TOPSIS

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 ^[1] with further developments by Yoon in 1987, ^[2] and Hwang, Lai and Liu in 1993. ^[3] TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).^{[ citation needed ]} A dedicated book in the fuzzy context was published in 2021 ^[4]

Description

It is a method of compensatory aggregation that compares a set of alternatives, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. The weights of the criteria in TOPSIS method can be calculated using Ordinal Priority Approach, Analytic hierarchy process, etc. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. Normalisation is usually required as the parameters or criteria are often of incongruous dimensions in multi-criteria problems. ^{[5] [6]} Compensatory methods such as TOPSIS allow trade-offs between criteria, where a poor result in one criterion can be negated by a good result in another criterion. This provides a more realistic form of modelling than non-compensatory methods, which include or exclude alternative solutions based on hard cut-offs. ^[7] An example of application on nuclear power plants is provided in. ^[8]

TOPSIS method

The TOPSIS process is carried out as follows:

Step 1

Create an evaluation matrix consisting of m alternatives and n criteria, with the intersection of each alternative and criteria given as ${\displaystyle x_{ij}}$, we therefore have a matrix ${\displaystyle (x_{ij})_{m\times n}}$.

Step 2

The matrix ${\displaystyle (x_{ij})_{m\times n}}$ is then normalised to form the matrix

${\displaystyle R=(r_{ij})_{m\times n}}$, using the normalisation method

${\displaystyle r_{ij}={\frac {x_{ij}}{\sqrt {\sum _{k=1}^{m}x_{kj}^{2}}}},\quad i=1,2,\ldots ,m,\quad j=1,2,\ldots ,n}$

Step 3

Calculate the weighted normalised decision matrix

${\displaystyle t_{ij}=r_{ij}\cdot w_{j},\quad i=1,2,\ldots ,m,\quad j=1,2,\ldots ,n}$

where ${\displaystyle w_{j}=W_{j}{\Big /}\sum _{k=1}^{n}W_{k},j=1,2,\ldots ,n}$ so that ${\displaystyle \sum _{i=1}^{n}w_{i}=1}$, and ${\displaystyle W_{j}}$ is the original weight given to the indicator ${\displaystyle v_{j},\quad j=1,2,\ldots ,n.}$

Step 4

Determine the worst alternative ${\displaystyle (A_{w})}$ and the best alternative ${\displaystyle (A_{b})}$:

${\displaystyle A_{w}=\{\langle \max(t_{ij}\mid i=1,2,\ldots ,m)\mid j\in J_{-}\rangle ,\langle \min(t_{ij}\mid i=1,2,\ldots ,m)\mid j\in J_{+}\rangle \rbrace \equiv \{t_{wj}\mid j=1,2,\ldots ,n\rbrace ,}$

${\displaystyle A_{b}=\{\langle \min(t_{ij}\mid i=1,2,\ldots ,m)\mid j\in J_{-}\rangle ,\langle \max(t_{ij}\mid i=1,2,\ldots ,m)\mid j\in J_{+}\rangle \rbrace \equiv \{t_{bj}\mid j=1,2,\ldots ,n\rbrace ,}$

where,

${\displaystyle J_{+}=\{j=1,2,\ldots ,n\mid j\}}$ associated with the criteria having a positive impact, and

${\displaystyle J_{-}=\{j=1,2,\ldots ,n\mid j\}}$ associated with the criteria having a negative impact.

Step 5

Calculate the L²-distance between the target alternative ${\displaystyle i}$ and the worst condition ${\displaystyle A_{w}}$

${\displaystyle d_{iw}={\sqrt {\sum _{j=1}^{n}(t_{ij}-t_{wj})^{2}}},\quad i=1,2,\ldots ,m,}$

and the distance between the alternative ${\displaystyle i}$ and the best condition ${\displaystyle A_{b}}$

${\displaystyle d_{ib}={\sqrt {\sum _{j=1}^{n}(t_{ij}-t_{bj})^{2}}},\quad i=1,2,\ldots ,m}$

where ${\displaystyle d_{iw}}$ and ${\displaystyle d_{ib}}$ are L²-norm distances from the target alternative ${\displaystyle i}$ to the worst and best conditions, respectively.

Step 6

Calculate the similarity to the worst condition:

${\displaystyle s_{iw}=d_{iw}/(d_{iw}+d_{ib}),\quad 0\leq s_{iw}\leq 1,\quad i=1,2,\ldots ,m.}$

${\displaystyle s_{iw}=1}$ if and only if the alternative solution has the best condition; and

${\displaystyle s_{iw}=0}$ if and only if the alternative solution has the worst condition.

Step 7

Rank the alternatives according to ${\displaystyle s_{iw}\,\,(i=1,2,\ldots ,m).}$

Normalisation

Two methods of normalisation that have been used to deal with incongruous criteria dimensions are linear normalisation and vector normalisation.

Linear normalisation can be calculated as in Step 2 of the TOPSIS process above. Vector normalisation was incorporated with the original development of the TOPSIS method, ^[1] and is calculated using the following formula:

${\displaystyle r_{ij}={\frac {x_{ij}}{\sqrt {\sum _{k=1}^{m}x_{kj}^{2}}}},\quad i=1,2,\ldots ,m,\quad j=1,2,\ldots ,n}$

In using vector normalisation, the non-linear distances between single dimension scores and ratios should produce smoother trade-offs. ^[9]

Online tools

•  : DeciGen A free MCDA Plugin for Grasshopper .
• Decision Radar  : A free online TOPSIS calculator written in Python.
• Yadav, Vinay; Karmakar, Subhankar; Kalbar, Pradip P.; Dikshit, A.K. (January 2019). "PyTOPS: A Python based tool for TOPSIS". SoftwareX. 9: 217–222. Bibcode:2019SoftX...9..217Y. doi: 10.1016/j.softx.2019.02.004 .

References

1. Hwang, C.L.; Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.
2. Yoon, K. (1987). "A reconciliation among discrete compromise situations". Journal of the Operational Research Society. 38 (3): 277–286. doi:10.1057/jors.1987.44. S2CID   121379674.
3. Hwang, C.L.; Lai, Y.J.; Liu, T.Y. (1993). "A new approach for multiple objective decision making". Computers and Operational Research. 20 (8): 889–899. doi:10.1016/0305-0548(93)90109-v.
4. El Alaoui, M. (2021). Fuzzy TOPSIS: Logic, Approaches, and Case Studies. New York: CRC Press. doi:10.1201/9781003168416. ISBN   978-0-367-76748-8. S2CID   233525185.
5. Yoon, K.P.; Hwang, C. (1995). Multiple Attribute Decision Making: An Introduction. SAGE publications.
6. Zavadskas, E.K.; Zakarevicius, A.; Antucheviciene, J. (2006). "Evaluation of Ranking Accuracy in Multi-Criteria Decisions". Informatica. 17 (4): 601–618. doi: 10.15388/Informatica.2006.158 .
7. Greene, R.; Devillers, R.; Luther, J.E.; Eddy, B.G. (2011). "GIS-based multi-criteria analysis". Geography Compass. 5 (6): 412–432. doi:10.1111/j.1749-8198.2011.00431.x.
8. Locatelli, Giorgio; Mancini, Mauro (2012-09-01). "A framework for the selection of the right nuclear power plant" (PDF). International Journal of Production Research. 50 (17): 4753–4766. doi:10.1080/00207543.2012.657965. ISSN   0020-7543. S2CID   28137959.
9. Huang, I.B.; Keisler, J.; Linkov, I. (2011). "Multi-criteria decision analysis in environmental science: ten years of applications and trends". Science of the Total Environment. 409 (19): 3578–3594. Bibcode:2011ScTEn.409.3578H. doi:10.1016/j.scitotenv.2011.06.022. PMID   21764422.