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Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

This paper presents an improved scheme for the state-of-health (SOH) estimation, applicable to battery management system (BMS) of electric vehicles. The original scheme requires the prior information for the estimation, which is the prior SOH identified from the last charging. This limit implies that if a battery or its BMS is replaced, the prior SOH stored within the BMS no longer matches with the actual SOH, resulting in a critical error. To avoid this potential but critical pitfall, we newly devise an improved SOH estimation scheme. The original scheme is revised by adopting the fixed-point iteration method into its parameter estimation. By removing dependencies on the prior information, the revised scheme can function regardless of such replacements. The revised scheme is experimentally validated and demonstrated that even without the prior information, it can satisfy the requirement of the SOH estimation (within 3%) thanks to its improved design. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a numerical study on laminar forced convection of water in offset strip-fin microchannels network heat sinks for microelectronic cooling. A 3-dimensional mathematical model, consisting of N–S equations and energy conservation equation, with the conjugate heat transfer between the heat sink base and liquid coolant taken into consideration is solved numerically. The heat transfer and fluid flow characteristics in offset strip-fin microchannels heat sinks are analyzed and the heat transfer enhancement mechanism is discussed. Effects of geometric size of strip-fin on the heat sink performance are investigated. It is found that there is an optimal strip-fin size to minimize the pressure drop or pumping power on the constraint condition of maximum wall temperature, and this optimal size depends on the input heat flux and the maximum wall temperature. The results of this paper are helpful to the design and optimization of offset strip-fin microchannel heat sinks for microelectronic cooling. [Copyright &y& Elsevier]

As a set theory-based convex model, the ellipsoidal model provides an attractive framework for treating uncertain-but-bounded variations in the structural reliability analysis and design optimization. However, improper modeling of the uncertainties may give rise to misleading non-probabilistic reliability analysis, thus result in either unsafe or over-conservative designs. This paper presents a systematic study on the mathematical formulation for constructing the minimum-volume ellipsoidal convex model using a given set of sample data, and shows its application in existing methods of non-probabilistic reliability analysis and design optimization of structures with bounded uncertainties. In this method, the uncertain parameters are first divided into groups according to their sources. For each individual group of uncertainties, the minimum-volume ellipsoid problem is reformulated into a semi-definite programming (SDP) problem and thus can be efficiently solved to its global optimum. Further, a linear transformation based on the eigenvalue analysis is employed to map the ellipsoidal model into a standard uncertainty space. This uncertainty modeling technique enables a compact and differentiable bound description of the parameter variations. Moreover, it has another useful property, the affine invariance, which is shown to be necessary for meaningful definition of a non-probabilistic reliability index. The effectiveness and efficiency of the present techniques for convex model construction and the corresponding reliability analysis are demonstrated with numerical examples of structural topology optimization problems with bounded variations arising from different sources. [ABSTRACT FROM AUTHOR]

Let D k , l ( m , n ) be the set of all the integer points in the transportation polytope of k n × l n matrices with row sums lm and column sums km . In this paper we find the sharp lower bound on the tropical determinant over the set D k , l ( m , n ) . This integer piecewise linear programming problem in arbitrary dimension turns out to be equivalent to an integer non-linear (in fact, quadratic) optimization problem in dimension two. We also compute the sharp upper bound on a modification of the tropical determinant, where the maximum over all the transversals in a matrix is replaced with the minimum. [ABSTRACT FROM AUTHOR]

Abstract: Model updating for the quadratic eigenvalue problem aims to update the model by given eigendata. In this paper, we consider the -weighted model updating problem which can not only preserve the symmetry and definiteness of the original model but also express our confidence in the original model through assigning different confidence weights. We propose an augmented Lagrangian dual method for the -weighted model updating problem. Under some mild assumptions, our method is shown to converge at least linearly. Numerical results illustrate the effectiveness of our method. In addition, we compare our method with the semi-definite programming (SDP) method. Numerical results illustrate that when the scale of the model becomes large our method still works but the SDP method failed to converge. [Copyright &y& Elsevier]

Abstract: Although the grey forecasting model has been successfully employed in many fields and demonstrated promising results, its prediction results may be inaccurate sometimes. For the purposes of enhancing the predictive performance of grey forecasting model and enlarging its suitable ranges, this paper puts forward a novel grey forecasting model termed NGM model and its optimized model, develops a calculative formula for solving the parameters of the novel NGM model through the least squares method, and obtains the time response sequence of NGM model by using differential equation as a procedure for reasoning. It performs a numerical demonstration on the prediction accuracy of NGM model and its optimized models. As shown in the results, the proposed model and it optimized model can enhance the prediction accuracy. Numerical results illustrate that the proposed NGM model and its optimized model are effective. They are suitable for predicting the data sequence with the characteristics of non-homogeneous exponential law. This work makes important contribution to the enrichment of grey prediction theory. [Copyright &y& Elsevier]

Abstract: Cam-modulated linkages that can achieve superior kinematic performance as compared with pure linkages have been widely applied in a variety of automation machinery. The tolerance analysis and synthesis are thus quite essential in the design and manufacture of precision cam-modulated linkages. This paper presents comprehensive and systematic mathematical tools for dealing with the tolerance analysis and synthesis of cam-modulated linkages. Based on the concept of equivalent linkage and the derived correlation between the radial-dimension errors and the normal-direction errors of the cam profile, the sensitivity analysis method for equivalent linkages is introduced for analyzing the tolerances in cam-modulated linkages, whose output error equations can be derived through an analytical means. Then, by incorporating the sensitivity analysis method and the concept of design for manufacturing and assembly (DFMA), an optimization model for synthesizing the tolerances in cam-modulated linkages is developed. The objective of this optimization model is to maximize the manufacturability and assembility of a cam-modulated linkage while maintaining acceptable kinematic accuracy of its output motion. A practical case study of analyzing and synthesizing the tolerances in a cam-modulated linkage type pick-and-place device is then performed to illustrate the presented methods. It shows that the mathematical tools presented can be helpful to the design and manufacture of precision cam-modulated linkages. [Copyright &y& Elsevier]

Abstract: Metamodelling is an increasingly more popular approach for alleviating the computational burden associated with computationally intensive optimization/management problems in environmental and water resources systems. Some studies refer to the metamodelling approach as function approximation, surrogate modelling, response surface methodology or model emulation. A metamodel-enabled optimizer approximates the objective (or constraint) function in a way that eliminates the need to always evaluate this function via a computationally expensive simulation model. There is a sizeable body of literature developing and applying a variety of metamodelling strategies to various environmental and water resources related problems including environmental model calibration, water resources systems analysis and management, and water distribution network design and optimization. Overall, this literature generally implies metamodelling yields enhanced solution efficiency and (almost always) effectiveness of computationally intensive optimization problems. This paper initially develops a comparative assessment framework which presents a clear computational budget dependent definition for the success/failure of the metamodelling strategies, and then critically evaluates metamodelling strategies, through numerical experiments, against other common optimization strategies not involving metamodels. Three different metamodel-enabled optimizers involving radial basis functions, kriging, and neural networks are employed. A robust numerical assessment within different computational budget availability scenarios is conducted over four test functions commonly used in optimization as well as two real-world computationally intensive optimization problems in environmental and water resources systems. Numerical results show that metamodelling is not always an efficient and reliable approach to optimizing computationally intensive problems. For simpler response surfaces, metamodelling can be very efficient and effective. However, in some cases, and in particular for complex response surfaces when computational budget is not very limited, metamodelling can be misleading and a hindrance, and better solutions are achieved with optimizers not involving metamodels. Results also demonstrate that neural networks are not appropriate metamodelling tools for limited computational budgets while metamodels employing kriging and radial basis functions show comparable overall performance when the available computational budget is very limited. [Copyright &y& Elsevier]

Abstract: Many practical control systems can be modeled as switched linear systems with stabilizable and unstabilizable subsystems. In this paper, control synthesis for such a class of systems with actuator saturation is well investigated in the continuous-time and discrete-time cases. Based on the time-dependent switching signals and multiple Lyapunov function method, stabilization conditions are established in terms of LMIs. To obtain an attractive region as large as possible, the optimization problems are formulated. As the applications, the asynchronously switched control and intermittent control under actuator saturation are discussed. Numerical examples and simulations are presented to illustrate the feasibility and values of the proposed results. [Copyright &y& Elsevier]

Abstract: Cross-efficiency evaluation is an extension of Data Envelopment Analysis (DEA) that permits not only the determination of a ranking of Decision Making Units (DMUs) but also the elimination of unrealistic weighting schemes, thereby rescinding the necessity for the inclusion of individual judgements in the models. The main deficiency of the procedure is the non-uniqueness of the optimal weights, which results in the peer evaluations dependences, for instance, on the software used to determine DMU’s efficiencies. This shortfall justifies the inclusion of secondary goals in order to determine cross-efficiency values. In this paper a new proposal of a secondary goal is studied. The idea is related with that proposed in Wu et al. (2009), in which the objective is the optimization of the rank position of the DMU under evaluation. In the procedure proposed here, an incentive to break level-pegging ties between alternatives is introduced by considering that efficiency scores induce a weak order of alternatives. The model is illustrated with a preference-aggregation application. [Copyright &y& Elsevier]

Abstract: The optimization problem addressed in this paper is an advanced form of the flexible job shop scheduling problem (FJSP) which also covers process plan flexibility and separable/non-separable sequence dependent setup times in addition to routing flexibility. Upon transforming the problem into an equivalent network problem, two mixed integer goal programming models are formulated. In the first model (Model A) the sequence dependent setup times are non-separable. In the second one (Model B) they are separable. Model B is obtained from Model A with a minor modification. The formulation of the models is described on a small sized numerical example and the solutions are interpreted. Finally, computational results are obtained on test problems. [Copyright &y& Elsevier]

Abstract: This paper investigates maintenance policy and length of extended warranty for repairable products from the seller’s viewpoint. When the product failed within the base and extended warranty periods, product failures are rectified using minimal repair. To reduce the number of product failures, the seller performs imperfect preventive maintenance (PM) actions when the age of the product reaches a controlled-limit within the base and extended warranty periods. After the base warranty period expires, the seller gives a discount of purchasing extended warranty expense if the consumer purchases the extended warranty for products. Under this maintenance scheme, the mathematical model of profit is constructed and then the optimal controlled-limit, the number and degree of PM and the length of extended warranty are obtained such that the expected total profit is maximized for the seller. Finally, numerical examples are given to illustrate the influences of the optimal length of the extended warranty and the maintenance policy for profit model. [Copyright &y& Elsevier]

Abstract: Most of the advanced integrated energy systems need a heat recovery steam cycle (HRSC), either fired or unfired, that recovers the waste heat from gas turbines and process units in order to generate electric power and supply mechanical power to compressors, heat to endothermic processes, and steam to external users. The key feature of such HRSCs is the integration between the heat recovery steam generator (HRSG) and the external heat exchangers. This paper presents a rigorous mathematical programming model, a linear approximation, and a two-stage algorithm for optimizing the design of integrated HRSGs and HRSCs, simultaneously considering the HRSG together with the heat recovery steam network and the intensive steam cycle variables. A detailed application of the methodology is described for an integrated gasification combined cycle plant with CO2 capture and results for other interesting plants are reported. A significant efficiency gain is obtained with respect to usual practice designs. [Copyright &y& Elsevier]

Abstract: Expensive optimization aims to find the global minimum of a given function within a very limited number of function evaluations. It has drawn much attention in recent years. The present expensive optimization algorithms focus their attention on metamodeling techniques, and call existing global optimization algorithms as subroutines. So it is difficult for them to keep a good balance between model approximation and global search due to their two-part property. To overcome this difficulty, we try to embed a metamodel mechanism into an efficient evolutionary algorithm, low dimensional simplex evolution (LDSE), in this paper. The proposed algorithm is referred to as the low dimensional simplex evolution extension (LDSEE). It is inherently parallel and self-contained. This renders it very easy to use. Numerical results show that our proposed algorithm is a competitive alternative for expensive optimization problems. [Copyright &y& Elsevier]

Abstract: Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. In the four dimensional variational (4D-Var) framework data assimilation is formulated as an optimization problem, which is solved using gradient based optimization methods. The 4D-Var gradient is obtained by forcing the adjoint model with observation increments. The construction of the adjoint model requires considerable development effort. In the continuous approach the adjoint differential equations are discretized. In the discrete approach the numerical solution of the forward equations is differentiated. The two routes lead to different gradients. In this paper we investigate numerically the effect of using discrete and continuous adjoints of the advection equation in chemical transport modeling. Continuous advection adjoints are easily implemented by calling the same advection subroutines as the forward model, with a sign change for the winds, and with rescaling the solution. Discrete advection adjoints involve the differentiation of a nonlinear, monotonic advection scheme like the piecewise parabolic method. The numerical experiments are carried out with CMAQ-ADJ. The results show that, while discrete advection adjoints are more accurate in point-to-point comparisons against finite differences, the continuous adjoints of advection perform better as gradients for optimization in 4D-Var data assimilation. [Copyright &y& Elsevier]

Abstract: The multiple attribute group decision making (MAGDM) problem with intuitionistic fuzzy information investigated in this paper is very useful for solving complicated decision problems under uncertain circumstances. Since experts have their own characteristics, they are familiar with some of the attributes, but not others, the weights of the decision makers to different attributes should be different. We derive the weights of the decision makers by aggregating the individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. The expert has a big weight if his evaluation value is close to the mean value and has a small weight if his evaluation value is far from the mean value. For the incomplete attribute weight information, we establish some optimization models to determine the attribute weights. Furthermore, we develop several algorithms for ranking alternatives under different situations, and then extend the developed models and algorithms to the MAGDM problem with interval-valued intuitionistic fuzzy information. Numerical results finally illustrate the practicality and efficiency of our new algorithms. [Copyright &y& Elsevier]

Abstract: In today’s competitive markets, effective product portfolio is critical for manufacturers that offer several products. From manufacturers’ perspective, the diversity must be maintained in a level in which the engineering costs do not exceed the acquired advantages of increased market share. On the other hand, product portfolio diversity is prominent for customers. In addition, manufacturers should always be careful about competitors activity. Therefore, we consider the problem of product portfolio management (PPM) in a competitive environment. This paper constructs a game theory-based mathematical model to deal with this new PPM problem. In this presented mathematical model, the PPM problem is formulated as a 2-person non-cooperative game with complete information. Each player has a set of strategies which correspond to the feasible product portfolios. Every payoff is determined by the procedure that considers the customer–engineering interaction in product portfolio planning, which aims to optimize product portfolio for a target market segment, and proposed a maximizing surplus share model for it. Therefore, obtaining the optimal product portfolio is determined by the Nash equilibrium point of this game. Finally, a numerical example is presented to demonstrate the feasibility of the approach. [Copyright &y& Elsevier]

Abstract: Total variation minimisation is a well-established method for digital image restoration. Its implicit preservation of edges permits the derivation of anisotropic models for a qualitative improvement at corners. This paper is a synopsis of anisotropic models with state-of-the-art insights into the numerics of isotropic models. We generalise two representative models from both branches of research. This formulation leads to a general convergent algorithm and a general highly efficient algorithm which apply for both cases. A transfer of the discretisation from the anisotropic model to the isotropic setting results in an improvement of rotational invariance. [ABSTRACT FROM AUTHOR]

Abstract: The multi-objective optimization strategy called physical programming (PP) provides engineers with a flexible tool to express design preferences with a ‘physical’ meaning. For each objective or specification design, preferences are established through linguistic categories to which numerical values are assigned. In PP, this mapping is made using preference functions as piecewise splines whose curvatures are calculated with an expensive and iterative algorithm. However, mapping between design parameter space and objective space may be largely non-convex and is uninfluenced by the use of gradient-based methods for solving the optimization problem. In this paper, the philosophy of the PP method has been used, but two components have been totally redesigned: a simpler algorithm is used for the construction of preference functions; and the optimizer is replaced by a genetic algorithm that avoids possible local minima problems. Three engineering applications are shown to illustrate the value of this new method. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on chemical process design and layout optimization based on a theoretical approach that takes into account the dynamic feature of price/cost fluctuations. As originally supposed, conceptual and systematic process design consider fixed prices and costs as input data in their models. These models do not account for well-known price and cost fluctuations such as electric energy and crude oil prices. The conventional modeling approach to conceptual design can find a sub-optimal solution because of neglecting the dynamic changes of economic terms within a given time horizon. This manuscript modifies the perspective, and considers the fluctuations of prices and costs within the conceptual design activity, in order to maximize a so-called economic potential. The term fluctuation refers to the deterministic variations of prices and costs while it does not quantify the stochastic oscillations of the market. These oscillations are bound to the mood of investors and require a forecasting approach, which is typical of forms of investment such as the exchange-traded derivatives. A straightforward case study, based on an energy intensive chemical process, shows the benefits and the opportunities of this approach. The mathematical model is based on the implementation of dynamic superstructures, which call for a MINLP formulation. Finally, the manuscript presents and discusses some numerical results and the economic benefits coming from the dynamic approach to conceptual design. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new nonmonotone trust-region method of conic model for solving unconstrained optimization problems. Both the local and global convergence properties are analyzed under reasonable assumptions. Numerical experiments are conducted to compare this method with some existed ones which indicate that the new method is efficient. [Copyright &y& Elsevier]

Abstract: Variable neighborhood search (VNS) metaheuristic as presented in Festa et al. [Randomized heuristics for the MAX-CUT problem, Optim. Methods Software 17 (2002) 1033–1058] can obtain high quality solution for max-cut problems. Therefore, it is worthwhile that VNS metaheuristic is extended to solve max-bisection problems. Unfortunately, comparing with max-cut problems, max-bisection problems have more complicated feasible region via the linear constraint . It is hard to directly apply the typical VNS metaheuristic to deal with max-bisection problems. In this paper, we skillfully combine the constraint with the objective function, obtain a new optimization problem which is equivalent to the max-bisection problem, and then adopt a distinct greedy local search technique to the resulted problem. A modified VNS metaheuristic based on the greedy local search technique is applied to solve max-bisection problems. Numerical results indicate that the proposed method is efficient and can obtain high equality solution for max-bisection problems. [Copyright &y& Elsevier]