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首页> 外文期刊>Journal of Environmental Management >IFRP: A hybrid interval-parameter fuzzy robust programming approach for waste management planning under uncertainty
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IFRP: A hybrid interval-parameter fuzzy robust programming approach for waste management planning under uncertainty

机译：IFRP：不确定性下废物管理计划的混合区间参数模糊鲁棒规划方法

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摘要

In this study, an interval-parameter fuzzy-robust programming (IFRP) model is developed and applied to the planning of solid waste management systems under uncertainty. As an extension of the existing fuzzy-robust programming and interval-parameter linear programming methods, the IFRP can explicitly address system uncertainties with complex presentations. Parameters in the IFRP model can be represented as interval numbers and/or fuzzy membership functions, such that the uncertainties can be directly communicated into the optimization process and resulting solution. Furthermore, highly uncertain information for the lower and upper bounds of interval parameters that exist due to the complexity of the real world can be effectively handled through introducing the concept of fuzzy boundary interval. Consequently, robustness of the optimization process and solution can be enhanced. Results of the case study indicate that useful solutions for planning municipal solid waste management practices can be generated. They reflect a compromise between optimality and stability of the study system. Willingness to pay higher costs will guarantee the system stability; however, a desire to reduce the costs will run the risk of potential instability of the system. The results also suggest that the proposed hybrid methodology is applicable to practical problems that are associated with highly complex and uncertain information.
机译：在这项研究中，建立了区间参数模糊鲁棒规划（IFRP）模型，并将其应用于不确定性条件下的固体废物管理系统的规划。作为现有模糊鲁棒编程和区间参数线性编程方法的扩展，IFRP可以通过复杂的表示方式显式解决系统不确定性。 IFRP模型中的参数可以表示为区间数和/或模糊隶属度函数，从而可以将不确定性直接传达给优化过程和得出的结果。此外，通过引入模糊边界区间的概念，可以有效地处理由于现实世界的复杂性而存在的区间参数上下限的高度不确定的信息。因此，可以增强优化过程和解决方案的鲁棒性。案例研究的结果表明，可以为规划城市固体废物管理实践提供有用的解决方案。它们反映了研究系统的最优性和稳定性之间的折衷。愿意支付更高的费用将保证系统的稳定性；但是，降低成本的愿望将带来系统潜在不稳定的风险。结果还表明，所提出的混合方法适用于与高度复杂和不确定的信息相关的实际问题。

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