An agile and environmentally conscious manufacturing paradigm refers to the ability to reconfigure a flexible system quickly, economically, and environmentally responsibly. In modern manufacturing enterprises, e-supply chains integrate Internet and web-based electronic market and are promising systems to achieve agility. A key issue in the strategic logistic planning of integrated e-supply chains (IESCs) is the configuration of the partner network. This paper proposes a single- and multiobjective optimization model to configure the network of IESCs. Considering an Internet-based distributed manufacturing system composed of different stages connected by material and information links, a procedure is presented to select the appropriate links. A set of performance indices is associated with the network links. Single-criterion and multicriteria optimization models are presented under structural constraint definitions. The integer linear programming (ILP) problem solution provides different network structures that allow to improve supply chain (SC) flexibility, agility, and environmental performance in the design process. The proposed optimization strategy is applied to two case studies describing two networks for desktop computer production.

Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a “robust” methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by design and analysis of simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify the variability in the estimated Kriging metamodels. In addition, we combine Kriging with nonlinear programming, and we estimate the Pareto frontier. We illustrate the resulting methodology through economic order quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that differ from the classic EOQ. We also compare our results with results we previously obtained using response surface methodology instead of Kriging.