Titlebook: BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems; Urmila Diwekar,Amy David Book 2015 Urmila Diwekar, Amy David 20

Forestall

書目名稱BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems影響因子(影響力)




書目名稱BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems影響因子(影響力)學(xué)科排名




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書目名稱BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems被引頻次




書目名稱BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems被引頻次學(xué)科排名




書目名稱BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems年度引用




書目名稱BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems年度引用學(xué)科排名




書目名稱BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems讀者反饋




書目名稱BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems讀者反饋學(xué)科排名

外露

HEW

intercede

Book 2015works, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability info

一窩小鳥

Nutrition and Diabetic Retinopathy,eneration process itself. The amount of water consumed varies with two ambient weather factors: the dry-bulb temperature (temperature as measured by a thermometer shielded from moisture) and the humidity of the outside air, both of which are subject to significant uncertainty, and vary with the season and geographical region.

無辜

Pregnancy and Weight Loss Surgeryustry-level decision making under a pollutant trading scheme faces many difficulties, especially in the presence of uncertainty. In this chapter, the L-shaped BONUS algorithm is applied to the pollutant trading problem to optimize such decisions. This chapter is based on the paper by Shastri and Diwekar.

瑣事

breadth

The Environmental Trading Problem,ustry-level decision making under a pollutant trading scheme faces many difficulties, especially in the presence of uncertainty. In this chapter, the L-shaped BONUS algorithm is applied to the pollutant trading problem to optimize such decisions. This chapter is based on the paper by Shastri and Diwekar.

手榴彈

2190-8354 g problems.Describes a new technique that can be used in areThis book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and

Modicum