This paper presents the application of the new method in the process of making the decisions on the scooters selection. The Fuzzy AHP method was used to obtain the weight coefficients of criteria, on the basis of which the alternatives were evaluated. The selection of criteria for evaluating scooters was based on an analysis of available manufacturer website. The evaluation and selection of scooters was carried out using a new multi-criteria decision making (MCDM) method. The present study tries to identify the attributes like kerb weight, mileage, top speed, fuel tank capacity and price playing prominent role in the buying behavior of the working women while purchasing scooters. Fuzzy-AHP method has been used to obtain the relative weights of the criteria, on the basis of which the alternatives are evaluated using new MCDM. The TOPSIS and MABAC methods were tested under the same conditions. The stability of the result obtained by this method is established through a sensitivity analysis.

This paper presents the application of the new method in the process of making the decisions on the scooters selection. The Fuzzy AHP method was used to obtain the weight coefficients of criteria, on the basis of which the alternatives were evaluated. The selection of criteria for evaluating scooters was based on an analysis of available manufacturer website. The evaluation and selection of scooters was carried out using a new multi-criteria decision making (MCDM) method. The present study tries to identify the attributes like kerb weight, mileage, top speed, fuel tank capacity and price playing prominent role in the buying behavior of the working women while purchasing scooters. Fuzzy-AHP method has been used to obtain the relative weights of the criteria, on the basis of which the alternatives are evaluated using new MCDM. The TOPSIS and MABAC methods were tested under the same conditions. The stability of the result obtained by this method is established through a sensitivity analysis.

Decision-making process is an integral part of every sporting event across the globe. This process uses inputs from single to multi-decision makers. To make a sporting event a success, appropriate decision making needs to be made starting from hosting-right to the end of the event. Thus, this study presents a framework that scientifically determines the hosting-right for a sporting event. The framework is based on techno-economic analysis of potential hosting locations for a sporting event. Techno-economic criteria are analysed using fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method. The analysis of the hosting-right is also performed through technical, economic, techno-economic and cost-benefit ratio perspectives. A case study of national sports festival in Nigeria is used to demonstrate the applicability of the proposed framework. Twelve technical and eight economic criteria are considered in the proposed framework. Six loca-tions are considered during the implementation of the proposed framework. The results obtained show that the issue of hosting-right award depends on the evaluation criteria that are considered by decision-makers.

Business process evaluation is a common norm in small-medium-large industries globally and information obtained during such evaluation have been used in simulating future performance of most industries using mathematical models such as Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN). This study explored the possibility of predicting port productivity and berth effectiveness of seaport using ANN and ARIMA. A comparative analysis of multi-layer perceptron (MLP) back propagation algorithm and ARIMA performance was carried out based on ships days at port, days at berth and tonnage which serves as model inputs, while port productivity and berth effectiveness were the model outputs. The MLP-ANN and ARIMA (1, 0, 4- port productivity) and (1, 0, 4-berth effectiveness) results were compared based on their coefficient of correlation and mean square error. The coefficient of correlation for port productivity prediction using MLP-ANN was 0.998. This value outperformed that of ARIMA (0.9862) for port productivity. The coefficient of correlation of 0.9956 and 0.9928 were obtained for berth effectiveness using MLR and ARIMA, respectively.

This paper presents an integrated granular framework of wavelet decomposition, DCC-GARCH, ADCC-GARCH, Diks-Panchenko nonlinear Granger’s causality and Diebold-Yilmaz spillover assessment techniques to understand temporal correlation, causal interplay and spillovers among volatile financial time series data exhibiting nonparametric behavior. The exercise has been carried out on daily closing observations of eight financial time series. Wavelet decomposition has been used to generate time varying components in which the other research models are applied to extract the interactive pattern of interaction to ascertain short and long run nexus. The findings rationalize the effectiveness of the presented research framework.

The main purpose of this paper is to know about the recent status of big data analytics (BDA) on various manufacturing and reverse supply chain levels (RSCL) in Indian industries. In particular, it emphasizes on understanding of BDA concept in Indian industries and proposes a structure to examine industries’ development in executing BDA extends in reverse supply chain management (RSCM). A survey was conducted through questionnaires on RSCM levels of 500 industries. Of the 500 surveys that were mailed, 125 completed surveys were returned, corresponding to a re-sponse rate of 25 percent, which was slightly greater than previous studies. The information of Indian industries with respect to BDA, the hurdles with boundaries to BDA-venture reception, and the connection with reverse supply chain levels and BDA learning were recognized. A structure was presented for the selection of BDA ventures in RSCM. This paper gives bits of knowledge to professionals to create activities including big data and RSCM, and presents utilitarian and predict-able direction through the BDA-RSCM triangle structure as an extra device in the execution of BDA ventures in the RSCM factors. This paper does not provide outside legitimacy owing to limitations for the speculation of the outcomes even in the Indian surroundings, which originates from the present test. Future research ought to enhance the understanding in this area and spotlight on the effect of big data on reverse supply chains in developed countries.

Uber and its main competitor Lyft are competing aggressively to attract more drivers and in turn limit the access of their competitors in the drivers’ supply base. To control the supply side, it is important to analyze the ride-sharing platforms from drivers’ perspective. Using Uber and Lyft drivers’ online reviews, it is shown that drivers are slightly rating Uber higher than Lyft. Additionally, comparing Uber and Lyft rating trend over time, the analysis shows that they are closely competing to attract more drivers. On the aggregate level, drivers count job flexibility, work/life balance, and meeting new people as the main advantages of working in the ride-sharing platforms. On the other hand, the results show that drivers suffer from insufficient compensation of their op-erating costs, poor job security, experiencing bad riders’ behavior, and poor customer service.

Inconel 600 is a super alloy known for its properties like low thermal conductivity and work hard-ening. The work hardening property of this alloy makes it harder and harder during successive passes of the tool during machining. Therefore, machining of this type of material demands inno-vation in tool material, selection of proper combination of parameters and their levels for economical machining. Coated carbide tool inserts are most widely used for machining Inconel alloys. These inserts are coated with special materials by PVD or CVD technique to reduce flank wear, improve surface finish of machined components and increase the material removal rate (MRR). In this work carbide insert coated with nanocomposite coatings like AlTiN and TiAlSiN commercially known as Hyperlox and HSN2 were used and their performance during machining of Inconel 600 was studied. As improper selection of process parameter influences on the quality of products and productivity, it is important to identify the optimum combination of input process parameters. Most of the time the influence of the input process parameters on the output parameters like MRR, surface roughness and flank wear is studied independently. Information obtained through single objective optimization may not be sufficient because industries desire to optimize all the output parameters, simultaneously. Multi-objective optimization is the only solution to satisfy the requirements of industries and genetic algorithm based multi-objective optimization is adopted in this work in order to get the optimum combination of input process parameters to obtain maximum material removal rate, minimum surface roughness and minimum flank wear simultaneously.

In this study, the efficiency of integration between Taguchi and TOPSIS in multi-response optimi-zation of powder mixed electrical discharge machining (PMEDM) process was evaluated. The in-put parameters, such as workpiece and tool electrode material, polarity, pulse on time (ton), pulse off time (toff), Current (I) and powder concentration have been selected to optimize two responses; namely surface roughness (Ra) and surface hardness (HV). The results show that titanium powder mixed dielectric fluid improves multi-response optimization efficiency in PMEDM. In addition, machining conditions, such as tool electrode material, powder concentration, pulse on time, polarity, current density, A×G and B×G interactions play a very important role on S/N ratio of C* whereby powder concentration has the strongest influence. TOPSIS -Taguchi is a potential method for multi-response optimization in PMEDM. However, the optimal results using ANOVA analysis show that there is a necessity to have more studies in TOPSIS-Taguchi to improve the in-tegration efficiency between two methods for optimizing multiple responses in PMEDM.

Form deviation of machined components need to be controlled within the required tolerance values for proper assembly and to serve the intended functional requirements. Methods like minimum zone circle (MZC) method, minimum circumscribed circle (MCC) method, maximum inscribed circle (MIC) method and least square circle (LSC) method are used to evaluate roundness error. Roundness error evaluation includes collection of co-ordinate points on the surface of the compo-nent and measurement using any of the above methods. Since, manual measurement of roundness error from these coordinate points is time consuming and less accurate, use of algorithms is highly appreciated. A detailed study of various optimization techniques showed that all evolutionary and swarm intelligence-based optimization algorithms require common control parameters and algorithm specific parameters. Improper tuning of these parameters either increases the computational effort or results in local optimal solution. Teaching Learning Based Optimization (TLBO) algorithm is used in this work as it does not require any algorithm specific parameters for the evaluation of roundness error. The results obtained are then compared with the results of Particle Swarm Optimization (PSO) algorithm to know the merits and demerits of both the algorithms when used for form measurement.