We present a multistage stochastic programming model for mean-risk optimization of electricity portfolios containing physical components and energy derivative products. We consider a medium-term time horizon of up to one year. Stochasticity enters the model via the uncertain (time-dependent) prices, electricity demand, and heat demand. The objective is to maximize the expected overall revenue a...

emission trading is one of the famous mechanisms under kyoto protocol to control environmental pollution. the aim of this paper is to design a strategic supply chain network under emission trading scheme with inclusion of stochastic parameters and budget limitation. demand and price of carbon credits are considered as the important stochastic parameters influencing the supply chain network. in ...

Multistage stochastic programs, which involve sequences of decisions over time, are usually hard to solve in realistically sized problems. In the two-stage case, several approaches based on different levels of available information has been adopted in literature such as the Expected Value Problem, EV , the Sum of Pairs Expected Values, SPEV , the Expectation of Pairs Expected Value, EPEV , solv...

Large corporations fund their capital and operational expenses by issuing bonds with a variety of indexations, denominations, maturities and amortization schedules. We propose a multistage linear stochastic programming model that optimizes the bond issuance policy to fund a predetermined project portfolio. Cash flows are uncertain, affected by financial, macroeconomic and business specific risk...

mathematical modeling of supply chain operations has proven to be one of the most complex tasks in the field of operations management and operations research. despite the abundance of several modeling proposals in the literature; for vast majority of them, no effective universal application is conceived. this issue renders the proposed mathematical models inapplicable due largely to the fact th...

In many planning problems under uncertainty the uncertainties are decision-dependent and resolve gradually depending on the decisions made. In this paper, we address a generic non-convex MINLP model for such planning problems where the uncertain parameters are assumed to follow discrete distributions and the decisions are made on a discrete time horizon. In order to account for the decision-dep...

in this study, a new stochastic model is proposed to deal with a multi-product, multi-period, multi-stage, multi-site production and transportation supply chain planning problem under demand uncertainty. a two-stage stochastic linear programming approach is used to maximize the expected profit. decisions such as the production amount, the inventory level of finished and semi-finished product, t...

Seasonal influenza is a major public health concern, and the first line of defense is the flu shot. Antigenic drifts and the high rate of influenza transmission require annual updates to the flu shot composition. The World Health Organization recommends which flu strains to include in the annual vaccine, based on surveillance and epidemiological analysis. There are two critical decisions regard...

Stochastic programming, despite its immense modeling capabilities, is well known to be computationally excruciating. In this paper, we introduce a unified framework of approximating multiperiod stochastic programming from the perspective of robust optimization. Specifically, we propose a framework that integrates multistage modeling with safeguarding constraints. The framework is computationall...