TISEM: A Two-Stage Interval-Stochastic Evacuation Management Model

Abstract

ABSTRACT. Traffic allocation planning is commonly required for mass evacuation management. It primarily relies on efficient coordination and appropriate utilization of roadway capacity and available traffic resources. However, traffic and evacuee information are usually difficult to be obtained and consequently of various uncertainties in data. Especially, stochastic information may often exist in evacuation management systems. In this study, a two-stage interval-stochastic evacuation management (TISEM) model was developed for supporting the evacuation planning under uncertainty, by which stochastic and interval evacuation information could be well reflected and communicated in the system. In addition, by adopting the proposed model, a case study abstracted from the City of Wuhan was introduced and solved through an interactive method. Results indicated that useful solutions for planning evacuation routes could be generated based on results of the model. As well, through the model, complex relationships between evacuation time, environmental influences and economic factors could be systematically analyzed. It demonstrated that the proposed TISP model is practical and applicable in real world, and is helpful for authorities to make decisions allocating vehicles before evacuation starts.

Development of an interval multi-stage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty

“…According to Lin and Huang [25], Huang et al [31], and Li et al [32], it can be solved through decomposition into two sets of deterministic sub-models. According to Lin and Huang [25], Huang et al [31], and Li et al [32], it can be solved through decomposition into two sets of deterministic sub-models.…”

“…Model 2 is an interval-parameter linear programming model (ILP). According to Lin and Huang [25], Huang et al [31], and Li et al [32], it can be solved through decomposition into two sets of deterministic sub-models.…”

Site Selection Models in Natural Disaster Shelters: A Review

“…Equation (38) helps to ensure that all affected people will be served by facilities of different levels at each evacuation phase. The minimum and maximum levels of capacity restriction for different facility types are constrained by Equations (39) and (40). Equation (41) is the closest assignment constraint, which mandates that demand nodes are served by their closest facility.…”

Environmental and Economic Optimization Model for Electric System Planning in Ningxia, China: Inexact Stochastic Risk-Aversion Programming Approach

“…Ji et al (2014) proposed a two-stage stochastic inexact robust optimization model for residential microgrid energy management, where combined cooling, heating, and electricity technology were introduced to satisfy various energy demands [17]. Among these techniques, inexact two-stage stochastic programming (ITSP) with recourse, integrated interval-parameter programming, and two-stage stochastic programming (TSP) could deal with uncertainties expressed as probability distributions and discrete intervals and received extensive attentions over the past years [18][19][20]. In the ITSP model, an initial energyrelated decision is first undertaken before the random events happen; after the random information associated with the stochastic nature of emission-reduction targets is known, a second-stage decision can be made in order to minimize "penalties" that may appear due to any infeasibility.…”