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 ...
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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.
In this study, a superiority-inferiority full-infinite mixed-integer programming (SFMP) method is developed for analyzing the effect of energy conversion efficiency under uncertainty. SFMP can effectively tackle uncer...
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In this study, a superiority-inferiority full-infinite mixed-integer programming (SFMP) method is developed for analyzing the effect of energy conversion efficiency under uncertainty. SFMP can effectively tackle uncertainties expressed as fuzzy sets, crisp intervals and functional intervals, it also can directly reflect relationships among multiple fuzzy sets through varying superiority and inferiority degrees with a high computational efficiency. Then the developed SFMP is applied to a real case of planning energy system for Bayingolin Mongol Autonomous Prefecture, where multiple scenarios related to different energy-conversion efficiency are concerned. Results for energy processing, energy conversion, capacity expansion, pollutant emission and system cost have been generated. It is proved that SFMP is an effective approach to deal with the uncertainties in energy systems with interactive and uncertain characteristics. A variety of uncertainties existed in energy conversion processes and impact factors could affect the modeling result. Results show that improvement of energy-conversion efficiency can effectively facilitate reducing energy resources consumption, optimizing energy generation pattern, decreasing capacity expansion, as well as mitigating pollutant emissions. Results also reveal that, for the study area, electric power has a highest energy saving potential among heating, oil processing, coal washing and refining. Results can help decision makers to generate desired alternatives that can facilitate policy enactment of conversion efficiency improvement and adjustment of regional energy structure under uncertainty. (C) 2015 Elsevier Ltd. All rights reserved.
Inexact fuzzy full-infinite mixed-integer programming (IFFMIP) was proposed as an extension of inexact full-infinite programming. It can deal with the uncertainties that exist in most of the parameters as intervals, e...
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Inexact fuzzy full-infinite mixed-integer programming (IFFMIP) was proposed as an extension of inexact full-infinite programming. It can deal with the uncertainties that exist in most of the parameters as intervals, economic data as functional intervals, fuzzy set in capacity constraint, and discrete variables in a facility-expansion plan. Although many models for dealing with waste planning and the air quality management problem exist, this is the first attempt to integrate waste flow allocation and an air quality control system. The proposed model was applied in an integrated air and waste management system. It can solve the waste allocation plan and air quality control, and reflect the interaction between these two systems. The solutions from the proposed IFFMIP model indicate that a landfill would be the first choice in period 1, whereas an incinerator would become the main approach for waste disposal at the end of the planning horizon. This is because of the increasingly strict environmental standards and loading capacity of SO(2) for industries, upon which waste planning prefers less operation costs for waste disposal. The solutions reflected the tradeoff between the environment and system cost. To control air quality at a safe level, the system must be willing to pay more operating costs;however, a desire to reduce the costs will run into the risk of potentially violating the emissions and/or ambient air quality standards. DOI: 10.1061/(ASCE)UP.1943-5444.0000088. (C) 2011 American Society of Civil Engineers.
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