Fuzzy multicriteria investment analyses and vehicle technology selection for a firm's fleet

Since the late of 1990's and beginning of 2000's, it has been seen that new shifts in transportation sector emerge and new vehicle technologies gain importance. One of the new shifts in transportation sector, especially the most dominating one, is electrification of transportation. Namely, the technology that the vehicle uses the electrical energy for fuel resource. The gases harmful to the environment caused by tailpipe emissions form the basis of new shifts in transportation sector. Besides, the reduction of fossil derived fuels' resources supports the new shift. At the beginning of 1990's, an alternative electric vehicle to the petroleum user vehicles were developed, however, it did not gain enough traction in those times. Recently, the electrification of transportation has gained importance. In 1997, Toyota released the first hybrid automobile to the market by leading the trend. Then, worldwide prominent automobile manufacturers tracked this trend. In parallel to this trend, plug-in hybrid electric vehicles started to penetrate to the market, and it was seen more distinctly in the market after 2008. The last motion in the sector is with battery electric vehicles. They have started to penetrate gradually to the market since then. In developed countries, considering the greenhouse gas emissions and reduction of fossil derived fuels' resources, new vehicle technologies and its R&D efforts are supported by incentives. Firms act with motto of ''respectful to the environment'' by aiming high profit gains in their operations. The firms that have the environmental sensitivity consider the environmentalism of the vehicles in their fleets. In this work, the most appropriate vehcile technology was determined for a firm operating in İstanbul whose vehicles have 2500 km monthly average mileage. In the selection process, fuzzy multricriteria investment analyses were made including environmentalism criteria. Fuzzy sets theory was developed by Azerbaijani man of science Lotfi Asker Zadeh in 1965. Fuzzy logic does not define an element's belonging to a set only with 0 or 1 as in Aristo logic. This logic defines the element's belonging to a set with a membership degree or function. Hence, fuzzy logic provides more flexible modelling of the system, and enables to construct more intelligent systems with this property. In fuzzy logic, amed as fuzzy sets theory, many fuzzy sets types have been developed. Firstly, type 1 fuzzy sets were developed by Zadeh. After that, type 2, interval type 2, hesitant and intiutionistic fuzzy sets were developed respectively. Fuzzy logic enables better modelling of the systems with respect to crisp sets in the uncertain environment which includes uncertainty of information and incomplete information. Since fuzzy logic reflects the disadvantages of assesments' subjectivity caused by lnguistic terms and the disadvanatges of uncertainty caused by incomplete information as little as possible to the modelling, it provides better modelling. As the uncertainty of the system arises, the type of fuzzy sets used in modelling changes for better handling the disadvantages. In this work, fuzzy logic is used for better modelling because experts assesments include linguistic terms and some of the information includes uncertainty. Besides that, some of new vehicle technologies' cost items are not certain now. In this situation, fuzzy logic is also used by engineering economics analysis tools. Type 1 and intuitionistic fuzzy sets were used in this work. Type 1 fuzzy sets do modelling more simple than intuitionistic fuzzy sets. It defines only an element's belonging to a set. However, intuitionistic fuzzy sets try to provide solutions by defining an element's belonging membership degree and non-membership degree to a set. Engineering economics analysis tools are frequently used techniques in cost analysis of an investment. The feasibility of an investment can be determined by these analyses and decision about whether invest or not is made with respect to the result of these analyses. The most used engineering economics analysis tools are net present value analysis and net annual worth analysis. In this work, net present worth analysis is used since the lives of investment alternatives are equal. Type 1 fuzzy numbers are used in which there is uncertainty in the considered cost item. Each alternative's total cost of ownership was computed and these results were presented to experts as inputs in the multicriteria analyses. In the case of more than one alternative to be selected with respect to multiple criteria, the decision making process is named as multicriteria decision making process. In decision making, criteria types may be different:cost or benefit criteria, and alternatives may not be absolutely dominating other alternatives with respect to all criteria. Hence, the decision is made with comparisons of alternatives with respect to all criteria. AHP (Analytical Hierarchy Process), TOPSIS (Technique for Order and Preference by Similarity to Ideal Solution) and VIKOR (VIsekriterijumsko KOmpromisino Rangiranje) are the most frequently used multicriteria decision making methods. AHP method is the multicriteria method that determines the best alternative by making pairwise comparisons of criteria vs. criteria, criteria vs. alternatives. TOPSIS method is the multicriteria method that determines the best alternative being the more distant to negative ideal point and closest to positive ideal point with respect to the other alternatives. VIKOR method is the multicriteria method that provides compromise solution by minimizing individual regret of opponents and maximizing group utility of proponents. In this work, the vehicles which a firm intended to purchase were examined for their requirements. These vehicles are B-class gasoline-powered, diesel-powered, hybrid, plug-in hybrid electric and battery eletric vehicles. These vehicles were evaluated with respect to vehicle cost, fuel repletion convenience, performance of the vehicle, ergonomics of the vehicle, environmentalism of the vehicle and safety of the vehicle criteria. Firstly, the solution, the best alternative, was obtained by using type 1 fuzzy integrated AHP-TOPSIS method. The criteria weights determined by type 1 fuzzy AHP were used as inputs for fuzzy TOPSIS after the consistency check was made. Secondly, the solution was obtained by using intuitionistic fuzzy integrated AHP-VIKOR method. In this stage, the criteria weights determined by intuitionistic AHP were used as inputs for fuzzy VIKOR. The criteria weights determined by type 1 fuzzy AHP are nearly the same as determined by intuitionistic fuzzy AHP. This situation shows the flexibility of used methods under fuzziness. It can be claimed that the difference of criteria weights determined by intuitionistic fuzzy sets and type1 fuzzy sets was caused by intuitionistic fuzzy sets' capability in better reflecting the uncertainty. For the same linguistic definitions, intuitionistic fuzzy sets require more mathematical expressions and equation. Fuzzy multcriteria investment analysis methods used in this work provide easiness and flexibility for modelling problems. At the end of the work, it has been determined that the best vehicle technology for the firm's fleet under the stated assumptions is diesel-powered vehicle. Hybrid and gasoline-powered vehicle follows the next two best alternatives, respectively. Plug-in hybrid electric vehicle and battery electric vehicles take the final two ranks, respectively. New vehicle technologies must be supported strongly to penetrate and hold on the market. The most important points in supporting the new techonologies are providing more incentives for dealers and inividual customers, increasing vehicle fiscal incentives and increasing charging infrastructure, generating public awareness of new vehicle technologies. The mass production of new vehicles and increasing R&D efforts will decrease the cost of new vehicle technologies. As it will be seen from the results, cost of the vehicle and the vehicle's fuel repletion convenience are the most important criteria among all constituting nearly % 50 of the weights, these stimulators will contribute to penetration and withholding of the new technology in the market. In the next 20 years, hybrid, plug-in hybrid electric and battery electric vehicles will possibly become the dominant vehicle technologies on the road due to some developments such as incentives, reduced costs and enough infrastructures.