available models, the resulting solutions are far more robust.

Keywords

End-of-life vehicle Risk control Conditional value-at-risk Two-stage stochastic programming Interval-parameter programming Uncertainty 

Notes

Acknowledgements

The author is grateful to the advisory editor and the anonymous reviewers for their insightful comments and suggestions.

Funding Information

This work was partially supported by Ministry of Education, Science and Technological Development of the Republic of Serbia through the project TR 36006 for the period 2011–2019.

Supplementary material

10666_2018_9648_MOESM1_ESM.docx (74 kb)
ESM 1 (DOCX 73 kb)

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Transport and Traffic EngineeringUniversity of BelgradeBelgradeSerbia

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