Determining criteria weights is paramount for any multi-criteria decision-making (MCDM) problem. Improper selection of the normalization scheme and inconsistencies in the weight calculation often affect the outcome of the MCDM models. In this regard, the symmetry point of criterion (SPC) method provides the advantage of no dependency on selecting an appropriate normalization scheme. However, it has no in-built mechanism to ensure consistency in weight calculation. This paper uses a novel integration of SPC and full consistent method (FUCOM). This innovative approach allows the analyst to examine the deviation from full consistency even when working with objective information. The proposed method is applied to a real-life issue of assessing the website quality of business schools from the perspective of digital marketing. Digital marketing is a crucial element of the promotional strategy for various organizations, including B-schools. We demonstrate the B-school’s final ranking using the evaluation by an area-based method of ranking (EAMR) approach. It is seen that F-SPC provides a consistent (DFC = 0.00003) and stable calculation of criteria weights. The analysis revealed that organic traffic holds the highest preference, while the stakeholders’ rating is consistent with the overall ranking. The present research shall help the decision-makers of marketing strategy and researchers dealing with complex real-life decision-making problems.

The main purpose of the current study is to develop a novel uncertain decision-making model using Type-2 Fermatean Fuzzy Set (T2FFS). The proposed model is intended for application in analysing financial securities from an investment perspective. The present paper first defines the T2FFS and various operations. It provides two novel definitions of the score functions and distance measures. Then, it extends the classical Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method using T2FFS. The T2FFS-TOPSIS model is applied to compare a set of six stocks representing their respective sectors listed on NSE, India. The sample was formulated by considering six representative stocks taken from the sectors that hold a major part in the benchmark index, such as NIFTY 50. Six criteria are considered to compare the stocks. These criteria are selected based on three major factors such as growth, valuation and risk factor. The underlying intentions are to achieve a return which may outperform the return of the benchmark index, and risk is comparatively lower. From the analysis it is seen that our stock selection method provided a reliable and stable solution. The result shows Infosys as the best stock to invest in and Maruti as the worst stock to invest in for the future, and at the end of the investment horizon, it was found to be true. To our best knowledge, the present work is a first of its kind that proposes the concept of T2FFS and defines the fundamental set theoretic operations along with the novel score function. Further, the current work fills the gap in the extant literature on security analysis in the stock market by utilizing uncertain modelling based on T2FFS.

COVID-19 continued to spread fast throughout the world since its outbreak from December, 2019. Most of the affected countries faced a huge challenge in managing the infection rate and providing the required treatments to the infected ones, which led the researchers to investigate the necessary causes and solutions regarding the infections. Researchers were also involved in estimating and forecasting the future trends and effects of COVID-19 as prediction is crucial to handling the unwanted pandemic situation. The uncertain nature of COVID-19 inspired researchers to adopt fuzzy sets for managing the pandemic. Researchers introduced various fuzzy logic-based models to analyze the pandemic situation and predict future directions. The aim of this study is to present an organized literature review to study the applicability of fuzzy set theory and its extensions in order to manage the pandemic situation. The COVID-19 related articles are grouped into six domains related to predictions (S1), related factor analysis (S2), prevention, control and managing the situation (S3), analysis of treatment (S4), after effects (S5), and distribution of vaccine (S6). Insights of the published articles are depicted using tabular representations. We have analyzed the significance of various categories to explore their societal impacts. This comprehensive review reveals a greater emphasis on experimenting with strategies to control the impact of COVID-19, while there is less focus on studying the effects of COVID-19, particularly in terms of vaccine distribution. The domain-wise data analysis from current research presents various approaches and directions. Additionally, this study predicts future research directions for each of the mentioned categories. Researchers initially focused on prevention, prediction, and control of COVID-19. The analysis reports illustrate the effects of COVID-19 factors and their social impacts on communities.

Career selection is a multi-criteria decision-making problem that can be challenging, especially when uncertainties are involved. To tackle such a problem, several decision-making techniques have been used to compare and rank the alternatives. Pythagorean Fuzzy Sets (PyFS), which are used to describe uncertainty, have also been used by some researchers in attempts to solve decision making applications. This paper introduces PyFS in type-2 Fuzzy Environments as type-2 PyFS (T2PyFS) to handle the uncertainty of a decision making problem more appropriately. Unlike conventional PyFS, T2PyFS incorporates secondary membership functions that capture higher-order uncertainty and hesitation in expert judgments, thereby providing a more realistic and flexible modeling framework. We define several arithmetic operations and algebraic properties related to it, and propose its level sets, we investigate related properties. We also develop the Hamming and Euclidean distance of our T2PyFS, and design three decision making algorithms based on the level sets, max-min-max composition and distance measure. Along the lines of academic performance, we apply the proposed decision making algorithms to quantify and compare the different criteria and alternatives systematically. Finally, we conclude this study with a comparative analysis of the proposed algorithms.

Modern healthcare systems operate under increasing pressure to deliver superior patient outcomes while constrained by limited financial and operational resources. Balancing cost efficiency with service quality remains a central challenge for healthcare administrators. This study introduces IntelliCare, a Pareto-efficient hybrid multi-objective optimization framework that models healthcare resource allocation as a bi-objective problem—minimizing Total Operational Cost (ToC) and maximizing Total Quality of Service (TQoS). The proposed Hybrid Multi-Objective Evolutionary Algorithm (Hybrid MOEA) integrates elitist selection and diversity preservation mechanisms from classical evolutionary paradigms to ensure reliable convergence and wide Pareto-front coverage. A nonlinear formulation captures diminishing returns in resource deployment, realistically reflecting hospital interdependencies across multiple departments. Experimental evaluations on synthetic healthcare datasets reveal that the Hybrid MOEA consistently surpasses baseline algorithms, yielding higher Hypervolume (HV) and lower Generational Distance (GD), Inverted GD (IGD), and Spread (Δ) values. Overall, the IntelliCare framework establishes a scalable, data-driven decision-support model that enables administrators to achieve cost-effective, quality-centered, and operationally sustainable healthcare management.

As urban logistics networks expand, there is a growing need for routing methods that are both cost-effective and environmentally sustainable. This study proposes the Sustainable Profit-Maximizing Vehicle Routing Problem (SPMVRP), a multi-objective model that aims to maximize operational profit while minimizing travel time. The model incorporates practical constraints such as vehicle capacity, service time, and carbon emissions. Unlike traditional routing models that focus on either cost or emissions, this framework integrates both economic and environmental objectives into a unified approach for sustainable logistics planning. To solve this intricate optimization problem, a hybrid metaheuristic algorithm is developed by combining Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). This hybrid method balances exploration and exploitation by using the fast convergence of MOPSO and the diversity-preserving ability of NSGA-II. The model is implemented in Python and tested on benchmark instances with 20, 50, and 100 customers. A real-world inspired case study based on an urban distribution network in Kolkata, India, is also considered. Results show that the hybrid approach outperforms individual algorithms, achieving 5-8% shorter travel times and 4-6% higher average profits while maintaining competitive emission levels. Statistical tests (p < 0.05) confirm the significance of these improvements, demonstrating the effectiveness of the proposed SPMVRP framework for sustainable urban logistics.

SentiVol-GA is an evolvable hybrid framework for stock price forecasting. This model integrates statistical models, deep learning architectures, financial sentiment analysis, and volatility-aware optimization through a Genetic Algorithm (GA). This framework combines five predictive models—Linear Regression, LSTM, GRU, Bi-LSTM, and ARIMA—with sentiment signals extracted from FinBERT, VADER, and the Loughran–McDonald dictionary. Its core innovation lies in a volatility-scaling mechanism that adjusts sentiment impact during turbulent periods, while the GA periodically optimizes model and sentiment weights to minimize forecasting error thus sustaining the model’s predictive stability. This empirical evaluation across eight Indian IT-sector stocks—covering large, mid- and small-cap segments, demonstrates up to 12

A non-green manufacturing process can destroy the environment rapidly and it creates global warming. On the other hand, green production processes and circular economic products can reduce environ mental pollution. Waste management through reuse, recycling, etc., is equally crucial for a healthy ecosystem and for saving natural resources. So, circular economic product based green manufacturing model development is very important in the current situation which can apply to the manufacturing industry. For this, a circular economic and green product based dual supply chain model is developed in this study. Here, three supply chain participants (manufacturer, online retailer and offline retailer) are considered where the manufacturer produces circular economic green products and fulfils the customers’ demand by retailers. For the online channel, the retailer purchases the products from the manufacturer and sales to the customers through online mode. But in the offline channel, the retailer completed the same tasks through offline mode. Also, it is assumed that the market demand is dependent on retailers’ selling price, product’s green level and circular economic index. Moreover, the retail price is influenced by the circular economic index. Then four different problems are proposed that maximizes the profits of manufacturer, retailers (online and offline) and integrated supply chain system and solved by centralized and decentralized methods. The objective of this study is to determine the optimal selling prices, product’s green level and circular economic index which maximizes the profits of the supply chain members. Finally, sensitivity analysis is carried out to determine the effects of model parameters on the optimal policy and draw a fruitful conclusion from this study.

An archived multi objective simulated annealing based energy efficient street lighting framework is proposed to maximize the energy efficiency and uniformity of the system while minimizing the power requirement of the luminaries. The proposed methodology considers the initial population in terms of the combination of the input parameters, such as the inter-distance between two consecutive street lights, luminary height, road width and average illuminance. The crossover and the clustering are used to check the domination status between the earlier and the new population as well as to reduce an excessive amount of possible solutions generated. The convergence and the diversity of the obtained solution set are evaluated for the proposed work over other alternative multiobjective optimization algorithms by four performance metrics, such as Generational Distance, Inverted Generational Distance, minimal-spacing, and purity, to highlight the energy efficiency in the proposed street lighting framework. The result obtained from the proposed approach amidst others, in terms of convergence and diversity, is validated using DIALux (an open source lighting software) to ensure the standard recommendation. Several improvements, in terms of various facets, resulting from the proposed work over other existing approaches are highlighted.

This study aims to introduce and examine the novel concepts of picture fuzzy linguistic hedges and variables, expanding upon existing intuitionistic and traditional fuzzy linguistic approaches. The motivation for this research stems from the increasing need to handle complex decision-making environments, where uncertainty, vagueness, and imprecision frequently challenge conventional methods. By incorporating linguistic hedges and a fuzzy-based dispersion mechanism, the study seeks to enhance the precision and stability of fuzzy decision-making frameworks. Specifically, the integration of dispersion-based uncertainty assessment is motivated by the necessity to systematically measure variations in fuzzy information, enabling more informed and accurate multi-criteria decision-making in specialized application domains. Initially, this study introduces the concepts of picture fuzzy linguistic hedges and picture fuzzy linguistic variables. These concepts are generalizations of similar ideas found in intuitionistic fuzzy linguistics and fuzzy linguistic variables with hedges. The key characteristics of these concepts are analyzed, and the behavior of picture fuzzy variables with hedges is examined. The application of these picture fuzzy linguistic hedges is also explored. The study incorporates the concept of dispersion into the fuzzy system, using proposed coefficients, normalized factors, and a range function to measure the uncertainty associated with different types of fuzzy sets. The fuzzy-based dispersion mechanism helps to establish the stability of the fuzzy system and is utilized to compare alternatives effectively. Finally, a decision-making process is proposed based on linguistic hedges by employing the linguistic factors and fuzzy-based dispersion to assess the importance of individual criteria. To validate the proposed approach, we present a case study that identifies the best resource person for each specific technical domain among the selected technical personnel of an IT company.

In the present scenario, the research outcomes related to hybrid electric vehicles are highly dominating the entire novelty sector. Initially, the long-term operation of electric vehicles was found to be ineffective in the case of electric vehicles (EVs), and then only the hybrid concept was encapsulated. Now, it is a real challenge to control the power split strategy for an ideal run and an economical run as well. Many papers explored and investigated with a few novel initiatives indeed. In this paper, one of the most unique analyses has been discussed by considering rotational inertia and angular displacement of the wheel in the case of running on a battery only. On the other hand, based on the battery state of charge (SOC), battery ampere, as well as power splitting takes place between the battery and the internal combustion engine. The entire concept is described in this paper using a type 1 fuzzy logic controller (FLC) by incorporating Triplets as membership functions (MFs). In this work, the centre of area (COA) method is used for defuzzification. In this work, cascaded fuzzy is introduced as an Artificial Intelligence approach and applied in hybrid electric vehicles for intelligent optimum power splitting strategic planning. The study is theoretically constructed and validated using a few simulation-based results.

Finding the centroid of a fuzzy set is one of the most crucial and significant computational tasks in fuzzy logic systems. Since Zadeh's time, centroids of type-1 and type-2 fuzzy sets have only been used in type-reduction. Although there are some applications for type-3 fuzzy control, there is currently no literature on the usage of centroid during type-reduction. The existing literature currently lacks a method for determining the centroid of the type-3 fuzzy set. In this study, we have attempted a relatively straightforward but rigorous way to find the centroid of a type-3 fuzzy set using weights in each direction of membership variables. This method may also be used to type-2 and type-1 fuzzy sets. Examples are provided that compare the computational results and show the computational efficiency of the proposed method.

This study examines an economic production quantity (EPQ) model for a single-item inventory with deterioration in a fuzzy environment. Conventional stochastic inventory models considered uncertainty in the demand functions; however, the majority of manufacturing systems experience supply (production) unavailability from time to time. The proposed Fuzzy Inventory Control (FIC) system considers both demand and supply as uncertain variables. As a result, the inventory level also remains uncertain. Uncertainty in production rate and inventory level is expressed by fuzzy valued functions of time. The unit density-dependent demand is taken as a fuzzy variable. The unit holding cost is considered as an uncertain variable and expressed by a fuzzy number. The total cost of production takes a known form, where the unit cost of production is also given by a fuzzy number. The deterioration rate obeys the generalized Pareto distribution with a negative shape parameter, which is given by a crisp function of time. By applying Pontryagin’s fuzzy maximum principle, the production inventory problem in the said fuzzy environment can be solved. The optimal inventory level and optimal production level are determined by closed bounded intervals of real numbers where the left and right-hand functions of their α-levels are the parameters. Now, under a special choice of the demand, holding cost, and production cost, left-hand and right-hand functions of the α-levels of the optimal inventory and of the production level are determined. Graphical representations of both types of functions are obtained separately for different α-levels for α = 0.25, 0.50, 0.75, 1. In all cases, graphical interpretations are explained in detail.

This work considers a production inventory system where the state equations involve two types of continuous dynamics, one for the supplier and the other for the buyer. The objective is to minimize the total cost resulting from the squares of the inventory of the supplier, the buyer and the production rate. As this is a multi-dimensional system, we express the model in the form of a linear quadratic regulator in matrix form, which ensures closed-form solutions to the problem. By applying Pontryagin’s maximum principle, analytical solutions are obtained and expressed as the solution of a Hamiltonian Jacobi matrix differential equation with time-varying coefficients. The final time is taken as finite, and the corresponding inventory level is taken as free. Under these assumptions, a change of the co-state variable is designed so that the new co-state variable is obtained as the solution of a matrix form of the Riccati differential equation. This can be solved analytically by following a suitable technique to determine the new costate variable. The optimal values of the state and control variables are then obtained with the help of this costate variable. Finally, the proposed model is illustrated through a numerical example.

This study investigates a sustainable supply chain operating under a cap-and-trade policy, involving a risk-averse retailer and a manufacturer that offers both traditional (non-green) and eco-friendly (green) products with uncertain demand. The demand is influenced by key factors such as pricing, warranty duration, advertising, and green initiatives. To promote sustainability, both the retailer and manufacturer invest in green efforts, and the manufacturer also remanufactures discarded non-green products into green ones for future sale. The study examines the environmental and economic impacts of traditional and green supply chains under both centralized and decentralized structures. Additionally, a trade credit contract is introduced to enhance liquidity, stimulate market demand, and improve profitability. A sensitivity analysis explores the effects of green investments, warranty length, advertising, and pricing on optimal supply chain decisions. The findings provide actionable insights for supply chain managers to enhance sustainability, balance between environmental and financial objectives, and adapt strategies to dynamic operational and policy conditions.

This paper introduces a new network optimization problem named the Star Tree Problem (STP), which deals with two-step service. This problem has a fixed center, while other nodes can be randomly divided into primary and secondary facility locations. Primary nodes together form a star graph by direct assignment with the center. Secondary nodes form some spanning trees, keeping their nearest primary nodes as the root vertices. STP aims to minimize the total edge installation cost by the star and trees. A discrete multi-verse optimizer (DMVO) algorithm is developed to solve the proposed STP, and the numerical experiments are performed on some benchmark problems. Results and statistical analyses of DMVO against the genetic algorithm (GA) and discrete antlion optimizer (DALO) are also presented. Finally, the proposed problem is applied to solve a particular problem for local cable operators and illustrated with realistic data as a case study.

The growing e-commerce grocery (e-grocery) sector drives e-grocers to pursue higher customer retention rather than acquisition. A good understanding of customer relationships is essential for customer retention; while accurate segmentation is essential for understanding this relationship. Traditional behavioral segmentation metrics such as recency (R), frequency (F), and monetary value (M) encounter accuracy and decision-making challenges amid evolving business dynamics and shifting consumer behavior. Hence, the RFM model must be improved by incorporating metrics that reflect modern consumer behavior. Cart abandonment has emerged as a pivotal metric of customer loyalty. This study enhances the RFM model by incorporating the delivery ratio (D) metric representing customer cart behavior and separates 'R' to present an innovative R + FMD model. By leveraging big data on e-groceries, we applied k-means, DBSCAN, agglomerative, FCM, and GMM clustering algorithms to compare the RFM and R + FMD models. The weights of the F, M, and D metrics were determined using the PF-AHP technique. Results demonstrate the superiority of the R + FMD model over the RFM model across all clustering algorithms, with the GMM showing the best performance. Using the GMM, we segmented customers at three recency levels and confirmed significant differences via MANOVA. Notably, 10 out of 26 clusters comprised 43.86 % of valuable customers who contributed 79.56 % of the firm's gains. This study also identified the customers' issues, preferred categories, and preferred brands. Additionally, we proved the robustness and generalizability of the R + FMD model relative to the RFMT and LRFM models in the literature by using another large-scale dataset. The insights from this study provide businesses with powerful tools to target customer retention through precise segmentation.

This research paper addresses the challenge of maximizing crop yield through an intelligent approach in crop rotation using Q-learning and rule-based decision-making. The objective is to develop an adaptable crop rotation system that continuously monitors and refines the crop rotation sequence for enhanced efficacy and sustainability. Traditional crop rotation methods based on expert knowledge often fail to optimize crop yields, necessitating the use of computational models. The proposed approach integrates the Markov decision process (MDP) and Q-learning to determine an optimal crop rotation plan. A Q-learning approach is employed here to iteratively update Q-values and identify the best sequence of crops to be planted. The rewards are estimated based on yield values, providing a realistic assessment of the impact of crop rotation on crop yield. The effectiveness of the rotation sequence is evaluated over 50 years through year-wise yield assessments. Additionally, a rule-based decision-making process is introduced to modify the rotation sequence based on yield evaluations, ensuring continuous improvement in crop yield. The model is assessed using reward, Q-value, and yield, revealing improvements of 36.13%, 2.04%, and 35.73% in respective metrics. Simulation results confirm the proposed approach’s superiority over existing methods.

This paper introduces a novel mechanism, the hybridization of rough set and game theory, that helps us evaluate the effectiveness of generalized rough set measures on various aspects. To calculate the effectiveness of different measures, we use various games where game players are considered as multiple rough set measures, and different game outcomes constitute the decision regions. Equilibrium analysis is used in the game to construct the rules of decision making. A practical implementation of the proposed approach is presented.

Restaurant selection by a consumer is a challenging task. The main problem is the proper optimization of ratings by food bloggers and the cost of food in the restaurant. In this paper, we propose an approach for a restaurant recommendation system that uses food blogger ratings and the cost of food items in the restaurant. The aim is to maximize the restaurant rating with minimization of overall cost. For this, we construct a multi-objective optimization problem to get restaurant recommendations that are appropriate for the customer's budget and desired rating. Three evolutionary optimization algorithms, namely Nondominated Sorting Genetic Algorithm II (NSGA II), Strength Pareto Evolutionary Algorithm 2 (SPEA 2), and Indicator-Based Evolution Algorithm (IBEA) have been used to identify approximated Pareto solutions for our proposed model. The effectiveness of the three evolutionary algorithms under consideration is given and evaluated against several performance indicators. Using Zomato restaurant data, we compare the results in terms of convergence and diversity, which confirms our suggestion for standardization. The suggested work contributes to an analytical approach based on evolutionary algorithm solutions to create a multi-objective restaurant recommendation system, which goes beyond the scope of previous works. We also present a comparative result analysis with the existing Zomato rating and proposed approach using statistical tools.