4.7 Article

End-of-life vehicles allocation management under multiple uncertainties: An interval-parameter two-stage stochastic full-infinite programming approach

期刊

RESOURCES CONSERVATION AND RECYCLING
卷 114, 期 -, 页码 1-17

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.resconrec.2016.06.019

关键词

End-of-life vehicle; Full-infinite programming; Two-stage stochastic programming; Interval programming; Uncertainty

资金

  1. Ministry of Education, Science and Technological Development of the Republic of Serbia [TR 36006]

向作者/读者索取更多资源

One of the main negative consequences of uncontrolled export of used vehicles from the European Union to developing countries is resource shortage for major players of European vehicle recycling systems. The resource scarcity puts serious pressure on vehicle recycling managers. An effective end-of-life vehicles (ELVs) allocation management is considered vital for mitigating the effect of the growing export of used vehicles. This paper proposes an interval-parameter two-stage stochastic full-infinite programming model for end-of-life vehicles allocation management under multiple uncertainties. A case study is conducted in order to demonstrate the potentials and applicability of the proposed model. Influences of parameter uncertainty on model solutions are thoroughly investigated. The developed model can efficiently handle uncertainties expressed as functional intervals, probability distributions and conventional crisp intervals. It is able to reduce risk of ELV management system failure due to the possible constraints violation. The formulated model can take into account connections of modeling parameters and their impact factors, thus reflecting external uncertainties of ELV management systems. It can provide a flexible ELV allocation management schemes adjustable with the variations in prices of secondary metals and end-of-life vehicles. The proposed model is able to reflect trade-off between conflicting waste management system revenues and the associated penalties for violating ELV allocation targets, thus providing a valuable insight for decision makers. (C) 2016 Elsevier B.V. All rights reserved.

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