CoCoSo - Combined Compromise Solution Method for MCDA

Provides a set of functions to implement the Combined Compromise Solution (CoCoSo) Method created by Yazdani, Zarate, Zavadskas and Turskis (2019) . This method is based on an integrated simple additive weighting and compromise exponentially weighted product model.

cocosoR

This program is an implementation of the Combined Compromise Solution (CoCoSo) Method (A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems, Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019), https://doi.org/10.1108/MD-05-2017-0458 ) based on an integrated simple additive weighting and compromise exponentially weighted product model.

Installation

You can install the development version of cocosoR from GitHub with:

# install.packages("devtools")
devtools::install_github("sergiofloquet/cocosoR")

library(cocosoR)

CoCoSo Method

This package is an implementation of the Combined Compromise Solution (CoCoSo) Method [1] based on an integrated simple additive weighting and compromise exponentially weighted product model. We can use the data from [1] as example:

data(example_cocoso)
example_cocoso
#>                                                                        
#> 1       Criteria Criteria_1 Criteria_2 Criteria_3 Criteria_4 Criteria_5
#> 2        Weights      0.036      0.192      0.326      0.326       0.12
#> 3  Optimal_value        MAX        MIN        MAX        MAX        MAX
#> 4  Alternative_1         60        0.4       2540        500        990
#> 5  Alternative_2       6.35       0.15       1016       3000       1041
#> 6  Alternative_3        6.8        0.1     1727.2       1500       1676
#> 7  Alternative_4         10        0.2       1000       2000        965
#> 8  Alternative_5        2.5        0.1        560        500        915
#> 9  Alternative_6        4.5       0.08       1016        350        508
#> 10 Alternative_7          3        0.1       1778       1000        920

CoCoSo method can be apply making the final ranking of all alternatives.

result<- cocoso(example_cocoso)
result
#>     Alternatives Order        k       ki_a     ki_b      ki_c        Si
#> 4  Alternative_1     5 2.041313 0.13062317 3.245282 0.7242238 0.4299734
#> 5  Alternative_2     2 2.787990 0.17546935 4.473498 0.9728678 0.6082495
#> 6  Alternative_3     1 2.882350 0.18036300 4.639646 1.0000000 0.6363392
#> 7  Alternative_4     4 2.416046 0.16332196 3.720904 0.9055181 0.4470733
#> 8  Alternative_5     7 1.298692 0.08779670 2.000000 0.4867778 0.2402679
#> 9  Alternative_6     6 1.443143 0.09737208 2.225040 0.5398672 0.2683310
#> 10 Alternative_7     3 2.519094 0.16505373 3.951291 0.9151197 0.5031435
#>          Pi
#> 4  3.291361
#> 5  4.390712
#> 6  4.502038
#> 7  4.205820
#> 8  2.260980
#> 9  2.505710
#> 10 4.199086

In addition we present in results all three aggregation strategies and the sum and product weight, used in [1].

References:

[1] Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501-2519.