Input data
sources for
Dalal, J. and
Uster, H., “Robust Emergency Relief Supply Planning for Foreseen Disasters
under Evacuation Side Uncertainty” submitted for publication.
A. Computational study data
The link to download the
instance data files used for computational study is given below. Please right
click on the link and click “Save Target As” to save it on your machine.
•
Computational study Data Instances - RO-dataset.zip
There are 12 folders representing
classes C1 – C12, and within each folder there are 10 “*.txt” instance files
that are randomly generated. The folder names follow a particular naming
convention to help a user match easily with the Table 2S in Online Supplement.
Folder name format: “c[A]-[B]-[C]-[D]-[E]-[F]-[G]”
where
A := class number, B :=
number of sources, C := number of UDCs , D := number of shelters,
E := number of DCs, F :=
number of CSLs, G := number of disaster events.
For example,
“c9-2000-400-200-40-30-10” contains 10 instance files of class 9 with
2000 sources, 400 UDCs, 200 shelters, 40 DCs, 30 CSL candidates, and 10
disaster events.
Each instance file within
such a folder is named as “c[i]_data_[j].txt”
where i = class number, j = 1, …, 10 (index number).
For example, the 7th
instance file of class 5 is names as: “c5_data7.txt”.
We now explain the content of
a data file.
The first line (header) of a
data file summarizes the number of different network entities and the events.
For example:
“CSL : 50 DC : 20 SH : 100 UDC : 200 SRC
: 1000 Scenarios:=10”
The legend used in these data
files are explained in Table 1.
Table 1: Legends used in data instance files
|
Symbol |
Details |
|
SourceList[ID,x,y,pop,
staybackReliefFrac] |
Source node ID, (x,y) coordinates,
population at that node, and the nominal fraction of the stay-back population
who seek relief (from UDCs). |
|
SHELTER_List[ID, x, y,
capa, VarCost_p] |
Shelter ID, (x,y)
coordinate, capacity (number of evacuees to accommodate), and variable cost
for evacuees in the shelter. |
|
UDCList[ID,x,y] |
UDC ID and (x,y)
coordinates. |
|
DCList[ID,x,y,VC_matl] |
DC ID, (x,y) coordinate,
and variable cost to handle relief item at that DC. |
|
DCCapa[k, p, Qdc_kp] |
At a DC (k), storage
capacity for item p |
|
CSLList[ID, x, y, fc_r] |
CSL ID, (x,y) coordinate,
fixed cost of opening the CSL. |
|
CSLCapa[ID, p, S_rp] |
CSL ID and storage capacity
for item p. |
|
TC_ppl[alpha_ppl] |
Unit transportation cost
for evacuee. |
|
ProductwiseTC[p, w_p,
alpha_smallTruck, alpha_medTruck, alpha_bigTruck] |
For each relief supply p,
demand per evacuee, and unit transportation cost in small, medium, and big
truck, respectively. |
|
ScenarioList[SID,
center_x, center_y, catg] |
Disaster event (scenario)
ID, (x,y) coordinate of its occurrence, and category (in 5 point scale) |
B.
Case study data
The
case study data are available in the folder “GIS-data” within dataset.zip. In
correspondence to the experiments in Section 7.1.1 and 7.1.2 the following 5
data files are prepared. These files differ only in the category data for each
of the 18 disaster event/scenarios. The table 2 below summarizes it.
|
File Name |
Explanation |
|
data_GIS_all_5.txt |
All 18 events represent of
category 5 hurricanes. (section 7.1.1) |
|
data_GIS_LO_1_HI_3.txt |
Events close to the low population areas are affected by
category 1 hurricane, and events close to high
population areas are hit by category 3 hurricane (Experiment 1 in section
7.1.2) |
|
data_GIS_LO_5_HI_3.txt |
Events close to the low population areas are affected by
category 5 hurricane, and events close to high
population areas are hit by category 3 hurricane (Experiment 2 in section
7.1.2) |
|
data_GIS_LO_3_HI_1.txt |
Events close to the low population areas are affected by
category 3 hurricane, and events close to high
population areas are hit by category 1 hurricane (Experiment 3 in section
7.1.2) |
|
data_GIS_LO_3_HI_5.txt |
Events close to the low population areas are affected by
category 3 hurricane, and events close to high
population areas are hit by category 5 hurricane (Experiment 4 in section
7.1.2) |