Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertai...Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.展开更多
For the system with the fuzzy failure state, the effects of the input random variables and the fuzzy failure state on the fuzzy probability of failure for the structural system are studied, and the moment-independence...For the system with the fuzzy failure state, the effects of the input random variables and the fuzzy failure state on the fuzzy probability of failure for the structural system are studied, and the moment-independence global sensitivity analysis（GSA） model is proposed to quantitatively measure these effects. According to the fuzzy random theory, the fuzzy failure state is transformed into an equivalent new random variable for the system, and the complementary function of the membership function of the fuzzy failure state is defined as the cumulative distribution function（CDF） of the new random variable. After introducing the new random variable, the equivalent performance function of the original problem is built. The difference between the unconditional fuzzy probability of failure and conditional fuzzy probability of failure is defined as the moment-independent GSA index. In order to solve the proposed GSA index efficiently, the Kriging-based algorithm is developed to estimate the defined moment-independence GSA index. Two engineering examples are employed to verify the feasibility and rationality of the presented GSA model, and the advantages of the developed Kriging method are also illustrated.展开更多
The influence of fuzzy uncertainty factors is considered on the analysis of chatter occurring during machine tool cutting process. Using fuzzy mathematics analysis methods, a detailed discussion over fuzzy stability a...The influence of fuzzy uncertainty factors is considered on the analysis of chatter occurring during machine tool cutting process. Using fuzzy mathematics analysis methods, a detailed discussion over fuzzy stability analysis problems is presented related to the mode coupling chatter with respect to intrinsic structure fuzzy factors, and the possibility distribution of the fuzzy stability cutting range and the confidence level expressions of the fuzzy stability cutting width are given.展开更多
A new numerical technique named as fuzzy finite difference method is proposed to solve the heat conduction problems with fuzzy uncertainties in both the phys- ical parameters and initial/boundary conditions. In virtue...A new numerical technique named as fuzzy finite difference method is proposed to solve the heat conduction problems with fuzzy uncertainties in both the phys- ical parameters and initial/boundary conditions. In virtue of the level-cut method, the difference discrete equations with fuzzy parameters are equivalently transformed into groups of interval equations. New stability analysis theory suited to fuzzy difference schemes is developed. Based on the parameter perturbation method, the interval ranges of the uncertain temperature field can be approximately predicted. Subsequently, fuzzy solutions to the original difference equations are obtained by the fuzzy resolution theorem. Two numerical examples are given to demonstrate the feasibility and efficiency of the presented method for solving both steady-state and transient heat conduction problems.展开更多
For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtaine...For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtained at the stage of flight test. Thus, those conventional evaluation methods cannot be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method（FNM） is proposed which combines the advantages of fuzzy theory and norm theory. The proposed method can deeply dig system information from limited data, which probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. In addition, the optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, computer simulation is used to prove the adaptability of FNM. Compared with statistical methods, FNM has superiority for evaluating expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%.展开更多
Offshore support operations must balance safety and sustainability under highly variable sea conditions.Deterministic motion analyses can underestimate extreme vessel responses,leading to insufficient operational limi...Offshore support operations must balance safety and sustainability under highly variable sea conditions.Deterministic motion analyses can underestimate extreme vessel responses,leading to insufficient operational limits and increased environmental impact.We develop a fuzzy‐enhanced multi‐body dynamics framework in which key inputs significant wave height,peak period,added mass,and radiation damping are represented as fuzzy numbers.Anα-cut decomposition yields interval bounds at each confidence level,and a fourth-order Runge-Kutta scheme integrates the six-degree-of-freedom equations of motion for both lower and upper“vertex”systems.A case study off the Karnataka coast applies both full 6-DoF and single-DOF heave approximations to demonstrate methodology.The heave response envelopes under calm(nominalα=1:0.73 m;full range atα=0:0.64–1.64 m)and severe(nominal 1.58 m;range 1.32–2.36 m)sea states reveal potential underestimations of 124%and 49%,respectively,when using only nominal values.By selecting an operationalα-level(e.g.,α^(*)=0.35 to cap heave≤1.8 m),decision-makers can balance risk tolerance and conservatism.Sensitivity analysis identifies significant wave height as the dominant uncertainty driver.Computational trade-offs and adaptiveα-sampling strategies are discussed.This work provides a self-contained,uncertainty-aware tool for deriving operational envelopes that improve risk-informed planning and enable fuel-efficiency optimization.By embedding fuzzy uncertainty quantification into vessel dynamics,the methodology supports safer,more sustainable marine operations and can be extended to real-time sensor fusion,multi-vessel interactions,and frequency-dependent hydrodynamics.展开更多
Offshore logistics operations must continuously balance safety,fuel efficiency,and emissions reduction while navigating under uncertain and highly variable sea states.To address this challenge,we present anα-cut inte...Offshore logistics operations must continuously balance safety,fuel efficiency,and emissions reduction while navigating under uncertain and highly variable sea states.To address this challenge,we present anα-cut interval framework in which environmental uncertainties,specifically wave height and wind speed,are modeled as fuzzy numbers.Their correspondingα-level intervals are systematically propagated through a discrete vessel dynamics model,focusing on surge and heave responses.This procedure generates families of nested motion envelopes that tighten monotonically with increasingα,thereby producing deterministic yet progressively refined safety bounds without relying on full probabilistic distributions.A case study off the Karnataka coast is used to demonstrate the approach for a 20 km offshore supply voyage.Route planning constrained byα-envelopes ensures adherence to vessel structural and stability limits while enabling optimized transit speed.Comparative evaluation indicates that,relative to standard interval analysis,α-cut propagation substantially reduces over-conservatism,while against Monte Carlo-based envelopes it achieves similar coverage with significantly lower computational effort.Sensitivity analyses further quantify the influence ofα-grid resolution,membership-function design,and hydrodynamic coupling coefficients on envelope width,fuel use,and emissions.In the tested scenario,higherαlevels allow up to~15%reduction in worst-case energy consumption and nearly 10%reduction in CO_(2)emissions,all while preserving safety margins.Overall,the proposed framework is transparent,computationally efficient,and easily integrable into digital-twin-enabled operational workflows,providing a practical and sustainable decision-support tool for adaptive offshore logistics planning.展开更多
基金The Science and Research Foundation of Shanghai Municipal Education Commission (No06DZ033)the Doctoral Science and Research Foundation of Shanghai Nor mal University ( No PL719)the Science and Research Foundation of Shanghai Nor mal University (NoSK200741)
文摘Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.
基金supported by the National Natural Science Foundation of China(11702281)the Science Challenge Project(TZ2018007)the Technology Foundation Project of State Administration of Science,Technology and Industry for National Defence,PRC(JSZL2017212A001)
文摘For the system with the fuzzy failure state, the effects of the input random variables and the fuzzy failure state on the fuzzy probability of failure for the structural system are studied, and the moment-independence global sensitivity analysis（GSA） model is proposed to quantitatively measure these effects. According to the fuzzy random theory, the fuzzy failure state is transformed into an equivalent new random variable for the system, and the complementary function of the membership function of the fuzzy failure state is defined as the cumulative distribution function（CDF） of the new random variable. After introducing the new random variable, the equivalent performance function of the original problem is built. The difference between the unconditional fuzzy probability of failure and conditional fuzzy probability of failure is defined as the moment-independent GSA index. In order to solve the proposed GSA index efficiently, the Kriging-based algorithm is developed to estimate the defined moment-independence GSA index. Two engineering examples are employed to verify the feasibility and rationality of the presented GSA model, and the advantages of the developed Kriging method are also illustrated.
文摘The influence of fuzzy uncertainty factors is considered on the analysis of chatter occurring during machine tool cutting process. Using fuzzy mathematics analysis methods, a detailed discussion over fuzzy stability analysis problems is presented related to the mode coupling chatter with respect to intrinsic structure fuzzy factors, and the possibility distribution of the fuzzy stability cutting range and the confidence level expressions of the fuzzy stability cutting width are given.
基金supported by the National Special Fund for Major Research Instrument Development(2011YQ140145)111 Project(B07009)+1 种基金the National Natural Science Foundation of China(11002013)Defense Industrial Technology Development Program(A2120110001 and B2120110011)
文摘A new numerical technique named as fuzzy finite difference method is proposed to solve the heat conduction problems with fuzzy uncertainties in both the phys- ical parameters and initial/boundary conditions. In virtue of the level-cut method, the difference discrete equations with fuzzy parameters are equivalently transformed into groups of interval equations. New stability analysis theory suited to fuzzy difference schemes is developed. Based on the parameter perturbation method, the interval ranges of the uncertain temperature field can be approximately predicted. Subsequently, fuzzy solutions to the original difference equations are obtained by the fuzzy resolution theorem. Two numerical examples are given to demonstrate the feasibility and efficiency of the presented method for solving both steady-state and transient heat conduction problems.
基金supported by Aeronautical Science Foundation of China (No. 20100251006)Technological Foundation Project of China (No. J132012C001)
文摘For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtained at the stage of flight test. Thus, those conventional evaluation methods cannot be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method（FNM） is proposed which combines the advantages of fuzzy theory and norm theory. The proposed method can deeply dig system information from limited data, which probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. In addition, the optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, computer simulation is used to prove the adaptability of FNM. Compared with statistical methods, FNM has superiority for evaluating expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%.
文摘Offshore support operations must balance safety and sustainability under highly variable sea conditions.Deterministic motion analyses can underestimate extreme vessel responses,leading to insufficient operational limits and increased environmental impact.We develop a fuzzy‐enhanced multi‐body dynamics framework in which key inputs significant wave height,peak period,added mass,and radiation damping are represented as fuzzy numbers.Anα-cut decomposition yields interval bounds at each confidence level,and a fourth-order Runge-Kutta scheme integrates the six-degree-of-freedom equations of motion for both lower and upper“vertex”systems.A case study off the Karnataka coast applies both full 6-DoF and single-DOF heave approximations to demonstrate methodology.The heave response envelopes under calm(nominalα=1:0.73 m;full range atα=0:0.64–1.64 m)and severe(nominal 1.58 m;range 1.32–2.36 m)sea states reveal potential underestimations of 124%and 49%,respectively,when using only nominal values.By selecting an operationalα-level(e.g.,α^(*)=0.35 to cap heave≤1.8 m),decision-makers can balance risk tolerance and conservatism.Sensitivity analysis identifies significant wave height as the dominant uncertainty driver.Computational trade-offs and adaptiveα-sampling strategies are discussed.This work provides a self-contained,uncertainty-aware tool for deriving operational envelopes that improve risk-informed planning and enable fuel-efficiency optimization.By embedding fuzzy uncertainty quantification into vessel dynamics,the methodology supports safer,more sustainable marine operations and can be extended to real-time sensor fusion,multi-vessel interactions,and frequency-dependent hydrodynamics.
文摘Offshore logistics operations must continuously balance safety,fuel efficiency,and emissions reduction while navigating under uncertain and highly variable sea states.To address this challenge,we present anα-cut interval framework in which environmental uncertainties,specifically wave height and wind speed,are modeled as fuzzy numbers.Their correspondingα-level intervals are systematically propagated through a discrete vessel dynamics model,focusing on surge and heave responses.This procedure generates families of nested motion envelopes that tighten monotonically with increasingα,thereby producing deterministic yet progressively refined safety bounds without relying on full probabilistic distributions.A case study off the Karnataka coast is used to demonstrate the approach for a 20 km offshore supply voyage.Route planning constrained byα-envelopes ensures adherence to vessel structural and stability limits while enabling optimized transit speed.Comparative evaluation indicates that,relative to standard interval analysis,α-cut propagation substantially reduces over-conservatism,while against Monte Carlo-based envelopes it achieves similar coverage with significantly lower computational effort.Sensitivity analyses further quantify the influence ofα-grid resolution,membership-function design,and hydrodynamic coupling coefficients on envelope width,fuel use,and emissions.In the tested scenario,higherαlevels allow up to~15%reduction in worst-case energy consumption and nearly 10%reduction in CO_(2)emissions,all while preserving safety margins.Overall,the proposed framework is transparent,computationally efficient,and easily integrable into digital-twin-enabled operational workflows,providing a practical and sustainable decision-support tool for adaptive offshore logistics planning.
