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
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
In intercontinental trade and economics goods are bought from a global supplier.On occasion,the expected lot may include a fraction of defective items.These imperfect items still have worth and can be sold to customer...In intercontinental trade and economics goods are bought from a global supplier.On occasion,the expected lot may include a fraction of defective items.These imperfect items still have worth and can be sold to customers after repair.It is cost-effective and sustainable to rework such items in nearby repair workshops rather than return them.The reworked items can be returned from the workshop to the buyer when shortages are equal to the quantity of imperfect items.In the meantime,the supplier correspondingly deals a multi-period delay-in-payments strategy with purchaser.The entire profit has been maximized with paybacks for interim financing.This study aims to develop a synergic inventory model to get the most profit by making an allowance for reworking,multi-period delay-in-payments policy,and shortages.The findings of the proposed model augment inventory management performance by monitoring cycle time as well as fraction of phase with optimistic inventory for a supply chain.The results demonstrate that profit is smaller if the permitted period given by supplier to buyer is equal to or greater than the cycle time,and profit is greater if the permitted period is smaller than the cycle time.The algebraic method is engaged to make a closed system optimum solution.The mathematical experiment of this study is constructed to provide management insights and tangible practices.展开更多
The general m-machine permutation flowshop problem with the total flow-time objective is known to be NP-hard for m ≥ 2. The only practical method for finding optimal solutions has been branch-and-bound algorithms. In...The general m-machine permutation flowshop problem with the total flow-time objective is known to be NP-hard for m ≥ 2. The only practical method for finding optimal solutions has been branch-and-bound algorithms. In this paper, we present an improved sequential algorithm which is based on a strict alternation of Generation and Exploration execution modes as well as Depth-First/Best-First hybrid strategies. The experimental results show that the proposed scheme exhibits improved performance compared with the algorithm in [1]. More importantly, our method can be easily extended and implemented with lightweight threads to speed up the execution times. Good speedups can be obtained on shared-memory multicore systems.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
With the increment of public awareness toward ecological environment protection,green building has gradually become an integral part of construction development.Green building refers to a form of architecture that con...With the increment of public awareness toward ecological environment protection,green building has gradually become an integral part of construction development.Green building refers to a form of architecture that conforms to the current social development form and meets the requisites of energy conservation and environmental protection.The budget and cost control of green building construction project play very important roles in improving the quality of the construction and reducing the cost of the project.This paper mainly analyzes the problems and control measures in the new green building engineering budget and cost control.展开更多
The trucking industry is a major force behind the United States （US） economy with approximately 750,000 interstate motor carriers. Trucks deliver 70% of all freight tonnage while 80% of U.S. communities receive good...The trucking industry is a major force behind the United States （US） economy with approximately 750,000 interstate motor carriers. Trucks deliver 70% of all freight tonnage while 80% of U.S. communities receive goods exclusively by truck. The motor carrier industry provides jobs, generating significant income and tax revenue, representing approximately 5% of U.S. GDP （gross domestic product）. Despite one of every 13 people employed in a trucking-related job, there is a significant driver shortage. The ATA （American Trucking Associations） currently projects a shortage of 20,000 to 25,000 drivers in the for-hire truckload market. Women are well positioned to address this shortage. In 2011, women represented 7% of total employment in the U.S. trucking industry. The purpose of this study was to identify truck design needs for women truck drivers. This research is significant due to limited data sources about truck design needs for women drivers. This study also contributes to the body of knowledge about women in the trucking industry. In response, a 33-question survey was developed using QualtricsTM survey software. In April, 2012, an electronic survey was distributed to 663 Women in Trucking Association members over a two-week period with a response rate of 18%. Results were separated into quantitative and qualitative data and analyzed accordingly. Adjustability was a central issue facing women truck drivers. Recommendations for seat design, side mirrors, steps, handrails, hydraulic hood lifters, and steering consoles will be presented. Limitations of the study and recommendations for future research will be discussed.展开更多
193 members of the United Nations approved the 17 Sustainable Development Goals in September 2015.According to the 2030 Agenda,the SDGs contemplated the ending of poverty,the protection of the Earth and the promotion ...193 members of the United Nations approved the 17 Sustainable Development Goals in September 2015.According to the 2030 Agenda,the SDGs contemplated the ending of poverty,the protection of the Earth and the promotion of prosperity for all.Sustainable Development Goal 17(SDG 17)deals specifically with the creation of global alliances for development.The underlying assumption respecting this point is that these stakeholder partnerships encourage the interchange of knowledge,experience,technology,and other resources to administer efficiently the other sixteen SDGs.Although SDG 17 is very well established in theory,in practice there are still appreciable downfalls as to how to successfully make this theory become a reality.This short review will analyse the potential viability of SDG 17“partnerships for the goals”with respect to SDG 7(affordable and clean energy),and thereupon SDG 13(associated with climate action)utilising two south-western France two wind farm initiatives.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
We consider dynamic capacity booking problems faced by multiple manufacturers each outsourcing certain operations to a common third-party firm. Each manufacturer, upon observing the current state of the third-party sc...We consider dynamic capacity booking problems faced by multiple manufacturers each outsourcing certain operations to a common third-party firm. Each manufacturer, upon observing the current state of the third-party schedule, books capacity with the objective to jointly minimize holding costs that result from early deliveries, tardiness penalties due to late deliveries, and third-party capacity booking costs. When making a reservation, each manufacturer evaluates two alternative courses of action： （i） reserving capacity not yet utilized by other manufactures who booked earlier; or （ii） forming a coalition with a subset or all of other manufacturers to achieve a schedule minimizing coalition costs, i.e., a centralized schedule for that coalition. The latter practice surely benefits the coalition as a whole; however, some manufacturers may incur higher costs if their operations are either pushed back too much, or delivered too early. For this reason, a cost allocation scheme making each manufacturer no worse than they would be when acting differently （i.e., participating in a smaller coalition or acting on their own behalf,） must accompany centralized scheduling for the coalition. We model this relationship among the manufacturers as a cooperative game with transferable utility, and present optimal and/or heuristic algorithms to attain individually and eoalitionally optimal schedules as well as a linear program formulation to find a core allocation of the manufacturers＇ costs.展开更多
Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player analytics.Player modeling refers to descriptions of players based on frameworks of data deri...Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player analytics.Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game.Player behavior focuses on dynamic and static information gathered at the time of gameplay.Player experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings.In this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral devices.Long Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer learning.Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems.The homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’engagement.Relevant not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.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
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.
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.
文摘In intercontinental trade and economics goods are bought from a global supplier.On occasion,the expected lot may include a fraction of defective items.These imperfect items still have worth and can be sold to customers after repair.It is cost-effective and sustainable to rework such items in nearby repair workshops rather than return them.The reworked items can be returned from the workshop to the buyer when shortages are equal to the quantity of imperfect items.In the meantime,the supplier correspondingly deals a multi-period delay-in-payments strategy with purchaser.The entire profit has been maximized with paybacks for interim financing.This study aims to develop a synergic inventory model to get the most profit by making an allowance for reworking,multi-period delay-in-payments policy,and shortages.The findings of the proposed model augment inventory management performance by monitoring cycle time as well as fraction of phase with optimistic inventory for a supply chain.The results demonstrate that profit is smaller if the permitted period given by supplier to buyer is equal to or greater than the cycle time,and profit is greater if the permitted period is smaller than the cycle time.The algebraic method is engaged to make a closed system optimum solution.The mathematical experiment of this study is constructed to provide management insights and tangible practices.
文摘The general m-machine permutation flowshop problem with the total flow-time objective is known to be NP-hard for m ≥ 2. The only practical method for finding optimal solutions has been branch-and-bound algorithms. In this paper, we present an improved sequential algorithm which is based on a strict alternation of Generation and Exploration execution modes as well as Depth-First/Best-First hybrid strategies. The experimental results show that the proposed scheme exhibits improved performance compared with the algorithm in [1]. More importantly, our method can be easily extended and implemented with lightweight threads to speed up the execution times. Good speedups can be obtained on shared-memory multicore systems.
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With the increment of public awareness toward ecological environment protection,green building has gradually become an integral part of construction development.Green building refers to a form of architecture that conforms to the current social development form and meets the requisites of energy conservation and environmental protection.The budget and cost control of green building construction project play very important roles in improving the quality of the construction and reducing the cost of the project.This paper mainly analyzes the problems and control measures in the new green building engineering budget and cost control.
文摘The trucking industry is a major force behind the United States （US） economy with approximately 750,000 interstate motor carriers. Trucks deliver 70% of all freight tonnage while 80% of U.S. communities receive goods exclusively by truck. The motor carrier industry provides jobs, generating significant income and tax revenue, representing approximately 5% of U.S. GDP （gross domestic product）. Despite one of every 13 people employed in a trucking-related job, there is a significant driver shortage. The ATA （American Trucking Associations） currently projects a shortage of 20,000 to 25,000 drivers in the for-hire truckload market. Women are well positioned to address this shortage. In 2011, women represented 7% of total employment in the U.S. trucking industry. The purpose of this study was to identify truck design needs for women truck drivers. This research is significant due to limited data sources about truck design needs for women drivers. This study also contributes to the body of knowledge about women in the trucking industry. In response, a 33-question survey was developed using QualtricsTM survey software. In April, 2012, an electronic survey was distributed to 663 Women in Trucking Association members over a two-week period with a response rate of 18%. Results were separated into quantitative and qualitative data and analyzed accordingly. Adjustability was a central issue facing women truck drivers. Recommendations for seat design, side mirrors, steps, handrails, hydraulic hood lifters, and steering consoles will be presented. Limitations of the study and recommendations for future research will be discussed.
文摘193 members of the United Nations approved the 17 Sustainable Development Goals in September 2015.According to the 2030 Agenda,the SDGs contemplated the ending of poverty,the protection of the Earth and the promotion of prosperity for all.Sustainable Development Goal 17(SDG 17)deals specifically with the creation of global alliances for development.The underlying assumption respecting this point is that these stakeholder partnerships encourage the interchange of knowledge,experience,technology,and other resources to administer efficiently the other sixteen SDGs.Although SDG 17 is very well established in theory,in practice there are still appreciable downfalls as to how to successfully make this theory become a reality.This short review will analyse the potential viability of SDG 17“partnerships for the goals”with respect to SDG 7(affordable and clean energy),and thereupon SDG 13(associated with climate action)utilising two south-western France two wind farm initiatives.
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 in part by Research Grants Council of Hong Kong,GRF No.410213the Hong Kong Government UGC Theme-based Research Scheme,Project No.T32-102/14N
文摘We consider dynamic capacity booking problems faced by multiple manufacturers each outsourcing certain operations to a common third-party firm. Each manufacturer, upon observing the current state of the third-party schedule, books capacity with the objective to jointly minimize holding costs that result from early deliveries, tardiness penalties due to late deliveries, and third-party capacity booking costs. When making a reservation, each manufacturer evaluates two alternative courses of action： （i） reserving capacity not yet utilized by other manufactures who booked earlier; or （ii） forming a coalition with a subset or all of other manufacturers to achieve a schedule minimizing coalition costs, i.e., a centralized schedule for that coalition. The latter practice surely benefits the coalition as a whole; however, some manufacturers may incur higher costs if their operations are either pushed back too much, or delivered too early. For this reason, a cost allocation scheme making each manufacturer no worse than they would be when acting differently （i.e., participating in a smaller coalition or acting on their own behalf,） must accompany centralized scheduling for the coalition. We model this relationship among the manufacturers as a cooperative game with transferable utility, and present optimal and/or heuristic algorithms to attain individually and eoalitionally optimal schedules as well as a linear program formulation to find a core allocation of the manufacturers＇ costs.
基金This study was supported by the BK21 FOUR project(AI-driven Convergence Software Education Research Program)funded by the Ministry of Education,School of Computer Science and Engineering,Kyungpook National University,Korea(4199990214394).This work was also supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea Government(MSIT)under Grant 2017-0-00053(A Technology Development of Artificial Intelligence Doctors for Cardiovascular Disease).
文摘Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player analytics.Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game.Player behavior focuses on dynamic and static information gathered at the time of gameplay.Player experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings.In this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral devices.Long Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer learning.Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems.The homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’engagement.Relevant not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.
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.
