This study presents a two-stage stochastic programming model for the design and management of a biomass co-firing supply chain network under feedstock supply uncertainty. To represent a more realistic case, we generat...
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This study presents a two-stage stochastic programming model for the design and management of a biomass co-firing supply chain network under feedstock supply uncertainty. To represent a more realistic case, we generate scenarios from prediction errors of the historical and forecasted biomass supply availabilities. We solve the model using a hybrid decomposition algorithm that combines Sample average approximation with an enhanced progressive hedging algorithm. The proposed algorithm is validated via a real-world case study using data from Mississippi and Alabama. Computational results indicate that the proposed algorithm is capable of producing high quality solutions in a reasonable amount of time. (C) 2016 Elsevier Ltd. All rights reserved.
This work expounds on implementing an effective dynamic (s, S) policy to solve a liner shipping refueling and speed determination problem under both bunker prices and consumption uncertainties. While solving an optimi...
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This work expounds on implementing an effective dynamic (s, S) policy to solve a liner shipping refueling and speed determination problem under both bunker prices and consumption uncertainties. While solving an optimization model which incorporates a continuous distribution is extremely challenging, we use sample average approximation method to solve it. However, the resulting problem is still a very large-scaled problem. Therefore, we propose two variations of the progressive hedging algorithm to tackle it. Numerical results show that our solution method is efficient and, in addition, our dynamic (s, S) policy model has significant cost reduction potential compared to stationary models. (C) 2014 Elsevier Ltd. All rights reserved.
Reservoir systems operations problems are in essence stochastic because of the uncertain nature of natural inflows. This leads to very large stochastic models that may not be easy to handle numerically. In this paper,...
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Reservoir systems operations problems are in essence stochastic because of the uncertain nature of natural inflows. This leads to very large stochastic models that may not be easy to handle numerically. In this paper, we revisit the decomposition method developed by Rockafellar and Wets (Math Oper Res 119-147, 1991) by proposing new heuristics to initialize and dynamically adjust the penalty parameter of the augmented Lagrangian function on which this method is based. The heuristics are tested on multi-reservoir problems generated randomly and compared with the traditional strategy of setting the penalty parameter to a fixed value.
Portfolio management deals with the allocation of wealth among different investment opportunities, considering investor's preferences on risk. In this paper we consider a multiperiod model where the investor rebal...
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ISBN:
(纸本)9780769536064
Portfolio management deals with the allocation of wealth among different investment opportunities, considering investor's preferences on risk. In this paper we consider a multiperiod model where the investor rebalances a portfolio at the beginning of each period facing uncertainty associated with the prices of the assets at future dates. Models of this decision problem tend to become very large because of the dynamic structure and uncertainty. We present a multiple period portfolio model over a finite horizon with transaction costs, a risk averse utility function and the uncertainty modeled using the scenario approach. We propose a new method for efficiently solving real problems;the procedure utilizes stochastic programming combined with decomposition and approximating techniques. Solving the resulting optimization problem relies on approximate dynamic programming techniques. The technique used for solving the portfolio problem provides a method whose effectiveness is proved by the experimental results.
We study a dynamic portfolio management problem over a finite horizon with transaction costs and a risk averse objective function. We assume that the uncertainty faced by the investor can be modelled or approximated u...
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We study a dynamic portfolio management problem over a finite horizon with transaction costs and a risk averse objective function. We assume that the uncertainty faced by the investor can be modelled or approximated using discrete probability distributions via a scenario approach. To solve the resulting optimization problem we use stochastic programming techniques;in particular a scenario decomposition approach. To take advantage of the structure of the portfolio problem we propose a further decomposition obtained by means of a discrete version of the Maximum Principle. The result is a double decomposition of the original problem: The first, given by the scenario approach, focuses on the stochastic aspect of the problem while the second, using the discrete Maximum Principle, concerns the dynamics over time. Applying the double decomposition to our portfolio problem yields a simpler and more direct solution approach which we illustrate with examples. (C) 2004 Elsevier B.V. All rights reserved.
This short note discusses some structural properties of the progressive hedging algorithm. It is based on the finite case, but allows for event trees that are unbalanced and where the nodes can have a varying number o...
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