This paper presents an up to date advances in time-domain system identification using fractional models. Both equation-error- and output-error-based models are detailed. In the former models, prior knowledge is genera...
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This paper presents an up to date advances in time-domain system identification using fractional models. Both equation-error- and output-error-based models are detailed. In the former models, prior knowledge is generally used to fix differentiation orders;model coefficients are estimated using least squares. The latter models allow simultaneous estimation of model coefficients and differentiation orders using nonlinear programing. As an example, a thermal system is identified using a fractional model and is compared to a rational one.
The objective of this study was to develop valid statistical collision models for multilane highway segments to examine the safety of curbs. For this, road geometric traffic and collision data for 2001 to 2003 were co...
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The objective of this study was to develop valid statistical collision models for multilane highway segments to examine the safety of curbs. For this, road geometric traffic and collision data for 2001 to 2003 were collected. The data included 2,274 collisions and 885 injury collisions that occurred on 191.85 mi of 199 directional segments in North Carolina. The authors applied a new modeling method of introducing variables into the model one by one in a multiplicative form. A nonlinear optimizing algorithm for estimating parameters using a negative binomial log likelihood function was adopted for the modeling. The functional form of the variable to be introduced was determined on the basis of the relationship between the recorded number of collisions and the number of collisions predicted by the current model without the variable. The integrate-differentiate method was applied to find candidate functional forms for each variable. Model selections were based on the -2 log likelihood and Bayesian information criterion statistics, and the cumulative residuals plot method to check the goodness of fit of the models was adopted. As a result of the modeling efforts, the annual average daily traffic, access point density, shoulder width, and shoulder type (including curb presence) variables were introduced to the final model for total collisions. The same variables except the shoulder type variable were introduced to the injury collision model. Overall, then, it appears that curbs mean fewer total collisions and no change in injury collisions as compared to no curbs on the sampled road segments.
In this paper, we propose a distributed algorithm to solve the yet explored distributed optimal power flow problem with discrete control variables of large distributed power systems. The proposed algorithm consists of...
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In this paper, we propose a distributed algorithm to solve the yet explored distributed optimal power flow problem with discrete control variables of large distributed power systems. The proposed algorithm consists of two distinguished features: 1) a distributed algorithm for solving continuous distributed optimal power flow to serve as a core technique in the framework of ordinal optimization (OO) strategy, and 2) implementing the OO strategy in a distributed power system to select a good enough discrete control variable solution. We have tested the proposed algorithm for several cases on the IEEE 118-bus and Tai Power 244-bus systems using a 4-PC network. The test results demonstrate the validity, robustness, and excellent computational efficiency of the proposed distributed algorithm in getting a good enough feasible solution.
This paper describes a new algorithm for solving nonlinear programming problems with equality constraints. The method introduces the idea of using trust cylinders to keep the infeasibility under control. Each time the...
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This paper describes a new algorithm for solving nonlinear programming problems with equality constraints. The method introduces the idea of using trust cylinders to keep the infeasibility under control. Each time the trust cylinder is violated, a restoration step is called and the infeasibility level is reduced. The radius of the trust cylinder has a nonincreasing update scheme, so eventually a feasible (and optimal) point is obtained. Global and local convergence of the algorithm are analyzed, as well as its numerical performance. The results suggest that the algorithm is promising.
Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of con...
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Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search algorithms can converge in a few iterations but lack a global perspective. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages. This paper proposes a new hybrid optimization technique that merges a genetic algorithm with a local search strategy based on the interior point method. The efficiency of this hybrid approach is demonstrated by solving a constrained multi-objective mathematical test-case. (C) 2007 Elsevier B.V. All rights reserved.
With the development and widespread use of large-scale nonlinear programming (NLP) tools for process optimization, there has been an associated application of NLP formulations with complementarity constraints in order...
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With the development and widespread use of large-scale nonlinear programming (NLP) tools for process optimization, there has been an associated application of NLP formulations with complementarity constraints in order to represent discrete decisions. Also known as mathematical programs with equilibrium constraints (MPECs), these formulations can be used to model certain classes of discrete events and can be more efficient than a mixed integer formulation. However, MPEC formulations and solution strategies are not yet fully developed in process engineering. In this study, we discuss MPEC properties, including concepts of stationarity and linear independence that are essential for well-defined NLP formulations. nonlinear programming based solution strategies for MPECs are then reviewed and examples of complementarity, drawn from chemical engineering applications are presented to illustrate the effectiveness of these formulations. (C) 2008 Elsevier Ltd. All rights reserved.
A standard Quadratic programming problem (StQP) consists in minimizing a (nonconvex) quadratic form over the standard simplex. For solving a SLQP we present an exact and a heuristic algorithm, that are based on new th...
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A standard Quadratic programming problem (StQP) consists in minimizing a (nonconvex) quadratic form over the standard simplex. For solving a SLQP we present an exact and a heuristic algorithm, that are based on new theoretical results for quadratic and convex optimization problems. With these results a StQP is reduced to a constrained nonlinear minimum weight clique problern in an associated graph. Such a Clique problem, which does not seem to have been Studied before, is then solved with all exact and a heuristic algorithm. Some computational experience shows that Our algorithms are able to solve StQP problems of at least one order of magnitude larger than those reported in the literature. (c) 2007 Elsevier B.V. All rights reserved.
Most pharmaceutical companies that rely heavily on their sales force for success do not fully understand the effect of details made in previous quarters have on the current quarter, which is also known as the carryove...
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Most pharmaceutical companies that rely heavily on their sales force for success do not fully understand the effect of details made in previous quarters have on the current quarter, which is also known as the carryover effect. This paper proposes an expert system that utilizes neural networks with nonlinear programming to accurately derive the carryover effect at the customer level. Results suggest that using this adaptive and easy-to-implement expert system helped a firm increase its sales by 3.4% while reducing its sales force expenditure by 8.9%, compared to the control group. The implications of this approach are considered. (C) 2007 Elsevier Ltd. All rights reserved.
Configurations of a four-column simulated moving bed chromatographic process are investigated by multi-objective optimization. Various existing column configurations are compared through a multi-objective optimization...
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Configurations of a four-column simulated moving bed chromatographic process are investigated by multi-objective optimization. Various existing column configurations are compared through a multi-objective optimization problem. Furthermore, an approach based on an SMB superstructure is applied to find novel configurations which have been found to outperform the standard SMB configuration. An efficient numerical optimization technique is applied to the mathematical model of the SMB process. It has been confirmed that although the optimal configuration highly depends on the purity requirement, the superstructure approach is able to find the most efficient configuration without exploring various existing configurations.
We consider a procurement problem where suppliers offer concave quantity discounts. The resulting continuous knapsack problem involves the minimization of a sum of separable concave functions. We identify polynomially...
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We consider a procurement problem where suppliers offer concave quantity discounts. The resulting continuous knapsack problem involves the minimization of a sum of separable concave functions. We identify polynomially solvable special cases of. this NP-hard problem, and provide a fully polynomial-time approximation scheme for the general problem. (c) 2007 Elsevier B.V. All rights reserved.
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