Top PDF Nonlinear Transportation Problems Algorithm

Nonlinear Transportation Problems Algorithm

Nonlinear Transportation Problems Algorithm

In the linear transportation problem (ordinary transportation problem) the cost per unit commodity shipped from a given source to a given destination is constant, regardless of the amount shipped. It is always supposed that the mileage (distance) from every source to every destination is fixed. To solve such transportation problem we have the streamlined simplex algorithm which is very efficient. However, in reality, we can see at least two cases that the transportation problem fails to be linear. First, the cost per unit commodity transported may not be fixed for volume discounts sometimes are available for large shipments. This would make the cost function either piecewise linear or just separable concave function. In this case the problem may be formulated as piecewise linear or concave programming problem with linear constraints.
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A regularization projection algorithm for various problems with nonlinear mappings in Hilbert spaces

A regularization projection algorithm for various problems with nonlinear mappings in Hilbert spaces

Since equilibrium problem (.) provides a unified model of several problems such as variational inequalities, fixed point problems and inclusion problems. In [], Takahashi and Takahashi further studied fixed points of a nonexpansive mapping and equilibrium problem (.) based on the viscosity approximation method, which was introduced by Moudafi []. To be more clear, they proved the following result.

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A new system of generalized nonlinear relaxed cocoercive variational inequalities

A new system of generalized nonlinear relaxed cocoercive variational inequalities

inequality problems and construct an iterative algorithm for approximating the solutions of the system of generalized relaxed cocoercive variational inequalities in Hilbert spaces. We prove the existence of the solutions for the system of generalized relaxed cocoercive variational inequality problems and the convergence of iterative sequences generated by the algorithm. We also study the convergence and stability of a new perturbed iterative algorithm for approximating the solution. The results presented in this paper improve and extend the previously known results in this area.
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Study on possibislitic multiobjective solid transportation problems

Study on possibislitic multiobjective solid transportation problems

known transportation problem (TP) in which three item demand and conveyance) are taken into account in the constraint set instead of two (supply and demand), and s of great use in public distribution systems. The STP was first stated by Shell (1955). Although STP was forgotten for long time, because of the new existing solution methodologies, recently it is receiving the attention and the interest of the researchers in this . 1993). Haley (1962) introduced the solution procedure of STP which is an extension of the modified distribution method. Patel and Tripathy (1989)developed a computationally superior method for a STP with mixed index transportation problem which is an extension of the modified . (1994) provided an algorithm for finding the optimum solution of solid fixed charge linear . (1997) designed a neural network approach for multicriteria STP. Jimenez and Verdegay (1996) developed a parametric approach for solving fuzzy STPs by an evolutionary algorithm (EA). Pandian and Natarajan (2010) are d the zeropoint method for finding an optimal solution to a classical TP. Pandian and Auradha, (2010) proposed a new method using the principle of zero point method introduced by Pandian and Natarajan (2010) for finding an optimal solution of . (2010) formulated a STP with discounted costs, fixed charges and vechicle costs as a linear programming problem. Qualitative analysis of some basic notions such as the set of feasible parameters, the solvability set and the stability set ind and the stability set of the second kind are introduced by Osman (1977). Luhandjula (1987) deals with multi- objective programming problems with possibilistic coefficients. Hussein (1998) introduced the complete solutions of multi- (1989) provided the concept of -Pareto objective nonlinear programming problems with differentiability for the considered objective functions. Ammar and Youness (2005) introduced n this paper, we deal with a multi- n problem (Poss MOSTP) with possibilistic coefficients, possibilistic supply values, possibilistic
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Wild Goats Optimization Approach for Capacitor Placement Problem

Wild Goats Optimization Approach for Capacitor Placement Problem

According to the table I, the WGA algorithm is a completely general method. This paper considered WGA Method in order to solve power system CP problems. The CP is a nonlinear, non-convex optimization problem. Every well- known heuristic method has some individual features. Meanwhile, some of these predominant features can be categorized as: mutation, swarm intelligence, memory based, and searching in groups. Mutation is the individual feature of GA which also considered in WGA. PSO algorithm is firmly based on Swarm intelligence and also it’s a perfectly memory based algorithm, both are considered in WGA. Searching in groups is another challenging and perfectly effective feature which is considered in ICA, this also considered in WGA. Also complete and well-sorted detail about WGA would found in [17]. As it obvious, WGA is a really powerful algorithm and this is the acceptable reason to consider this algorithm for complex non-linear and non-convex problems like CP.
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Newton Krylov Type Algorithm for Solving Nonlinear Least Squares Problems

Newton Krylov Type Algorithm for Solving Nonlinear Least Squares Problems

Trust region methods for unconstrained minimization are blessed with both strong theoretical convergence properties and a good accurate results in practice. The trial computational step in these methods is to find an approximate minimizer of some model of the true objective function within a trust region for which a suitable norm of the correction lies inside a given bound. This restriction is known as the trust region constraint, and the bound on the norm is its radius. The radius is adjusted so that successive model problems minimized the true objective function within the trust region 7.
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Solving Large-Scale Inverse Problems via Approximate Message Passing and Optimization.

Solving Large-Scale Inverse Problems via Approximate Message Passing and Optimization.

A standard way for solving inverse scattering problem is via optimization, where a sequence of estimates is generated by minimizing a cost function. For ill-posed problems with an additive measurement noise model, a cost function usually consists of a quadratic data-fidelity term and a regularization term, which incorporates prior information such as transform-domain sparsity. The challenge of such a formulation for nonlinear diffractive imaging is that the data-fidelity term is nonconvex due to the nonlinearity and that sparsity-promoting regularizers are usually nondifferentiable. For such nonsmooth and nonconvex problems, the proximal gradient method, also known as iterative shrinkage/thresholding algorithm (ISTA) [10, 32, 43], is a natural choice and enjoys convergence guarantees. However, it usually converges slowly. Fast iterative shrink- age/thresholding algorithm (FISTA) [8] is an accelerated variant of ISTA, which is proved to converge fast for convex problems. Unfortunately, its convergence analysis for nonconvex prob- lems has not been established. This work proposes a relaxed variant of FISTA for the nonsmooth and nonconvex problems and provides its convergence guarantee.
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An algorithm for finding common solutions of various problems in nonlinear operator theory

An algorithm for finding common solutions of various problems in nonlinear operator theory

In Section , we defined a Lipschitz continuous mapping and an inverse strongly mono- tone mapping. Inverse strongly monotone mappings arise in various areas of optimization and nonlinear analysis (see, for example, [–]). It follows from the Cauchy-Schwarz inequality that if a mapping A : D(A) ⊆ H → R(A) ⊆ H is 

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Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics

Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics

In most real world application and problems, a homogeneous product is carried from an origin to a destination by using different transportation modes (e.g., road, air, rail and water). This paper investigates a fixed charge transportation problem (FCTP), in which there are different routes with different capacities between suppliers and customers. To solve such a NP-hard problem, four meta- heuristic algorithms include Red Deer Algorithm (RDA), Stochastic Fractal Search (SFS), Genetic Algorithm (GA), and Simulated Annealing (SA) and two new hybrid meta-heuristics include hybrid RDA & GA (HRDGA) algorithm and Hybrid SFS & SA (HSFSA) algorithm are utilized. Regarding the literature, this is the first attempt to employ such optimizers to solve a FCTP. To tune up their parameters of algorithms, various problem sizes are generated at random and then a robust calibration is applied by using the Taguchi method. The final output shows that Simulated Annealing (SA) algorithm is the better than other algorithms for small-scale, medium-scale, and large-scale problems. As such, based on the Gap value of algorithms, the results of LINGO software shows that it reveals better outputs in comparison with meta-heuristic algorithms in small- scale and simulated annealing algorithm is better than other algorithms in large- scale and medium-scale problems. Finally, a set of computational results and conclusions are presented and analyzed.
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An efficient hybrid algorithm based on genetic algorithm (GA) and Nelder–Mead (NM) for solving nonlinear inverse parabolic problems

An efficient hybrid algorithm based on genetic algorithm (GA) and Nelder–Mead (NM) for solving nonlinear inverse parabolic problems

This simplex search method, first proposed by Spendley, Hext, and Himsworth [14] and later refined by Nelder and Mead [9]. Their methods is one of the most efficient pattern search method currently available. This method is a derivative-free line search method that was particularly designed for tradi- tional unconstrained minimization scenarios, such as the problems of non- linear least squares, nonlinear simultaneous equations, and other types of function minimization [10]. In this method for N vertices of an initial sim- plex, evaluate cost function for each vertex at the first. Then the worth vertex replace by newly reflected and better point, which can be approximately lo- cated in the negative gradient direction. In the minimization problem with three initial simplex vertices, the method can be mention as follows [6, 17]: x h : Vertex with highest cost function value.
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Fuzzy One Point Method for Finding the Fuzzy Optimal Solution for FTP and FUAP

Fuzzy One Point Method for Finding the Fuzzy Optimal Solution for FTP and FUAP

problems. Nagoor Gani and Abdul Razak [4] obtained a fuzzy solution for a two stage cost minimizing fuzzy transportation problem in which supplies and demands are trapezoidal fuzzy numbers. Nagoor Gani et al. [5] used an improved version of Vogel’s Approximation Method to find the efficient initial solution for the large scale transshipment problems. Dinagar and Palanivel [1] investigated fuzzy transportation problem, with the aid of trapezoidal fuzzy numbers and proposed fuzzy modified distribution method to find the optimal solution in terms of fuzzy numbers. Pandian and Natarajan [6] proposed a new algorithm namely, fuzzy zero point method for finding a fuzzy optimal solution for a fuzzy transportation problems, where the transportation cost, supply and demand are represented by trapezoidal fuzzy numbers. An assignment problem (AP) which is a special type of linear programming problem plays an important role in industry and other applications. In an assignment problem, the main task is to assign exactly one job to one person, so that for performing all the jobs, the total cost is minimum or the total profit is maximum. A fuzzy assignment problem (FAP) is an assignment problem in which the assignment costs are fuzzy quantities. It is a special case of FTP. Lin and Wen [3] solved the assignment problem by a labeling algorithm with fuzzy interval number costs. Hadi Basir Zadeh [10] proposed ones assignment method for solving assignment problems. Srinivasan and Geetharamani [7] applied Robust’s ranking technique for solving fuzzy assignment problem using ones assignment method. Thus numerous papers have been published in Fuzzy Transportation Problem and Fuzzy Assignment Problem. This paper is structured as follows: In section 2, we have reviewed the preliminary concepts of fuzzy set theory and coined the defuzzification formulae for triangular fuzzy number. In section 3 and subsection 3.1, we have proposed new algorithms namely, fuzzy one point method for finding a fuzzy optimal solution for a FTP and FUAP respectively where all parameters are triangular fuzzy numbers. Through illustrative examples, the fuzzy optimal solution obtained in this paper is compared with the existing crisp result. Section 4, concludes the paper.
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An Evolutionary Algorithm Based on a New Decomposition Scheme for Nonlinear Bilevel Programming Problems

An Evolutionary Algorithm Based on a New Decomposition Scheme for Nonlinear Bilevel Programming Problems

In this paper, we consider a special class of nonlinear BLPP with nonconvex objective functions, in which the follower’s objective is a function of the linear expression of all variables, and the follower’s constraints are convex with respect to the follower’s variables. There are no restrictions of convexity and differentiability on both the leader’s and the follower’s objective functions, which makes the model different from those proposed in the open literature. In view of the nonconvexity of the leader’s objective, we develop an evolutionary algorithm to solve the problem. First, based on the structural fea- tures of the follower’s objective, we give a new decom- position scheme by which the (approximate) optimal solution y to the follower’s problem can be obtained in a finite number of iterations. At the same time, the popula- tions are composed of such points (x, y) satisfying the follower’s optimization problem, that is, y is an optimal solution of the follower’s problem when x is fixed, which improves the feasibility of individuals. Then, to improve the efficiency of proposed algorithm, the better individu- als than crossover parents are employed to design new crossover operator, which is helpful to generate better offspring of crossover. Moreover, we design a single- side mutation operator, which can guarantee the diversity of individual in the process of evolution.
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Conditionally Suboptimal Filtering in Nonlinear Stochastic Differential System

Conditionally Suboptimal Filtering in Nonlinear Stochastic Differential System

This paper presents a novel conditionally suboptimal filtering algorithm on estimation problems that arise in discrete nonlinear time-varying stochastic difference systems. The suboptimal state estimate is formed by summing of conditionally nonlinear filtering estimates that their weights depend only on time instants, in contrast to conditionally optimal filtering, the proposed conditionally suboptimal filtering allows parallel processing of information and reduce online computational requirements in some nonlinear stochastic dif- ference system. High accuracy and efficiency of the conditionally suboptimal nonlinear filtering are demon- strated on a numerical example.
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Maximum network flow approach for fuzzy transportation problems

Maximum network flow approach for fuzzy transportation problems

Fuzzy transportation problem is much more natural to originate them in terms of nodes and arcs, taking advantage of the special structure of the problem. The core objective in a maximum network flow technique is that seek to maximize the flow through a flow network from a single source to a single sink, while minimizing the cost of that flow. As in the MODI method, the maximum flow is evenly distributed to entire cells in our proposed method. This proposed algorithm often reaches an optimal solution much faster than the linear programming solvers. In future, many optimized algorithms can be developed to solve the network flow problems in more efficient manner.
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A Modified Interactive Stability Algorithm for Solving Multi Objective NLP Problems with Fuzzy Parameters in Its Objective Functions

A Modified Interactive Stability Algorithm for Solving Multi Objective NLP Problems with Fuzzy Parameters in Its Objective Functions

In an earlier work, Osman [2] introduced the notions of the stability set of the first kind and the second kind, and analyzed these concepts for parametric convex nonlinear programming problems. Osman and El-Banna [3] presented the qualitative analysis of the stability set of the first kind for fuzzy parametric multi-objective nonli- near programming problems. Kassem [4] dealt with the interactive stability of multi-objective nonlinear pro- gramming problems with fuzzy parameters in the constraints. Sakawa and Yano [5] introduced the concept of α - multi-objective nonlinear programming and α -Pareto optimality. Katagiri and Sakawa [6] dealt with fuzzy ran- dom programming, Loganathan and Sherali [7] presented an interactive cutting plane algorithm for determining a best-compromise solution to a multi-objective optimization problem in situations with an implicitly defined utility function. Jameel and Sadeghi [8] solved nonlinear programming problem in fuzzy enlivenment. Recently, Elshafei [9] and Parag [10] gave an interactive stability compromise programming method for solving fuzzy multi-objective integer nonlinear programming problems.
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Solution Algorithm for Transportation Problem

Solution Algorithm for Transportation Problem

The study of the TP laid the foundation for further theoretical and algorithmic development of the minimal cost network flow problems. Transportation problem is famous in operation research for its wide application in real life. This is a special kind of the network optimization problems in which goods are transported from a set of sources to a set of destinations subject to the supply and demand of the source and destination, respectively, such that the total cost of transportation is minimized.

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The Newton-like properties of the updating mechanism of a model-reality differences algorithm

The Newton-like properties of the updating mechanism of a model-reality differences algorithm

Abstract The Dynamic Integrated Systems Optimization and Parameter Esti- mation (DISOPE) algorithm is an algorithm for solving nonlinear optimal con- trol problems and is of the gradient descent type. The updating step of DISOPE plays an important role in terminating the iterations of the algorithm and hence in determining its rate of convergence. In this paper, the mechanism was shown to have Newton-like properties and the order convergence established.

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Convergence results of a matrix splitting algorithm for solving weakly nonlinear complementarity problems

Convergence results of a matrix splitting algorithm for solving weakly nonlinear complementarity problems

In this paper, by reformulating the complementarity problem () as an implicit fixed point equation based on splittings of the system matrix A, we establish an accelerated modulus- based matrix splitting iteration algorithm and show the convergence analysis when the involved matrix of the WNCP is a P-matrix.

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Vol. 2, Issue 9, September 2013

Vol. 2, Issue 9, September 2013

Recently, Yousria Abo-elnaga et al (2012) [12] introduced a trust region globalization strategy to solve multi-objective transportation, assignment, and transshipment problems. Khurana et al (2011) [13] studied a transshipment problem with mixed constraints. Also. In (2012) Khurana et al [14] they introduced an algorithm for solving time minimizing capacitated transshipment problem.

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A conjugate gradient algorithm for large scale unconstrained optimization problems and nonlinear equations

A conjugate gradient algorithm for large scale unconstrained optimization problems and nonlinear equations

It is well known that the model of small- and medium-scale smooth functions is simple since it has many optimization algorithms, such as Newton, quasi-Newton, and bundle algorithms. Note that three algorithms fail to effectively address large-scale optimization problems because they need to store and calculate relevant matrices, whereas the conju- gate gradient algorithm is successful because of its simplicity and efficiency.

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