Top PDF Using Interval Graphs in an Order Processing Optimization Problem

Using Interval Graphs in an Order Processing Optimization Problem

Using Interval Graphs in an Order Processing Optimization Problem

Figure 2: Non simultaneous orders can share stack space This means that it is natural to associate the lifetime of a costumers’ order in the solution with intervals of time measured not in minutes or hours but measured in terms of the number of different products in the manufactur- ing sequence. We have seen that we can start solving a MOSP problem with a graph, and that in the solution of the problem we can consider an interval for the time that each stack is open. We will see that an interval graph can be associated to the set of intervals in the solution and we will also use some properties of interval graphs to find the solution of MOSP instances.
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Order Reduction of Linear Interval Systems Using Genetic Algorithm

Order Reduction of Linear Interval Systems Using Genetic Algorithm

applied to non-differentiable objective functions. Recently, Genetic Algorithm (GA) technique appeared as a promising algorithm for handling the optimization problems. GA can be viewed as a general-purpose search method, an optimization method, or a learning mechanism, based loosely on Darwinian principles of biological evolution, reproduction and ‘‘the survival of the fittest’’ [10]. GA maintains a set of candidate solutions called population and repeatedly modifies them. At each step, the GA selects individuals from the current population to be parents and uses them to produce the children for the next generation. In general, the fittest individuals of any population tend to reproduce and survive to the next generation, thus improving successive generations. However, inferior individuals can, by chance, survive and also reproduce. GA is well suited and has been extensively applied to solve complex design optimization problems because it can handle both discrete and continuous variables, non-linear objective and constrained functions without requiring gradient information
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High-performance Scientific Computing using Parallel Computing to Improve Performance Optimization Problems

High-performance Scientific Computing using Parallel Computing to Improve Performance Optimization Problems

The scientific computing represents the design and analysis activity for the nu- meric solving of mathematical problems in science and engineering, and tradition- ally this activity is called numeric analysis. The scientific computing is used in order to simulate natural phenomena, using virtual prototypes of models in engineering [7]. The general strategy is to replace a difficult problem with an easier one having the same solution, or a very similar solution, that is, they wish to replace an infinite problem with a finite problem, the differential one with the algebraically one, the non-linear with the linear, and eventually, a complicated one with a simple one, and the solution obtained can only approximate the solution of the original prob- lem.
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J. Braz. Soc. Mech. Sci. & Eng.  vol.34 número4

J. Braz. Soc. Mech. Sci. & Eng. vol.34 número4

In deterministic optimization, the uncertainties of the structural system (i.e. dimension, model, material, loads, etc) are not explicitly taken into account. Hence, resulting optimal solutions may lead to reduced reliability levels. The objective of reliability based design optimization (RBDO) is to optimize structures guaranteeing that a minimum level of reliability, chosen a priori by the designer, is maintained. Since reliability analysis using the First Order Reliability Method (FORM) is an optimization procedure itself, RBDO (in its classical version) is a double-loop strategy: the reliability analysis (inner loop) and the structural optimization (outer loop). The coupling of these two loops leads to very high computational costs. To reduce the computational burden of RBDO based on FORM, several authors propose decoupling the structural optimization and the reliability analysis. These procedures may be divided in two groups: (i) serial single loop methods and (ii) uni- level methods. The basic idea of serial single loop methods is to decouple the two loops and solve them sequentially, until some convergence criterion is achieved. On the other hand, uni-level methods employ different strategies to obtain a single loop of optimization to solve the RBDO problem. This paper presents a review of such RBDO strategies. A comparison of the performance (computational cost) of the main strategies is presented for several variants of two benchmark problems from the literature and for a structure modeled using the finite element method.
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Using the Firefly optimization method to weight an ensemble of rainfall forecasts from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS)

Using the Firefly optimization method to weight an ensemble of rainfall forecasts from the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS)

Abstract. In this paper we consider an optimization problem applying the metaheuristic Firefly algorithm (FY) to weight an ensemble of rainfall forecasts from daily precipitation simulations with the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) over South Amer- ica during January 2006. The method is addressed as a pa- rameter estimation problem to weight the ensemble of pre- cipitation forecasts carried out using different options of the convective parameterization scheme. Ensemble simulations were performed using different choices of closures, repre- senting different formulations of dynamic control (the mod- ulation of convection by the environment) in a deep convec- tion scheme. The optimization problem is solved as an in- verse problem of parameter estimation. The application and validation of the methodology is carried out using daily pre- cipitation fields, defined over South America and obtained by merging remote sensing estimations with rain gauge ob- servations. The quadratic difference between the model and observed data was used as the objective function to deter- mine the best combination of the ensemble members to re- produce the observations. To reduce the model rainfall bi- ases, the set of weights determined by the algorithm is used to weight members of an ensemble of model simulations in order to compute a new precipitation field that represents the observed precipitation as closely as possible. The validation of the methodology is carried out using classical statistical scores. The algorithm has produced the best combination of the weights, resulting in a new precipitation field closest to the observations.
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Multiobjective deterministic economic-emission load dispatch optimization problem using firefly algorithm

Multiobjective deterministic economic-emission load dispatch optimization problem using firefly algorithm

Genetic algorithms were formally introduced in the United States in the 1970s by John Holland at University of Michigan. Genetic algorithm is based on the mechanics of natural selection and natural genetics [7].Its fundamental principle is that the fittest member of population has the highest probability for survival. The genetic algorithm, works only with objective function information in a search for an optimal parameter set. In particular, genetic algorithms work very well on mixed (continuous and discrete), combinatorial problems. They are less susceptible to getting 'stuck' at local optima than gradient search methods. But they tend to be computationally expensive. To use a genetic algorithm, it must represent a solution to your problem as a genome (or chromosome)[6] The genetic algorithm then creates a population of solutions and applies genetic operators such as mutation and crossover to evolve the solutions in order to find the best one(s) which is shown in Fig:2, as no of iteration increased the value of fitness function(objective function) improves.
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PARALLEL PROCESSING OF BIG POINT CLOUDS USING Z-ORDER-BASED  PARTITIONING

PARALLEL PROCESSING OF BIG POINT CLOUDS USING Z-ORDER-BASED PARTITIONING

The core abstraction of Spark is the resilient distributed dataset (RDD), which is a distributed collection of elements. Instead of immediately executing an operation on an RDD, Spark instead builds an execution graph until it encounters an operation, known as an action, to force the graph to be executed. This allows the actual execution to be optimized based on succeeding operations. For example, instead of reading an entire file, Spark may read only those parts that will be processed in succeeding steps. Spark has also introduced the DataFrames application program- ming interface (API), which essentially wraps an RDD into SQL- like tables. This API also eases optimization by adding more con- text to the desired action. Another benefit of the API is that the methods are implemented in Java and Scala, the native languages of Spark, regardless of the language used by the user. This is a major boost for Python and R users, which are typically executed more slowly than Java or Scala.
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Pesqui. Oper.  vol.23 número1

Pesqui. Oper. vol.23 número1

Sometimes we have to solve small and median size combinatorial optimization problems using commercial codes of mixed linear integer programming as CPLEX, LINDO, OSL, XPRESS, etc. The use of branch and cut techniques to solve these problems needs a hard work of implementation. For the traveling salesman problem (TSP) and the Steiner tree problem (STP) in graphs one uses mathematical models with an exponential number of constraints to avoid subtours, these constraints are implicitly considered. The aim of this paper is to present 0-1 mixed linear programs with a polynomial number of variables and constraints to solve some combinatorial optimization problems using commercial codes. When we solve integer linear programming by column generation techniques, we have to generate paths, cycles and trees with additional constraints in small graphs, 40 to 60 nodes, 120 to 180 edges, at each iteration. For this we can use a commercial code to solve the master problem and the same code to solve the optimization problem which generates at each iteration a new column.
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Query Optimization Architecture for Data Grid Environment

Query Optimization Architecture for Data Grid Environment

In last few decades, many researchers have been devoted for the query processing in grid environment [1, 2, 3, 4, 5]. In this context, design and implementation of an efficient query optimization technique for grid environment is utmost important. Taking into account the constraints of the grid, a cost model for calculating the query execution cost, was introduced in [1]. In order to optimize the cost of query processing considering the constraints in grid environment, a linear programming optimization problem (LPP) is formulated based on the cost model., and they also deal a constraint-based query optimization technique using the linear programming optimization problem. In [2], another cost model is defined for dynamic grid database environment, and also gives the dynamic query optimization algorithm used for the query plan to make adaptive evolvement along with the fluctuation of gird environment. The authors of [3] propose a new model for distributed query optimization that integrates three distinct phases namely, (1) creation of single node plan, (2) generation of parallel plan, and (3) optimal site selection for plan execution. They also present different heuristic approaches for solving the proposed integrated distributed query processing problem. In [4], a semantic query optimizer for a grid environment is proposed; it mainly implements optimization of the following three modules: semantic extension of the user query, resources selection, and parallel processing. In [5], the Hameurlain team defined an execution model based on mobile agents to the distributed dynamic query optimization in large-scale systems. The idea is to execute each relational operator using a mobile agent, which allows decentralizing the decisions taken by the optimizer and adapting dynamically to estimation errors on the profile of relations.
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Convalesce Optimization for Input Allocation Problem Using Hybrid Genetic Algorithm

Convalesce Optimization for Input Allocation Problem Using Hybrid Genetic Algorithm

Lamarckian learning: The Lamarckian approach is based on the inheritance of acquired characteristics obtained through learning. This approach forces the genetic structure to reflect the result of local search. The genetic structure of an individual and its fitness are changed to match the solution found by a local search method. In the Lamarckian approach, the local search method is used as a refinement operator that modifies the genetic structure of an individual and places it back. Lamarckian evolution can accelerate the search process of genetic algorithm (Whiteley et al., 1994), on the other hands can disrupt the schema processing which can badly affect the exploring abilities of genetic algorithm, which may lead to premature convergence. Most of the hybrid genetic algorithms that repair chromosomes to satisfy constraints are Lamarckian and
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Comput. Appl. Math.  vol.31 número3

Comput. Appl. Math. vol.31 número3

3.2 Fuzzy decision making using fuzzy optimization – flexible optimization Fuzzy decision making using fuzzy optimization was first operationalized by Tanaka, Okuda, and Asai [31, 32], and then by Zimmermann [38]. This approach, based on Bellman and Zadeh [1], transforms systems of inequalities Ax ≤ b and the objective function into aspirations. The results are what is commonly called soft constraints, where the number b to the right of the inequality is a target such that, if the constraint is less than or equal to b, the membership value is one (the constraint is satisfied with certainty), and, if the constraint is greater than b + d, (for an a-priori given d ≥ 0), the membership is zero (the constraint is not satisfied with certainty). Here, the objective function is translated into a target, say z = f (x, c) ≥ t ∗ , and t ∗ translated into an aspiration. In between, the membership function is interpolated so that it is consistent with the definition of a fuzzy interval membership function in the context of the problem. Linear interpolation was the original form [38]. This models a fuzzy meaning of inequality that is translated into a fuzzy membership function and is the source of our use of the designation of flexible programming for these classes of optimization problems. The α-level represents the degree of feasibility of the constraints, consistent with the aspiration that the inequality be less than b but definitely not more than b + d. Thus, the objective, according to [38], is to simultaneously satisfy all constraints at the highest possible level of preference as measured by the α-levels of the membership functions. The approach of [38] is not always Pareto optimal. It must be iterated – fix the constraints at bounds and re-optimize.
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Sba Controle & Automação  vol.17 número2

Sba Controle & Automação vol.17 número2

This paper presents a fuzzy switching controller for uncer- tain nonlinear systems which are represented by a class of TS fuzzy systems with uncertainties. The controller pro- posed uses local guaranteed cost control laws and a switching scheme based on local quadratic Lyapunov functions when the state is on the boundary of defined subspaces of the state space. A sufficient condition for the stability of the uncertain nonlinear system with state feedback is given in terms of a piecewise quadratic Lyapunov function. This approach pro-

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Economic Load Dispatch Using Grey Wolf Optimization

Economic Load Dispatch Using Grey Wolf Optimization

Electrical power plays a pivotal role in the modern world to satisfy various needs. It is therefore very important that the electrical power generated is transmitted and distributed efficiently in order to satisfy the power requirement. Electrical power is generated in several ways. The economic scheduling of all generators in a system to meet desired demand is important problem in operation and planning of power system. The Economic Load Dispatch (ELD) problem is the most significant optimization problem in scheduling the generation of thermal generators in power system. In ELD problem, ultimate goal is to decrease the operation cost of the power generation system, while supplying the required power demanded. In addition to this, the various operational constraints of the system should also be satisfied. Traditional methods to solve ELD problem include the linear programming method, gradient method, lambda iteration method and Newton‟s method [1].
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J. Braz. Comp. Soc.  vol.7 número3

J. Braz. Comp. Soc. vol.7 número3

39 Edge-Clique Graphs and the l- Coloring Problem Tiziana Calamoneri, Rossella Petreschi... 40.[r]

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Strength Pareto Evolutionary Algorithm based Multi-Objective Optimization for Shortest Path Routing Problem in Computer Networks

Strength Pareto Evolutionary Algorithm based Multi-Objective Optimization for Shortest Path Routing Problem in Computer Networks

A computer network is an interconnected group of computers with the ability to exchange data. Today, computer networks are the core of modern communication. Routing problem is one of the important research issues in communication networks (Jayakumar and Gopinath, 2008). An ideal routing algorithm should strive to find an optimal path for packet transmission within a specified time so as to satisfy the Quality of Service (QoS). The objective functions related to cost, time, reliability and risk are appropriated for selecting the most satisfactory route in many communication network optimization problems. Traditionally, the routing problem has been a single-objective problem of minimization of either cost or delay. However, it is necessary to take into account that many real world problems are multi-objective in nature and so is the shortest path routing problem in computer networks.
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390 GENETIC ALGORITHM FOR OPTIMIZATION OF THE AEDES AEGYPTI CONTROL STRATEGIES

390 GENETIC ALGORITHM FOR OPTIMIZATION OF THE AEDES AEGYPTI CONTROL STRATEGIES

important advantage of the combination of these three controls is the large decrease in the popu- lation of natural mosquitoes, (Figure 11e), which minimizes the risk of diseases outbreak. The summer in Brazil occurs in the first four months of the year (January-April), it is a hot and humid period presenting optimal conditions for the growth of the Aedes aegypti mosquito population, and represents the time of the year when the greatest number of mosquito transmitted diseases takes place. For this reason there is frequent need for sanitary surveillance authorities to apply vector controls in this period. In this context and in order to conduct sensitivity analysis with respect to the duration of an outbreak, we also investigate some instances in which control was applied during part or throughout the year, beginning in the summer period, during which the population is at high levels.
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DETERMINATION OF THE STACKING VELOCITY FIELD VIA OPTIMIZATION METHODS

DETERMINATION OF THE STACKING VELOCITY FIELD VIA OPTIMIZATION METHODS

The global search optimization method, Genetic Algorithm, used for the automatic interpretation of Semblance, randomly generates an initial population of time interval velocities models [r]

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eve cccavalcante a (y)

eve cccavalcante a (y)

Abstract—Wireless data usage is growing now faster than ever before. In order to attend the increasing demand for wireless services and considering that frequency spectrum is a scarce and expensive resource, wireless are required to operate as efficiently as possible. In this context, the application of mathematical optimization methods in the study and design of key function- alities of wireless systems has acquired great relevance. This papers surveys some applications of optimization methods to wireless communications problems. Among them, game theory and majorization theory have got increasing attention in the last few years and are described in some more details. An application of optimization methods to solve a concrete problem in modern wireless communications, namely, the maximization of the ergodic capacity of a Coordinated Multi-Point system with statistical Channel State Information at the Transmitter is also provided.
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Bol. Ciênc. Geod.  vol.20 número4

Bol. Ciênc. Geod. vol.20 número4

Furthermore, non-spherical celestial bodies such as planets, physical satellites, asteroids and comets can be modeled by a triaxial ellipsoid. Also, the present day accuracy requirements and the modern computational capabilities push toward the study on the triaxial ellipsoid as a geometrical and a physical model in geodesy and related interdisciplinary sciences Panou et al (2013).

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Hat problem on graphs with exactly three cycles

Hat problem on graphs with exactly three cycles

This paper is devoted to investigation of the hat problem on graphs with exactly three cycles. In the hat problem, each of n players is randomly fitted with a blue or red hat. Everybody can try to guess simultaneously his own hat color by looking at the hat colors of the other players. The team wins if at least one player guesses his hat color correctly, and no one guesses his hat color wrong; otherwise the team loses. The aim is to maximize the probability of winning. Note that every player can see everybody excluding himself. This problem has been considered on a graph, where the vertices correspond to the players, and a player can see each player to whom he is connected by an edge. We show that the hat number of a graph with exactly three cycles is 3
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