ArXiv:1303.2247v1 [math.DS] 9 Mar 2013 Robust Adaptive Dynamic Programming for Optimal Nonlinear Control Design Yu Jiang and Zhong-Ping Jiang Department of. Good Programming Practices in Healthcare Creating Robust Programs. The Test-First Design principles discussed in software development circles. A robust possibilistic programming model for a responsive closed loop supply chain network design. Cara Terbaru 2013 Download Gratis Film Semi. Robust Design Techniques. It's important to understand that robust programs generally need to deal with three kinds of 'exceptional' conditions: user errors, when invalid input is passed to the program. Exhaustions, when the program tries to acquire shared resources. Internal errors, due to bugs (e.g. Dangling pointers).

Recently, governmental legislations, limitation of natural resources and adverse effects of End-of-Life products on ecological system have spurred researchers to design closed-loop supply chains (CLSCs). Accordingly, designing green supply chains (SCs) that manage greenhouse gas emissions and prevent air pollution can be helpful for companies to heighten profitability and customer loyalty, besides, uncertainty of parameters and disruption strikes could adversely affect performance of SCs and lower quality of output decisions. Effective planning prevents great losses and increases reliability of manager's decisions against uncertainties.

Dtm Race Driver 3 Challenge Psp Iso. Therefore, this paper is proceeding to design a reliable bi-objective green CLSC that minimizes total costs of network aside with minimizing harmful gas emissions. The proposed model is capable of controlling adverse effects of disruptions via applying scenario-based stochastic programming approach. Also, an effective hybrid robust fuzzy stochastic programming method is extended to effectively control uncertainty of parameters and risk-aversion level of output decisions. Extended model analyzing is based on lead-acid battery SC case study that output results approve applicability and effectiveness of model.