Research On The Resilience-based Decision Optimization Of The Post-disaster Road Network Recovery

Road network closely connects with society and the public.Particularly,it is crucial and irreplaceable to traffic activities within areas.In recent years,major disasters and accidents occur frequently,which incur not only heavy casualties and property losses to the affected areas,but also severe damage to the local road network and other infrastructures.The damaged road network can't ensure normal social and economic activities,and,more importantly,also can't satisfy the need of disaster relief and post-disaster reconstruction in time.Therefore,it is necessary to recover the damaged road network in time.A road network recovery from major disasters is usually divided into multi phases.In each phase,decision-makers should determine the critical road segments required to be restored and the repair time sequence,according to the budget,resources,urgency and recovery objectives of the corresponding phase,and considering the travel choice behavior of road network users and the uncertainty of the recovery.The decision-making process is extremely complex and difficult.To address the above issues,this paper studies the resilience-based decision optimization of the post-disaster road network recovery.In this study,resilience engineering and network optimization problem are combined to solve the network recovery problem.A resilience-based optimization method of the integrated selection and scheduling problem of network recovery is proposed,which is conducive to the systematic and in-depth study of the integrated selection and scheduling problem that is still in the initial research stage.Based on the above optimization method,a resilience-based decision optimization method of the road network recovery during emergency recovery phase,and a resilience-based decision optimization method of the road network recovery during comprehensive recovery phase are proposed,which solve the decision-making problem of post-disaster road network recovery in different phases,enrich the research of post-disaster road network recovery,and provide scientific decision basis for decision-makers.The main research work is as follows:(1)Resilience-based optimization method of the integrated selection and scheduling problem of network recovery.An optimization method of the integrated selection and scheduling problem of network recovery is proposed,without considering the characteristics of each recovery phase and the travel choice behavior of road network users.The optimization method includes:two resilience metrics,which measure system resilience from both the recovery rapidity of network performance and the cumulative loss of network performance during the recovery;a resilience-based optimization model of the integrated selection and scheduling decision of network recovery;a genetic algorithm(GA)to solve the model.The procedure and effectiveness of the optimization method are demonstrated via a numerical example.(2)Resilience-based decision optimization method of the road network recovery during emergency recovery phase.By applying network connectivity as the key performance of road networks to be recovered in emergency recovery phase,the integrated selection and scheduling problem of road network recovery during emergency recovery phase is studied.First,the measurement of road network connectivity in emergency recovery phase is established.Second,on the basis of research(1),considering the recovery uncertainty in emergency recovery phase,a resilience-based bi-level optimization model of the road network recovery during emergency recovery phase is established for both deterministic and stochastic cases.The recovery objective of the optimization model in research(1)is changed to the road network recovery objective of emergency recovery phase,in order to obtain the upper level model.The upper level model determines which road segments need to be restored and the repair time sequence in emergency recovery phase to maximize the road network resilience.The lower level model formulates the behavior response of road network users under the upper decision as a User Equilibrium(UE)with a time series.Then,a novel algorithm that integrates the GA proposed in research(1)and the Frank-Wolfe algorithm for the UE is designed.Finally,via the freight transportation network of a given area,the effectiveness of the proposed model and algorithm is verified.The effects of resources,funds,tolerance factor of travel times and decision-maker preference on the recovery decisions and results are analyzed,in order to provide decision-makers with management implications and suggestions on road network recovery in emergency recovery phase.(3)Resilience-based decision optimization method of the road network recovery during comprehensive recovery phase.By applying network capacity as the key performance of road networks to be recovered in comprehensive recovery phase,the integrated selection and scheduling problem of road network recovery during comprehensive recovery phase is studied.First,the measurement of road network capacity in comprehensive recovery phase is established.Second,on the basis of research(1),a resilience-based tri-level optimization model of the road network recovery during comprehensive recovery phase is established.The recovery objective of the optimization model in research(1)is changed to the road network recovery objective of comprehensive recovery phase,in order to obtain the upper level model.The upper level model determines which road segments need to be restored and the repair time sequence in comprehensive recovery phase to maximize the road network resilience.The middle level model and the lower level model constitute a bi-level optimization model of road network capacity with a time series,where the middle level model solves the recovery of road network capacity under the upper decision,and the lower level model formulates the behavior response of road network users under the upper and middle decisions as a combined equilibrium trip distribution and traffic assignment model with a time series.Then,a novel algorithm that integrates the GA proposed in research(1),a heuristic algorithm for one-dimensional search and the convex combination algorithm for the combined equilibrium trip distribution and traffic assignment model is designed.Finally,via the freight transportation network of a given area,the effectiveness of the proposed model and algorithm is verified.The effects of resources,funds,recovery objective of road network capacity,and tolerance factor of average travel time of road network on the recovery decisions and results are analyzed,in order to provide decision-makers with management implications and suggestions on road network recovery in comprehensive recovery phase.