With the rapid economic development in recent years, the peak load demands of China are experiencing a booming period. As a regional power grid with the maximum electrical load in the world, the East China Power Grid ...
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With the rapid economic development in recent years, the peak load demands of China are experiencing a booming period. As a regional power grid with the maximum electrical load in the world, the East China Power Grid (ECPG) is in charge of coordinating simultaneously the power generation of its own power plants to several subordinate provincial power grids. However, due to unreasonable power structure, there is a lack of flexible energy to quickly respond the peak loads of multiple power grids, which has brought a new real challenge for the dispatching center of most regional power grids in China. Hence, to meet the practical requirement of peak shaving operation in China, a novel linear programming optimization model is proposed in this paper to find out the optimal quarter-hourly generation allocation plan while satisfying a group of complex constraints. In this model, the objective is to minimize the summation of peak-valley difference of the residual load series by subtracting the total allocated generation from the original load of each power grid. This model is used to solve the day-head peak operation of 14 hydro-thermal-nuclear plants serving multiple power grids in ECPG. The results from different cases show that compared with the current method used in practical engineering, the proposed model is capable of providing results with smoother remaining load series for each power grid. Thus, this method proves to be effective technique to provide scientific decision support for large-scale generation allocation of plants serving multiple interconnected power grids in China. (C) 2017 Elsevier Ltd. All rights reserved.
Supplier selection is a critical process in sustainable supply chain management. Increased pressure from stakeholders has forced companies to search for methodologies that help in arriving at intelligent supplier sele...
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Supplier selection is a critical process in sustainable supply chain management. Increased pressure from stakeholders has forced companies to search for methodologies that help in arriving at intelligent supplier selection decisions. This is a unique study as it illustrates how to optimise orders among various suppliers while taking into consideration all three dimensions of sustainability -economic, social, and environmental. Previous studies have mostly relied on simple ranking of suppliers on the basis of past performance for selection. Those studies that did emphasise on optimisation of orders among suppliers, did not consider all three dimensions of sustainability. To establish an improved sustainable supply chain, this study uses integrated fuzzy AHP and fuzzy multi-objective linear programming approach for order allocation among suppliers. fuzzy AHP has been used for weighing various factors such as quality, lead time, cost, energy use, waste minimisation, emission, and social contribution, and weights of the factors have been considered for developing linear programming. Demand has been taken as a fuzzy variable in this model. The case of an Indian automobile company has been taken as illustration.
This paper presents a logarithmic barrier method for solving a linear programming problem. We are interested in computation of the direction by the Newton's method and in computation of the displacement step using...
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This paper presents a logarithmic barrier method for solving a linear programming problem. We are interested in computation of the direction by the Newton's method and in computation of the displacement step using majorant functions instead line search methods in order to reduce the computation cost. This purpose is confirmed by numerical experiments, showing the efficiency of our approach, which are presented in the last section of this paper. (C) 2016 Elsevier B.V. All rights reserved.
In this paper we consider robust linear programs with uncertainty sets defined by the convex hull of a finite number of mxn matrices. Embedded within the matrices are related robust linear programs defined by the rows...
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In this paper we consider robust linear programs with uncertainty sets defined by the convex hull of a finite number of mxn matrices. Embedded within the matrices are related robust linear programs defined by the rows, columns, and coefficients of the matrices. This results in a nested set of primal (and dual) linear programs with predictably different optimal objective values. The set of matrices also embed a covariance structure for the matrix coefficients and we show that when negative covariances predominate in the rows, more favorable optimal objective values for the primal can be expected. (C) 2016 Elsevier Ltd. All rights reserved.
The max-product belief propagation (BP) is a popular message-passing heuristic for approximating a maximum-a-posteriori assignment in a joint distribution represented by a graphical model. In the past years, it has be...
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The max-product belief propagation (BP) is a popular message-passing heuristic for approximating a maximum-a-posteriori assignment in a joint distribution represented by a graphical model. In the past years, it has been shown that BP can solve a few classes of linear programming (LP) formulations to combinatorial optimization problems including maximum weight matching, shortest path, and network flow, i.e., BP can be used as a message-passing solver for certain combinatorial optimizations. However, those LPs and corresponding BP analysis are very sensitive to underlying problem setups, and it has been not clear what extent these results can be generalized to. In this paper, we obtain a generic criteria that BP converges to the optimal solution of given LP and show that it is satisfied in LP formulations associated to many classical combinatorial optimization problems including maximum weight perfect matching, shortest path, traveling salesman, cycle packing, vertex/edge cover, and network flow.
In this paper, an application of linear programming in optimizing the procurement and movement of coal for an Indian coal-fired thermal power-generating company is presented. Results show that there is immense potenti...
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In this paper, an application of linear programming in optimizing the procurement and movement of coal for an Indian coal-fired thermal power-generating company is presented. Results show that there is immense potential not only for significant cost savings but also for reduced logistics between different coal source-power plant pairs. The target plant load factor at each power plant can be achieved without the need of any imported coal which would not only save precious foreign exchange, but also reduce the logistics involved in the import of coal and transport to power plants. Sensitivity analyses have also been performed with varying coal supply and coal quality levels. The issue of greenhouse gas (GHG) emissions from coal-fired power plants has also been addressed. The trade-off between the optimal total cost and GHG emission targets has been explored. Results show that it is possible to significantly reduce carbon footprints with a marginal increase in the optimal total cost and without the need of import of coal. However, if it is desired to further reduce GHG emission targets, optimal total costs rise substantially with imported coal gradually substituting domestic coal. It is believed that the results presented in this paper would provide a fresh perspective with regard to the allocation and movement of coal. Finally, recommendations and concluding remarks are presented.
We consider a linear programming problem with interval data. We discuss the problem of checking whether a given solution is optimal for each realization of interval data. This problem was studied for particular forms ...
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We consider a linear programming problem with interval data. We discuss the problem of checking whether a given solution is optimal for each realization of interval data. This problem was studied for particular forms of linear programming problems. Herein, we extend the results to a general model and simplify the overall approach. Moreover, we inspect computational complexity, too. Eventually, we investigate a related optimality concept of semi-strong optimality, showing its characterization and complexity.
Women of reproductive age are at nutritional risk due to their need for nutrient-dense diets. Risk is further elevated in resource-poor environments. In one such environment, we evaluated feasibility of meeting micron...
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Women of reproductive age are at nutritional risk due to their need for nutrient-dense diets. Risk is further elevated in resource-poor environments. In one such environment, we evaluated feasibility of meeting micronutrient needs of women of reproductive age using local foods alone or using local foods and supplements, while minimizing cost. Based on dietary recall data from Ouagadougou, we used linear programming to identify the lowest cost options for meeting 10 micronutrient intake recommendations, while also meeting energy needs and following an acceptable macronutrient intake pattern. We modeled scenarios with maximum intake per food item constrained at the 75th percentile of reported intake and also with more liberal maxima based on recommended portions per day, with and without the addition of supplements. Some scenarios allowed only commonly consumed foods (reported on at least 10% of recall days). We modeled separately for pregnant, lactating, and nonpregnant, nonlactating women. With maxima constrained to the 75th percentile, all micronutrient needs could be met with local foods but only when several nutrient-dense but rarely consumed items were included in daily diets. When only commonly consumed foods were allowed, micronutrient needs could not be met without supplements. When larger amounts of common animal-source foods were allowed, all needs could be met for nonpregnant, nonlactating women but not for pregnant or lactating women, without supplements. We conclude that locally available foods could meet micronutrient needs but that to achieve this, strategies would be needed to increase consistent availability in markets, consistent economic access, and demand.
In this work we develop a novel Input-Output linear programming model to study the energy-economic recovery resilience of an economy by analyzing the relationships between energy production disruption, impacts on sect...
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In this work we develop a novel Input-Output linear programming model to study the energy-economic recovery resilience of an economy by analyzing the relationships between energy production disruption, impacts on sectoral production and demands, and post-disruption recovery efforts. The proposed model evaluates the minimum level of recovery investments required to restore production levels so that total economic impacts are acceptable over a stipulated post-disruption duration. It is assumed that disruptions are uncertain and can occur at different sectors and possibly simultaneously. The optimization model is then solved using a cutting plane method which involves computing a small sequence of mixed integer programming problems of moderate dimensions. A case study using China 2012 Input-Output data is performed, and we demonstrate the model's ability to uncover critical inter-sectoral dependencies at different disruption levels. This provides decision-makers with important information in evaluating and improving the energy-economic resilience in a systematic and rigorous manner. (C) 2017 Elsevier B.V. All rights reserved.
Motivation: Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Results: Here, we present Aether (http://***), an intuitive, eas...
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Motivation: Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Results: Here, we present Aether (http://***), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimallybid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users' existing HPC pipelines.
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