Abstract: Due to today’s globalization as well as outsourcing
practices of the companies, the Supply Chain (SC) performances
have become more dependent on the efficient movement of material
among places that are geographically dispersed, where there is more
chance for disruptions. One such disruption is the quality and
delivery uncertainties of outsourcing. These uncertainties could lead
the products to be unsafe and, as is the case in a number of recent
examples, companies may have to end up in recalling their products.
As a result of these problems, there is a need to develop a
methodology for selecting suppliers globally in view of risks
associated with low quality and late delivery. Accordingly, we
developed a two-stage stochastic model that captures the risks
associated with uncertainty in quality and delivery as well as a
solution procedure for the model. The stochastic model developed
simultaneously optimizes supplier selection and purchase quantities
under price discounts over a time horizon. In particular, our target is
the study of global organizations with multiple sites and multiple
overseas suppliers, where the pricing is offered in suppliers’ local
currencies. Our proposed methodology is applied to a case study for a
US automotive company having two assembly plants and four
potential global suppliers to illustrate how the proposed model works
in practice.
Abstract: We consider power system expansion planning under
uncertainty. In our approach, integer programming and stochastic
programming provide a basic framework. We develop a multistage
stochastic programming model in which some of the variables are
restricted to integer values. By utilizing the special property of the
problem, called block separable recourse, the problem is transformed
into a two-stage stochastic program with recourse. The electric power
capacity expansion problem is reformulated as the problem with first
stage integer variables and continuous second stage variables. The
L-shaped algorithm to solve the problem is proposed.