Information Technology Reference
In-Depth Information
Q
i
,
v
i
,
u
i
≥
0 (4)
Objective function (1) minimizes the overall cost of holding excess aid supplies. Ob-
jection function (2), minimizes the shortage cost (number of lives affected) of not
having an aid supply. Constraint (3) guarantees that the number of aid supplies re-
quired for a specific disaster corresponds with the expected demand of a scenario,
while taking excess inventory and shortages into consideration. Constraint (4) ensures
that decision variables
Q
i
,
v
i
and
u
i
remain greater or equal to 0. It is assumed that no
excess inventory is present during the first usage of the model.
3.2
Data Gathering
Predicting a disaster is challenging, and in most cases impossible. However, a proba-
bility can be determined to pre-determine the likelihood of such an event. The ap-
proach used to determine these estimates was to observe the number of times the
identical disasters have occurred in Somalia in the past 30 years. We use data from
the Emergency Disaster Database (EM-DAT) as provided by the Centre for Research
on the Epidemiology of Disasters (CRED) [24]. In this database, an event qualifies as
a disaster if at least one of the following criteria are fulfilled: 10 or more people are
reported killed; 100 or more people are reported affected, injured and/or homeless;
there has been a declaration of a state of emergency; or there has been a call for inter-
national assistance. We measure the severity of a disaster in Somalia by the number of
people affected.
The repetition of occurrences of each disaster is then divided by the overall total of
Somalia disasters, giving the result of
q
k
of each disaster. The parameter
n
k
, represents
the estimated number of victims likely to be affected by a disaster in its worst magni-
tude. Therefore, if a drought occurs, it is most likely that the entire population (100%)
may be affected. These values are multiplied by the total population of an area to give
an indication of the total victims affected.
Holding cost comprises the cost of carrying one unit of inventory for one time pe-
riod, and usually indicates storage and insurance cost, taxes on inventory, labor cost,
and cost of spoilage, theft, or obsolescence [25]. However, the inventory carrying cost
will vary according to each individual warehouse, but for testing purposes it is as-
sumed that inventory carrying cost equals 25% of product value per annum [13]. The
shortage cost represents the amount of people who will be affected if an aid supply is
not available during and after the disaster event.
The preemptive optimization model performs multi-objective optimization by first
optimizing objective function (1), i.e., the cost of holding an aid supply. Objective
function (2), i.e., the cost of the total shortages, is optimized subject to the require-
ment that 1 has achieved its optimal value [23].
4
Results and Findings
The model was solved to construct four efficient frontier curves, each representing a
category. The efficient frontier indicates the efficient points when considering the
