Multi-objective multi-item four dimensional green transportation problem in interval-valued intuitionistic fuzzy environment

This paper investigates the multi-objective multi-item four dimensional green transportation problem in an interval-valued intuitionistic fuzzy environment. Carbon emission is a critical problem in recent years and transportation activity is one of the main sources of carbon emission. In the transportation system, the rate of carbon emission depends on several factors such as type of fuel used, type of vehicles, load of the vehicles, driving style, traffic on the road, etc. In this study, we have described the impact of driving style on the carbon emission as well as on the transportation costs. The model has been proposed for the comparison of the amount of carbon emitted under two different driving styles, i.e., right(defensive) and wrong(aggressive). Also, the parameters of real-life multi-objective transportation problems are inherently unpredictable due to variety of factors. To handle the impreciseness and vagueness, interval-valued triangular intuitionistic fuzzy numbers (IVTIFNs) are used in the proposed model. Further, the proposed problem with IVTIFNs has been converted into a crisp form by using the expected value operator. Then the three programming techniques: weighted Tchebycheff metrics programming, interval-valued intuitionistic fuzzy programming and fuzzy TOPSIS method are employed to obtain the Pareto-optimal solution of the suggested model. A comparison is drawn between the Pareto-optimal solutions extracted with respect to two driving styles. The extracted results show that the right driving style provides a better solution in both environmental and economic aspects. Finally, a numerical application is given to demonstrate the applicability of the proposed study. Conclusions with the future research scope of this study are presented at last.