Modeling Resilience Enhancement Strategies for International Express Industries

碩士 === 國立交通大學 === 交通運輸研究所 === 100 ===   There has been a general awareness that natural and man-made disasters may occur suddenly. When disruptions occur, the previously operational plans may become far from optimal or even infeasible, and means are needed for adjusting or re-optimizing the original...

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Bibliographic Details
Main Authors: Tsai, Ya-Hsuan, 蔡亞璇
Other Authors: Feng, Cheng-Min
Format: Others
Language:en_US
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/29788422490699488773
Description
Summary:碩士 === 國立交通大學 === 交通運輸研究所 === 100 ===   There has been a general awareness that natural and man-made disasters may occur suddenly. When disruptions occur, the previously operational plans may become far from optimal or even infeasible, and means are needed for adjusting or re-optimizing the original plan to adapt the changing environment and to get back on track in a timely manner while effectively using the available resources.   International express is one most time-sensitive industry, which may need to respond disruptions quickly so as to improve service quality and to avoid losing their competitiveness with other express service providers. 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 assignment concept, regardless of how the available resources are located with respect to the studied logistics network or how many capacities we can rent from others.   The studied problem is formulated as a multi-hubs, multi-modes, multi-carriers, and multi-commodities network problem. The analytical model is developed for determining the alternative routes and rent activities (including the mode choice and carrier selection) after the disruption occurs. It also takes into account nonlinear cargo value functions of time to reflect the feature of the express industry that allows company transport different types of cargo with different ways to achieve higher customers’ satisfaction. Numerical experiments are conducted to examine our model applied in more complex networks and real world cases. Through a series of sensitivity analysis, some managerial implications are suggested to decision makers and potential stakeholders.