Evaluation of decision support system for disaster management using multi-criteria decision techniques: a case study of Alappuzha, Kerala

Figures & data

Table 1. List of recent major flood events in India (source: NDMA).

Flood Event	Year	Affected Regions	Details
Bengaluru Floods	2020	Karnataka (Bengaluru)	In Bengaluru, heavy rains caused floods, exposing the city’s insufficient drainage system and creating discomfort to inhabitants, especially in low-lying regions.
Kerala Floods (2019)	2019	Kerala	Like 2018 the state experienced devastating floods caused by heavy mansoon rains. The floods resulted in loss of life, displacement of people, and extensive damage to infrastructure and culture.
Kerala Floods (2018)	2018	Kerala	Heavy monsoon rains triggered extensive flooding and landslides, killing over 400 people and displacing thousands more. The state experienced the worst flooding in nearly a century.
Gujarat Floods	2017	Gujarat	Heavy monsoon rains caused flooding in several Gujarat districts, displacing people and destroying homes and agricultural land. The government was forced to conduct relief and rescue activities.
Chennai Floods	2015	Tamil Nadu (Chennai)	Severe flooding in Chennai was caused by unusually heavy rainfall, disrupting regular life and causing considerable damage to the city’s infrastructure, including roads and buildings.
Jammu and Kashmir Floods	2014	Jammu and Kashmir	Heavy rains caused disastrous floods and landslides across the state, killing people and destroying houses and infrastructure.
Uttarakhand Floods	2013	Uttarakhand	Heavy rains caused catastrophic flash floods and landslides. Thousands of people were killed, and infrastructure, including as roads and bridges, was seriously damaged.
Assam Floods	Various Years	Assam	During the monsoon season, Assam endures annual flooding, affecting millions of people and causing damage to houses, crops, and infrastructure. The degree of intensity fluctuates from year to year.
Bihar Floods	Various Years	Bihar	Every year, monsoon flooding causes loss of life, crop destruction, and displacement of people in Bihar. Flooding severity changes from year to year.

Table 2. Literature review on various techniques used for sustainable supply chain management.

Author(s)	Year	Solution Technique	Research Overview	Disaster Phase	Model/Formulation
Dekle et al.	2005	Integer Programming, GIS Software Simulation	Maximal Covering Location Model	Mitigation	Maximal Covering Location Model
Tzeng et al.	2007	AHP	Optimal planning for designing relief logistics.	Preparation & Response	Maximal Covering Location Model
Chang et al.	2007	Mixed-Integer Programming, Sample Average Approximation Scheme, Spatial Data Analysis with ArcGIS	Demand and Location of Demand	Preparation & Response	Location-Allocation Model
Balcik and Beamon	2008	Mixed-Integer Linear Programming	Demand and Location of Demand	Preparation & Response	Maximal Covering Location Model
Rawls and Turnquist	2008	Mixed-Integer Programming, Solution Computation using CPLEX	Short Term Demand and Demand Location	Preparation & Response	Dynamic Allocation Model
Horner and Downs	2008	Mixed-Integer Programming, C++ Programming, Geographic Information System (GIS) Spatial Data Management, and Trans CAD Simulation	Warehouse Location	Mitigation	Warehouse Location Model
Rawls and Turnquist	2010	Mixed-Integer Linear Program, Lagrangian L-Shaped Method, Heuristic Algorithm	Demand and Route Reliability	Preparation & Response	Location-Allocation Model
Zhang et al.	2010	Integer Programming, Local Search Heuristic	Resource Allocation	Response	Resource Allocation Model
Bozorgi-Amiri et al.	2013	Particle Swarm Optimization Metaheuristic, Mixed-Integer Nonlinear Programming (Robust Optimization)	Demand, Location, Cost of Procurement, and Transportation	Preparation & Response	Location-Allocation Model
Mahdi Alinaghian et al.	2017	Fuzzy Theory	Locating temporary logistics hubs in disaster scenarios.	Preparation & Response	Location-Allocation Model
Barzinpour and Esmaeili	2017	Goal Programming, Mixed-Integer Linear Programming	Location-Allocation Model	Preparation & Response	Location-Allocation Model
Xiangyang Ren and Juan Tan	2021	Simulation Algorithm	Dynamic relief-demand management in emergency logistics.	Preparation & Response	Location-Allocation Model
Ghasemi et al.	2021	Genetic Algorithm	Bi-objective logistics distribution location-routing problem.	Preparation & Response	Bi-objective Location-Routing Model

Figure 1. Study area map of Alappuzha district, Kerala.

Figure 2. Land use distribution (in %) of Alappuzha district.

Figure 3. Road network (left) and relief camp density (right) maps in Alappuzha district.

Figure 4. Detailed methodology.

Table 3. Scaling of FAHP.

Categories	Triangular FAHP
Equally Important	1 1 1 1 1 1
Intermediate preference	1 2 3 1/3 1/2 1
Moderately more important	2 3 4 1/4 1/3 1/2
Intermediate preference	3 4 5 1/5 1/4 1/3
Strongly more important	4 5 6 1/6 1/5 1/4
Intermediate preference	5 6 7 1/7 1/6 1/5
Very Strong more important	6 7 8 1/8 1/7 1/6
Intermediate preference	7 8 9 1/9 1/8 1/7
Extremely more important	9 9 9 1/9 1/9 1/9

Figure 5. FAHP methodology.

Table 4. Scaling of BWM.

Categories	BWM Weights
Equally Important	1
Intermediate preference	2
Moderately more important	3
Intermediate preference	4
Strongly more important	5
Intermediate preference	6
Very Strong more important	7
Intermediate preference	8
Extremely more important	9

Figure 6. Hierarchical structure of criteria selection.

Table 5. Sampling.

Experts	Sample size	Designation	Qualification	Experience
Department of disaster management	2	Deputy Collector, Hazard analyst, financial advisor	Post Graduation	Above 15 years
Revenue department	2	Sub collector	Post Graduation	Above 20 years
Taluk office	1	Tehsildar	Post Graduation &	10 years &
Fire force department	1	Senior fire force officer	Graduation	Above 15 years
Police department	2	CI and SI	Post Graduation & Graduation	15 years & 12 years
Civil supply office	1	Asst. Civil supplies officer	Post Graduation	18 years
SWTD directorate	2	Director, Asst. engineer, Traffic Superintendent	Post Graduation	Above 15 years
AEO Office	1	Asst. Educational officer	Post Graduation	Above 20 years
Regional and National NGO	2	Member	Graduation	10 years
Town and Country planning	1	Chief Town planner	Post Graduation	15 years
NATPAC	1	Logistics analyst	Post Graduation	10 years

Table 6. Pairwise comparison matrix of sub criteria with respect to commodity.

	Right Commodity	Excess Commodity	Excess demand	Quality of Commodity
Right Commodity	1 1 1	.11 .11 .11	.11 .11 .11	.125 .142 .166
Excess commodity	9 9 9	1 1 1	.125 .142 .166	.125 .142 .166
Excess demand	9 9 9	6 7 8	1 1 1	.166 .2 .25
Quality of commodity	6 7 8	6 7 8	4 5 6	1 1 1

Table 7. Pairwise comparison matrix of sub criteria with respect to cost.

	Cost of Commodity	Transportation cost	Storage cost	Operation cost	Maintenance cost
Cost of commodity	1 1 1	.11 .11 .11	.125 .142 .166	.125 .142 .166	2 3 4
Transportation cost	9 9 9	1 1 1	6 7 8	4 5 6	9 9 9
Storage cost	6 7 8	.125 .142 .166	1 1 1	2 3 4	2 3 4
Operation cost	4 5 6	.11 .11 .11	.25 .33 .5	1 1 1	.25 .33 .5
Maintenance cost	.25 .33 .5	.11 .11 .11	.25 .33 .5	2 3 4	1 1 1

Table 8. Pairwise comparison matrix of sub criteria with respect to cost.

	Right Commodity	Excess Commodity	Excess demand	Quality of Commodity
Mode	1 1 1	.125 .142 .166	.166 .2 .25	.125 .142 .166
Network resilience	6 7 8	1 1 1	6 7 8	4 5 6
Ontime delivery	4 5 6	.125 .142 .166	1 1 1	.166 .2 .25
Frequency of delivery	6 7 8	.166 .2 .25	4 5 6	1 1 1

Table 9. Pairwise comparison matrix of sub criteria with respect to Infrastructure.

	IT Infrastructure	Electricity	Gas and water supply	Health care
IT Infrastructure	1 1 1	.125 .142 .166	.11 .11 .11	.166 .2 .25
Electricity	6 7 8	1 1 1	.125 .142 .166	2 3 4
Gas and water supply	9 9 9	6 7 8	1 1 1	4 5 6
Health care	4 5 6	.25 .33 .5	.166 .2 .25	1 1 1

Table 10. Pairwise comparison matrix of sub criteria with respect to location.

	Closeness to major supply point	District center	Closeness to potential point
Closeness to major supply point	1 1 1	.166 .2 .25	4 5 6
District center	4 5 6	1 1 1	6 7 8
Closeness to potential point	.166 .2 .25	.125 .142 .166	1 1 1

Figure 7. FAHP result weights.

Table 11. Pairwise comparison matrix of Commodity.

commodity	Right Commodity	Excess commodity	Excess demand	Quality of commodity	Ksi
Weights	.07	.20	.28	.45	.11

Table 12. Pairwise comparison matrix of Cost.

Costs	Commodity cost	Transportation cost	Storage cost	Operation cost	Maintenance cost	Ksi
weights	.05	.58	.16	.11	.10	.15

Table 13. Pairwise comparison matrix of transportation.

Transportation	Mode	Network resilience	Ontime delivery	Frequency of delivery	Ksi
Weights	.05	.53	.17	.25	.18

Table 14. Pairwise comparison matrix of Infrastructure.

Infrastructure	IT Infrastructure	Electricity	Gas and water supply	Health care	Ksi
Weights	.05	.24	.55	.15	.18

Table 15. Pairwise comparison matrix of location.

Location	Closeness to major supply point	District center	Closeness to potential point	Ksi
Weights	.30	.65	.5	.21

Figure 8. FAHP and BWM result comparison.

Figure 9. Statical test results.