This study introduces an adaptive robust approach for optimally sizing hybrid renewable energy systems (HRESs) comprising solar panels, wind turbines, batteries, and a diesel generator. It integrates vector auto-regressive models (VAR) neural networks (NN) into dynamic uncertainty sets (DUSs) to address temporal auto-correlations cross-correlations among uncertain parameters like demand supply. These DUSs are compared static independent based on time series (TS) from the literature. An exact...

Recently, some researchers suggested using a single chart to monitor both location and scale parameters for process simultaneously, in order resolve difficulties control interpretation arising from the traditional approach. This study focuses on Maximum Exponentially Weighted Moving Average Mean Squared deviation (MAX EWMAMS) presence of measurement error. An important issue this is that error adversely affects performance chart. In study, we investigate effects MAX EWMAMS by calculating...

Purpose – The purpose of this paper is to compare the performances np -VP control chart with estimated parameter known using average time-to-signal ( ATS ), standard deviation SDTS and number observations signal ANOS ) as performance measures. Design/methodology/approach approach used in study probabilistic which expected values measures are calculated probabilities different estimators estimate process parameter. Findings Numerical results indicate for cases. It obvious that when not Phase...

In the design of control charts, it is usually assumed that process parameters are known. However, in many practical applications values these unknown and should be estimated using historical in-control observations. this study, performance adaptive c-chart with parameter evaluated. It demonstrated by increasing size number samples estimating parameter, chart converges to known case. Finally best phase I sampling scenarios presented make perform as well parameter.

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

This study proposes an adaptive robust method for optimally sizing a hybrid renewable energy system (HRES) consisting of wind turbines, solar photovoltaic panels, battery bank, and diesel generator. We employ vector auto-regressive models (VAR) neural networks (NN) within dynamic uncertainty sets (DUSs) that account the cross-correlation between uncertain parameters, namely demand supply, as well their temporal auto-correlations. The constructed DUSs are compared to static independent used...

This article presents an extended facility location model for multiproduct supply chain network design, that accounts concave capacity, transportation, and inventory costs (induced by economies of scale, quantity discounts risk pooling, respectively). The problem is formulated as a mixed-integer nonlinear program (MINLP) with linear constraints large number separable terms in the objective function. We propose solution approach combines stabilized Lagrangian relaxation novel Benders...

In the literature of scheduling, many studies have been devoted to schedule single machine activities. Despite numerous done bridge gap between mathematical models and real-life scheduling problems, there is still remained be covered. this study, position-based, time-based experience-based learning effect calculations are employed simultaneously in order extend applicability proposed model. At first, modified problem formulated as a mixed integer model with non-linear terms. Finally, hybrid...

This paper presents three approaches to deal with the uncertainty of energy output and demand in problem optimal sizing an isolated hybrid renewable system limited capacity. The is composed wind turbines, photovoltaic solar panels, a battery bank, diesel generator. Applied are adaptive robust optimization unmet as reliability measure, scenario-based satisficing optimization, stochastic-free optimization. connection between their formulations derived. Adaptive formulation solved using...