Recovering, recycling and reusing are some processes whose popularity is intense nowadays due to the increasing concern about sustainability and environmental issues. These processes are composed by some input variabl...
详细信息
Recovering, recycling and reusing are some processes whose popularity is intense nowadays due to the increasing concern about sustainability and environmental issues. These processes are composed by some input variables that can be adjusted to optimize related relevant responses. The present paper, focusing on multiobjectiveoptimization, proposes the Two-Phased optimization Methodology based on the use of factor analysis, the Normal Boundary Intersection method and stochastic programming. A real application is developed in a cladding process of ABNT 1020 carbon steel plate using austenitic ABNT 316L stainless steel cored wire to exemplify the approach. The first stage of the methodology focuses on optimizing the geometric characteristics of the weld bead in order to improve the quality of the final product. The achieved values for the input variables were wire feed rate = 8.96 m/min, arc voltage = 29.38 V, welding speed = 24.21 cm/min, contact tip to the workpiece distance = 17.90 mm. From the comparison of the optimized geometry from Phase 1 with the real DoE experiments geometry, the scrap and rework areas are measured through a computer graphics software. Then, in the Phase 2, which focuses on a sustainability aspect, it is solved the multiobjectivestochastic problem aiming the minimization of the scrap and rework jointly with the energy consumption. In this case, the optimized values for the input variables were wire feed rate = 9.95 m/min, arc voltage = 28 V, welding speed = 33.51 cm/min, contact tip to the workpiece distance = 25.41 mm. The methodology provides consistent results when dealing with a large number of responses considering the quality of the product and the environmental issues.
Study Region: The eastern route of the South-to-North Water Diversion Project in Jiangsu Province, China, a critical national interbasin water diversion system for alleviating water shortages. Study Focus: This study ...
详细信息
Study Region: The eastern route of the South-to-North Water Diversion Project in Jiangsu Province, China, a critical national interbasin water diversion system for alleviating water shortages. Study Focus: This study proposed a risk-based multiobjectiveoptimization model for interbasin water diversion, with chance constraint on total water use. Probabilistic forecasting of local streamflow and water demand was adopted to identify operation risks. multiobjective stochastic optimization was then introduced to minimize the risks of water shortages and spillages. Furthermore, a decomposition method was proposed to investigate the regime of water use under different hydrological conditions, and the decomposed chance constraint was incorporated into the optimization model. Finally, two indices were designed to assess the value of forecasts and water utilization efficiency. New Hydrological Insights for the Region: Developing a robust and efficient water diversion strategy based on forecast information is crucial. The proposed method with case study provides the following new hydrological insights: (1) conflict occurs between water diversion, spillage, and shortage, with water shortage and diversion representing major contradictions. (2) high-skilled forecasting helps reduce water diversion (22.3 %), spillage (over 60 %), and shortage (approximately 10 %), indicating considerable value for promoting the benefits of water diversion operations. (3) water use constraint focuses restricting excessive water diversion (30.8 %), exploiting the potential of local water supply, increasing in local water utilization efficiency from 92.8 % to 93.4 %.
In this article, we propose a new method for multiobjectiveoptimization problems in which the objective functions are expressed as expectations of random functions. The present method is based on an extension of the ...
详细信息
In this article, we propose a new method for multiobjectiveoptimization problems in which the objective functions are expressed as expectations of random functions. The present method is based on an extension of the classical stochastic gradient algorithm and a deterministic multiobjective algorithm, the Multiple Gradient Descent Algorithm (MGDA). In MGDA a descent direction common to all specified objective functions is identified through a result of convex geometry. The use of this common descent vector and the Pareto stationarity definition into the stochastic gradient algorithm makes the algorithm able to solve multiobjective problems. The mean square and almost sure convergence of this new algorithm are proven considering the classical stochastic gradient algorithm hypothesis. The algorithm efficiency is illustrated on a set of benchmarks with diverse complexity and assessed in comparison with two classical algorithms (NSGA-II, DMS) coupled with a Monte Carlo expectation estimator. (C) 2018 Elsevier B.V. All rights reserved.
Better battery swapping station (BSS) allocation helps reduce range anxiety. But the uncertain nature of battery swapping demand (BSD) deserves attention, and few models discussed the multiobjective scooter BSS alloca...
详细信息
Better battery swapping station (BSS) allocation helps reduce range anxiety. But the uncertain nature of battery swapping demand (BSD) deserves attention, and few models discussed the multiobjective scooter BSS allocation. To reduce the aforesaid problem, this paper advocates the research on innovation by developing a grid-based multiobjectivestochastic allocation model for scooter BSS (MSASBSS). Based on the concept of sample average approximation (SAA), Monte Carlo simulation (MCS) and traffic flow, the MSASBSS mode produced large numbers of different BSD scenarios to solve the uncertain BSD problem. Meanwhile, this mode also optimized the BSS allocation and satisfied both the various BSD scenarios and the minimal construction cost of BSSs with the average driving distance of battery swapping taken into account. In this semi-realistic study case, the MSASBSS was validated. Finally, the grid-based MSASBSS model could provide the multiobjective and visually optimized BSS allocation to decision-makers for their more flexible selection of the exact BSS locations shown in a grid. Uncertainty analyses demonstrated that the use of SAA-based algorithm could resolve the uncertain problem of BSD in relation to the scooter BSS allocation.
暂无评论