In this article, a Leslie-Gower prey-predator model that incorporates cooperation among the predator populations during hunting and a refuge mechanism for the prey are proposed and then analyzed. Additionally, a strong Allee effect in prey growth is included to address both biological and mathematical considerations. Initially, the topological equivalence method is employed to discuss the dynamical behavior of the system in the neighborhood of the origin. Subsequently, the existence and stability of the model's non-negative equilibria are examined. We identify parameter subsets where the system exhibits co dimension-one local bifurcations, specifically saddle-node and Hopf bifurcations, and demonstrate the presence of bistability. The first Lyapunov number is calculated to determine the stability of limit cycles that emerge from the Hopf bifurcation. Using Sotomayor's theorem, we derive the existence of a saddle-node bifurcation in the system. Furthermore, we analyze the influence of hunting cooperation on the model both analytically and numerically, revealing that hunting cooperation not only reduces the density of the prey population, but also destabilizes the system's dynamics. We also investigate the impact of refuge on the model numerically, finding that refuge stabilizes the system's dynamics.

The increasing complexity of the global food supply chain network under demand uncertainties calls for solutions that improve efficiency, transparency, and sustainability. This study develops a bi-objective, multi-period omnichannel framework that integrates Internet of Things (IoT), blockchain technology, and food waste reuse for biofuel production. Omnichannel strategies optimize the flow of goods across multiple channels, addressing the fragmented nature of modern supply chains. The study highlights the role of blockchain in improving transparency in food transportation and tracking, while IoT-enabled RFID tracking facilitates real-time data collection and monitoring. Together, these technologies improve brand value by strengthening consumer trust and overall supply chain credibility. To address uncertainties in demand and supply, the framework uses robust optimization techniques. The model is solved using an epsilon-constraint method, capturing the trade-off between minimizing costs and reducing carbon emissions. We then use a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to select the most preferred solutions for each instance. Numerical experiments on small-, medium-, and large-scale instances show that the integrated blockchain-IoT framework increases cost by 59%, 55%, and 39%, respectively, compared to 1-21% for IoT-only and 24-38.57% for blockchain-only implementations. Carbon emissions decrease by up to 3.81% in large-scale scenarios. The results also demonstrate that brand value increases with scale, with the highest brand value achieved when both IoT and blockchain are integrated. These findings provide valuable insights for designing sustainable, transparent, and efficient supply chains that balance cost, environmental impact, and brand reputation.

Bioethanol as renewable energy is receiving more attention due to increasing demand for sustainable energy on a global scale. This study presents an innovative optimization strategy that makes use of biomass resources to plan and design a sustainable supply chain for the production of bioethanol. The increasing interest in sustainability creates difficulties for decision makers (DMs) in selecting sustainable vehicles. It introduces an advanced cash credit-based mixed-integer nonlinear programming model to optimize bioethanol supply chain design, balancing cost efficiency with sustainability. The approach minimizes overall costs while ensuring employment generation and reduced green-house gas emissions, making the bioethanol supply chain both economically and environmentally sustainable. The problem is extended under type-2 intuitionistic fuzzy sets under carbon cap, tax, reward policies with proper sustainable vehicle selection. Next, a triangular intuitionistic type-2 fuzzy (TrIT2F)-analytic hierarchy process (AHP) is chosen to evaluate the weight of sustainability parameters. Thereafter, a TrIT2F-data envelopment analysis (DEA) is done to evaluate the efficiency score of each vehicle type according to sustainable criteria. A new ranking function is introduced to convert the TrIT2F number to a crisp form. A novel neutrosophic-technique for order of preference by similarity to ideal solution (TOPSIS) method is incorporated to obtain a Pareto-optimal solution for the formulated model. Furthermore, we evaluate the deterministic model using an LP-metric approach; an analogy is described between the executed solutions evaluated from two methods by considering the decisions of six DMs. The proposed optimization approach is validated through a numerical experiment and enhanced with a multi-criteria decision-making method to identify the best option among six alternatives based on six DM’ preferences.

. This study focuses on how to establish a connection between game theory and decision making under an uncertain environment. To tackle this uncertainty, an expansion of the hesitant fuzzy set and linguistic term set, i.e., the hesitant fuzzy linguistic term set (HFLTS), is considered here. The HFLTS is an effective technique for addressing ambiguity or uncertainty in multiple criteria decision making (MCDM). MCDM is a technique of evaluating multiple criteria for the sake of selecting the best alternative. It is an emerging topic in game theory and decision making. Nowadays, researchers are paying more attention to MCDM games with HFLTS. Numerous MCDM techniques are presented in the literature, but very few studies take into account MCDM challenges by using game theoretic models to ascertain the decision makers' preferences and attribute weights. Our main objective is to build the methods for solving an HFLTS-MCDM game. First, we present a novel HFLTS distance measure. To address the hesitant fuzzy linguistic-MCDM game, three novel distance measures, namely HFL-TOPSIS, HFL-VIKOR, and lambda-fuzzy measure, are devised. The resulting outcomes from the three methodologies are compared and contrasted. To exhibit the viability and effectiveness of the suggested measures, we illustrate a real-world example. The numerical results depict that lambda-fuzzy measure is the most suitable approach to find the best alternative among the set of alternatives. Moreover, the Spearman correlation coefficient and p-value between HFL-VIKOR and lambda-fuzzy measure (0.574125and0.045893, respectively) demonstrate the validity and robustness of the results.

This study aims to support decision-making in vendor selection for water supply problem which is often complex and uncertain. Vendor selection involves multiple criteria, including technical capability, quality of proposals, financial strength, and risk management. Evaluating vendors in water supply problem is therefore a challenging task. To address this challenge, this research proposes a systematic framework for vendor assessment based on the judgements of technical experts, environmental scientists, local committees, project managers, and government authorities. Thereafter, we introduce a new integrated approach that synergizes power Bonferroni aggregation operators and Schweizer-Sklar operation with p, q-quasirung orthopair fuzzy information and grey relational analysis, providing a robust framework to tackle real-life challenges. Moreover, the proposed model integrates the MEREC (MEthod based on the Removal Effects of Criteria) and DEMATEL (DEcision-MAking Trial and Evaluation Laboratory) methods to derive more precise and reliable weights for both attributes and experts. This robust framework enhances multi-attribute decision-making by enabling decision-making to make wellinformed choices and providing structured, accurate solutions that minimize uncertainty and enhance decision quality. What is more, using a numerical example based on the process of considering problem, we construct a novel decision-making method. Finally, we include sensitivity analysis and a comparison between the existing approaches and the proposed method in this study.

This research implements the impact of an advance payment policy on inventory management while incorporating preservation technology to mitigate product deterioration. In a real scenario, the sum of the membership and non-membership degrees of uncertain parameters is greater than one. Hence, the primary objective is to optimize cycle time, selling price and maximum profit by utilizing an advanced payment policy, hybrid price-dependent demand rate under interval-valued Pythagorean fuzzy numbers to handle imprecise parameters. Two inventory models are developed: one incorporating preservation technology and another without it. Then, the corresponding fuzzy models are obtained under interval-valued Pythagorean fuzzy environment. A novel ranking method is employed to defuzzify the models and then the defuzzified models are solved by using analytic solution method of maximization problem. The models are validated through numerical examples by assuming hypothetical data, and the results are compared across crisp, Pythagorean fuzzy, and intuitionistic fuzzy frameworks. Key findings indicate that adopting preservation technology significantly improves profitability and reduces deterioration losses. Moreover, the Pythagorean fuzzy approach proves to be more effective in capturing uncertainty compared to intuitionistic fuzzy sets. These findings suggest that businesses can enhance inventory decision-making by leveraging advanced fuzzy techniques to optimize financial and operational outcomes.

Waste generation across various categories such as municipal waste, industrial byproducts, medical discards continues to grow in both quantity and complexity due to population growth, urbanization, and advancements in technology. Improper waste management (WM), driven by human activities, is a major contributor to environmental pollution, making effective waste handling a critical priority for all nations. This study focuses on managing waste by minimizing total transportation cost, restricting carbon emission, and mitigating impacts on public health through a multi-objective optimization (MOO) framework. To address uncertainties in supply and demand fluctuations, the model incorporates type-2 intuitionistic fuzzy sets. Global criterion method (GCM) is applied to obtain Pareto-optimal solutions that balance multiple conflicting objectives. To validate the practicality of the proposed approach, a real-world case study based on Addis Ababa, Ethiopia, is presented, alongside several generated benchmark instances. Furthermore, the performance of GCM is compared with two existing MOO techniques to demonstrate its effectiveness in generating solutions. The study also includes a comparative analysis, sensitivity analysis, and managerial insights to support decision-making.

In the face of increasing uncertainty and sustainability challenges, inventory management must develop to balance financial, environmental, and social objectives. This study presents a multi-objective sustainable inventory model that integrates machine learning for demand prediction, addressing uncertainty through robust programming. By using machine learning techniques, demand forecasting is made more accurate, even under uncertain conditions, improving decision-making in inventory control. Preservation and green technologies are employed to reduce product deterioration and decrease carbon emissions. Additionally, the model accounts for the influence of inflation on costs, ensuring financial sustainability over time. Weighted goal programming approach is adopted to address three key objectives: maximum profit, increasing labour payment and decreasing carbon emissions. This method allows for balancing the conflicting goals by optimizing inventory levels, ultimately promoting sustainability while ensuring labour payments and emissions. The results demonstrate that the proposed model can successfully achieve sustainable and social responsible inventory management, and the proposed model is the superior model of traditional model. Sensitivity analyses are conducted to assess the robustness of the proposed model under varying parameters, providing valuable insights for decision-makers. The study concludes with a discussion of the findings and suggestions for future research directions.

This study intends to present a Stackelberg game model for design of the fractional type-2 fuzzy programming. In achieving this aspiration, we develop a probabilistic fuzzy multi-objective fractional linear programming where the entire parameters are of type-2 fuzzy numbers apart from the right-hand side of the constraints are follow Weibull distribution. In the projected approach, the membership function allied with each objective function is generated by using the first-order Taylor series approximation and converted into a single objective function by assuming the weights of the objective functions are equal. Type conversion is made in two ways by existing methods, and using stochastic programming, the probabilistic constraints are transformed into a deterministic form. The accessible model incorporates the non-linear programming viewpoint of the decision-maker and is solved with the help of intuitionistic fuzzy programming (IFS). A comparison study on the optimum results by genetic algorithm (GA) and particle swarm optimization (PSO) with the LINGO 15.0 iterative scheme is offered to resolve the created bi-level programming problem (BLPP) in the course of the Stackelberg game. To make obvious the feasibility of the projected representation and solution methodology, realistic data are measured and results are presented through several discussions.

This study proposes a sustainable model involving a single supplier, a logistics provider, and multiple retailers. The presented model incorporates deteriorating items by accounting for carbon emissions with green technology investment. Emissions arising from inventory storage, transportation, and storage of deteriorating items. To minimize both production and overall system costs, the supplier adopts discrete setup cost reduction. The paper evaluates the effectiveness of green technology investment within a supply chain model under the effect of a cap-and-trade policy. Preservation technology is also applied to mitigate item deterioration. Economic sustainability is maintained by minimizing total cost via optimized shipments, setup cost reduction, cycle time, and investments in preservation and green technologies. Environmental sustainability is preserved through carbon emission reduction and the implementation of green technology, while social sustainability is supported by ensuring a healthier, low-carbon environment for future generations. Sensitivity analysis is performed and graphical representations are provided to evaluate the effectiveness of key parameters in the study. An important perspective of the study is on reducing carbon emissions while minimizing total cost. Numerical experiments illustrate that a global optimum solution is obtained at the optimum values of the decision variables, and carbon emission reduction is sustained. This study contributes to sustainable supply chain research in developing countries by exploring investment strategies such as green technology implementation, cap-and-trade policies, and preservation technology to reduce product deterioration and emissions.

Decision-making is a fundamental and complex cognitive process that influences our daily lives. Moreover, as uncertainty looms over decision-making processes, effective management becomes imperative. Therefore, the objective of this study is to establish a novel hybrid multi-attribute decision-making methodology, with a specific focus on the following objectives: (i) addressing ambiguity and interrelationships inherent in decision-making problems, (ii) integrating uncertain attribute weights rather than relying solely on crisp values, and (iii) presenting ranking outcomes that incorporate conflict analysis among alternatives and accommodate preference relations within multiple conflicting elements. Firstly, q-rung orthopair fuzzy sets is initiated for handling uncertainties in expert opinions during evaluations process. Secondly, leveraging the flexibility and generality of Aczel-Alsina norms, weighted geometric Bonferroni mean is developed within q-rung orthopair fuzzy sets framework. Additionally, an advanced form of Dice and Jaccard similarity measures are devised, using new quadratic form to accurately measure similarity between q-rung orthopair fuzzy sets. Furthermore, a logarithmic methodology of additive weights method is proposed within the q-rung orthopair fuzzy sets environment to derive weights and minimize information loss. Subsequently, organisation, rangement et Synth & egrave;se de donn & eacute;es relarionnelles (in French) method is extended to rank alternatives, employing a new generalized form of the developed similarity measures. Finally, harnessing these advancements, a hybrid multi-attribute decision-making methodology is developed and applied to determine the most suitable electrolysis for green hydrogen production in the eastern part of India.

This study develops a sustainable inventory model for complementary products by integrating a two-level credit policy with controllable carbon emissions. The model captures key operational complexities such as sustainability-sensitive demand, product interdependence, carbon-cap-andtrade regulations, and deterioration effects. The analysis reveals that offering a coordinated two-level credit period significantly enhances retailer liquidity and stimulates higher demand for both complementary items, thereby increasing overall system profitability. The results show that (i) offering two-level credit enhances retailer profitability and encourages higher-order quantities for complementary goods, (ii) controllable carbon emission investment reduces environmental impact while maintaining cost efficiency, and (iii) joint consideration of credit terms and carbon control creates a win-win scenario for economic and environmental performance. Results further show that investing in carbon-reduction efforts not only lowers total emissions but also reduces regulatory costs under cap-and-trade schemes, leading to economically and environmentally balanced decisions. Sensitivity analysis highlights the critical roles of credit duration, emission-control cost, and deterioration rate in shaping optimal order quantity, carbon-reduction level, and total profit. The findings provide actionable insights for retailers seeking to align financial incentives with sustainable operational strategies while managing interrelated product lines.

The indisputable concerns about the environment are compelled the rapid spread of electric vehicles (EVs), turquoise hydrogen (TH) and renewable energy facilities (REFs). TH production is environmentally friendly and less harmful than conventional hydrogen, but further research is needed to make its eventual use feasible. Thus, this study explores India's potential organic waste for pyrolysis, highlighting potential challenges in maintaining supply-demand equilibrium in the electric distribution system (EDS) due to unpredictable and variable sources. The study proposes a distributionally robust optimization approach for waste legislation-based multi-objective mixed-integer sustainable closed-loop supply chain in integrated natural gas and electricity distribution networks (INEDNs) in order to overcome these issues. The method not only simplifies waste separation in TH manufacturing but also significantly diverts recyclable waste to recycling facilities. Furthermore, EDS utilizes demand response activities (DRAs) to prevent peak load hours from overlapping with natural gas distribution system (NGDS), and utilizes linepack technology to store natural gas in NGDS pipes for short-term versatility. Next, the proposed multi-objective model is solved using a novel approach called utility function based multi-volition conic goal programming. A 123-EDS and a 40-NGDS are used for the simulations. The analysis of hydrogen's closed-loop supply chain using flexible energy sources, DRA, linepack technology, and smart charging simulations shows potential for an 11.22 % reduction in emissions. Linepack technology reduces operating expenses for natural gas pipelines by 6.5 % in S 2 , to EDS responsive loads 4.2 % in S 3 , and 9.49 % in S 4 lower than those of S 1 when all energy sources are used.

In today's data-driven world, making informed decisions in dynamic fields like culinary tourism is crucial. An enhanced multi-attribute decision-making (MADM) model is presented in this study to tackle the uncertainty and interdependencies of India's culinary tourism landscape. The main goals are to (i) address uncertainty and correlations in MADM scenarios, (ii) calculate objective attribute weights, and (iii) resolve conflicts among alternatives based on preference, indifference, and incomparability. To manage uncertainty, the proposed model incorporates r, s-quasirung orthopair fuzzy set (r, s-QOFS), while to capture relational dynamics among factors Aczel-Alsina operations based geometric Heronian mean operator is developed. Attribute weighting is performed with MEREC (method based on the removal effects of criteria) method, while a modified ORESTE (organisation, rangement et Synth & egrave;se dedonn & eacute;es relarionnelles (in French)) method within the r, s-QOFS is initiated to rank alternatives, introducing a new ranking measure in place of Besson's traditional rank. Finally, to test the effectiveness and practical value, a case study of culinary tourism destinations across 36 Indian states and union territories is conducted and then ranked using the proposed model. The results highlight southern Indian states as preferred destinations. Thus, this work contributes in two ways: first, by providing a general decision-making model for imprecise and data, and second, by offering valuable insights into the future of Indian culinary tourism.

The study of the nervous system is mainly known as neuroscience. Neuroscience is concerned with the brain, spinal chord and all the nervous systems throughout the human body. The main goal of neuroscience is to understand human brain and its function, to understand how the Central Nervous System (CNS) develops and matures, to understand the psychiatric and neurological disorders and how to cure or prevent them. Historically, the lack of available information about brain technology and human psychology has a limited scope of application for Neuroscience in business management. In this article, we describe, in detail, the advantages and disadvantages of neuroscience in business strategies. When it is about a business strategy, neuromarketing is a powerful tool to add a positive impact in the direction of marketing research. Therefore, this paper provides an overview of marketing research to understand the subconscious mind of the customer and discusses the ethical issues and professional challenges in neuromarketing and recommendations. Besides, the purpose of this study is to highlight the relationship between green pro duct knowledge, green marketing, consumer effectiveness and green purchase intention, which influence consumers' decisions to purchase green products in India. For this purpose, a survey via questionnaire is performed by collecting data from different industry in India. Data are analyzed through measurement model and hypothesis testing and the research findings can guide the invenand sustainability. Not only that, but this study provides new insights for chase intention. The study ends with a conclusion and an outlook to future research and application.

In military logistics and operational planning, selecting an optimal highway for war-plane landings and take-offs is a critical and strategic decision. This process involves several key factors that directly affect mission success, operational safety, and public security. Among the most important attributes are the highway's straight and long stretch with sufficient width to accommodate war-plane landing distances, and its surface condition, which must be free from obstacles, debris, and damage. Low traffic density is crucial to avoid the risk of collisions during landing. Additionally, favourable weather conditions, proximity to military camps, availability of emergency services and fuel, and a secure and hazard-free surrounding terrain are essential for safe and efficient operations. These factors collectively form the backbone of a reliable and tactical approach to highway selection for military air operations. Thus, in order to assess and rank various options for the landing and take-off of war planes, a strong and trustworthy procedure for making decisions is required. The purpose of this experiment is to build a comprehensive structure in multi-attribute decision making environment, using suggested p, q-quasirung orthopair fuzzy Frank power averaging as well as p, q-quasirung orthopair fuzzy Frank power geometric operators to capture ambiguity and uncertainty in highway selection. Furthermore, p, q-quasirung orthopair fuzzy Frank power weighted aggregation along with p, q-quasirung orthopair fuzzy Frank power weighted geometric operators are implemented for integrating the distance as well as similarity measures. Finally, sensitivity analysis and a comparison with the present technique are included to further demonstrate the superiority and validity of the technique that is suggested.

This study advances the classical economic production quantity model by incorporating multiple real-world complexities within the context of deteriorating items under uncertain environment. A novel framework is developed that integrates preservation technology to reduce deterioration rates, along with the optimal investment decisions required for its implementation. To better reflect in contemporary market dynamics, the model includes a demand rate influenced by both price and greening level. Further the model accommodates shortages, allowing partial back ordering by reserving shortfall quantities for customers in subsequent production time enhancing customer retention and operational flexibility. In alignment with global sustainability goals, the formulated model introduces carbon emission taxation, incorporating four distinct environmental policies: simple tax, cap, cap and reward, and strict under permitted cap policy. In addition to that, a unique concept, development cost is used in the proposed model. To account for economic variability, money inflation is considered, and fuzzy-random cost parameters are employed to model for tackling imprecise and uncertain market information. The model’s effectiveness is demonstrated through three hypothetical case studies, which illustrate the model’s capacity to handle uncertainty and sustainability simultaneously. Finally, the results illustrate that a sustainable version incorporating controllable carbon emission, preservation technology and green investments are more realistic and profitable as compared to other existing models.

This study analyses the multi-objective inventory optimisation, where demand is influenced by both selling price and marketing efforts. In traditional inventory problem, profit or cost is optimised through single objective function. Here, we identify two conflicting aspects and treat them as separate objective functions which present multi-objective scenarios. To deal with real scenario in the formulated model, shortages, emissions, fuzzy-random numbers, RFID, money inflation are permitted. During production, some products are defective; among these, some are repairable while others are not. Repairable products are fixed, whereas non-repairable products are sent to a reclamation centre. From there, usable parts are retained, and remaining parts are disposed. Application example is included and then solved. The findings indicate that this model is the superior model of the traditional inventory models. Subsequently, a sensitivity analysis subject to the primary parameters is incorporated to verify the reliability of the proposed study which leads to some intriguing managerial insights that helped the decision maker to make some suitable and qualitative decisions.