複合運輸路網回復力之模式建構

Abstract

貨物運輸在國際貿易中扮演重要的角色，現今，運輸路網越趨複雜，物流運輸通常以復合運輸的形式運送，因此，任何意料之外的天災人禍都將對整個路網造成傷害，例如颱風、地震等，造成路網中斷，貨物無法送達目的地，增加貨物運輸過程中的不確定性，導致龐大的經濟損失。此時，要讓該運輸路網維持與災前相同的服務水準是不容易的事情，路網回復力指的是一個路網受到干擾後，回復到原先的狀態或者可接受的狀態的能力，因此，若要減低災害所造成的損失，提升運輸路網回復力是很重要的。而這些災害是無法預期且無法避免的，因此在災害還沒發生之前，政府須重視災前的整備與應變，避免災害發生時措手不及，而當災害發生之後，針對受損之路段或場站，進行復原重建，透過災前預防及災後復原工作，將災害帶來的負面影響降到最低。 本研究針對複合運輸路網進行探討，提出複合運輸路網回復力之模式，來探討如何在災害發生後，有效的提升該路網之回復力，此模式透過災前的預防工作及災後的復原工作，來提升路網的容量，實施預防工作及復原工作皆能提升該節點或節線之容量，用以檢視該路網之服務水準的提升程度。 此模式是以兩階段隨機規劃方法建構，政府決策者在有限的預防工作預算下，考量未來可能發生的所有災害，決定實施對整個路網最佳的災前預防工作，當特定災害發生過後，有效運用有限的復原工作預算，決定針對受損地方實施復原工作，來提升路網回復力。第一階段模式主要決定預防工作的實施地點，目標式為最大畫整體路網回復力，決策變數為路網中之節點、節線是否實施災前預防工作，並受預算限制；第二階段模式則是決定災後復原工作的實施地點，為一個最大流量問題。 透過本研究，在有限的預算下，找出運輸路網中應實施預防工作及復原工作的地方，也就是路網中關鍵的地方，作為政府決策單位制訂政策時的參考依據，可以檢視國家擁有多少預算，如何有效的運用這筆預算，對路網進行預防及復原工作，讓更多貨物順利送抵目的地，也就是提升複合運輸路網之回復力，同時提升該路網之競爭力。

Freight transportation plays an important role in world economies. Nowadays, the transportation network becomes more and more complicated due to intermodal freight transportation. Any unexpected disruption occurring can impact the whole transportation network, such as typhoon, earthquake. These disruptions impose uncertainties to the operation and impact the level of service for freight transportation. Degradation of handling capacity causes the industry facing huge economic loss. To overcome the loss due to disruptions, it is imperative for the intermodal transportation network to improve its resilient ability. Resilience is defined as the ability of the network to return to its original state or an accepted state after being attacked by a disruption. The objective of this study is to develop a mathematical model to evaluate the performance of a freight network after a disruption. By implementing preparedness actions and recovery actions, the restoring capability can be enhanced. A two-stage stochastic program formulation is proposed in this study. Under a limited budget of preparedness actions, the decision maker decides the optimal places where preparedness actions are taken. After the disruption occurs, the decision maker decides the optimal places where recovery actions are taken under a limited budget of recovery actions. The first stage decides for the preparedness actions that can improve the capability of the network to face vulnerability under any disruption. The objective function is to maximize the resilience of the network subjecting to limited budget. And prepareness actions are the decision variables. The second stage is a max flow problem decides for the recovery actions. The resulting ability to quantify the resilience level of the network would aid decision makers or the operators in determining the best set of preparedness and recovery actions to mitigate disaster effects after the disruption occurs. Policy makers can also use this framework to test some preparedness-related policy effects. Furthermore, the result would be able to enhance the resilience and hence the competitiveness of the transportation system.