Conclusion

1. This paper studies the recovery problem for the natural and man-made disaster. Although this problem was extensively discussed in the literature, no models is tailored for the international express companies to consider the cargo time value and incorporates several resilient strategies, for example renting measure.

2. Instead of arbitrarily making rush decisions during the post-disruption phase, this paper contributes a method for quantifying and optimizing the resilience strategies based on an integrated resource allocation concept, regardless of how the available resources are located with respect to the studied logistics network or how many capacities we should rent from others .

3. The mathematical programming model is employed in the numerical experiments to present the applicability of our model. The results show that different resilient strategies or recovery activities are adopted depending on different failure types and affected locations. In general, the strategies will increase and be more complex when the situation becomes worse.

4. From several scenario analysis and sensitivity analysis, we explore some rules from the results and give some suggestions to the express company. Due the assumption of our cargo time value function, we find that time will become not sensitive to express companies during the disaster. The model can choose any routes which allow the cargos be delivered before the cargo time value declines even if the travel time is not the shortest. The renting cost becomes the more significant factor in the recovery activities, carrier selection and route choice. They are based on our cargo time value function type. If it changes, there is the different rule in the result.

5.2 Extensions to Future Research

1. In the future research, we suggest to obtain the practical and accurate data in the

transportation time, renting time and available rental capacities, etc., and acquire more useful solutions to examine the nodes or links in advance.

2. The usefulness of the numerical results can be increased by further developing commercial software for real-time control operations.

3. Although our model can examine the reliability of primary hubs and links within the existing operational networks before the disruptions, we can’t decide which proactive

strategies the express company should invest in. Thus, in the future, the model may extend to consider both proactive decision and reactive decision for disaster recovery problem. It can let companies do more useful decisions in investment before disruption and in cost spending after disruption.

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