Recent advances in computer architecture has started to in uence the eld of numerical optimisation. Several methods to solve large mathematical programming problems which were tried and given up in the last three decades has gained renewed interest. stochastic optimisation is such area which has a huge potential of providing successful results due to its high parallel content. In most practical...

We recently proposed a new hybrid algorithm to solve linear two-stage stochastic integer programs (2SIPs) based on stage wise (primal) decomposition [1]. The master algorithm performs a search on the first stage variables by an evolutionary algorithm (EA), the decoupled second stage scenario problems are solved by mathematical programming. In this paper, the performance of the EA approach is co...

Uncertainties are widespread in the optimization of process systems, such as uncertainties technologies, prices, and customer demands. In this paper, we review basic concepts recent advances a risk-neutral mathematical framework called “stochastic programming” its applications solving systems engineering problems under uncertainty. This intends to provide both tutorial for beginners without pri...

This paper deals with a realistic multi-period liner ship fleet planning problem by incorporating stochastic dependency of the random and period-dependent container shipment demand. This problem is formulated as a multi-period stochastic programming model with a sequence of interrelated two-stage stochastic programming (2SSP) problems characterized ship fleet planning in each single period. A s...

We present a multistage model for allocation of financial resources to bond indices in different currencies. The model was tested on historical data of interest and exchange rates. We compare a two-stage and a three-stage stochastic programming model from a financial performance point of view. For solving two-stage and three-stage stochastic programs the interior point method (IPM) in the frame...

In this paper, a stochastic two-stage model is offered for optimization of the day-ahead scheduling of the microgrid. System uncertainties including dispatchable distributed generation and energy storage contingencies are considered in the stochastic model. For handling uncertainties, Monte Carlo simulation is employed for generation several scenarios and then a reduction method is used to decr...

Unit commitment (UC) is a key operational problem in power systems used to determine an optimal daily or weekly generation commitment schedule. Incorporating uncertainty in this already difficult mixed-integer optimization problem introduces significant computational challenges. Most existing stochastic UC models consider either a two-stage decision structure, where the commitment schedule for ...

• First treatment of standard quadratic optimization problems under uncertainty with two-stage stochastic approach. Efficient and tractable upper lower bounds for this NP-hard problem. Modern first-order method (pairwise Frank-Wolfe) enhanced copositive techniques aim at scalability bounds. Two-stage decision are characterized by the property that in stage one part has to be made before relevan...