scholarly journals Characterization of the stability set for non-differentiable fuzzy parametric optimization problems

2005 ◽  
Vol 2005 (12) ◽  
pp. 1995-2003
Author(s):  
Mohamed Abd El-Hady Kassem
Keyword(s):  
Parametric Optimization ◽  
Optimization Problems ◽  
Objective Functions ◽  
Triangular Fuzzy Numbers ◽  
Convex Programs ◽  
Nonlinear Programs ◽  
Multiobjective Nonlinear Programming ◽  
The Stability ◽  
Fuzzy Parameters

This note presents the characterization of the stability set of the first kind for multiobjective nonlinear programming (MONLP) problems with fuzzy parameters either in the constraints or in the objective functions without any differentiability assumptions. These fuzzy parameters are characterized by triangular fuzzy numbers (TFNs). The existing results concerning the parametric space in convex programs are reformulated to study for multiobjective nonlinear programs under the concept ofα-Pareto optimality.

scholarly journals A fuzzy multi-objective linear programming with interval-typed triangular fuzzy numbers

2019 ◽  
Vol 17 (1) ◽  
pp. 607-626 ◽  
Author(s):  
Chunquan Li
Keyword(s):  
Linear Programming ◽  
Fuzzy Numbers ◽  
Programming Model ◽  
Optimal Solution ◽  
Objective Functions ◽  
Triangular Fuzzy Numbers ◽  
Matlab Toolbox ◽  
Multi Objective ◽  
Cut Set ◽  
The Stability

Abstract A multi-objective linear programming problem (ITF-MOLP) is presented in this paper, in which coefficients of both the objective functions and constraints are interval-typed triangular fuzzy numbers. An algorithm of the ITF-MOLP is provided by introducing the cut set of interval-typed triangular fuzzy numbers and the dominance possibility criterion. In particular, for a given level, the ITF-MOLP is converted to the maximization of the sum of membership degrees of each objective in ITF-MOLP, whose membership degrees are established based on the deviation from optimal solutions of individual objectives, and the constraints are transformed to normal inequalities by utilizing the dominance possibility criterion when compared with two interval-typed triangular fuzzy numbers. Then the equivalent linear programming model is obtained which could be solved by Matlab toolbox. Finally several examples are provided to illuminate the proposed method by comparing with the existing methods and sensitive analysis demonstrates the stability of the optimal solution.

Preservation of Supermodularity in Parametric Optimization: Necessary and Sufficient Conditions on Constraint Structures

2020 ◽  
Author(s):  
Xin Chen ◽  
Daniel Zhuoyu Long ◽  
Jin Qi
Keyword(s):  
Operations Research ◽  
Parametric Optimization ◽  
Optimization Problems ◽  
Lattice Structure ◽  
Sufficient Conditions ◽  
Comparative Statics ◽  
Necessary And Sufficient Conditions ◽  
Objective Functions ◽  
New Concepts ◽  
Necessary And Sufficient

The concept of supermodularity has received considerable attention in economics and operations research. It is closely related to the concept of complementarity in economics and has also proved to be an important tool for deriving monotonic comparative statics in parametric optimization problems and game theory models. However, only certain sufficient conditions (e.g., lattice structure) are identified in the literature to preserve the supermodularity. In this article, new concepts of mostly sublattice and additive mostly sublattice are introduced. With these new concepts, necessary and sufficient conditions for the constraint structures are established so that supermodularity can be preserved under various assumptions about the objective functions. Furthermore, some classes of polyhedral sets that satisfy these concepts are identified, and the results are applied to assemble-to-order systems.

TEM characterization of Au/Polysilicon interactions

1990 ◽  
Vol 48 (4) ◽  
pp. 650-651
Author(s):  
N. David Theodore ◽  
Leslie H. Allen ◽  
C. Barry Carter ◽  
James W. Mayer
Keyword(s):  
Solid Phase ◽  
Single Crystal Silicon ◽  
Chemical Vapor ◽  
Electrical Contacts ◽  
Silicon Substrates ◽  
Crystal Silicon ◽  
Transmission Electron ◽  
Polysilicon Films ◽  
The Stability

Metal/polysilicon investigations contribute to an understanding of issues relevant to the stability of electrical contacts in semiconductor devices. These investigations also contribute to an understanding of Si lateral solid-phase epitactic growth. Metals such as Au, Al and Ag form eutectics with Si. reactions in these metal/polysilicon systems lead to the formation of large-grain silicon. Of these systems, the Al/polysilicon system has been most extensively studied. In this study, the behavior upon thermal annealing of Au/polysilicon bilayers is investigated using cross-section transmission electron microscopy (XTEM). The unique feature of this system is that silicon grain-growth occurs at particularly low temperatures ∽300°C).Gold/polysilicon bilayers were fabricated on thermally oxidized single-crystal silicon substrates. Lowpressure chemical vapor deposition (LPCVD) at 620°C was used to obtain 100 to 400 nm polysilicon films. The surface of the polysilicon was cleaned with a buffered hydrofluoric acid solution. Gold was then thermally evaporated onto the samples.

Preparation and Characterization of β-glucosidase Films for Stabilization and Handling in Dry Configurations

2020 ◽  
Vol 21 (8) ◽  
pp. 741-747
Author(s):  
Liguang Zhang ◽  
Yanan Shen ◽  
Wenjing Lu ◽  
Lengqiu Guo ◽  
Min Xiang ◽  
...  
Keyword(s):  
Tensile Strength ◽  
Protein Function ◽  
Protein Stabilization ◽  
Functional Protein ◽  
Elongation At Break ◽  
Gelatin Films ◽  
The Stability ◽  
And Function ◽  
General Method

Background: Although the stability of proteins is of significance to maintain protein function for therapeutical applications, this remains a challenge. Herein, a general method of preserving protein stability and function was developed using gelatin films. Method: Enzymes immobilized onto films composed of gelatin and Ethylene Glycol (EG) were developed to study their ability to stabilize proteins. As a model functional protein, β-glucosidase was selected. The tensile properties, microstructure, and crystallization behavior of the gelatin films were assessed. Result: Our results indicated that film configurations can preserve the activity of β-glucosidase under rigorous conditions (75% relative humidity and 37°C for 47 days). In both control films and films containing 1.8 % β-glucosidase, tensile strength increased with increased EG content, whilst the elongation at break increased initially, then decreased over time. The presence of β-glucosidase had a negligible influence on tensile strength and elongation at break. Scanning electron-microscopy (SEM) revealed that with increasing EG content or decreasing enzyme concentrations, a denser microstructure was observed. Conclusion: In conclusion, the dry film is a promising candidate to maintain protein stabilization and handling. The configuration is convenient and cheap, and thus applicable to protein storage and transportation processes in the future.

Scalable Computing Architecture for Time-Dependent Transportation Optimization Problems Based on High-Performance Computing Techniques

2019 ◽  
Vol 2673 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Pengfei (Taylor) Li ◽  
Peirong (Slade) Wang ◽  
Farzana Chowdhury ◽  
Li Zhang
Keyword(s):  
Objective Function ◽  
High Performance Computing ◽  
High Performance ◽  
Optimization Problems ◽  
Dynamic Network ◽  
Alternative Formulation ◽  
High Fidelity ◽  
Objective Functions ◽  
Performance Computing ◽  
Transportation Optimization

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.

scholarly journals R-Regularity of Set-Valued Mappings Under the Relaxed Constant Positive Linear Dependence Constraint Qualification with Applications to Parametric and Bilevel Optimization

2021 ◽  
Author(s):  
Patrick Mehlitz ◽  
Leonid I. Minchenko
Keyword(s):  
Linear Dependence ◽  
Constraint Qualification ◽  
Parametric Optimization ◽  
Optimization Problems ◽  
Sufficient Conditions ◽  
Bilevel Optimization ◽  
Regularity Property ◽  
Constant Rank Constraint Qualification ◽  
Existence Criterion ◽  
Set Valued Mappings

AbstractThe presence of Lipschitzian properties for solution mappings associated with nonlinear parametric optimization problems is desirable in the context of, e.g., stability analysis or bilevel optimization. An example of such a Lipschitzian property for set-valued mappings, whose graph is the solution set of a system of nonlinear inequalities and equations, is R-regularity. Based on the so-called relaxed constant positive linear dependence constraint qualification, we provide a criterion ensuring the presence of the R-regularity property. In this regard, our analysis generalizes earlier results of that type which exploited the stronger Mangasarian–Fromovitz or constant rank constraint qualification. Afterwards, we apply our findings in order to derive new sufficient conditions which guarantee the presence of R-regularity for solution mappings in parametric optimization. Finally, our results are used to derive an existence criterion for solutions in pessimistic bilevel optimization and a sufficient condition for the presence of the so-called partial calmness property in optimistic bilevel optimization.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

Steady-State Parametric Optimization and Transient Characterization of Heat Flow Regulation With Binary Diffusion

2020 ◽  
Vol 10 (12) ◽  
pp. 1996-2007
Author(s):  
Tanya Liu ◽  
James W. Palko ◽  
Joseph S. Katz ◽  
Feng Zhou ◽  
Ercan M. Dede ◽  
...  
Keyword(s):  
Steady State ◽  
Heat Flow ◽  
Parametric Optimization ◽  
Flow Regulation ◽  
Binary Diffusion

Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R767-R781 ◽  
Author(s):  
Mattia Aleardi ◽  
Silvio Pierini ◽  
Angelo Sajeva
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Model Space ◽  
Global Search ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Fireworks Algorithm ◽  
Highly Nonlinear ◽  
Whale Optimization

We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular, we are interested in assessing the exploration and exploitation capabilities of each method as the dimension of the model space increases. First, we test the different algorithms on two multiminima and two convex analytic objective functions. Then, we compare them using the residual statics corrections and 1D elastic full-waveform inversion, which are highly nonlinear geophysical optimization problems. Our results demonstrate that FA, FWA, and WOA are characterized by optimal exploration capabilities because they outperform the other approaches in the case of optimization problems with multiminima objective functions. Differently, QPSO and PSO have good exploitation capabilities because they easily solve ill-conditioned optimizations characterized by a nearly flat valley in the objective function. QPSO, PSO, and WCA offer a good compromise between exploitation and exploration.

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