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 ...

In this work we propose a stochastic model for the design and planning of closed-loop supply chains. Uncertainties in demand and return volumes are modelled together with uncertain transportation costs. A two-stage stochastic programming is developed and a sensitivity analysis to the worst-case probability is performed in order to test the solution robustness. Finally, in order to prove the goo...

The significant penetration rate of wind turbines in power systems made some challenges in the operation of the systems such as large-scale power fluctuations induced by wind farms. Gas-fired plants with fast starting ability and high ramping can better handle natural uncertainties of wind power compared to other traditional plants. Therefore, the integration of electrical and natural gas syste...

In this paper, a Multi-Choice Stochastic Bi-Level Programming Problem (MCSBLPP) is considered where all the parameters of constraints are followed by normal distribution. The cost coefficients of the objective functions are multi-choice types. At first, all the probabilistic constraints are transformed into deterministic constraints using stochastic programming approach. Further, a general tran...

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...

In this paper we develop and test scenario generation methods for asset liability management models. We propose a multi-stage stochastic programming model for a Dutch pension fund. Both randomly sampled event trees and event trees tting the mean and the covariance of the return distribution are used for generating the coeecients of the stochastic program. In order to investigate the performance...

Multi-stage stochastic programming problems arise in many practical situations, such as production and manpower planning, portfolio selections and so on. In general, the deterministic equivalences of these problems can be very large, and may not be solvable directly by general-purpose optimization approaches. Sequential quadratic programming methods are very effective for solving medium-size no...

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...

Production planning is a key area of operations management. An important methodology for production planning is mathematical programming. Traditional mathematical programming models for production planning are deterministic, and cannot provide robust production plans in the presence of uncertainty. As such, deterministic planning models may yield unsatisfactory decisions. Stochastic programming...