E-Rickshaw is an E-vehicle that has three wheels, a rechargeable battery driven electric motor as engine. E-rickshaw has become very popular due to low operating cost, low maintenance cost, eco-friendliness and ease of driving. It is perfect for small distance transport. As a last mile connector, it has transformed the public transport system in India. The low cost electric vehicle carries enough people to make a decent income and hence has become a source of livelihood for many. For considering the issues in this paper, detailed attributes of E-rickshaw are studied and Analytical Hierarchy Process (AHP) has been applied to calculate criteria weights for the sorted attributes. Subsequently, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi Criteria Decision Making (MCDM) technique has been applied for the selection of best E-Rickshaw. In this paper, sensitivity analysis and comparative analysis have been conducted for further insight.
PrahaladC., ‘Fortune at the bottom of the pyramid: eradicating poverty through profit’. USA: Wharton School Publishing (2005).
12.
HangC., ChenJ. and SubramianA.M., ‘Developing disruptive products for emerging economies: lessons from Asian cases’, IEEE41(4) (2010), 119–126.
13.
ChandranN. and BrahmachariS.K., Technology, knowledge and markets: connecting the dots—electric rickshaw in India as a case study, J Frugal Innovation1 (2015) 3. https://doi.org/10.1186/s40669-015-0003-5
14.
MajumdarD. and JashT., Merits and Challenges of E-Rickshaw as An Alternative form of Public Road Transport System: A Case Study in the State of West Bengal in India, Energy Procedia79 (2015), 307–314.
15.
SrikrishnaS., Sreenivasulu ReddyS. and VaniS., ‘A New Car Selection in the Market using TOPSIS Technique’.International Journal of Engineering Research and General Science2 (2014), ISSN: 2091–2730.
16.
SahuJ. and NishantS., Application of multiple criteria decision making mathematical model for selecting best automobile, International Journal of Scientific Research and Development 5(04) (2017), ISSN (online): 221-0613.
17.
MitraS. and KunduS., Application of TOPSIS for best Domestic Refrigerator Selection, International Journal of Research and Analytical Reviews5(3) (2018), ISSN (print): 2349–5138.
18.
UlkhaqM.M., WijayantiW.R., ZainM.S., BaskaraE. and LeonitaW., Combining the AHP and TOPSIS to Evaluate Car Selection, Proceedings of the 2nd International Conference on High Performance Compilation, Computing and Communication- HP3C. (2018). Doi: http://doi.org/10.1145/3195612.3195628.
19.
SenthilvelS., MuruganandhamR., RavichandranK., ManokaranE. and SubhashiniS., Analysis of corporate stress using TOPSIS Method- A case study on Nationalised banks in Madurai City, International Journal of Management and Business Studies1(3) (2011), ISSN 2231-2463 (print).
20.
BulgurcuB., Application of TOPSIS technique for Financial Performance Evaluation of Technology Firms in Istanbul Stock Exchange Market, Procedia- Social and Behavioural Sciences62 (2012), 1033–1040.
21.
SamiM. and Murat, Audit Company Selection by using the AHP and TOPSIS Methods, International Research Journal of Applied FinanceVIII(10) (2017), ISSN: 2229–6891.
22.
TugbaS. and KayralI.E., Applying Two-stage TOPSIS Approach and Step-wise Regression in Evaluating Bank Performance: Evidence from Turkish Banks, Banks and Banking System14(4) (2019), doi: 10.21511/bbs.14 (4).2019.11.
23.
StevićŽ., PamučarD., PuškaA. and ChatterjeeP., Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to Compromise solution (MARCOS), Computers and Industrial Engineering140 (2020), 106231.
24.
ZavadskasE.K., KalibatasD. and KalibatieneD., A multi-attribute assessment using WASPAS for choosing an optimal indoor environment, Archives of Civil and Mechanical Engineering16 (2016), 76–85.
25.
PinemA.P.R., AsmiatunS. and PutriA.N., Determination of Industrial Location Using the WASPAS Method with Spatial Data as Criteria Data, Jurnal RESTI (RekayasaSistem dan TeknologiInformasi)4(4) (2020), 691–696.
26.
SimanavicieneR. and UstinovichiusL., Sensitivity analysis for multiple criteria decision making methods: TOPSIS and SAW, Procedia-Social and Behavioral Sciences2(6) (2010), 7743–7744.
27.
SalehiA. and IzadikhahM., A novel method to extend SAW for decision-making problems with interval data, Decision Science Letters3(2) (2014), 225–236.
28.
HandokoD., MesranM., NasutionS.D., YuhandriY. and NurdiyantoH., Application Of Weight Sum Model (WSM) In Determining Special Allocation Funds Recipients, The IJICS (International Journal of Informatics and Computer Science)1(2) (2017).
29.
DwivediS.K. and DwivediA., Application of MOORA and WSM method for supplier selection in manufacturing, International Journal for Advance Research and Development3(7) (2018), 114–117.
30.
LinM., HuangC. and XuZ., TOPSIS method based on correlation coefficient and entropy measure for linguistic pythagorean fuzzy sets and its application to multiple attribute decision making, Complexity (2019).
31.
ChenK., ChenP., YangL. and JinL., Grey clustering evaluation based on AHP and interval grey number, International Journal of Intelligent Computing and Cybernetics (2019).
32.
CanG.F. and DemirokS., Universal usability evaluation by using an integrated fuzzy multi criteria decision making approach, International Journal of Intelligent Computing and Cybernetics (2019).
33.
LiH., LvL., LiF., WangL. and XiaQ., A novel approach to emergency risk assessment using FMEA with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment, International Journal of Intelligent Computing and Cybernetics (2020).
34.
WangL., XiaQ., LiH. and CaoY., Multi-criteria decision making method based on improved cosine similarity measure with interval neutrosophic sets, International Journal of Intelligent Computing and Cybernetics (2019).
35.
LinM., ChenY. and ChenR., Bibliometric analysis on Pythagorean fuzzy sets during 2013–2020, International Journal of Intelligent Computing and Cybernetics (2020).
36.
HwangC.L. and YoonK., ‘Multiple Attribute Decision Making: Methods and Applications’, Berlin Heidelberg New York, Springer- Verlag (1981).
37.
HungC.C. and ChenL.H., ‘A fuzzy TOPSIS decision making model with entropy weight under intuitionistic fuzzy environment’, In Proceedings of the international multi-conference of engineers and computer scientists. IMECS Hong Kong1 (2009), 13–16.
38.
“Mahindra launches e-Alfa Mini electric rickshaw: A complete three-wheeler, specifically designed for pollution-free last mile connectivity, it boasts of the tough and rugged Mahindra DNA”, Economic Times Auto published on September 09, 2017.
39.
StevićŽ. and BrkovićN., A Novel Integrated FUCOM-MARCOS Model for Evaluation of Human Resources in a Transport Company, Logistics4(1) (2020), 4.
40.
Watson,Robert and McCarthy,James and Canziani,Pablo and Nakicenovic,Nebojsa and Hisas,Liliana, Watson, Robert and McCarthy, James and Canziani, Pablo and Nakicenovic, Nebojsa and Hisas, Liliana, ‘The Truth Behind the Climate Pledges’. (2019). ISBN: 978-0-9831909-3-6. DOI:10.13140/RG.2.2.23744.28169
41.
‘India has put in place a game plan for electric vehicles, components to reduce imports, says NITI Aayog CEO Amitabh Kant’, Press Trust of India, published on July 27, 2019
MalikY., DwivediR., PrakashN. and KapoorA., Impact assessment of E-Rickshaws while analyzing entrepreneurial success of rickpullers, Indian Journal of Economics and Business17(3) (2018), 287–294.
44.
ShandilyaN., SainiV. and GhorpadeA.R., ‘Handbook: E-Rickshaw Deployment In Indian Cities’, ICLEI South Asia (2019)
45.
RahamanM., MondalS.P., ShaikhA.A., PramanikP., RoyS., MaitiM.K. and DeD., Artificial bee colony optimization-inspired synergetic study of fractional-order economic production quantity model, Soft Computing (2020), 1–19.
46.
SenB., HussainS.A.I., MiaM., MandalU.K. and MondalS.P., Selection of an ideal MQL-assisted milling condition: an NSGA-II-coupled TOPSIS approach for improving machinability of Inconel 690, The International Journal of Advanced Manufacturing Technology103(5–8) (2019), 1811–1829.
47.
SenB., MiaM., MandalU.K. and MondalS.P., GEP-and ANN-based tool wear monitoring: a virtually sensing predictive platform for MQL-assisted milling of Inconel 690, The International Journal of Advanced Manufacturing Technology105(1–4) (2019), 395–410.
48.
SenB., MiaM., MandalU.K., DuttaB. and MondalS.P., Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS, Neural Computing and Applications31(12) (2019), 8693–8717.
49.
Garg,Harish, A hybrid PSO-GA algorithm for constrained optimization problems, Applied Mathematics and Computation274 (2016), 292–305.
50.
Garg,Harish and RaniMonica, An approach for reliability analysis of industrial systems using PSO and IFS technique, ISA Transactions52.6 (2013), 701–710.
51.
SamantaS., SarkarB., ShinD. and PalM., Completeness and regularity of generalized fuzzy graphs, SpringerPlus5(1) (2016), 1979.
52.
SamantaS. and SarkarB., A study on generalized fuzzy graphs, Journal of Intelligent & Fuzzy Systems35 (3) (2018), 3405–3412.
53.
JaarsveldM.J.V. and GouwsR., An active hybrid energy storage system utilising a fuzzy logic rule-based control strategy, World Electric Vehicle Journal11(2) (2020), 34.
54.
MakrygiorgouJ.J. and AlexandridisA.T., Fuzzy logic control of electric vehicles: Design and analysis concepts. In twelfth international conference on ecological vehicles and renewable energies (EVER) (pp. 1-6). IEEE, (2017).
55.
SarkarB., MondalS.P., HurS., AhmadianA., SalahshourS., GuchhaitR. and IqbalM.W., An optimization technique for national income determination model with stability analysis of differential equation in discrete and continuous process under the uncertain environment, RAIRO-Operations Research53(5) (2019), 1649–1674.
56.
RahamanM., MondalS.P., ShaikhA.A., AhmadianA., SenuN. and SalahshourS., Arbitrary-order economic production quantity model with and without deterioration: generalized point of view, Advances in Difference Equations2020(1) (2020), 16.
57.
SattyT.L., The Analytic Hierarchy Process; McGraw-Hill: New York, NY, USA, 1980.
