Natural gas is one of the most important sources of energy for many of the industrial and residential users in the world. It has a complex and huge supply chain which is in need of heavy investments in all the stages ...
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Natural gas is one of the most important sources of energy for many of the industrial and residential users in the world. It has a complex and huge supply chain which is in need of heavy investments in all the stages of exploration, extraction, production, transportation, storage and distribution. The aim of this study is evaluation and optimization of natural gas supply chain using a multi-objective multi-period fuzzy linear programming model considering economic and environmental objectives. In the proposed model, to deal with uncertainty, the parameters of problem including demand, capacity and cost are considered as fuzzy parameters. To solve the problem, a combination of possibilistic programming approach based on previous approach is used to verify and validate the model. A small-sized problem was solved using GAMS 23.2 software and sensitivity analysis is conducted on its parameters. To the best of our knowledge, this is the first study that presents a multi-objective fuzzy linear programming model for optimization of natural gas supply chain through a greenhouse gas reduction approach. (C) 2015 Elsevier B.V. All rights reserved.
In this paper we explore the decision regions of linear programming (LP) decoding. We compare the decision regions of an LP decoder, a Belief Propagation (BP) decoder and the optimal Maximum Likelihood (ML) decoder. W...
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In this paper we explore the decision regions of linear programming (LP) decoding. We compare the decision regions of an LP decoder, a Belief Propagation (BP) decoder and the optimal Maximum Likelihood (ML) decoder. We study the effect of minimal-weight pseudocodewords on LP decoding. We present global optimization as a method for finding the minimal pseudoweight of a given code as well as the number of minimal-weight generators. We present a complete pseudoweight distribution for the [24, 12, 8] extended Golay code, and provide justifications of why the pseudoweight distribution alone cannot be used for obtaining a tight upper bound on the error probability.
Most existing linear programming (LP) models have optimization objectives that are very different from Fisher's linear discriminant function (FLDF). An LP technique that adapts to FLDF to solve the two-group class...
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Most existing linear programming (LP) models have optimization objectives that are very different from Fisher's linear discriminant function (FLDF). An LP technique that adapts to FLDF to solve the two-group classification problem is desirable, as FLDF is one of the most popular classification rules. Therefore, this paper introduces a piecewise linear programming (PLP-p) approach that has an optimization objective very similar to that of FLDF to solve the two-group classification problem in discriminant analysis. Moreover, the paper compares the classificatory performance between FLDF and the new PLP-p model, and shows that the results from both approaches are as good as each other when applied to three published data sets. However, the new PLP-p is more flexible than FLDF in terms of adding different types of constraints and weighting individual observations. The results of a simulation. experiment confirm the value of our proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.
In this paper we represent two new methods for the solution of canonical form linear programming problems. In order to solve this linear programming problem we must minimize energy function of the corresponding neural...
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In this paper we represent two new methods for the solution of canonical form linear programming problems. In order to solve this linear programming problem we must minimize energy function of the corresponding neural network. Here energy function is considered as a Liapunov function and we use treated Hopfield neural network. First new method finds optimal solution for primal problem, using neural network, while second new method composes primal and dual problem and therefore finds optimal solution for both problems. Numerical results compared with simplex solution, and find that the convergence of two new methods to the correct solution is too fast, even faster than Neguyen's method. The new methods are fully stable. (c) 2004 Elsevier Inc. All rights reserved.
We use the models of cognitive psychology and the early literature on linear programming models to understand how experts organize their thinking about models. We show that several different patterns appear in the way...
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We use the models of cognitive psychology and the early literature on linear programming models to understand how experts organize their thinking about models. We show that several different patterns appear in the way models are related to each other. This paper also is a kind of cognitive history of the formulation of linear programming models in the first decade after the invention of the field.
Presents an experiment in implementing a supersparsity technique due to J. Kalan together with a data compacting scheme in a linear programming code. Provision of a solution to a number of supersparse linear programmi...
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Presents an experiment in implementing a supersparsity technique due to J. Kalan together with a data compacting scheme in a linear programming code. Provision of a solution to a number of supersparse linear programming problems; Determination of the effect of memory space saving techniques on the speed of computations; Provision of a comparative computational statistics and a discussion of implementation issues.
Supply chain management (SCM) is concerned with a complex business relations network that contains interrelationships between various entities, such as suppliers, manufacturers, distribution centers and customers. SCM...
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Supply chain management (SCM) is concerned with a complex business relations network that contains interrelationships between various entities, such as suppliers, manufacturers, distribution centers and customers. SCM integrates these entities and manages their interrelationships through the use of information technology to meet customer expectations (i.e., higher product variety and quality, lower costs and faster responses) effectively along the entire value chain. Thus, one of the vital issues in supply chain management is the design of the value chain network. In this paper, a fuzzy linear programming model for the optimization of the multi-stage supply chain model with triangular and trapezoidal membership functions is presented. The model determines the fuzzy capacities of the facilities (plants or distribution centers (DCs)) and the design of the network configuration with a minimum total cost. The total cost involves the shipping cost from suppliers;transportation costs between plants and DCs;distribution costs between DCs and customer zones;and opportunity costs from not having the material at the right time. The developed model is solved by a professional software package (LINDO), and the computational results are discussed. (C) 2011 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
The aim of this paper is to apply a fuzzy/possibilistic approach focus in the context of the Caribbean. The fuzzy model is obtained taking into account the behaviour of salinity and total dissolved solids content acco...
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The aim of this paper is to apply a fuzzy/possibilistic approach focus in the context of the Caribbean. The fuzzy model is obtained taking into account the behaviour of salinity and total dissolved solids content according to the season (dry or hurricane season). The methodology is based on the fact that minimal/maximal values of these parameters are target values along the year rather than fixed real numbers. The results of this paper indicate that the operating costs of any desalination plant based on reverse osmosis can change up to 30% in one year.
The continually increasing energy consumption represents a critical issue in modern heterogeneous computing systems. With the aid of dynamic voltage frequency scaling (DVFS), task scheduling is considered an effective...
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The continually increasing energy consumption represents a critical issue in modern heterogeneous computing systems. With the aid of dynamic voltage frequency scaling (DVFS), task scheduling is considered an effective software-based technique for reducing the total energy consumption and minimizing the overall schedule length (makespan). A natural solution is to reclaim the slack time in a given time-efficient schedule, which is also referred to as a two-pass method or a rescheduling method. A number of studies have focused on slack reclamation to achieve energy reductions through heuristics;although, these methods offer suboptimal solutions. In this article, the rescheduling optimization problem is formulated as a linear program for minimizing an energy objective function subject to precedence and deadline constraints implied in the given schedule. Two types of decision variables, ie, frequency duty factors and task intervals, are defined to set up the linear model. Consequently, an optimal solution to the problem can be provided in a straightforward manner by a linear programming solver, which suggests that such a rescheduling problem belongs to the P (polynomial time) class. The experimental results show the effectiveness of the proposed approach and demonstrate that the performance is superior to that of other competitive algorithms in terms of both energy saving and runtime efficiency.
We describe an iterative refinement procedure for computing extended-precision or exact solutions to linear programming (LP) problems. Arbitrarily precise solutions can be computed by solving a sequence of closely rel...
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We describe an iterative refinement procedure for computing extended-precision or exact solutions to linear programming (LP) problems. Arbitrarily precise solutions can be computed by solving a sequence of closely related LPs with limited-precision arithmetic. The LPs solved share the same constraint matrix as the original problem instance and are transformed only by modification of the objective function, right-hand side, and variable bounds. Exact computation is used to compute and store the exact representation of the transformed problems, and numeric computation is used for solving LPs. At all steps of the algorithm the LP bases encountered in the transformed problems correspond directly to LP bases in the original problem description. We show that this algorithm is effective in practice for computing extended-precision solutions and that it leads to direct improvement of the best known methods for solving LPs exactly over the rational numbers. Our implementation is publically available as an extension of the academic LP solver SOPLEX.
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