The optimization box of Matlab was used in agricultural planting programming in this paper, and the optimization problem of crop’s planting structure was solved successfully. First we set up a mathematical model, the...
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In this paper,the problem of time optimal feedrate generation under confined feedrate,axis accelerations,and axis tracking errors is *** main contribution is to reduce the tracking error constraint to constraints abou...
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In this paper,the problem of time optimal feedrate generation under confined feedrate,axis accelerations,and axis tracking errors is *** main contribution is to reduce the tracking error constraint to constraints about the axis velocities and accelerations,when the tracking error satisfies a second order linear ordinary differential *** on this simplification on the tracking error,the original feedrate generation problem is reduced to a new form which can be efficiently solved with linear programming *** results are used to validate the methods.
linear programming was used to optimize the economic, environmental, and social impacts of forest biomass used for bioenergy production. Sixteen scenarios (combinations of feedstocks, products, markets, and end use) w...
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linear programming was used to optimize the economic, environmental, and social impacts of forest biomass used for bioenergy production. Sixteen scenarios (combinations of feedstocks, products, markets, and end use) were studied. Two feedstocks (roundwood and wood residues), two densified bioenergy products (white pellet, torrefied pellet), two markets (domestic, international), and two end uses (power generation, district heating) were evaluated. The social, environmental, and economic sustainability attributes were quantified and monetized using peer-reviewed literature to analyze the trade-offs. Using the economic criteria alone, the model showed that the best solution was use of 70% roundwood and 30% forest residue feedstock to produce torrefied pellets (TP) sold for district heating in the EU. The model predicts $5.4 million annual profit which is driven by the use of lower cost forest residue feedstocks, and relatively higher prices for the heating market in the EU. Inclusion of all three sustainability attributes led to a different optimized solution. TP produced from roundwood and sold to the EU market for heating was the optimum, due to the social benefits derived from increased local income to landowners and reduced shipping costs. It also had added benefits of reductions in emissions across the transportation system on an energy basis. TP consistently had higher social benefits than WP due to the need for more biomass per unit of final product, and providing more local jobs and income from feedstock production. The increasing costs of carbon emissions increased the environmental benefits of TP compared to WP or coal. (c) 2016 Society of Chemical Industry and John Wiley & Sons, Ltd
Intelligent behavior that appears in a decision process can be treated as a point y, the dynamic state observed and controlled by the agent, moving in a factor space impelled by the goal factor and blocked by the cons...
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Intelligent behavior that appears in a decision process can be treated as a point y, the dynamic state observed and controlled by the agent, moving in a factor space impelled by the goal factor and blocked by the constraint factors. Suppose that the feasible region is cut by a group of hyperplanes, when point y reaches the region's wall, a hyperplane will block the moving, and the agent needs to adjust the moving direction such that the target is pursued as faithfully as possible. Since the wall is not able to be represented by a differentiable function, the gradient method cannot be applied to describe the adjusting process. We, therefore, suggest a new model, named linear step-adjusting programming (LSP) in this paper. LSP is similar to a kind of relaxed linear programming (LP). The difference between LP and LSP is that the former aims to find the ultimate optimal point, while the latter just does a direct action in a short period. Where will a blocker encounter? How do you adjust the moving direction? Where further blockers may be encountered next, and how should the direction be adjusted again? horizontal ellipsis If the ultimate best is found, that's a blessing;if not, that's fine. We request at least an adjustment should be got at the first time. However, the former is idealism, and the latter is realism. In place of a gradient vector, the projection of goal direction g in a subspace plays a core role in LSP. If a hyperplane block y goes ahead along with the direction d, then we must adjust the new direction d' as the projection of g in the blocking plane. Suppose there is only one blocker at a time. In that case, it is straightforward to calculate the projection, but how to calculate the projection when more than one blocker is encountered simultaneously? It is still an open problem for LP researchers. We suggest a projection calculation using the Hat matrix in the paper. LSP will attract interest in economic restructuring, financial prediction, and reinforce
This work proposes a novel linear programming approach for the joint detection and decoding of LDPC-based space-time (ST) coded signals in multi-antenna orthogonal frequency division multiplexing (OFDM) systems. While...
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This work proposes a novel linear programming approach for the joint detection and decoding of LDPC-based space-time (ST) coded signals in multi-antenna orthogonal frequency division multiplexing (OFDM) systems. While traditional receivers typically decouple the detection and decoding processes as two disjunctive blocks or require iterative turbo exchange of extrinsic information between the soft detector and decoder, we formulate a joint linear program (LP) by exploiting the constraints imposed on the data symbols, training symbols, noise subspace as well as channel code. In consideration of the vast amount of LDPC parity check inequalities, we further present an adaptive procedure to significantly reduce the complexity of the joint LP receiver. Our LP-based receivers outperform existing receivers with substantial performance gains. Moreover, the proposed joint LP receiver demonstrates strong robustness when pilot symbols are sparsely arranged on subcarriers.
High mix-low volume operations face difficulties determining a specific order of processing, due to constant-changing market requirements and variability in processing times. Having an optimal and effective process fl...
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ISBN:
(纸本)9780983762447
High mix-low volume operations face difficulties determining a specific order of processing, due to constant-changing market requirements and variability in processing times. Having an optimal and effective process flow is essential for any manufacturing industry to meet customer requirements and revenue goals. linear programming and job sequencing techniques have been applied to determine the best sequence for processing group products. The constraints have been defined based in machine availability, production lot size, market demand, monthly revenue goals, and machines' capacity per type of product. The program selects the unit that enters the station comparing processing times and revenue, so that the waiting time between stations is minimized while simultaneously guaranteeing the revenue goal is fulfilled. The program have significantly reduced the cycle time and established effective sequences for every component to enter the production cycle. The total benefits have been maximized, the market demand has been attend, and the business goal in terms of revenue on deadline has been covered.
Resource management for large-scale high performance computing systems poses difficult challenges to system administrators. The extreme scale of these modern systems require task scheduling algorithms that are capable...
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Resource management for large-scale high performance computing systems poses difficult challenges to system administrators. The extreme scale of these modern systems require task scheduling algorithms that are capable of handling at least millions of tasks and thousands of machines. Highly scalable algorithms are necessary to efficiently schedule tasks to maintain the highest level of performance from the system. In this study, we design a novel linear programming based resource allocation algorithm for heterogeneous computing systems to efficiently compute high quality solutions for minimizing makespan. The novel algorithm tightly bounds the optimal makespan from below with an infeasible schedule and from above with a fully feasible schedule. The new algorithms are highly scalable in terms of solution quality and computation time as the problem size increases because they leverage similarity in tasks and machines. This novel algorithm is compared to existing algorithms via simulation on a few example systems. (C) 2015 Elsevier Inc. All rights reserved.
This paper models the reserve capacity procurement problem in Denmark to account for the sequential clearing of the reserve availability market and the energy market, and the forecast error of system wind power and de...
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ISBN:
(纸本)9781509041695
This paper models the reserve capacity procurement problem in Denmark to account for the sequential clearing of the reserve availability market and the energy market, and the forecast error of system wind power and demand. The problem is modeled as a stochastic mixed-integer bilevel linearing programming problem. A real case of Western Denmark is studied. In the case analyzed, the EENS induced by the forecast error reduces from 312.4 MWh to 0 when 40 MW reserve capacity is procured. It is also shown that procuring more reserve capacity does not necessarily reduce the EENS.
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.
The improved primal simplex (IPS) was recently developed by Elhalaloui et al. to take advantage of degeneracy when solving linear programs with the primal simplex. It implements a dynamic constraint reduction based on...
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The improved primal simplex (IPS) was recently developed by Elhalaloui et al. to take advantage of degeneracy when solving linear programs with the primal simplex. It implements a dynamic constraint reduction based on the compatible columns, i.e., those that belong to the span of a given subset of basic columns including the nondegenerate ones. The identification of the compatible variables may however be computationally costly and a large number of linear programs are solved to enlarge the subset of basic variables. In this article, we first show how the positive edge criterion of Raymond et al. can be included in IPS for a fast identification of the compatible variables. Our algorithm then proceeds through a series of reduction and augmentation phases until optimality is reached. In a reduction phase, we identify compatible variables and focus on them to make quick progress toward optimality. During an augmentation phase, we compute one greatest normalized improving direction and select a subset of variables that should be considered in the reduced problem. Compared with IPS, the linear program that is solved to find this direction involves the data of the original constraint matrix. This new algorithm is tested over Mittelmann's benchmark for linear programming and on instances arising from industrial applications. The results show that the new algorithm outperforms the primal simplex of CPLEX on most highly degenerate instances in which a sufficient number of nonbasic variables are compatible. In contrast, IPS has difficulties on the eleven largest Mittelmann instances. (C) 2015 Elsevier B.V. All rights reserved.
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