Forecasting Sugarcane Yield of India based on rough set combination approach

This study applied a novel rough set combination approach for forecasting sugarcane production in India. The paper uses autoregressive integrated moving average (ARIMA), double exponential smoothing (DES) and Grey model (GM) to generate the single forecasts. Also, the weight coefficient is evaluated by underlying the rough set approach to combine the single forecasts obtained from different models. To validate our proposed analysis, Sugarcane from 1950 to 2011 was used for the overall empirical analysis and generate out-sample forecasts from 2012 to 2021 for the comparative analysis. Also, ARIMA (2, 1, 1) model is found more appropriate for forecasting Sugarcane production.

Designing optimized ternary catalytic alloy electrode for efficiency improvement of semiconductor gas sensors using a machine learning approach

Catalytic noble metal (s) or its alloy (s) has long been used as the electrode material to enhance the sensing performance of the semiconducting oxide based gas sensors. In the present paper, design of optimized ternary metal alloy electrode, while the database is in pure or binary alloy compositions, using a machine learning methodology is reported for detection of CH4 gas as a test case. Pure noble metals or their binary alloys as the electrode on the semiconducting ZnO sensing layer were investigated by the earlier researchers to enhance the sensitivity towards CH4. Based on those research findings, an artificial neural network (ANN) models were developed considering the three main features of the gas sensor devices, viz. response magnitude, response time and recovery time as a function of ZnO particle size and the composition of the catalytic alloy. A novel methodology was introduced by using ANN models considered for optimized ternary alloy with enriched presentation through the multi-objective genetic algorithm (GA) wherever the generated pareto front was used. The prescriptive data analytics methodology seems to offer more or less convinced evidences for future experimental studies.

Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.

Evaluation of tanker vehicle selection using a novel hybrid fuzzy MCDM technique

Petroleum product transportation considered as one of the crucial parts of dangerous material transportation is a risky logistics activity. The selection of the appropriate tanker vehicles may be a suitable solution to reduce the risks and increase the efficiency and performance of the fuel transportation companies. However, the selection of a suitable road tanker vehicle is not an easy task for decision-makers as there are many conflicting criteria and many decision alternatives. In addition, decision-makers may have to decide with insufficient information since collecting crisp values may not be possible at all times. Hence, many ambiguities affecting the evaluation results exist in an assessment process performed to select the best tanker vehicle option. This paper suggests a novel integrated fuzzy approach to solve these decision-making problems. Sensitivity analysis is conducted to test the validation of the proposed integrated fuzzy approach and its results was performed by forming 130 scenarios. The results of sensitivity analysis prove that the proposed model can be applied to solve these kinds of decision-making problems.

A novel prediction algorithm for multivariate data sets

Regression analysis is a statistical technique that is most commonly used for forecasting. Data sets are becoming very large due to continuous transactions in today's high-paced world. The data is difficult to manage and interpret. All the independent variables can’t be considered for the prediction because it costs high for maintenance of the data set. A novel algorithm for prediction has been implemented in this paper. Its emphasis is on extraction of efficient independent variables from various variables of the data set. The selection of variables is based on Mean Square Errors (MSE) as well as on the coefficient of determination r2p, after that the final prediction equation for the algorithm is framed on the basis of deviation of actual mean. This is a statistical based prediction algorithm which is used to evaluate the prediction based on four parameters: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and residuals. This algorithm has been implemented for a multivariate data set with low maintenance costs, preprocessing costs, lower root mean square error and residuals. For one dimensional, two-dimensional, frequent stream data, time series data and continuous data, the proposed prediction algorithm can also be used. The impact of this algorithm is to enhance the accuracy rate of forecasting and minimized average error rate.

Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD

The comparative analysis of objective methods for determining the weights of criteria in the problems of multi-criteria decision-making is carried out. It is shown that the use of methods for determining the weights of criteria, based on formal processing of the decision matrix (Entropy, CRITIC, Standard deviation) for MCDM problems in some cases is not correct. It is demonstrated that the Entropy weighting method (EWM) is highly sensitive to evaluation of probabilities of states based on the decision matrix. For the Entropy method two modifications of estimation of probabilities of states are proposed that partially eliminate the contradictions of the basic EWM method. The first modification (EWM.df) is based on a statistical approach and it estimates the probabilities of states based on attribute distribution function. The second modification (EWM.dsp) estimates the probabilities of states based on the relative dispositions of attributes. Two options both have their supporting rationale. The analysis of integrated weighing methods is carried out and various options for aggregation of weights are given. An integrated EWM-Corr-method is proposed which allows to re-allocate the weights obtained by the Entropy method among correlated criteria.

Refinement of acyclic-and-asymmetric payoff aggregates of pure strategy efficient Nash equilibria in finite noncooperative games by maximultimin and superoptimality

A theory of refining pure strategy efficient Nash equilibria in finite noncooperative games under uncertainty is outlined. The theory is based on guaranteeing the corresponding payoffs for the players by using maximultimin, which is an expanded version of maximin. If a product of the players’ maximultimin subsets contains more than one efficient Nash equilibrium, a superoptimality rule is attached wherein minimization is substituted with summation. The superoptimality rule stands like a backup plan, and it is involved if maximultimin fails to produce just a single refined efficient equilibrium (a metaequilibrium). The number of the refinement possible outcomes is 10. There are 3 single-metaequilibrium cases, 3 partial reduction cases, and 4 fail cases. Despite successfulness of refinement drops as the game gets bigger, pessimistic estimation of its part is above 54 % for games with no more than four players.

Incorporating heterogeneity of travelers’ preferences into the overall hostel performance rating

Hostels have become a very popular form of accommodation and their varieties have grown steadily in recent years. To ensure the sustainability of this business model, it is necessary to understand the main drivers influencing travelers to choose a hostel accommodation. For this purpose, we conducted an online survey using convenience sampling and purposive sampling techniques. Respondents' preferences to six hostel attributes (cleanliness, location, staff, atmosphere, facilities, and cancellation policy) were determined using discrete choice analysis. Sample results showed that the most important attributes are cleanliness and location, while the atmosphere is the least important one. However, widespread heterogeneity in preferences was observed, and cluster analyzes identified three distinct groups of travelers: “cleanliness sticklers”, “location demanders” and “party seekers”. Facilities and atmosphere were found to be very important attributes for particular clusters. These findings can help design a marketing strategy for each of the identified segments to ensure sustainable business. Finally, we have proposed a new approach to calculating the hostel overall rating based on attribute importance, which shows much better discriminatory power compared to the traditional average-based approach.

Investigation into the efficiencies of European football clubs with bi-objective multi-criteria data envelopment analysis

A financially successful football club can achieve sporting achievements as well as become financially stable. From this point of view, in this study, the efficiencies of clubs were investigated with the Bi-Objective Multi-Criteria Data Envelopment Analysis (BiO-MCDEA) model by using financial and sporting data of the 2015-2016, 2016-2017 and 2017-2018 seasons of 10 football clubs in the Big-Five League which is the locomotive of the football industry. In the study, the number of social media followers, the average number of viewers and total market value were used as input, and the UEFA club score and total revenues were used as output. As a result, Arsenal, Paris Saint-Germain, and Juventus were determined as efficient in the 2015-2016 season, Paris Saint-Germain and Liverpool in the 2016-2017 season, Manchester United, Paris Saint-Germain and Chelsea in the 2016-2017 season. The reasons as to why Paris Saint-Germain was efficient in all three seasons were also examined. In addition, in the sensitivity analysis conducted to determine the effect of inputs and outputs on the model, it was concluded that efficiency was highly related to financial data.

An objectıve criteria proposal for the comparison of MCDM and weighting methods in financial performance measurement: An application in Borsa Istanbul

Financial performance research with multi-criteria decision making (MCDM) methods, is a common subject of study not only for researchers in the finance literature but also in the applied sciences. Financial performance manifests itself in an internal universe that a firm can directly control, while the share return of the same firm is shaped synchronically in an external universe that cannot be controlled directly. On the other hand, preferring the most suitable MCDM and weighting method to use in measuring financial performance is often regarded as a source of uncertainty. In this study, the share price is used as an external proxy and a tool for comparing MCDM methods, completely different from the previously proposed approaches based on the superiority of internal features. This study was conducted on 131 manufacturing companies in Borsa Istanbul, covering entire 20-quarter period between 2014 and 2018. The experimental findings of the study provide valid solutions for the MCDM and weighting selection problem, that can be proposed as a practical and indirect solution. The results show that preference ranking organization method for enrichment of evaluations (PROMETHEE) method used with hybrid weighting technique produced by far the best performance rankings in 19 out of 20 quarterly periods when compared to the technique for order preference by similarity to ideal solution (TOPSIS) and weighted sum approach (WSA).