Waste management strategies employed by emerging economies worldwide are often insufficient to address the new-age sustainability challenges.Especially in chemicalintensive manufacturing industries,increasing levels o...Waste management strategies employed by emerging economies worldwide are often insufficient to address the new-age sustainability challenges.Especially in chemicalintensive manufacturing industries,increasing levels of waste production are a significant environmental threat.Adopting a circular supply chain(CSC)can be a viable solution to this problem since it incorporates the components of the circular economy into an organization's supply chain,bringing an engaging viewpoint to the supply chain sustainability field.Nevertheless,the adoption of CSC in chemical-intensive manufacturing industries faces various intricate challenges in emerging economies.This study,therefore,aims to explore and evaluate the challenges associated with adopting CSC in the chemical-intensive manufacturing industries,using the empirical case of an emerging economy,Bangladesh.After a thorough literature review and expert validation,26 challenges were analyzed using a probabilistic group decision-making approach,i.e.,the Bayesian best-worst method(BWM).The result showed that the most significant challenge is the chemical composition-related complexity(global weight=0.0801),followed by strong emphasis on the take-make-dispose policy(0.0705),and insufficient investment and financial resources(0.0697).On the contrary,the least important challenge is the resistance toward the transition from conventional supply chain to CSC(0.0078).The outcomes of this study are expected to enrich existing knowledge and comprehension of the challenges linked to implementing CSC practices in Bangladesh and contribute to achieving Sustainable Development Goals(SDGs),such as SDG 3(good health and wellbeing),SDG 11(sustainable cities and communities),SDG 12(responsible consumption and production),SDG 13(climate action),and so on.展开更多
Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effect...Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.展开更多
Hypersoft set is an extension of soft set as it further partitions each attribute into its corresponding attribute-valued set.This structure is more flexible and useful as it addresses the limitation of soft set for d...Hypersoft set is an extension of soft set as it further partitions each attribute into its corresponding attribute-valued set.This structure is more flexible and useful as it addresses the limitation of soft set for dealing with the scenarios having disjoint attribute-valued sets corresponding to distinct attributes.The main purpose of this study is to make the existing literature regarding neutrosophic parameterized soft set in line with the need of multi-attribute approximate function.Firstly,we conceptualize the neutrosophic parameterized hypersoft sets under the settings of fuzzy set,intuitionistic fuzzy set and neutrosophic set along with some of their elementary properties and set theoretic operations.Secondly,we propose decision-making-based algorithms with the help of these theories.Moreover,illustrative examples are presented which depict the structural validity for successful application to the problems involving vagueness and uncertainties.Lastly,the generalization of the proposed structure is discussed.展开更多
Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 20...Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.展开更多
Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses...Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities.A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route problem.The model is enhanced to include possible discounts in fuel prices,which are performed by adding dummy variables and some restrictive constraints,or by fitting a suitable distribution function that relates prices to purchased quantities.The obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight route.The relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered.A case study is introduced for a certain flight rotation in domestic US air transport route.The mathematical model including stepped discounted fuel prices is formulated.The problem has a stochastic nature as the total flight time is a random variable,the stochastic nature of the problem is realistic and more appropriate than the deterministic case.The stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real situation.Many similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact methods.Therefore,metaheuristic approaches are widely used in treating such different optimization tasks.In this paper,a gaining sharing knowledge-based procedure is used to handle the mathematical model.The algorithm basically based on the process of gaining and sharing knowledge throughout the human lifetime.The generated simulation runs of the example are solved using the proposed algorithm,and the resulting distribution outputs for the optimum purchased fuel amounts from each airport and for the total cost and are obtained.展开更多
The present paper has two-fold purposes.First,the current work provides an integrated theoretical framework to compare popular mobile wallet service providers based on users’views in the Indian context.To this end,we...The present paper has two-fold purposes.First,the current work provides an integrated theoretical framework to compare popular mobile wallet service providers based on users’views in the Indian context.To this end,we propose a new grey correlationbased Picture Fuzzy-Evaluation based on Distance from Average Solution(GCPF-EDAS)framework for the comparative analysis.We integrate the fundamental framework of the Technology Acceptance Model and Unified theory of acceptance and use of technology vis-a-vis service quality dimensions for criteria selection.For comparative ranking,we conduct our analysis under uncertain environments using picture fuzzy numbers.We find that user-friendliness,a wide variety of use,and familiarity and awareness about the products help reduce the uncertainty factors and obtain positive impressions from the users.It is seen that PhonePe(A3),Google Pay(A2),Amazon Pay(A4)and PayTM(A1)hold top positions.For validation of the result,we first compare the ranking provided by our proposed model with that derived by using picture fuzzy score based extensions of EDAS and another widely used algorithm such as The Technique for Order of Preference by Similarity to Ideal Solution.We observe a significant consistency.We then carry out rank reversal test for GCPF-EDAS model.We notice that our proposed GCPF-EDAS model does not suffers from rank reversal phenomenon.To examine the stability in the result for further validation,we carry out the sensitivity analysis by varying the differentiating coefficient and exchanging the criteria weights.We find that our proposed method provides stable result for the present case study and performs better as ranking order does not get changed significantly with the changes in the given conditions.展开更多
Linear equality systems With fuzzy parameters and crisp variables defined by the Zadeh's extension principle are called possibilistic linear equality systems. This study focuses on the problem of stability (with r...Linear equality systems With fuzzy parameters and crisp variables defined by the Zadeh's extension principle are called possibilistic linear equality systems. This study focuses on the problem of stability (with respect to small changes in the membership function of fuzzy parameters) of the solution in these systems.展开更多
In this article,mathematical modeling for the evaluation of reliability is studied using two methods.One of the methods,is developed based on possibility theory.The performance of the reliability of the system is of p...In this article,mathematical modeling for the evaluation of reliability is studied using two methods.One of the methods,is developed based on possibility theory.The performance of the reliability of the system is of prime concern.In view of this,the outcomes for the failure are required to evaluate with utmost care.In possibility theory,the reliability information data determined from decision-making experts are subjective.The samemethod is also related to the survival possibilities as against the survival probabilities.The other method is the one that is developed using the concept of approximation of closed interval including the piecewise quadratic fuzzy numbers.In this method,a decision-making expert is not sure of his/her estimates of the reliability parameters.Numerical experiments are performed to illustrate the efficiency of the suggested methods in this research.In the end,the paper is concluded with some future research directions to be explored for the proposed approach.展开更多
Time and space complexity is themost critical problemof the current routing optimization algorithms for Software Defined Networking(SDN).To overcome this complexity,researchers use meta-heuristic techniques inside the...Time and space complexity is themost critical problemof the current routing optimization algorithms for Software Defined Networking(SDN).To overcome this complexity,researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow(OF)based large scale SDNs.This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs.Due to the dynamic nature of SDNs,the proposed algorithm uses amutation operator to overcome the memory-based problem of the ant colony algorithm.Besides,it uses the box-covering method and the k-means clustering method to divide the SDN network to overcome the problemof time and space complexity.The results of the proposed algorithm compared with the results of other similar algorithms and it shows that the proposed algorithm can handle the dynamic network changing,reduce the network congestion,the delay and running times and the packet loss rates.展开更多
The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics.A problem arises for a decision maker who wants to opt...The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics.A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specic time period.A nonlinear binary mathematical programming model for the problem is formulated.The decision variables are binary ones that represent whether to choose a specic consumer,and design constraints are formulated to keep track of the chosen route.To better illustrate the problem,objective,and problem constraints,a real application case study is presented.The case study involves the optimum delivery of safeguarding substances to several hospitals in the Al-Gharbia Governorate in Egypt.The hospitals are selected to represent the consumers of safeguarding substances,as they are the rst crucial frontline for mitigation against a pandemic outbreak.A distribution truck is used to distribute the substances from the main store to the hospitals in specied required quantities during a given working shift.The objective function is formulated in order to maximize the total amount of delivered quantities during the specied time period.The case study is solved using a novel Discrete Binary Gaining Sharing Knowledge-based Optimization algorithm(DBGSK),which involves two main stages:discrete binary junior and senior gaining and sharing stages.DBGSK has the ability of nding the solutions of the introduced problem,and the obtained results demonstrate robustness and convergence toward the optimal solutions.展开更多
In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung n...In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.展开更多
Data Envelopment Analysis(DEA)is a powerful mathematical optimization method widely used for measuring,evaluating and improving the performance of Decision Making Units(DMUs).These used in the various forms,such as ho...Data Envelopment Analysis(DEA)is a powerful mathematical optimization method widely used for measuring,evaluating and improving the performance of Decision Making Units(DMUs).These used in the various forms,such as hospitals,government agencies,educational institutions,air force,bank branches,business finns,sport teams and even people including the performance of countries,regions,etc.Recently DEA has been extended to examine the performance through the different sport types.In this paper,a Stochastic Input Oriented Data Envelopment Analysis(SIODEA)Model is conducted for measuring and evaluating the relative efficiency scores of football teams selected from different European countries during 2014/2015 season each with some of inputs are stochastic with normally distributed and recent inputs are deterministic and outputs,to shed light on the professional football teams performance.展开更多
Overall bank performance in a particular year or period is important to all banking industry stakeholders,as it indicates their success or failure relative to predetermined targets.Due to conflicting criteria and unce...Overall bank performance in a particular year or period is important to all banking industry stakeholders,as it indicates their success or failure relative to predetermined targets.Due to conflicting criteria and uncertainties,assessing bank performance is a complicated decision-making problem.The current paper proposes the Fuzzy Level Based Weight Assessment(F-LBWA),the Fuzzy Logarithm Methodology of Additive Weights(F-LMAW),and the Measurement Alternatives and Ranking according to the Compromise Solution(MARCOS)combination as a practical and robust decisionmaking tool to cope with many complex ambiguities.In the first phase,the suggested hybrid Multi-Criteria Decision-Making(MCDM)approach estimates the weight coefficients of the performance criteria with the aid of a combined version of the F-LBWA and F-LMAW methods.In the second phase,the MARCOS method determines the ranking performance of the decision alternatives.The introduced model is tested and validated on a case study assessing publicly traded bank performance in Pakistan.The findings obtained from the sensitivity analysis revealed that the presented F-LBWAF-LMAW-MARCOS approach produces consistent solutions and is a reliable and effective procedure in rational decision-making.展开更多
This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed o...This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies.展开更多
Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy.This study develops the novel Gromov-Hausdorff distance for r...Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy.This study develops the novel Gromov-Hausdorff distance for rock quality(GHDQR)methodology for rock mass quality rating based on multi-criteria grey metric space.It usually presents the quality of surrounding rock by classes(metric spaces)with specified properties and adequate interval-grey numbers.Measuring the distance between surrounding rock sample characteristics and existing classes represents the core of this study.The Gromov-Hausdorff distance is an especially useful discriminant function,i.e.,a classifier to calculate these distances,and assess the quality of the surrounding rock.The efficiency of the developed methodology is analyzed using the Mean Absolute Percentage Error(MAPE)technique.Seven existing methods,such as the Gaussian cloud method,Discriminant method,Mutation series method,Artificial neural network(ANN),Support vector machine(SVM),Grey wolf optimizer and Support vector classification method(GWO-SVC)and Rock mass rating method(RMR)are used for comparison with the proposed GHDQR method.The share of the highly accurate category of 85.71%clearly indicates compliance with actual values obtained by the compared methods.The results of comparisons showed that the model enables objective,efficient,and reliable assessment of rock mass quality.展开更多
This socialized environment among educated and developed people causes themto focusmore on their appearance and health,which turns them towards medical-related treatments,leading us to discuss anti-aging treatment met...This socialized environment among educated and developed people causes themto focusmore on their appearance and health,which turns them towards medical-related treatments,leading us to discuss anti-aging treatment methods for each age group,particularly for urban people who are interested in this.Some anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative effects.Five anti-aging therapies such as microdermabrasion or dermabrasion,laser resurfacing anti-aging skin treatments,chemical peels,dermal fillers for aged skin,and botox injections are considered in this study.Based on the criteria of safety risk,investment cost,customer happiness,and side effects,the optimal alternative is picked.As a result,a NormalWiggly Hesitant Pythagorean Fuzzy Set(NWHPFS)is constructed and used in Multi-Criteria Decision-Making(MCDM)using traditional wavy mathematical approaches.The entropy approach is utilized to determine weight values,and the Normal Wiggly Hesitant Pythagorean-VlseKriterijumska Optimizacija I Kompromisno Resenje(NWHPF-VIKOR)method is utilized to rank alternatives using MCDM methodologies.Sensitivity analysis and comparative analysis were performed to ensure the robustness and reliability of the proposed method.The smart final choice will undoubtedly assist Decision Makers(DM)in making the right judgments,and the MCDM approach will undoubtedly assist individuals in understanding the medicine.展开更多
Food Waste(FW)is a pressing environmental concern that affects every country globally.About one-third of the food that is produced ends up as waste,contributing to the carbon footprint.Hence,the FW must be properly tr...Food Waste(FW)is a pressing environmental concern that affects every country globally.About one-third of the food that is produced ends up as waste,contributing to the carbon footprint.Hence,the FW must be properly treated to reduce environmental pollution.This study evaluates a few available Food Waste Treatment(FWT)technologies,such as anaerobic digestion,composting,landfill,and incineration,which are widely used.A Bipolar Picture Fuzzy Set(BPFS)is proposed to deal with the ambiguity and uncertainty that arise when converting a real-world problem to a mathematical model.A novel Criteria Importance Through Intercriteria Correlation-Stable Preference Ordering Towards Ideal Solution(CRITIC-SPOTIS)approach is developed to objectively analyze FWT selection based on thirteen criteria covering the industry’s technical,environmental,and entrepreneurial aspects.The CRITIC method is used for the objective analysis of the importance of each criterion in FWT selection.The SPOTIS method is adopted to rank the alternative hassle-free,following the criteria.The proposed model offers a rank reversal-free model,i.e.,the rank of the alternatives remains unaffected even after the addition or removal of an alternative.In addition,comparative and sensitivity analyses are performed to ensure the reliability and robustness of the proposed model and to validate the proposed result.展开更多
In South Asia,Pakistan has a long and deadly history of floods that cause losses to various infrastructures,lives,and industries.This study aims to identify the most appropriate flood risk mitigation strategies that t...In South Asia,Pakistan has a long and deadly history of floods that cause losses to various infrastructures,lives,and industries.This study aims to identify the most appropriate flood risk mitigation strategies that the government of Pakistan should adopt.The assessment of flood risk mitigation strategies in this study is based on certain criteria,which are analyzed using the fuzzy full consistency method.Moreover,flood risk mitigation strategies are evaluated by using the fuzzy weighted aggregated sum product assessment(WASPAS)method,considering previously prioritized criteria.According to the results,lack of governance,lack of funding and resources,and lack of flood control infrastructure are the most significant flood intensifying factors and act as major criteria for assessing flood risk mitigation strategies in Pakistan.Adopting hard engineering strategies(e.g.,dams,reservoirs,river straightening and dredging,embankments,and flood relief channels),maintaining existing infrastructure,and adopting soft engineering strategies(flood plain zoning,comprehensive flood risk assessment,and sophisticated flood modeling)are identified as the top three flood risk mitigation strategies by the fuzzy WASPAS method.The highest weight(0.98)was assigned to the adoption of hard engineering strategies to mitigate flood risks.The study introduces a novel dimension by analyzing the real-time impact of the unprecedented 2022 floods,during which approximately one-third of the nation was submerged.This focus on a recent and highly significant event enhances the study’s relevance and contributes a unique perspective to the existing literature on flood risk management.The study recommends that the government of Pakistan should prioritize hard engineering strategies for effective flood risk mitigation.It also recommends that the government should incorporate these strategies in the national policy framework to reduce flood losses in the future.展开更多
Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that we...Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that were known a priori. Model averaging approaches, on the other hand, try to combine estimators for a set of candidate models. Specifically, instead of deciding which model is the 'right' one, a model averaging approach suggests to fit a set of candidate models and average over the estimators using data adaptive weights.In this paper we establish a general frequentist model averaging framework that does not set any restrictions on the set of candidate models. It broaden, the scope of the existing methodologies under the frequentist model averaging development. Assuming the data is from an unknown model, we derive the model averaging estimator and study its limiting distributions and related predictions while taking possible modeling biases into account.We propose a set of optimal weights to combine the individual estimators so that the expected mean squared error of the average estimator is minimized. Simulation studies are conducted to compare the performance of the estimator with that of the existing methods. The results show the benefits of the proposed approach over traditional model selection approaches as well as existing model averaging methods.展开更多
文摘Waste management strategies employed by emerging economies worldwide are often insufficient to address the new-age sustainability challenges.Especially in chemicalintensive manufacturing industries,increasing levels of waste production are a significant environmental threat.Adopting a circular supply chain(CSC)can be a viable solution to this problem since it incorporates the components of the circular economy into an organization's supply chain,bringing an engaging viewpoint to the supply chain sustainability field.Nevertheless,the adoption of CSC in chemical-intensive manufacturing industries faces various intricate challenges in emerging economies.This study,therefore,aims to explore and evaluate the challenges associated with adopting CSC in the chemical-intensive manufacturing industries,using the empirical case of an emerging economy,Bangladesh.After a thorough literature review and expert validation,26 challenges were analyzed using a probabilistic group decision-making approach,i.e.,the Bayesian best-worst method(BWM).The result showed that the most significant challenge is the chemical composition-related complexity(global weight=0.0801),followed by strong emphasis on the take-make-dispose policy(0.0705),and insufficient investment and financial resources(0.0697).On the contrary,the least important challenge is the resistance toward the transition from conventional supply chain to CSC(0.0078).The outcomes of this study are expected to enrich existing knowledge and comprehension of the challenges linked to implementing CSC practices in Bangladesh and contribute to achieving Sustainable Development Goals(SDGs),such as SDG 3(good health and wellbeing),SDG 11(sustainable cities and communities),SDG 12(responsible consumption and production),SDG 13(climate action),and so on.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R259)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.Ashit Kumar Dutta would like to thank AlMaarefa University for supporting this research under project number MHIRSP2025017.
文摘Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.
文摘Hypersoft set is an extension of soft set as it further partitions each attribute into its corresponding attribute-valued set.This structure is more flexible and useful as it addresses the limitation of soft set for dealing with the scenarios having disjoint attribute-valued sets corresponding to distinct attributes.The main purpose of this study is to make the existing literature regarding neutrosophic parameterized soft set in line with the need of multi-attribute approximate function.Firstly,we conceptualize the neutrosophic parameterized hypersoft sets under the settings of fuzzy set,intuitionistic fuzzy set and neutrosophic set along with some of their elementary properties and set theoretic operations.Secondly,we propose decision-making-based algorithms with the help of these theories.Moreover,illustrative examples are presented which depict the structural validity for successful application to the problems involving vagueness and uncertainties.Lastly,the generalization of the proposed structure is discussed.
基金funded by Deanship of Scientific Research,King Saud University,through the Vice Deanship of Scientific Research.
文摘Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.
基金The research is funded by Deanship of Scientific Research at King Saud University research group number RG-1436-040.
文摘Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities.A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route problem.The model is enhanced to include possible discounts in fuel prices,which are performed by adding dummy variables and some restrictive constraints,or by fitting a suitable distribution function that relates prices to purchased quantities.The obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight route.The relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered.A case study is introduced for a certain flight rotation in domestic US air transport route.The mathematical model including stepped discounted fuel prices is formulated.The problem has a stochastic nature as the total flight time is a random variable,the stochastic nature of the problem is realistic and more appropriate than the deterministic case.The stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real situation.Many similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact methods.Therefore,metaheuristic approaches are widely used in treating such different optimization tasks.In this paper,a gaining sharing knowledge-based procedure is used to handle the mathematical model.The algorithm basically based on the process of gaining and sharing knowledge throughout the human lifetime.The generated simulation runs of the example are solved using the proposed algorithm,and the resulting distribution outputs for the optimum purchased fuel amounts from each airport and for the total cost and are obtained.
文摘The present paper has two-fold purposes.First,the current work provides an integrated theoretical framework to compare popular mobile wallet service providers based on users’views in the Indian context.To this end,we propose a new grey correlationbased Picture Fuzzy-Evaluation based on Distance from Average Solution(GCPF-EDAS)framework for the comparative analysis.We integrate the fundamental framework of the Technology Acceptance Model and Unified theory of acceptance and use of technology vis-a-vis service quality dimensions for criteria selection.For comparative ranking,we conduct our analysis under uncertain environments using picture fuzzy numbers.We find that user-friendliness,a wide variety of use,and familiarity and awareness about the products help reduce the uncertainty factors and obtain positive impressions from the users.It is seen that PhonePe(A3),Google Pay(A2),Amazon Pay(A4)and PayTM(A1)hold top positions.For validation of the result,we first compare the ranking provided by our proposed model with that derived by using picture fuzzy score based extensions of EDAS and another widely used algorithm such as The Technique for Order of Preference by Similarity to Ideal Solution.We observe a significant consistency.We then carry out rank reversal test for GCPF-EDAS model.We notice that our proposed GCPF-EDAS model does not suffers from rank reversal phenomenon.To examine the stability in the result for further validation,we carry out the sensitivity analysis by varying the differentiating coefficient and exchanging the criteria weights.We find that our proposed method provides stable result for the present case study and performs better as ranking order does not get changed significantly with the changes in the given conditions.
基金This work has been supported by Hungarian Young Scholars' Fund under No. 400-0113.
文摘Linear equality systems With fuzzy parameters and crisp variables defined by the Zadeh's extension principle are called possibilistic linear equality systems. This study focuses on the problem of stability (with respect to small changes in the membership function of fuzzy parameters) of the solution in these systems.
文摘In this article,mathematical modeling for the evaluation of reliability is studied using two methods.One of the methods,is developed based on possibility theory.The performance of the reliability of the system is of prime concern.In view of this,the outcomes for the failure are required to evaluate with utmost care.In possibility theory,the reliability information data determined from decision-making experts are subjective.The samemethod is also related to the survival possibilities as against the survival probabilities.The other method is the one that is developed using the concept of approximation of closed interval including the piecewise quadratic fuzzy numbers.In this method,a decision-making expert is not sure of his/her estimates of the reliability parameters.Numerical experiments are performed to illustrate the efficiency of the suggested methods in this research.In the end,the paper is concluded with some future research directions to be explored for the proposed approach.
文摘Time and space complexity is themost critical problemof the current routing optimization algorithms for Software Defined Networking(SDN).To overcome this complexity,researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow(OF)based large scale SDNs.This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs.Due to the dynamic nature of SDNs,the proposed algorithm uses amutation operator to overcome the memory-based problem of the ant colony algorithm.Besides,it uses the box-covering method and the k-means clustering method to divide the SDN network to overcome the problemof time and space complexity.The results of the proposed algorithm compared with the results of other similar algorithms and it shows that the proposed algorithm can handle the dynamic network changing,reduce the network congestion,the delay and running times and the packet loss rates.
基金funded by Deanship of Scientic Research,King Saud University through the Vice Deanship of Scientic Research.
文摘The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics.A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specic time period.A nonlinear binary mathematical programming model for the problem is formulated.The decision variables are binary ones that represent whether to choose a specic consumer,and design constraints are formulated to keep track of the chosen route.To better illustrate the problem,objective,and problem constraints,a real application case study is presented.The case study involves the optimum delivery of safeguarding substances to several hospitals in the Al-Gharbia Governorate in Egypt.The hospitals are selected to represent the consumers of safeguarding substances,as they are the rst crucial frontline for mitigation against a pandemic outbreak.A distribution truck is used to distribute the substances from the main store to the hospitals in specied required quantities during a given working shift.The objective function is formulated in order to maximize the total amount of delivered quantities during the specied time period.The case study is solved using a novel Discrete Binary Gaining Sharing Knowledge-based Optimization algorithm(DBGSK),which involves two main stages:discrete binary junior and senior gaining and sharing stages.DBGSK has the ability of nding the solutions of the introduced problem,and the obtained results demonstrate robustness and convergence toward the optimal solutions.
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(No.RS-2023-00218176)Korea Institute for Advancement of Technology(KIAT)Grant funded by the Korea government(MOTIE)(P0012724)The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.
文摘Data Envelopment Analysis(DEA)is a powerful mathematical optimization method widely used for measuring,evaluating and improving the performance of Decision Making Units(DMUs).These used in the various forms,such as hospitals,government agencies,educational institutions,air force,bank branches,business finns,sport teams and even people including the performance of countries,regions,etc.Recently DEA has been extended to examine the performance through the different sport types.In this paper,a Stochastic Input Oriented Data Envelopment Analysis(SIODEA)Model is conducted for measuring and evaluating the relative efficiency scores of football teams selected from different European countries during 2014/2015 season each with some of inputs are stochastic with normally distributed and recent inputs are deterministic and outputs,to shed light on the professional football teams performance.
文摘Overall bank performance in a particular year or period is important to all banking industry stakeholders,as it indicates their success or failure relative to predetermined targets.Due to conflicting criteria and uncertainties,assessing bank performance is a complicated decision-making problem.The current paper proposes the Fuzzy Level Based Weight Assessment(F-LBWA),the Fuzzy Logarithm Methodology of Additive Weights(F-LMAW),and the Measurement Alternatives and Ranking according to the Compromise Solution(MARCOS)combination as a practical and robust decisionmaking tool to cope with many complex ambiguities.In the first phase,the suggested hybrid Multi-Criteria Decision-Making(MCDM)approach estimates the weight coefficients of the performance criteria with the aid of a combined version of the F-LBWA and F-LMAW methods.In the second phase,the MARCOS method determines the ranking performance of the decision alternatives.The introduced model is tested and validated on a case study assessing publicly traded bank performance in Pakistan.The findings obtained from the sensitivity analysis revealed that the presented F-LBWAF-LMAW-MARCOS approach produces consistent solutions and is a reliable and effective procedure in rational decision-making.
基金supported by the Natural Science Foundation of China under Grant 61873017 and Grant 61473016in part by the Beijing Natural Science Foundation under Grant Z180005supported in part by the National Research Foundation of South Africa under Grant 113340in part by the Oppenheimer Memorial Trust Grant
文摘This paper presents a distributed optimization strategy for large-scale traffic network based on fog computing. Different from the traditional cloud-based centralized optimization strategy, the fog-based distributed optimization strategy distributes its computing tasks to individual sub-processors, thus significantly reducing computation time. A traffic model is built and a series of communication rules between subsystems are set to ensure that the entire transportation network can be globally optimized while the subsystem is achieving its local optimization. Finally, this paper numerically simulates the operation of the traffic network by mixed-Integer programming, also, compares the advantages and disadvantages of the two optimization strategies.
文摘Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy.This study develops the novel Gromov-Hausdorff distance for rock quality(GHDQR)methodology for rock mass quality rating based on multi-criteria grey metric space.It usually presents the quality of surrounding rock by classes(metric spaces)with specified properties and adequate interval-grey numbers.Measuring the distance between surrounding rock sample characteristics and existing classes represents the core of this study.The Gromov-Hausdorff distance is an especially useful discriminant function,i.e.,a classifier to calculate these distances,and assess the quality of the surrounding rock.The efficiency of the developed methodology is analyzed using the Mean Absolute Percentage Error(MAPE)technique.Seven existing methods,such as the Gaussian cloud method,Discriminant method,Mutation series method,Artificial neural network(ANN),Support vector machine(SVM),Grey wolf optimizer and Support vector classification method(GWO-SVC)and Rock mass rating method(RMR)are used for comparison with the proposed GHDQR method.The share of the highly accurate category of 85.71%clearly indicates compliance with actual values obtained by the compared methods.The results of comparisons showed that the model enables objective,efficient,and reliable assessment of rock mass quality.
基金funded by the Korean Government(MSIT)Grant NRF-2022R1C1C1006671.
文摘This socialized environment among educated and developed people causes themto focusmore on their appearance and health,which turns them towards medical-related treatments,leading us to discuss anti-aging treatment methods for each age group,particularly for urban people who are interested in this.Some anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative effects.Five anti-aging therapies such as microdermabrasion or dermabrasion,laser resurfacing anti-aging skin treatments,chemical peels,dermal fillers for aged skin,and botox injections are considered in this study.Based on the criteria of safety risk,investment cost,customer happiness,and side effects,the optimal alternative is picked.As a result,a NormalWiggly Hesitant Pythagorean Fuzzy Set(NWHPFS)is constructed and used in Multi-Criteria Decision-Making(MCDM)using traditional wavy mathematical approaches.The entropy approach is utilized to determine weight values,and the Normal Wiggly Hesitant Pythagorean-VlseKriterijumska Optimizacija I Kompromisno Resenje(NWHPF-VIKOR)method is utilized to rank alternatives using MCDM methodologies.Sensitivity analysis and comparative analysis were performed to ensure the robustness and reliability of the proposed method.The smart final choice will undoubtedly assist Decision Makers(DM)in making the right judgments,and the MCDM approach will undoubtedly assist individuals in understanding the medicine.
文摘Food Waste(FW)is a pressing environmental concern that affects every country globally.About one-third of the food that is produced ends up as waste,contributing to the carbon footprint.Hence,the FW must be properly treated to reduce environmental pollution.This study evaluates a few available Food Waste Treatment(FWT)technologies,such as anaerobic digestion,composting,landfill,and incineration,which are widely used.A Bipolar Picture Fuzzy Set(BPFS)is proposed to deal with the ambiguity and uncertainty that arise when converting a real-world problem to a mathematical model.A novel Criteria Importance Through Intercriteria Correlation-Stable Preference Ordering Towards Ideal Solution(CRITIC-SPOTIS)approach is developed to objectively analyze FWT selection based on thirteen criteria covering the industry’s technical,environmental,and entrepreneurial aspects.The CRITIC method is used for the objective analysis of the importance of each criterion in FWT selection.The SPOTIS method is adopted to rank the alternative hassle-free,following the criteria.The proposed model offers a rank reversal-free model,i.e.,the rank of the alternatives remains unaffected even after the addition or removal of an alternative.In addition,comparative and sensitivity analyses are performed to ensure the reliability and robustness of the proposed model and to validate the proposed result.
文摘In South Asia,Pakistan has a long and deadly history of floods that cause losses to various infrastructures,lives,and industries.This study aims to identify the most appropriate flood risk mitigation strategies that the government of Pakistan should adopt.The assessment of flood risk mitigation strategies in this study is based on certain criteria,which are analyzed using the fuzzy full consistency method.Moreover,flood risk mitigation strategies are evaluated by using the fuzzy weighted aggregated sum product assessment(WASPAS)method,considering previously prioritized criteria.According to the results,lack of governance,lack of funding and resources,and lack of flood control infrastructure are the most significant flood intensifying factors and act as major criteria for assessing flood risk mitigation strategies in Pakistan.Adopting hard engineering strategies(e.g.,dams,reservoirs,river straightening and dredging,embankments,and flood relief channels),maintaining existing infrastructure,and adopting soft engineering strategies(flood plain zoning,comprehensive flood risk assessment,and sophisticated flood modeling)are identified as the top three flood risk mitigation strategies by the fuzzy WASPAS method.The highest weight(0.98)was assigned to the adoption of hard engineering strategies to mitigate flood risks.The study introduces a novel dimension by analyzing the real-time impact of the unprecedented 2022 floods,during which approximately one-third of the nation was submerged.This focus on a recent and highly significant event enhances the study’s relevance and contributes a unique perspective to the existing literature on flood risk management.The study recommends that the government of Pakistan should prioritize hard engineering strategies for effective flood risk mitigation.It also recommends that the government should incorporate these strategies in the national policy framework to reduce flood losses in the future.
基金supported by National Science Foundation of USA (Grant Nos.DMS1812048,DMS-1737857,DMS-1513483 and DMS-1418042)National Natural Science Foundation of China (Grant No.11529101)
文摘Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that were known a priori. Model averaging approaches, on the other hand, try to combine estimators for a set of candidate models. Specifically, instead of deciding which model is the 'right' one, a model averaging approach suggests to fit a set of candidate models and average over the estimators using data adaptive weights.In this paper we establish a general frequentist model averaging framework that does not set any restrictions on the set of candidate models. It broaden, the scope of the existing methodologies under the frequentist model averaging development. Assuming the data is from an unknown model, we derive the model averaging estimator and study its limiting distributions and related predictions while taking possible modeling biases into account.We propose a set of optimal weights to combine the individual estimators so that the expected mean squared error of the average estimator is minimized. Simulation studies are conducted to compare the performance of the estimator with that of the existing methods. The results show the benefits of the proposed approach over traditional model selection approaches as well as existing model averaging methods.
