An interval-parameter mean-CVaR two-stage stochastic programming approach for waste management under uncertainty

Optimal strategies for carbon capture, utilization and storage based on an inexact m λ -measure fuzzy chance-constrained programming

“…Accurate accounting of the potential impact of such uncertainties is vital for understanding the financial risks that will come with the implementation and deployment of CCUS. Sensitivity analysis can be used to evaluate the effects of parameter uncertainties on the modeling results, involving changing one or several input parameters to analyze the responses of the results [17,52]. However, too many uncertain parameters in the linear programming model may result in infinite possibilities from the uncertain information so that conducting sensitivity analysis for all uncertain parameters is timeconsuming.…”

“…Also, many CCUS system parameters such as CO 2 emission amount, capacity of CO 2 disposal facility, carbon emission permit, and transportation cost, may appear uncertain in multiple forms that can be expressed as fuzzy sets, probability density functions, and intervals [14,16]. Furthermore, such uncertainties may be further multiplied by site-specific features of many system components, factors, and parameters [17]. These uncertainties have caused many concerns over reliability and sustainability of CCUS projects, casting many doubts upon this prospective technology.…”

Development of a constructed wetland network for mitigating nonpoint source pollution through a GIS-based watershed-scale inexact optimization approach

“…Recently, a number of research efforts were undertaken to deal with uncertainties C. Dai et al / Ecological Engineering xxx (2015) xxx-xxx through inexact optimization methods, such as stochastic and fuzzy mathematical programming (Cai et al, 2011a,b;Dai et al, 2015;Dong et al, 2013;Ganji et al, 2008;Han et al, 2013;Housh et al, 2012;Tan et al, 2011;Uddameri et al, 2014;Zarghami and Szidarovszky, 2009;Zeng et al, 2011;Zhang and Huang, 2011). Among them, two-stage stochastic programming (TSP), a typical stochastic mathematical programming method, is effective for tackling optimization problems where an analysis of policy scenarios is desired and the model's coefficients are random with known probability distributions (Dai et al, 2014). In TSP, a decision is first made before values of random variables are known; after the random events have happened and their values are known, a second decision is made to minimize "penalties" that may appear due to any infeasibility (Maqsood et al, 2005).…”

Risk-Averse Multi-Stage Stochastic Programming to Optimizing Vaccine Allocation and Treatment Logistics for Effective Epidemic Response

“…Therefore, many previous studies considered mean-risk models with CVaR in stochastic programming models (Ahmed, 2006;Miller and Ruszczyński, 2011;Rockafellar and Uryasev, 2002;Schultz and Tiedemann, 2006). CVaR-based mean-risk stochastic programming has been studied in various applications, such as supply chain management (Alem and Morabito, 2013), reverse logistic network design problem (Soleimani and Govindan, 2014), solid waste management system (Dai et al, 2014), water resources allocation (Zhang et al, 2016), and forestry invasive species control planning (Bushaj et al, 2020(Bushaj et al, , 2021.…”