Traditional truss layout optimization employing the ground structure method will often generate layouts that are too complex to fabricate in practice. To address this, mixed integer linear programming can be used to e...
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Traditional truss layout optimization employing the ground structure method will often generate layouts that are too complex to fabricate in practice. To address this, mixed integer linear programming can be used to enforce buildability constraints, leading to simplified truss forms. Limits on the number of joints in the structure and/or the minimum angle between connected members can be imposed, with the joints arising from crossover of pairs of members accounted for. However, in layout optimization, the number of constraints arising from 'crossover joints' increases rapidly with problem size, along with computational expense. To address this, crossover constraints are here dynamically generated and added at runtime only as required (so-called lazy constraints);speedups of more than 20 times are observed whilst ensuring that there is no loss of solution quality. Also, results from the layout optimization step are shown to provide a suitable starting point for a non-linear geometry optimization step, enabling results to be obtained that are in agreement with literature solutions. It is also shown that symmetric problems may not have symmetric optimal solutions, and that multiple distinct and equally optimal solutions may be found.
Consideration was given to the two-step problem of stochastic optimization with a bilinear model which describes the problem of forming the securities portfolio consisting of some risk assets and one riskless asset. T...
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Consideration was given to the two-step problem of stochastic optimization with a bilinear model which describes the problem of forming the securities portfolio consisting of some risk assets and one riskless asset. The probability of exceeding the given threshold of capital is used as the optimality criterion. At the second step, the piecewise constant control is used as the capital control. Determined were the upper and lower estimates of the probability functional. The problems of maximizing the upper and lower estimates of the probability functional were reduced to the problems of mixed integer linear programming by means of discretizing the probabilistic measure. An algorithm to seek an approximate solution to the original problem was proposed, and an example was considered.
Square-based fiducial markers are one of the most popular approaches for camera pose estimation due to its fast detection and robustness. In order to maximize their error correction capabilities, it is required to use...
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Square-based fiducial markers are one of the most popular approaches for camera pose estimation due to its fast detection and robustness. In order to maximize their error correction capabilities, it is required to use an inner binary codification with a large inter-marker distance. This paper proposes two mixed integer linear programming (MILP) approaches to generate configurable square-based fiducial marker dictionaries maximizing their inter-marker distance. The first approach guarantees the optimal solution, however, it can only be applied to relatively small dictionaries and number of bits since the computing times are too long for many situations. The second approach is an alternative formulation to obtain suboptimal dictionaries within restricted time, achieving results that still surpass significantly the current state of the art methods. (C) 2015 Elsevier Ltd. All rights reserved.
Unmanned air vehicles (UAVs), which have been popular in military context, have recently attracted attention of many researchers because of their potential civilian applications. However, before the UAVs can be used f...
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Unmanned air vehicles (UAVs), which have been popular in military context, have recently attracted attention of many researchers because of their potential civilian applications. However, before the UAVs can be used for civilian applications, a systematic integration of UAVs in the National Airspace System (NAS) is needed that can allow safe operation of UAVs along with other manned aircrafts. One of the critical capabilities needed for safe operation of the UAVs in the NAS is the ability of UAV to carry out sense and avoid task which would allow the UAV to amend its path to avoid collision with other aircrafts. Despite recent technological advances, such as availability of automatic dependent surveillance broadcast that can transmit position of an aircraft to others in the vicinity, planning a collision-free path autonomously is still challenging in a dynamic environment. The objective of this paper is to develop a methodology for discovering a path for the UAV that meets mission goals, avoids collision, is optimal in terms of path length and, more importantly, is feasible. The paper formulates the problem of path planning using the mathematical paradigm of mixed integer linear programming and provides a solution strategy for solving this problem in the dynamic sense. The tasks of avoidance of obstacles and waypoint navigation are incorporated as constraints in the MILP problem. The paper presents the solution of the MILP problem and verifies the performance of the proposed methodology with regard to optimality of the solution and computational time requirement via several simulated scenarios of different complexities.
Due to booming economic development over the past decades, energy demands in most of China's provincial power grids have increased sharply, and it has become challenging to guarantee the energy balance at peak per...
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Due to booming economic development over the past decades, energy demands in most of China's provincial power grids have increased sharply, and it has become challenging to guarantee the energy balance at peak periods. In many provincial electric systems of China, gas-fired generators are one of the most important peaking power sources to respond the load change at peak periods. To meet this practical necessity, a novel mixed integer linear programming model is proposed in this paper for the peak operation of gas-fired generating units with disjoint-prohibited operating zones. In the developed model, the objective function is chosen to minimize the peak-valley difference of the remaining load series that is obtained by subtracting the total generation of all the gas-fired units from the original load curve. The real-world simulations in several cases show that the developed model is able to generate satisfying scheduling results by reasonably allocating the power outputs of all the gas-fired generators in the scheduling horizon. Then, the management implications obtained lie in the fact that it is necessary to increase the share of peak power sources in the mid- to long-term planning of an electrical power system;and in the daily operation of the power grid, greater flexibility should be given to the gas-fired units to reduce peak pressure.
mixed integer linear programming is one of the main approaches used to solve unit commitment problems. Due to the computational complexity of unit commitment problems, several researches remark the benefits of using l...
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mixed integer linear programming is one of the main approaches used to solve unit commitment problems. Due to the computational complexity of unit commitment problems, several researches remark the benefits of using less binary variables or relaxing them for the branch-and-cut algorithm. However, integrality constraints relaxation seems to be case dependent because there are many instances where applying it may not improve the computational burden. In addition, there is a lack of extensive numerical experiments evaluating the effects of the relaxation of binary variables in mixed integer linear programming based unit commitment. Therefore, the primary purpose of this work is to analyze the effects of binary variables and compare different relaxations, supported by extensive computational experiments. To accomplish this objective, two power systems are used for the numerical tests: the IEEE118 test system and a very large scale real system. The results suggest that a direct link between the relaxation of binary variables and computational burden cannot be easily assured in the general case. Therefore, relaxing binary variables should not be used as a general rule-of-practice to improve computational burden, at least, until each particular model is tested under different load scenarios and formulations to quantify the final effects of binary variables on the specific UC implementation. The secondary aim of this work is to give some preliminary insight into the reasons that could be supporting the binary relaxation in some UC instances. (C) 2018 Elsevier B.V. All rights reserved.
When operating a compressor station, given its mass flow rate, inlet pressure and temperature, and discharge pressure, dispatchers need to decide which compressors to run and at what flow rates, i.e., the operating sc...
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When operating a compressor station, given its mass flow rate, inlet pressure and temperature, and discharge pressure, dispatchers need to decide which compressors to run and at what flow rates, i.e., the operating scheme of the station, to cut its energy costs. This paper addresses this problem under unsteady states. This means that at least one of the four given operating parameters is time-dependent, and therefore so is the operating scheme. The key constraints of the problem are the minimum uptime and downtime of each compressor, which interconnect the operating schemes at each time step and complicate the problem. The energy cost of a compressor unit is almost a linear function of its flow rate for a given inlet pressure, inlet temperature, and discharge pressure. Therefore, the optimization problem was formulated as a mixed integer linear programming (MILP) model, which was solved by CPLEX. The optimal operating schemes given by CPLEX were simulated to reevaluate the objective function, and the error of the linearized energy cost model was shown to be within 5%. The recalculated objective function values were 0.22%-1.18% higher than those of the true optimum. However, the MILP method was 0.49-64.95 times faster than the dynamic programming approach yielding global optimal solutions. (C) 2016 Elsevier B.V. All rights reserved.
An optimization problem for designing a nonuniformly spaced, linear-phase FIR filter with minimal complexity is formulated and solved by mixed integer linear programming (MILP). Examples illustrate that the proposed m...
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An optimization problem for designing a nonuniformly spaced, linear-phase FIR filter with minimal complexity is formulated and solved by mixed integer linear programming (MILP). Examples illustrate that the proposed method is useful for designing a wide range of filter types and can outperform subset selection-based design methods.
To address the lack of rapid and economical methods for assessing the power substitution potential of enterprises and industrial parks under the dual-carbon goals, this study employs a mixed-integerlinearprogramming...
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ISBN:
(数字)9798331506797
ISBN:
(纸本)9798331506803
To address the lack of rapid and economical methods for assessing the power substitution potential of enterprises and industrial parks under the dual-carbon goals, this study employs a mixed-integerlinearprogramming (MILP) approach to construct a year-round, 8760-hour simulation and optimization model of the energy system at the industrial park level. A quantitative analysis framework for power substitution potential is proposed. Through a practical analysis of power substitution in a pharmaceutical park in Guizhou, it was found that compared with the traditional gas boiler solution, the optimized power substitution scheme using high-temperature heat pumps could save the target park approximately 95,000 RMB in annual energy costs, reduce unit energy cost by about 3%, and decrease unit energy-related carbon emissions by about 4%. This study provides a scientific method and simulation framework for quantitatively analyzing power substitution potential in industrial parks.
The cyclotomic polynomial (CP) prefilter design problem is formulated as an optimization problem with linear objective functions by applying logarithms to the transfer function of the CP prefilter, This problem is the...
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The cyclotomic polynomial (CP) prefilter design problem is formulated as an optimization problem with linear objective functions by applying logarithms to the transfer function of the CP prefilter, This problem is then solved by mixed integer linear programming (MILP). Design examples demonstrate that this method leads to more efficient cascaded finite impulse response (FIR) prefilter-equalizers than existing methods.
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