Leafy green vegetables are highly susceptible to microbial contamination because they are minimally processed. Pathogenic bacteria of concern include Escherichia coli O157:H7, Salmonella spp., and Listeria monocytogen...
详细信息
Leafy green vegetables are highly susceptible to microbial contamination because they are minimally processed. Pathogenic bacteria of concern include Escherichia coli O157:H7, Salmonella spp., and Listeria monocytogenes. Leafy greens are a highly perishable commodity, and in some cases have a postharvest shelf-life limited to one week. This study provides an approach to optimize storage temperature of leafy greens in the supply chain, considering the cost of refrigeration, sensory quality parameters (i.e., fresh appearance, wilting, browning, and off-odor), and microbial safety using nonlinear programming (NLP). The loss of sensory quality parameters was expressed as Arrhenius equations and pathogen growth were represented by three-phase linear (primary) and square-root (secondary) models. The objective function was refrigeration cost, which was to be minimized. The constraints were growth of pathogens and the loss of sensory characteristics. An interactive graphical user interface was developed in MATLAB. Pathogen growth is of more concern than loss of sensory quality in fresh-cut Iceberg lettuce when considering a shelf-life of up to two days, and the model indicates is difficult to maintain sensory qualities for longer shelf-life values. Browning is of maximum concern for fresh-cut Iceberg and Romaine lettuce, whereas off-odor is the biggest concern for fresh-cut chicory. (C) 2016 Elsevier Ltd. All rights reserved.
To satisfy component concentration constraints in crude oil operations, it is necessary to blend different oil types, resulting in a mixed integer nonlinear programming (MINLP) formulation for the scheduling problem o...
详细信息
To satisfy component concentration constraints in crude oil operations, it is necessary to blend different oil types, resulting in a mixed integer nonlinear programming (MINLP) formulation for the scheduling problem of crude oil operations. Because of the intractability of such a nonlinear problem, approximate methods were proposed in the literature. However, by the existing methods, a composition concentration dicrepancy may occur, leading to an infeasible solution;or a feasible solution cannot be found even if such a solution exists for some cases. Based on a priority-slot modeling method, this paper copes with the crude-oil scheduling problem suffering from composition concentration discrepancy. To find a solution without composition concentration discrepancy, a valid inequality is added to the MINLP model. Also, the model size is significantly reduced by properly determining the number of slots. Then, a novel solution method is proposed. By this method, the problem is iteratively solved and, at each iteration step, only a reduced MILP problem is solved. Consequently, a solution can be found such that the composition concentration discrepancy is completely eliminated and it is computationally more efficient than the existing ones. Experiments are done to test the performance of the proposed method. Results show that the proposed method outperforms the existing ones.
We present a filter line-search algorithm that does not require inertia information of the linear system. This feature enables the use of a wide range of linear algebra strategies and libraries, which is essential to ...
详细信息
We present a filter line-search algorithm that does not require inertia information of the linear system. This feature enables the use of a wide range of linear algebra strategies and libraries, which is essential to tackle large-scale problems on modern computing architectures. The proposed approach performs curvature tests along the search step to detect negative curvature and to trigger convexification. We prove that the approach is globally convergent and we implement the approach within a parallel interior-point framework to solve large-scale and highly nonlinear problems. Our numerical tests demonstrate that the inertia-free approach is as efficient as inertia detection via symmetric indefinite factorizations. We also demonstrate that the inertia-free approach can lead to reductions in solution time because it reduces the amount of convexification needed.
Phasor measurement units (PMUs) provide synchronized measurements of voltage and current phasors and can make state estimation more accurate. The objective of optimal PMU placement (OPP) problem is to minimize the num...
详细信息
ISBN:
(纸本)9781538626993
Phasor measurement units (PMUs) provide synchronized measurements of voltage and current phasors and can make state estimation more accurate. The objective of optimal PMU placement (OPP) problem is to minimize the number of PMUs required for the system to be completely observable. This paper presents two different formulations of optimal PMU placement (OPP) problem: mixed integer linear programming (MILP) and nonlinear programming (NLP). For each formulation, modeling of power flow measurements, zero injection, limited communication facility, and single PMU failure is studied. The contribution of our paper is to conduct a comparison between the MILP and NLP formulations and show the advantages and disadvantages of each formulation.
In this paper we present a chaos-based evolutionary algorithm (EA) for solving nonlinear programming problems named chaotic genetic algorithm (CGA). CGA integrates genetic algorithm (GA) and chaotic local search (CLS)...
详细信息
In this paper we present a chaos-based evolutionary algorithm (EA) for solving nonlinear programming problems named chaotic genetic algorithm (CGA). CGA integrates genetic algorithm (GA) and chaotic local search (CLS) strategy to accelerate the optimum seeking operation and to speed the convergence to the global solution. The integration of global search represented in genetic algorithm and CLS procedures should offer the advantages of both optimization methods while offsetting their disadvantages. By this way, it is intended to enhance the global convergence and to prevent to stick on a local solution. The inherent characteristics of chaos can enhance optimization algorithms by enabling it to escape from local solutions and increase the convergence to reach to the global solution. Twelve chaotic maps have been analyzed in the proposed approach. The simulation results using the set of CEC'2005 show that the application of chaotic mapping may be an effective strategy to improve the performances of EAs. (C) 2016 Elsevier Ltd. All rights reserved.
The Quadratic Finite Element Model Updating Problem (QFEMUP) concerns with updating a symmetric second-order finite element model so that it remains symmetric and the updated model reproduces a given set of desired ei...
详细信息
The Quadratic Finite Element Model Updating Problem (QFEMUP) concerns with updating a symmetric second-order finite element model so that it remains symmetric and the updated model reproduces a given set of desired eigenvalues and eigenvectors by replacing the corresponding ones from the original model. Taking advantage of the special structure of the constraint set, it is first shown that the QFEMUP can be formulated as a suitable constrained nonlinear programming problem. Using this formulation, a method based on successive optimizations is then proposed and analyzed. To avoid that spurious modes (eigenvectors) appear in the frequency range of interest (eigenvalues) after the model has been updated, additional constraints based on a quadratic Rayleigh quotient are dynamically included in the constraint set. A distinct practical feature of the proposed method is that it can be implemented by computing only a few eigenvalues and eigenvectors of the associated quadratic matrix pencil. (C) 2015 Elsevier Ltd. All rights reserved.
A novel idea is proposed for solving optimization problems with equality constraints and bounds on the variables. In the spirit of sequential quadratic programming and sequential linearly-constrained programming, the ...
详细信息
A novel idea is proposed for solving optimization problems with equality constraints and bounds on the variables. In the spirit of sequential quadratic programming and sequential linearly-constrained programming, the new proposed approach approximately solves, at each iteration, an equality-constrained optimization problem. The bound constraints are handled in outer iterations by means of an augmented Lagrangian scheme. Global convergence of the method follows from well-established nonlinear programming theories. Numerical experiments are presented.
This paper proposes a new necessary condition for the infeasibility of nonlinear optimization problems, that becomes also sufficient under a convexity assumption, which is stated as a Pareto-criticality condition of a...
详细信息
This paper proposes a new necessary condition for the infeasibility of nonlinear optimization problems, that becomes also sufficient under a convexity assumption, which is stated as a Pareto-criticality condition of an auxiliary multi-objective optimization problem. This condition is evaluated in a search that either leads to a feasible point or to a point at which the infeasibility conditions hold. The resulting infeasibility certificate has global validity in convex problems and has at least a local meaning in generic nonlinear problems. (C) 2016 Elsevier B.V. All rights reserved.
In this paper, we study Fritz John type optimality conditions for constrained nonlinear programming in which equality and inequality constraints are together present. We introduce a generalized Fritz John condition wh...
详细信息
In this paper, we study Fritz John type optimality conditions for constrained nonlinear programming in which equality and inequality constraints are together present. We introduce a generalized Fritz John condition which is necessary and sufficient for a feasible point to be an optimal solution under weak invexity. In particular, by combining the introduced generalized Fritz John condition with the invexity with respect to different functions, we obtain sufficient optimality conditions which extend and generalize various results in the literature, and their importance and usefulness are illustrated on examples.
The purpose of this study is to develop a crisp nonlinear method of programming to address production inventory problems based on both production inventory and conditions. In addition, we use a statistical confidence ...
详细信息
The purpose of this study is to develop a crisp nonlinear method of programming to address production inventory problems based on both production inventory and conditions. In addition, we use a statistical confidence interval to derive level (1-beta, 1-alpha) interval-valued fuzzy numbers, in order to solve problems in nonlinear programming for production inventory in the fuzzy sense.
暂无评论