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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.

This research addresses the problem of restaurant locations ranking with applications for a cosmopolitan big city like Kolkata, India. A restaurant selection is based on occasions, spending capability,  environment, location, comfort, quality of the food etc. In this research paper an exhaustive set of factors and sub-factors is taken into consideration to select and rank restaurants situated at different locations in the city of Kolkata with a population of around fifteen million. The ranking of restaurants depends on complex, conflicting qualitative attributes. In the paper Hexagonal Fuzzy Numbers (HFN) have been used to suitably depict the imprecise uncertain environment. HFN, its distance measure and defuzzification have been applied to deal with the hesitancy and impreciseness of the decision makers. Analytic Hierarchy Process (AHP) has been used as a Multi Criteria Decision Making (MCDM) tool to obtain factors and sub-factors weights. TOPSIS and COPRAS methods were used for ranking different restaurant locations. Using comparative analysis it is shown that HFN with the TOPSIS and COPRAS method gives better result than other fuzzy numbers. The sensitivity analysis portion also gives a direction for taking a suitable decision in different possible scenario.

In this paper, an attempt to find a more generalized version of the classical EOQ model is made under the setup of fuzzy fractional calculus. Intuitively, the notions of fuzziness and fractional derivative represent the sense of uncertainty and memory, respectively. Thus, manipulating the classical EOQ model in terms of the fuzzy fractional differential equation (FFDE), the impacts of the uncertainty and system memory on the lot-sizing model are manifested here. The model describes the FFDE under Caputo gH derivative and Riemann–Liouville integration. A trapezoidal fuzzy environment is considered to find a better environment for the cost minimization perspectives. From the numerical simulation, memory introduction is established to be fruitful in favor of the cost minimization objective. Moreover, the discussed model contains the other versions (crisp integer order, fuzzy integer order, and crisp fractional order) of the classical EOQ model as particular cases.