Optimization of simulated systems is the goal many methods, but most methods assume known environments. We, however, develop a “robust” methodology that accounts for uncertain Our uses Taguchi's view world replaces his statistical techniques by design and analysis simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify variability in estimated metamodels. In addition, combine with nonlinear programming, estimate Pareto frontier. We...

The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. DSS combines unique tool sales forecasting with order planning which includes an individual model selection equipped ARIMA, ARIMAX transfer function families, latter two accounting impact prices. Forecasting parameters are chosen via alternative tuning algorithms: two-step statistical analysis, sequential parameter optimisation framework automatic tuning. selects to apply...

The paper describes the methodology used for developing an electric load microforecasting module to be integrated in Energy Management System (EMS) architecture designed and tested within “Energy Router” (ER) project. This Italian R&D project is aimed at providing non-industrial active customers prosumers with a monitoring control device that would enable demand response through optimization of their own distributed energy resources (DERs). optimal organized hierarchical structure...

This paper addresses the problem of optimal management consumer flexibility in an electric distribution system. Aggregation a number consumers clustered according to appropriate criteria, is one most promising approaches for modifying daily load profile at nodes network. Modifying recognized as strongest needs both safe and efficient operation The proposes optimization approach allowing aggregator, i.e., operator which manages aggregated consumers, gather generate bids energy market, with...

This paper deals with the issue of forecasting energy production a Photo-Voltaic (PV) plant, needed by Distribution System Operator (DSO) for grid planning. As PV plant is strongly dependent on environmental conditions, DSO has difficulties to manage an electrical system stochastic generation. implies need have reliable irradiance level next day in order setup whole distribution network. To this aim, proposes use transfer function models. The assessment quality and accuracy proposed method...

Optimization of simulated systems is the goal many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain Our uses Taguchi's view world, replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We illustrate resulting through well-known Economic Order Quantity (EOQ) model.

This paper concerns the problem of optimally scheduling a set appliances at end-user premises. The user's energy fee varies over time, and moreover, in context smart grids, user may receive reward from an aggregator if he/she reduces consumption during certain time intervals. In household, is to decide when schedule operation appliances, order meet number goals, namely overall costs, climatic comfort level timeliness. We devise model accounting for typical household user, present...

Optimization of simulated systems is the goal many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain Our uses Taguchi's view world, replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We illustrate resulting through well-known Economic Order Quantity (EOQ) model.

Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a 'robust' methodology for uncertain uses Taguchi's view the world, replaces his statistical techniques Response Surface Methodology (RSM). George Box originated RSM, and Douglas Montgomery recently extended RSM to robust optimization real (non-simulated) systems. We combine with systems, apply resulting classic Economic Order Quantity (EOQ) inventory...

Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (Gaussian Process) metamodels (response surfaces). These are combined Non-Linear Mathematical Programming (NLMP) to find a robust optimal solution. Varying the constraint values NLMP model gives an estimated Pareto frontier. To account variability of frontier, uses bootstrapping which confidence regions...

Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (also called Gaussian Process) metamodels (emulators, response surfaces, surrogates). These are combined Non-Linear Mathematical Programming (NLMP) to find robust solutions. Varying the constraint values NLMP gives an estimated Pareto frontier. To account variability of frontier, contribution considers...

Optimization of simulated systems is the goal many methods, but most methods assume known environments. We, however, develop a `robust' methodology that accounts for uncertain Our uses Taguchi's view world, replaces his statistical techniques by Kriging. We illustrate resulting through classic Economic Order Quantity (EOQ) inventory models. results suggest robust optimization requires order quantities differ from EOQ. also compare our latest with previous do not use Kriging Response Surface...

In this work, the multidisciplinary design optimization (MDO) methodology is applied to a case arising in automotive engineering which of mechanical and control features system are simultaneously carried out with an evolutionary algorithm based method. The under study regulator injection pressure innovative Common Rail for Compressed Natural Gas (CNG) engines, whose includes several practical numerical difficulties. To tackle such situation, paper proposes constrained multi-objective method,...

Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (also called Gaussian Process) metamodels (emulators, response surfaces, surrogates). These are combined Non-Linear Mathematical Programming (NLMP) to find robust solutions. Varying the constraint values NLMP gives an estimated Pareto frontier. To account variability of frontier, contribution considers...