Geoscience Reference
In-Depth Information
We now review the related literature in the area of disaster relief supply chains.
About half of the research has applied quantitative techniques (Charles and Lauras
2011
). The other half utilizes case studies and empirical research to investigate past
disasters and to present best practices. Time is frequently a criterion/objective aimed
to be maintained along with the total cost being minimized (see, e.g., Zhenling
2009
;
Tzeng et al.
2007
). Such a formulation, however, may result in excessive delivery
times to the demand points if the distribution to certain locations is too costly. In our
model, in contrast, rather than evenly minimizing all travel/delivery times, we seek to
minimize the time deviations of activities on paths with respect to the pre-determined
target times. Thus, one can prioritize the demand points based on their locations,
demand patterns, number of elderly and children, etc.
Sheu (
2010
) constructed a dynamic fuzzy model of disaster relief response in
large-scale problems. Nagurney et al. (
2011
) presented a supply chain network
design model for critical needs with the possibility of outsourcing. Nagurney
et al. (
2012b
) developed a multiproduct supply chain for the production and
distribution of disaster relief items. Nagurney et al. (
2013
) presented supply chain
network frameworks for various timesensitive, perishable healthcare products such
as human blood, pharmaceuticals, medical nuclear products, etc. Nagurney and Yu
(
2014
), in turn, constructed a game theory supply chain network model for the case
of time-based oligopolistic competition.
Hale and Moberg (
2005
) proposed a set covering location model to identify
secure sites for the storage of emergency supplies. Barbarosoglu and Arda (
2004
)
and Falasca and Zobel (
2011
) developed two-stage stochastic models for the
procurement and transportation of the vital disaster relief items. Also, Mete and
Zabinsky (
2010
) proposed a two-stage stochastic model for the storage and distri-
bution of medical supplies to be used in case of emergencies. Balcik and Beamon
(
2005
) studied facility location in humanitarian relief. Huang et al. (
2012
) presented
performance measures for the efficiency, efficacy, and equity of relief distribution.
Liu and Nagurney (
2013
), in turn, constructed a supply chain network model
with quickresponse production and outsourcing under uncertain demand and cost.
Nagurney and Qiang (
2012
) developed network robustness and performance
measures in addition to synergy measurement of network integration in the case
of humanitarian partnerships. Qiang and Nagurney (
2012
) proposed a bi-criteria
indicator to evaluate the performance of supply chains of critical needs under
capacity and demand disruptions. MacKenzie and Barker (
2011
) integrated a
risk-management approach with a Multiregional Input-Output model using ideas
from Isard et al. (
1998
) to quantify the regional economic impacts of a supply
shortage. Simpson and Hancock (
2009
) applied simulation to the case of resource
allocation in an emergency response system. Rottkemper et al. (
2012
) presented a
bi-criteria mixed-integer programming model for the inventory relocation of relief
items. Furthermore, Ortu˜o et al. (
2011
) and Vitoriano et al. (
2011
) developed goal
programming frameworks for the distribution of relief goods while considering
targets for attributes such as the cost and travel time. A recent edited volume on
disaster management and emergencies is by Vitoriano et al. (
2013
) with a survey on
decision aid models for humanitarian logistics therein by Ortu˜o et al. (
2013
).
