Abstract
This study presents a multi-stage random regret minimization (RRM) model as an emergency rescue decision support system to determine the emergency resource pre-allocation schedule for the freeway network. The proposed methodology consists of three steps: (1) improved accident frequency approach to identify the black spots on the freeway network, (2) stochastic programming (SP) model to determine the initial allocation plan sets, and (3) regret-based model in the logarithmical specification to select the most minimal regret one considering the factors of the response time, total cost and demand. The model is applied to the case study of 2014–2016 freeway network in Shandong, China. The results show that the random regret minimization (RRM) model can improve the full-compensation of SP model to a certain degree. RRM in logarithmical specification performs lightly better than random utility maximization (RUM) and RRM in the linear-additive specification in this case. This approach emerges as a valuable tool to help decision makers to allocate resources before traffic accident occurs, with the aim of minimizing the total regret of their decisions.
Highlights
Since the subject of emergency management emerged, it has already become a worldwide-noticeable theme for natural or man-made disasters
Pre-allocation of emergency supplies can be an effective mechanism for improving response to traffic accidents
We have developed a multi-stage stochastic programming (SP)-regret minimization (RRM) model whose solution provides a pre-allocation strategy for the storage and distribution of emergency supplies under different scenarios
Summary
Since the subject of emergency management emerged, it has already become a worldwide-noticeable theme for natural or man-made disasters. Previous studies have developed several supply allocation models to support the decisionmaking process, but there is still an understanding on how to improve the pre-allocation. A multi-stage emergency supplies pre-allocation approach for freeway black spots: a Chinese case study process in emergency management. The design of our emergency resource pre-arrangement strategy includes the identification of black spots and the preparedness of rescue supplies in advance. Notwithstanding the obvious success of the SP model for emergency management purposes, there is much scope for the characteristics of full compensation It assumes that one of the attributes outperforms other attributes, which can compensate other attributes with bad performance greatly. 2. A new SP-RRM model for emergency resource pre-arrangement introduces the regret theory into emergency management, which provides a different perspective to look at emergency system decisions.
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