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In order to improve the prediction accuracy of GM(1,1) this paper points out the disadvantages of using least square method to solve the parameters of model, attempts to use particle swarm optimization algorithm (PSO) to calculate the parameter of GM(1,1), introduces the stochastic strategy into PSO to endow the inertia weight of particle randomly, and then selects high-rising exponential sequence...
Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding large-scale brain networks that underlie higher-level cognition in human. However, suitable network analysis computational tools are still lacking in human brain connectivity research. To address this problem, we propose a novel probabilistic multi-graph...
When an optimization via simulation (OvS) procedure designed for known input distributions is applied to a problem with input uncertainty (IU), it typically does not provide the target statistical guarantee. In this paper, we focus on a discrete OvS problem where all systems share the same input distribution estimated from the common input data (CID). We define the CID effect as the joint impact of...
This paper provides a new generalized minimum variance (GMV) strategy based on input-output measurements. The proposed method can perform with no need for plant characteristics nor disturbance ones. The paper shows that the optimization of the proposed criterion results in the control parameters which achieve the GMV control. The application to datasets obtained from a continuous stirred tank reactor...
This paper presents two strategies to speed up the alternating direction method of multipliers (ADMM) for distributed data. In the first method, inspired by stochastic gradient descent, each machine uses only a subset of its data at the first few iterations, speeding up those iterations. A key result is in proving that despite this approximation, our method enjoys the same convergence rate in terms...
The papers in this special issue seek to report cutting edge research on stochastic simulation and optimisation methodologies, and their application to challenging SP problems that are not well addressed by existing methodologies.
System dynamics, which is an approach built on information feedbacks and delays in the model in order to understand the dynamical behavior of a system, has successfully been implemented for supply chain management problems for many years. However, research within in multi-objective optimization of supply chain problems modelled through system dynamics has been scares. Supply chain decision making...
This paper deals with the forecasting of tropical cyclone (TC) landed intensity change problem in which multi-level and multi-attribute decision are considered. A novel index model of tropical cyclone intensity change based on projection pursuit (PP) and evolution strategy (ES) is proposed to forecast the TC intensity change. We propose to use projection pursuit to project the high-dimensional TC...
Stochastic optimization facilitates decision making in uncertain environments. In typical problems, probability distributions are fit to historical data for the chance variables and then optimization is carried out, as if the estimated probability distributions are the “truth”. However, this perspective is optimistic in nature and can frequently lead to sub-optimal or infeasible results because the...
This paper designs a holistic global workload management solution which explores diversities of a set of geo-distributed data centers and energy buffering in order to minimize the electricity cost, reduce the peak power drawn from utilities while maintaining the carbon capping requirement of the data centers. The prior work often designed solutions to address each of the aforementioned energy and...
This paper investigates empirically two-range robust optimization (2R-RO) as an alternative to stochastic programming in terms of computational time and solution quality. We consider a number of possible projects with anticipated costs and cash flows, and an investment decision to be made under budget limitations. In 2R-RO, each uncertain parameter is allowed to take values from more than one uncertainty...
Performance analysis via stochastic simulation is often subject to input model uncertainty, meaning that the input model is unknown and needs to be inferred from data. Motivated especially from situations with limited data, we consider a worst-case analysis to handle input uncertainty by representing the partially available input information as constraints and solving a worst-case optimization problem...
We consider the problem of parameter synthesis for black-box systems whose operations are jointly influenced by a set of “tunable parameters” under the control of designers, and a set of uncontrollable stochastic parameters. The goal is to find values of the tunable parameters that ensure the satisfaction of given performance requirements with a high probability. Such problems are common in robust...
This paper develops a method for building non-parametric stochastic models of multivariate distributions from large data sets. The motivation is stochastic optimization based on time series forecasting models. The proposed non-parametric stochastic modeling approach is based on multiple quantile regressions with inter-quantile smoothing. The models are built using ADMM optimization approach scalable...
The paper considers stochastic optimization of the electricity procurement in the day-ahead power market. The novelty is in addressing the random errors of time series forecasting of electrical power loads and prices in the procurement. This problem is currently important because of the increased random variability in the power grid that is caused by growing integration of renewable generation. This...
Research in business process optimization is valuable in improving business process models. However, it is difficult to find real datasets of business processes to evaluate process improvement techniques and tools. In this paper, a symbolic process generator, namely G-DCBP, is introduced to stochastically generate symbolic data-centric business processes that can be used to analyze their properties...
We present scalable algebraic modeling software, StochJuMP, for stochastic optimization as applied to power grid economic dispatch. It enables the user to express the problem in a high-level algebraic format with minimal boiler-plate. StochJuMP allows efficient parallel model instantiation across nodes and efficient data localization. Computational results are presented showing that the model construction...
Financial markets change precipitously and on-demand pricing and risk models must be constantly recalibrated to reduce risk. However, certain classes of models are computationally intensive to robustly calibrate to intraday pricesstochastic volatility models being an archetypal example due to the non-convexity of the objective function. In order to accelerate this procedure through parallel implementation,nancial...
Considering near-real-time data available on the smart grid, analytics can be used to determine the best-case scenario for optimal and reliable distribution of power. However, the distributed integration of renewable sources and demand response adds complexity to the modeling, control and optimization of smart grid operations. Latest concepts aim for using new model-based computational intelligence;...
Textbooks sometimes describe building models, running experiments, analyzing outputs, and implementing results as distinct activities in a simulation project. This paper demonstrates advantages of combining these activities in the context of system performance optimization. Simulation optimization algorithms can be improved by exploiting the ability to observe and change literally anything at any...
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