Multicriteria decision making and goal programming for determination of electric automobile aimed at sustainable green environment: a case study

Abstract

In this paper, we consider the problem of automobile selection for transportation in inner city using a hybrid multicriteria decision making approach. The electric automobiles that are a relatively new concept in the world of the automotive industry, are widely viewed as attractive among its alternatives day by day. Fuel-vehicles produce a lot of carbon emissions that are ejected into our natural atmosphere, leaving us vulnerable to things like pollution and greenhouse gases. So, electric vehicle and automobiles have emerged as a more efficient alternative and these vehicles have been a great step forward to help positively the environment with zero emissions and total energy consumption in their lifecycle. Many companies focus on electric vehicle production with the development of electric vehicle technology. Therefore, the selection process emerges among the various electric automobile technologies for the users. The selection process includes several conflicting factors which are such as economic, technical and technological factors. In the present study, we propose a hybrid approach for electric automobile selection that combines analytic hierarchy process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS) and goal programming (GP) is used to determine the weights to assign to the factors that go into these selection decisions and TOPSIS method is used for preference ranking. These weights founded by AHP are inputted into a GP model to determine the best alternative among the electric automobiles. Finally, the study used three methods TOPSIS, AHP- TOPSIS and AHP-GP for better comparison and evaluation. The most suitable electric automobile is selected among their alternatives by using analytic methods and goal programming.

Selection of EVs as Tourist and Logistic Means of Transportation in Bosnia and Herzegovina’s Nature Protected Areas Using Z-Number and Rough Set Modeling

“…In Bosnia and Herzegovina, two areas have been designated as strict nature reserves (SNRs): SNR Prašuma Janj and SNR Prašuma Lom. Furthermore, the country is home to a total of four national parks, including National Park (NP) Kozara, NP Sutjeska, NP Una, and the most recently established NP [20] Advanced battery technologies selection MCDM-WPM Babar et al [21] Viability of EV in the current market Fuzzy SWOT, fuzzy LP Büyüközkan and Uztürk [22] Sustainable urban logistics SAW, VIKOR Hamurcu and Eren [23] Electric bus selection AHP, TOPSIS Khan et al [24] HEV selection TOPSIS Ziemba [25] EV selection for government use PROSA-C, Monte Carlo Ecer [26] Comprehensively assessing BEV alternatives SECA, MARCOS, MAIRCA, COCOSO, ARAS, COPRAS Khan and Ali [27] Smart waste management adoption framework Fuzzy SWARA, fuzzy VIKTOR Ren et al [28] Selection strategies for BEVs LDA, DEMATEL, DANP, VIKTOR Ziemba [29] Selection of city and compact EVs NEAT F-PROMETHEE Ziemba [30] Analysis and recommendation of the EV Monte Carlo, fuzzy TOPSIS, fuzzy SAW, NEAT F-PROMETHEE II Hamurcu and Eren [31] EV selection for transportation in inner city AHP, GP, TOPSIS He [32] Selection of battery electric bus EWM Oztaysi et al [33] EV selection problem F-SMART Ozdagoglu et al [34] Bus selection for intercity transportation PIPRECIA, COPRAS-G Stopka et al [35] Evaluation of selected passenger EVs AHP Štilić et al [36] Taxi service EV selection SWARA, MSDM, MABAC Wei and Zhou [37] EV supplier selection BWM, fuzzy VIKOR Ziemba and Gago [38] Selection and analysis of e-scooters PROMETHEE GDSS, GAIA Puška et al [39] EV selection DNMEREC, DNCRADIS Ba ˛czkiewicz and Wa ˛tróbski [40] EV selection Entropy, standard deviation (SD), CRITIC, Gini coefcient-based, MEREC, statistical variance, CILOS, IDOCRIW, VIKOR Dwivedi and Sharma [41] EV selection Entropy, TOPSIS Drina. In this research, the focus will be on creating a sustainable transportation system based on the selection of EVs for the needs of the NP Kozara.…”

“…Vehicle cost is another signifcant criterion. Te cost price of individual vehicle models was used to calculate the vehicle cost [31]. Te cost of a vehicle varies depending on its size and equipment, and the values of two versions of the same EV are taken here as it is essential to keep expenses as low as possible in order to obtain a high-quality EV at the lowest possible cost.…”

“…When decision-making includes uncertainty, Z-numbers may be applied in all MCDA approaches. [18]; Ziemba [25]; Ecer [26]; Ziemba [29,30]; Hamurcu and Eren [31]; Stopka et al [35] Beneft…”

“…Vehicle cost Euro Biswas and Das [18]; Ziemba [25]; Ecer [26]; Ziemba [29,30]; Hamurcu and Eren [31]; Stopka et al [35] Cost C3 Vehicle consumption wh/km Ziemba [25]; Ecer [26]; Ziemba [29,30]; Hamurcu and Eren [31]; Stopka et al [35] Cost C4…”

BV2TrS Appraiser Model: Enforcing BHARAT Version2 in Tree Soft Modelling for Appraising E-Mobility Hurdles

“…One growing concern among these forecasts of affluence in the future is global warming. Thereby [5] fossil fuel-powered vehicles are starting to be banned by several territories. An impending shift in the transportation system [6] toward a low-carbon, ecologically sustainable regime is necessary to combat the danger posed by greenhouse gas (GHG) emissions, air pollution, and reliance on finite fossil fuels.…”