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ARTICLES • Pesqui. Oper. 43 • 2023 • https://doi.org/10.1590/0101-7438.2023.043.00263808 copy

STUDY FOR DECENTRALIZATION OF NAVAL POWER IN THE BRAZILIAN NAVY: STRUCTURING AND MATHEMATICAL MODELING IN THE LIGHT OF THE ELECTRE-MOR METHOD

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The Brazilian Navy (BN) has its combatant force concentrated in an area with only one exit to the sea. Nevertheless, Brazil has an extensive coast that needs to be patrolled and, in case of any kind of conflict, the BN must be able to defend the sovereignty and interests of the country. For this purpose, a robust and decentralized naval force is necessary, which allows the fulfillment of its mission. This paper aims to classify the most suitable cities in Brazil to be established as naval bases by applying the ELECTRE-MOr hybrid method. Seventeen cities were analyzed, which have an opening to the sea and a pre-existing minimum port structure. The alternatives were evaluated by specialists from the BN, with more than twenty years of active service, in the light of operational, economic and strategic criteria. As a result, five alternatives were classified as the most indicated headquarters for the decentralization of the naval power: Belém/PA, São Luis/MA, Fortaleza/CE, Recife/PE and Aratu/BA. This article presents relevance to academia and society, as it represents the application of a method in the state of the art to support the decision-making process in a significant problem for the sovereignty of Brazil, presenting a methodology that can be applied in issues of tactical, operational, strategic and political levels.

Keywords:
multicriteria analysis; ELECTRE-MOr; Brazilian Navy

MCDA method	Percentage
AHP	43.80%
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)	22.17%
Analytic Network Process (ANP)	6.30%
Condorcet	3.56%
Preference Ranking Organization Method for Enriching Evaluations (PROMETHEE)	3.56%
ELimination Et Choix Traduisant la REalité (ELECTRE)	3.56%
Decision Making Trial and Evaluation Laboratory (DEMATEL)	3.01%
Organísation, rangement et synthe`se de données relarionnelles (ORESTE)	3.00%
VIseKriterijumska Optimizacija I KOmpromisno Resenje (VIKOR)	2.44%
Borda	2.06%
Verbal Decision Analysis (VDA)	1.13%
Multiattribute Utility Theory (MAUT)	1.10%
THOR	0.94%
Reference Ideal Method (RIM)	0.56%
Full Consistency Method (FUCOM)	0.56%
Simple Multi Attribute Rating Technique (SMART)	0.56%
REGIME	0.56%
TODIM	0.38%
Utility Additive (UTA)	0.38%
FITradeoff	0.38%
Copeland	0.38%

Authors	Source	Methods
(Sennaroglu & Varlik Celebi, 2018SENNAROGLU B & VARLIK CELEBI G. 2018. A military airport location selection by AHP integrated PROMETHEE and VIKOR methods. Transportation Research Part D: Transport and Environment, 59: 160-173. Available at: https://doi.org/10.1016/j.trd.2017.12.022 https://doi.org/10.1016/j.trd.2017.12.02... )	Transportation Research Part D: Transport and Environment	AHP, PROMETHEE and VIKOR
(Farahani & Asgari, 2007FARAHANI RZ & ASGARI N. 2007. Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study. European Journal of Operational Research, 176(3): 1839-1858. Available at: https://doi.org/10.1016/j.ejor.2005.10.039 https://doi.org/10.1016/j.ejor.2005.10.0... )	European Journal of Operational Research	TOPSIS
(Gigovićyh et al., yth016GIGOVIĆ L, PAMUĈAR D, BAJIĆ Z & MILIĆEVIĆ M. 2016. The combination of expert judgment and GIS-MAIRCA analysis for the selection of sites for ammunition depots. Sustainability (Switzerland), 8(4). Available at: https://doi.org/10.3390/su8040372 https://doi.org/10.3390/su8040372... )	Sustainability	DEMATEL and ANP
(Bozanic et al., 2018BOZANIC D, TESIC D & MILICEVIC J. 2018. A hybrid fuzzy ahp-mabac model: Application in the serbian army -the selection of the location for deep wading as a technique of crossing the river by tanks. Decision Making: Applications in Management and Engineering, 1(1): 143-164. Available at: https://doi.org/10.31181/dmame1801143b https://doi.org/10.31181/dmame1801143b... )	Decision Making: Applications in Management and Engineering	AHP and Multi-Attributive Border Approximation Area Comparison (MABAC)
(Akgün & Erdal, 2019CROES GAAKGÜN Í & ERDAL H. 2019. Solving an ammunition distribution network design problem using multi-objective mathematical modeling, combined AHP-TOPSIS, and GIS. Computers and Industrial Engineering, 129: 512-528. Available at: https://doi.org/10.1016/j.cie.2019.02.004 https://doi.org/10.1016/j.cie.2019.02.00... )	Computers & Industrial Engineering	AHP and TOPSIS
(Lai, 2019LAI C.-M. 2019. Integrating simplified swarm optimization with AHP for solving capacitated military logistic depot location problem. Applied Soft Computing Journal, 78: 1-12. Available at: https://doi.org/10.1016/j.asoc.2019.02.016 https://doi.org/10.1016/j.asoc.2019.02.0... )	Applied Soft Computing	AHP
(Curran et al., 2014CURRAN RW, BATES ME & BELL HM. 2014. Multi-criteria decision analysis approach to site suitability of U.S Department of Defense humanitarian assistance projects (SD & VA. (eds.); Vol. 78, pp. 59-63). Elsevier Ltd. Available at: https://doi.org/10.1016/j.proeng.2014.07.039 https://doi.org/10.1016/j.proeng.2014.07... )	Procedia Engineering	Multi-Attribute Value Theory (MAVT) and PROMETHEE II

Relationship	Scale
is much less important than	− 2
is less important than	− 1
is as important as	0
is more important than	1
is much more important than	2

Alternatives	C1	C2	C3	C4	C5	C6	C7
A1 - Santana/AP	2544	607	165	5890	2	0	1
A2 - Belém/PA	2217	1200	100	5213	4	1	4
A3 - São Luís/MA	1910	4700	15	5291	4	0	5
A4 - Luís Correia/PI	1731	170	4	5921	0	0	1
A5 - Fortaleza/CE	1505	1000	1.7	5882	3	0	5
A6 - Natal/RN	1233	200	5.5	5177.5	3	1	4
A7 - Cabedelo/PB	1165	1300	2	4473	2	0	3
A8 - Recife/PE	1086	300	4	6176	4	0	6
A9 - Maceió/AL	972	500	2.3	5101	2	0	4
A10 - Aracajú/SE	875	750	4	4013	0	0	3
A11 - Aratu/BA	755	450	27	5191	4	1	5
A12 - Porto Seguro/BA	539	470	1	5970	0	1	3
A13 - Vitória/ES	252	140	7.3	6993	5	0	5
A14 - Santos/SP	220	300	11	5512	7	0	6
A15 - Paranaguá/PR	350	2100	25	3521	5	0	4
A16 - Itajáı/SC	389	240	4.5	6382	6	0	5
A17 - Rio Grande/RS	755	500	18	3488	4	0	4

Criterion	Weights
C1	Distance to Rio de Janeiro by the Sea	1.8
C2	Output width	3
C3	Distance to the Sea	1.6
C4	Square Meter Value	1.4
C5	Port Structure	2.6
C6	It has a Naval Base	2.1
C7	City Infrastructure	2.3

	C1	C2	C3	C4	C5	C6	C7
A1	2544	607	-165	-5890	2	0	1
A2	2217	1200	-100	-5213	4	1	4
A3	1910	4700	-15	-5291	4	0	5
A4	1731	170	4	-5921	0	0	1
A5	1505	1000	-1,7	-5882	3	0	5
A6	1233	200	-5,5	-5177.5	3	1	4
A7	1165	1300	-2	-4473	2	0	3
A8	1086	300	-4	-6176	4	0	6
A9	972	500	-2,3	-5101	2	0	4
A10	875	750	-4	-4013	0	0	3
A11	755	450	-27	-5191	4	1	5
A12	539	470	-1	-5970	0	1	3
A13	252	140	-7.3	-6993	5	0	5
A14	220	300	-11	-5512	7	0	6
A15	350	2100	-25	-3521	5	0	4
A16	389	240	-4.5	-6382	6	0	5
A17	755	500	-18	-3488	4	0	4
q	150	100	15	1000	1	0.5	1
p	300	200	50	2000	2	1	3
v	1200	500	120	5000	5	2	5
Weight	1,8	3	1.6	1,4	2,6	2,1	2,3
b _h2	1769.33	3180.00	-52.33	-4656.33	4.67	0.67	4.33
b _h1	994.67	1660.00	-108.67	-5824.67	2.33	0.33	2.67
b _n2	1233.00	750.00	-4.00	-5177.50	4.00	0.00	5.00
b _n1	755.00	300.00	-15.00	-5890.00	2.00	0.00	4.00

bh			bn
Alternative	Pessimist	Optimist	Alternative	Pessimist	Optimist
A1	C	B	A1	C	A
A2	C	B	A2	A	A
A3	A	A	A3	A	A
A4	C	B	A4	C	C
A5	C	B	A5	A	A
A6	C	C	A6	B	B
A7	B	B	A7	A	A
A8	C	B	A8	A	A
A9	C	C	A9	B	B
A10	C	C	A10	B	B
A11	C	B	A11	A	A
A12	C	C	A12	B	B
A13	C	B	A13	B	B
A14	C	B	A14	B	B
A15	B	B	A15	A	A
A16	C	B	A16	B	B
A17	C	C	A17	A	A

	Cities	Class
A2	Belém/PA
A3	São Luís/MA
A5	Fortaleza/CE	A
A8	Recife/PE
A11	Aratu/BA
A7	Cabedelo/PB
A13	Vitória/ES
A14	Santos/SP	B
A15	Paranaguá/PR
A16	Itajáı/SC
A1	Santana/AP
A4	Luís Correia/PI
A6	Natal/RN
A9	Maceió/AL	C
A10	Aracajú/SE
A12	Porto Seguro/BA
A17	Rio Grande/RS

λ	0.55	0.6	0.65	0.7	0.8	0.9	1
A1 - Santana/AP	C	C	C	C	C	C	C
A2 - Belém/PA	A	A	A	B	C	C	C
A3 - São Luís/MA	A	A	A	A	A	A	B
A4 - Luís Correia/PI	C	C	C	C	C	C	C
A5 - Fortaleza/CE	A	A	A	A	B	C	C
A6 - Natal/RN	C	C	C	C	C	C	C
A7 - Cabedelo/PB	B	B	B	B	C	C	C
A8 - Recife/PE	A	A	A	B	C	C	C
A9 - Maceió/AL	C	C	C	C	C	C	C
A10 - Aracajú/SE	B	C	C	C	C	C	C
A11 - Aratu/BA	A	A	A	B	C	C	C
A12 - Porto Seguro/BA	C	C	C	C	C	C	C
A13 - Vitória/ES	B	B	B	C	C	C	C
A14 - Santos/SP	B	B	B	C	C	C	C
A15 - Paranaguá/PR	B	B	B	B	B	C	C
A16 - Itajáı/SC	B	B	B	C	C	C	C
A17 - Rio Grande/RS	C	C	C	C	C	C	C
Class A	29.41%	29.41%	29.41%	11.76%	5.88%	5.88%	0.00%
Class B	35.29%	29.41%	29.41%	29.41%	11.76%	0.00%	5.88%
Class C	35.29%	41.18%	41.18%	58.82%	82.35%	94.12%	94.12%

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