Other risk measures are also available, but there is currently no consensus on which is the best one, which makes quantitative risk management a rather inexact science.
This paper presents a review on application of inexact programming methods to waste management under uncertainty.
The literature review highlights the development and applications of inexact programming methods to waste management under uncertainty, which have become a popular research area.
To obtain a snapshot of these studies, this paper gives a review on recent developments, applications, challenges, and barriers associated with inexact programming techniques in supporting waste management.
Sun et al. ([2010a]) proposed an inexact chance-constrained quadratic solid waste management (ICQSWM) model, which integrates two-stage stochastic, chance-constrained, and interval-parameter quadratic programming together.
Ahluwalia and Nema ([2011]) developed a multi-time-step and multi-objective inexact decision-support model for computer waste management.
During the past several decades, various inexact programming methods were applied to waste management; they were mainly categorized as stochastic mathematical programming (SMP), fuzzy mathematical programming (FMP), interval-parameter mathematical programming (IMP), and combinations of these methods (Singh [2012]).
Fishery management has historically been an inexact and reactionary discipline, often taking action only after a critical stock suffers overfishing or collapse.
This paper proposed an inexact reverse logistics model for municipal solid waste management systems (IRWM).
Li et al. ([2009c]) developed inexact fuzzy-stochastic constraint-softened programming for waste management through incorporation of multistage stochastic programming (MSP), ILP, and FFP.
