Summary

There are varieties of definitions available in the literatures, however, the resilience for international express in this research can be defined as “the ability to take opportune proactive responses to quickly recover to an acceptable level of service within a reasonable timeframe when the goods flow in the transportation network is blocked up”. The resilient strategy in our study is the set of activities that can enhance the above mentioned resilience in international express company.

In section 2.2, we mention the resilient strategies in the transportation and supply chain areas. The suggested recovery activities in transportation are reviewed from government perspective and more likely to be risk management principles. On the private sectors

dimension, most reviewed supply chain resilient strategies appertain to the actual operational measures. Most of them belong to supply management and product management category and deal with supply change for inbound logistics. However, the flexible transportation is one of the possible resilient strategies in the supply chain covering both inbound and outbound flow logistics. On the whole, supply chain resilient strategies put more stress on the resistance in advance, for example network design, supply management, process improvement and product management. The transportation resilient strategies emphasize the salvage after the event.

According to the literatures and expert opinions, we will develop a set of resilient strategies for the express company in next chapter and add the adequate part in our model.

In section 2.3, we review the quantitative methods of resilience and try to focus on the transportation area. We find there are different methodologies used by the scholars and most of them put the emphasis on the transportation networks when they study the problem of freight transportation. These papers can be divided into two categories. One is to optimize the structure design of network (Wang & Ip, 2009); the other is going to choose recovery

activities after disruption to maximum the network freight flow (Nair et al., 2010, Chen &

Miller-Hooks, 2011). The model we proposed in the next chapter combines the resilient

concept from Nair et al. (2010) and Chen & Miller-Hooks (2011) and the disaster relief model.

The differences are that we consider the different recovery activities and objectives and

literatures considering the four decisions (route choice, trucks re-allocation, renting decision, cargo selection ) together. We also arrange the mathematical programming literatures and compare the difference between their model and ours in Table 2.5.

According the sorting table, our model has different objective functions and decision variables which are tailored with express company and resilient strategies.

Table 2.5 The sorting table of mathematical programming model

Author Objective function Constraints Decision variables Decision maker

Seferlis et al. (2008)

(operation level)

Minimize back-orders penalty, transportation costs ,inventory costs and changing routes penalty

(The resilience concept resides in collaboration among all stakeholders so he proposes multi-echelon supply chain model .)

(1) flow conservation

(2) inventory balance equation (3) back-orders balance

equation

(4) node capacity of carrying inventory not to exceed a maximum allowable inventory level

(5) route capacity for transportation

(6) the utilization of resource for manufacturing not to exceed available resource (4) production rate

Contractual supply

Maximize node resilience Investment limitation (budget) Whether the project is selected for construction or not

Airport operator who decide to construct aircraft maintenance and service centers

Chen & Miller-Hooks (2011)

(operation level)

Minimize number of shipments that cannot be satisfied for O-D pair

(1) flow on each arc to be less than the affected link capacity and recovery capacity

(2) the traversal time not to exceed a given maximum duration

(3) the total cost of selected recovery actions does not exceed budget

(4) only one recovery activity can be selected for each arc

(1) number of shipments transported on path p (number of shipments that can’t be satisfied for O-D pair)

(2) binary variable indicating whether or not path p will suffice given maximum traversal time (3) binary variable indicating whether or not recovery activity is transfer cargos and the cost generated by holding vehicle

(The resilience concept resides in handling the

Total missed-connection cargos cannot exceed the available storage area at the transfer terminal

Hold for late inbound vehicle or to dispatch it immediately

schedule disruptions)

Disaster relief model

Author Objective function Constraints Decision variables Decision maker

Barbarosoglu &

(1) capacity constraint (2) flow conservation (3) mode shifting constraint (4) supplies are equal to the commodity flow

(1) amount of commodity sent over arc(i,j) by mode v in stage one and two

(2) mode-shifting amount of commodity in two stages (3) excess and shortage amount of commodity in stage two

Government (relief planner)

Stage two:

Minimize transportation cost, mode-shifting cost , the penalty costs of inventory holding and shortage

(1) capacity constraint (2) flow conservation (3) mode shifting constraint (4) determine the excess and shortage amounts of demands Tzeng et al. (2007) (1) Minimize setup cost and

total transportation cost

(2) Minimize total travel time (3) Maximize satisfaction

(1) the sum of amount of relief items doesn’t exceed the available demand and

available supply (2) flow conservation

(1) the amount of relief item transport from collection point to demand point (2) whether or not the candidate point is chosen as

Government (relief planner)

the transfer depot Our Model (1) Maximizing the total cargo

time value

(2) Minimizing the total incremental resilient costs

(1) capacity constraints (2) flow conservation (3) time constraints

(1) the amount of freights (2) the re-allocated capacities of own trucks

(3) recovery capacity

International express company