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Supply chain planning during disasters can be challenging due to uncertainty in demand and travel time, leading to limited stocks and delivery delays. While previous studies have focused on network planning for disaster relief supply chains under uncertainty, they have not fully integrated all network components while considering various potential factors. This integration is crucial for successful humanitarian relief operations. To address this issue, we propose a comprehensive model using a two-stage mixed-integer stochastic linear programming. The model incorporates facility location, pre-positioning, direct allocation, and multi-depot vehicle routing under demand and travel time uncertainties while examining multi-echelon, multi-commodity, response deadlines, and deprivation costs. We also create an improved random forest algorithm to enhance the accuracy of demand and travel time forecasts. To obtain accurate information for effective decision-making, we develop a data-driven, exact algorithm by combining an improved random forest algorithm and Benders decomposition. Computational experiments show that our proposed algorithm outperforms the L-shaped method in finding a better solution with less running time. We provide a real case to validate our model and algorithms. Our model and solution scheme can help improve efficiency and timeliness while minimizing deficiencies in disaster relief efforts. [ABSTRACT FROM AUTHOR]

In time-variant reliability problems, there are a lot of uncertain variables from different sources. Therefore, it is important to consider these uncertainties in engineering. In addition, time-variant reliability problems typically involve a complex multilevel nested optimization problem, which can result in an enormous amount of computation. To this end, this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model. In this method, the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a time-independent reliability problem. Further, to solve the double nested optimization problem in hybrid reliability calculation, an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point (MPP). The limit state function is linearized at these points, and an innovative random variable is defined to solve the equivalent static reliability analysis model. The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mechanical Strength / Jixie Qiangdu is the property of Zhengzhou Research Institute of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the problem of reliability analysis of hybrid structures containing interval variables and probabilistic variables, a hybrid structure reliability calculation method based on chaotic sparrow search algorithm (CSSA) and Bayesian regularized (BR) optimization algorithm optimized BP neural network is proposed. First, a reliability analysis model is constructed based on the mixed uncertainty variables, evidence theory is introduced to characterize the uncertainty of the interval variables, and the interval variables are transformed into probabilistic variables by using uniform probability processing method, which in turn decouples the two-layer nested reliability solving problem into a single-layer reliability solving problem. Next, the weights and thresholds of the BP neural network are optimized using CSSA and BR techniques, leading to the development of the CSSA-BR-BP neural network surrogate model. Finally, the HL-RF method in Advanced First Order Second Moment (AFOSM) is employed to efficiently compute the structural reliability index. The results demonstrate that the proposed method exhibits excellent fitting accuracy and enhances the efficiency of reliability analysis for hybrid structures, confirming the feasibility of the approach. [ABSTRACT FROM AUTHOR]

The turbine rotor is the key component of the turbine, which has a great impact on the construction cost and power generation efficiency of an entire hydropower station. Receiving the torque of the runner transmission and completing the specified power generation is its main function. There are many uncertain factors in the design, manufacture, and operation environment of a turbine rotor. Therefore, it is necessary to optimize the mechanism on the premise of ensuring that the mechanical system meets high reliability and high safety levels. This article uses the multidisciplinary reliability analysis and optimization method under random and interval uncertainty to quantitatively analyze the uncertainty factors, and then optimally solves the RBMDO problem of the turbine rotor mechanism. Through the finite element simulation analysis of the optimized design scheme, the rationality and feasibility of the obtained results are further verified. [ABSTRACT FROM AUTHOR]

This article presents a method for structured robust control design for systems with a mixture of parametric and dynamic uncertainty. The proposed method alternates between an analysis step and a synthesis step. Samples of the parametric uncertainty are computed during the analysis steps, thus yielding an array of uncertain systems containing only dynamic uncertainty. The controller is then synthesized on this array of uncertain models. This synthesis step itself involves an alternation between constructing a D-scale for each of the uncertain systems and tuning a single controller for the entire collection of scaled plants. The controller tuning is performed using structured control design techniques. The proposed method is utilized to design a flutter suppression controller for a flexible aircraft. The aircraft dynamics are described by both a high-fidelity and a reduced-order model. The design objectives for flutter suppression are to achieve robust stabilization in the presence of mixed uncertainty. The proposed structured design method yields a single, low-order, linear time-invariant (LTI) controller, which increases the flutter speed by 15%. Additional robustness analyses and high-fidelity simulations are provided to assess the controller performance. [ABSTRACT FROM AUTHOR]

This article proposes a novel design of a disaster relief blood supply chain network with multiple echelons and multiple products. It incorporates: (1) the blood donors' behavior and preference for the selection of facilities where the blood donation takes place, (2) an estimating the quantity of the injured people under each disaster scenario, (3) the inherent uncertainty in input parameters and (4) the remaining capacity for satisfying the demand affected by the disruption in blood facilities (i.e., both blood collection and production centers). To incorporate the utility of blood collection facilities, a utility assessment approach, called Logit model, is developed, which measures the donors' preference. The disrupted capacity and the number of injured people under each disaster scenario are estimated based on the disaster severity, the distance from the disaster (particularly earthquake) epicenter and the civil infrastructure. The uncertainty of parameters (e.g., cost parameters as well as blood supply and demand) along with the disruption in blood facilities are two unavoidable concerns of disaster relief blood supply chain planning. Accordingly, to handle the parameters under mixed uncertainty, a new approach which is two-stage stochastic-robust is utilized. Finally, using real data of a case study in Iran, the presented formulation and its solution approach are examined. The obtained results demonstrate that the related managers should be aware of blood donors' behavior, injured people in disaster, and the effect of disruption in the blood supply chain network design. Moreover, when it comes to the uncertainty of parameters, the obtained results from the robust model overcome those of deterministic one. Finally, based on findings, applicable managerial insights are derived for the decision-makers in blood supply chain network design for disaster relief cases. [ABSTRACT FROM AUTHOR]

This paper deals with the effects of the interval and probability uncertain parameters imposed on the process of unbalance parameters identification from our previous paper (Mao et al. in: Shock Vib 2016:1–9, 2016) in Shock and Vibration. After employing uncertain parameters, we proposed the unbalance parameters identification method, and compared the results with those of previous studies. [ABSTRACT FROM AUTHOR]

The turbine rotor is the key component of the turbine, which has a great impact on the construction cost and power generation efficiency of an entire hydropower station. Receiving the torque of the runner transmission and completing the specified power generation is its main function. There are many uncertain factors in the design, manufacture, and operation environment of a turbine rotor. Therefore, it is necessary to optimize the mechanism on the premise of ensuring that the mechanical system meets high reliability and high safety levels. This article uses the multidisciplinary reliability analysis and optimization method under random and interval uncertainty to quantitatively analyze the uncertainty factors, and then optimally solves the RBMDO problem of the turbine rotor mechanism. Through the finite element simulation analysis of the optimized design scheme, the rationality and feasibility of the obtained results are further verified.

The Kreiss constant K(A) of a stable matrix A conveys information about the transient behavior of system trajectories in response to initial conditions. We present an efficient way to compute the Kreiss constant K(A), and we show how feedback can be employed to make the Kreiss constant K(Acl) in closed loop significantly smaller. This is expected to reduce transients in the closed loop trajectories. The proposed method is compared to potential competing techniques. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reliability-based design optimization (RBDO) is a powerful tool for addressing design problems involving variables with uncertainty characteristics. In practical engineering problems, there may not be sufficient information to build probabilistic distribution models for some crucial parameters. Evidence theory has emerged to deal with inaccuracies of parameters that lack information or knowledge. In this study, the incremental shifting vector and mixed uncertainty analysis method (ISVMUA) is proposed to improve the efficiency in dealing with RBDO problems with both aleatory and epistemic uncertainties based on probability and evidence theory. Higher efficiency is achieved in ISVMUA using the following strategies: (1) the result of the mixed uncertainty analysis is directly employed to update the deterministic optimization formulation by the incremental shifting vector; (2) a new allocation strategy is proposed to reasonably decompose the total target failure probability into all the focal elements of the epistemic uncertainties; and (3) a strategy of feasibility checking is employed to identify the inactive constraints among all the quasi-equivalent probabilistic constraints to simplify the deterministic optimization problem. Four examples are investigated to demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

A mixed reliability analysis algorithm based on two parameters is proposed for the mixed reliability problem of mechanical structure with random variables and interval variables. Interval variables make the reliability analysis problem become a double-loop optimization problem. In order to reduce the influence of double-loop optimization model on computational efficiency, it is decoupled into a high-efficiency sequence iterative model for probability and interval analysis. In order to optimize design point, two adjusting parameters are introduced to control the search step length and the search direction, respectively, in probability analysis, which ensures the convergence stability and the search efficiency. The interval analysis problem is transformed into a more solvable quadratic programming problem, and the gradient projection method is used to search for the interval optimum point. The analyzed results of two cases show that the relative error of maximun failure probability calculated by the proposed algorithm is within 5%, and the number of function calls can reach convergence within 1000 times compared with Monte Carlo sampling. When the performance function has a high degree of nonlinearity, the proposed algorithm also has high calculation accuracy, efficiency and convergence stability. [ABSTRACT FROM AUTHOR]

We present new approaches to designing structured controllers, which are robust in the presence of mixed real parametric and complex dynamic uncertainty. As the synthesis of such controllers is inherently NP-hard, we discuss inner and outer relaxation techniques, which make this problem amenable to computations. Our relaxation methods are positively evaluated and compared on the basis of a rich test set and for a challenging missile pilot design problem. [ABSTRACT FROM AUTHOR]

A novel framework based on stochastic modeling methods and likelihood analysis to address large‐scale monetization processes of converting shale gas to polymers under the mixed uncertainties of feedstock compositions, estimated ultimate recovery, and economic parameters is presented. A new stochastic data processing strategy is developed to quantify the feedstock variability through generating the appropriate number of scenarios. This strategy includes the Kriging‐based surrogate model, sample average approximation, and the integrated decline‐stimulate analysis curve. The feedstock variability is then propagated through performing a detailed techno‐economic modeling method on distributed‐centralized conversion network systems. Uncertain economic parameters are incorporated into the stochastic model to estimate the maximum likelihood of performance objectives. The proposed strategy and models are illustrated in four case studies with different plant locations and pathway designs. The results highlight the benefits of the hybrid pathway as it is more amenable to reducing the economic risk of the projects. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2017–2036, 2018 [ABSTRACT FROM AUTHOR]

On the basis of fuzzy random variables, a universal approach to squeal analysis of the disc brakes involving various types of uncertainty is proposed in this paper. In the proposed approach, first, the brake stability analysis function related to reliability is constructed with fuzzy random variables. Next, the fuzziness represented by fuzzy random variables is decomposed into interval uncertainties by using a level-cut strategy. Then, the expectations and the variances of the brake stability analysis function are approximately solved by the random moment method at different cut levels, and the lower bounds and the upper bounds of the expectations and the variances are calculated by using a first-order Taylor expansion and a subinterval analysis. Finally, by combining the different interval solutions with the corresponding cut levels, the fuzzy solutions of the brake stability analysis function are obtained, which can be employed to evaluate the brake squeal instability. The proposed approach provides a universal framework for dealing with various types of uncertainty that may exist in automotive brakes. The universality, the accuracy and the efficiency of the proposed approach to the squeal instability analysis of the brakes involving various types of uncertainty are verified by the analysis results from nine different numerical examples. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is aimed at proposing a novel uncertainty analysis approach based on polynomial chaos expansion method for composite structures, where mixed uncertainties including normal random and interval variables can be considered simultaneously. Firstly, existing uncertain elastic parameters in composites can be separately dealt with as normal random and interval variables according to the amount of available information and the uncertain response analysis problem with mixed uncertainties is proposed. Then some traditional uncertainty analysis methods are introduced, whose purpose is to point out the shortcomings or deficiencies for given approaches. Furthermore, the basic knowledge for polynomial chaos expansion (PCE) method and a novel uncertainty analysis method on the basis of PCE method for composite structures with mixed uncertainties are explained in details, where the polynomial coefficients are viewed as quadratic polynomial functions of interval variables. Finally, two numerical examples are carried out to account for the validity and feasibility of the developed method. The results show that the developed PCE method can be efficiently applied in uncertainty analysis of composite structures. [ABSTRACT FROM AUTHOR]

• Presenting a novel model to manage different risks in the oil supply chain network. • Proposing resilience measures as optimization tools in the proposed model. • Employing a fuzzy approach based on Me measure to cope with epistemic uncertainty. • Applying a Monte Carlo and forward approach for scenario generation and reduction. • Tailoring a real-life case of Tehran zone to validate the proposed model and method. The main goal of this research is to design and plan a downstream oil supply chain network under mixed uncertainty. To reach this goal, a bi-objective mixed-integer linear model is proposed to make decisions at tactical and strategic levels. Notably, to deal with mixed uncertainty, a hybrid uncertain programming approach including adjustable possibilistic programming, chance-constrained programming, scenario-based programming, and p-robust optimization is developed and applied. Finally, the proposed model is implemented in a real-life case from the National Iranian Oil Products Distribution Company to illustrate the applicability and efficacy of the proposed approach. The analysis of the results indicates that the benefit of the used methods leads to the improvement of network performance against operational and disruption risks. Moreover, the analysis of the results indicates that the tailored approaches improve the total costs, the resiliency of the network, and the reliability of the chain in comparison to the deterministic situation. [ABSTRACT FROM AUTHOR]

With the continuous development of ship design and application requirements, the deterministic design optimization based on design conditions has been difficult to meet the current demands of ship design. Some parameters regarded as fixed values in ship design inevitably fluctuate when sailing in the real marine environment, and the changes of these uncertainties will directly affect the performance of the ship scheme. Therefore, higher requirements are proposed by the designers for the robustness and reliability of the ship scheme under the influence of uncertainty. Generally, uncertainty can be divided into two types, which are aleatory uncertainty an epistemic uncertainty. In most researches, due to the modeling of epistemic uncertainty is not mature, uncertain parameters which might belong to epistemic uncertainty are often modeled as the stochastic distribution belonging to aleatory uncertainty, thus insufficient distribution information will lead to false modeling and result in wrong analysis. Also, few studies have taken the influence of aleatory and epistemic uncertainty into consideration simultaneously in ship design. Therefore, in this research, evidence theory is used to model interval variables of epistemic uncertainty, and the influence of mixed uncertainty on constraints and objective functions is analyzed. By deriving the failure probability of the constraint function and the expectation and standard deviation of the objective function in the framework of evidence theory, and the reliability and robustness expression under mixed uncertainty are completed. Finally, a unified analysis and propagation method of mixed uncertainty is proposed based on evidence theory. By applying this method to the engineering optimization of a bulk carrier, the optimal scheme under mixed uncertainty is obtained, which has better reliability and robustness than the deterministic optimal scheme. • Three uncertain variables of aleatory and epistemic uncertainty are considered. • The interval variables of epistemic uncertainty are expressed using evidence theory. • The expressions of robustness and reliability under mixed uncertainty are derived based on evidence theory. The unified analysis and propagation of mixed uncertainty is completed. • The method is applied to the engineering optimization to improve the robustness and reliability. [ABSTRACT FROM AUTHOR]

This paper addresses the planning and optimization of intermodal hub-and-spoke (IH&S) network considering mixed uncertainties in both transportation cost and travel time. Different from previous studies, this paper develops a novel modeling framework for the IH&S network design problem to jointly minimize the expected value of total transportation costs and the maximum travel time requirement in term of critical value. A new hybrid methodology by combining fuzzy random simulation (FRS) technique and multi-start simulated annealing (MSA) algorithm is designed to solve the proposed model. Numerical experiments are implemented to verify the effectiveness of the proposed model and solution approach. [ABSTRACT FROM AUTHOR]

The disturbance observer (DOB) and H ∞ based robust control methods are widely used in feedback control applications due to their disturbance rejection capability and robustness improvement. However, the simultaneous synthesizing of DOB and H ∞ controller considering the real uncertain parameters and dynamic uncertainties of the models is an open problem. This paper presents a new fixed-order μ -synthesis algorithm based optimization methodology for the combined design of DOB and robust controller in the presence of mixed uncertainty. The fixed-order robust H ∞ control framework is adopted to optimize structured plant inverse, so-called Q filter and feedback controller. In order to guarantee the robust stability of the main feedback loop and inner DOB loop, the overall closed-loop system is considered with uncertainties during the design procedure. The proposed approach renders the need for an explicit plant inverse unnecessary. Thanks to this advantage and generality of μ -synthesis techniques, the proposed method can be directly applied to both the non-minimum phase and multiple-input and multiple-output (MIMO) uncertain systems without any approximation. The theoretical design approach is experimentally verified on an electromechanical control actuation system of an air vehicle and numerically on a non-minimum phase system. • A novel fixed-order μ -synthesis based DOB and controller design method is proposed. • The H ∞ framework is adapted to optimization of DOB, plant inverse and controller. • The approach is experimentally verified on an electromechanical actuation system. [ABSTRACT FROM AUTHOR]

An improved upper bound of structured singular value for mixed uncertainties with purely real uncertainty blocks is proposed. [ABSTRACT FROM AUTHOR]