Optimal Fleet Size When National Quotas Can Be Traded

Assuming stochastic quotas for a fish stock that is shared between two nations, we find the optimal fleet size for one of them by maximizing expected profit under the assumption that national quotas can be traded and that stable national quotas is a political goal. As an example we use the Norwegian purse seiner fleet and the summer capelin fishery in the Barents Sea.Environmental Economics and Policy, International Development, International Relations/Trade, Resource /Energy Economics and Policy,

Stochastic programming in energy

We give the reader a tour of good energy optimization models that explicitly deal with uncertainty. The uncertainty usually stems from unpredictability of demand and/or prices of energy, or from resource availability and prices. Since most energy investments or operations involve irreversible decisions, a stochastic programming approach is meaningful. Many of the models deal with electricity investments and operations, but some oil and gas applications are also presented. We consider both traditional cost minimization models and newer models that reflect industry deregulation processes. The oldest research coincides with the birth of linear programming, and most models within the market paradigm have not yet found their final form.stochastic programming, energy, regulated markets, deregulation, uncertainty, electricity, natural gas, oil

Problem-driven scenario generation:an analytical approach for stochastic programs with tail risk measure

Scenario generation is the construction of a discrete random vector to represent parameters of uncertain values in a stochastic program. Most approaches to scenario generation are distribution-driven, that is, they attempt to construct a random vector which captures well in a probabilistic sense the uncertainty. On the other hand, a problem-driven approach may be able to exploit the structure of a problem to provide a more concise representation of the uncertainty. There have been only a few problem-driven approaches proposed, and these have been heuristic in nature. In this paper we propose what is, as far as we are aware, the first analytic approach to problem-driven scenario generation. This approach applies to stochastic programs with a tail risk measure, such as conditional value-at-risk. Since tail risk measures only depend on the upper tail of a distribution, standard methods of scenario generation, which typically spread there scenarios evenly across the support of the solution, struggle to adequately represent tail risk well

The impact of design uncertainty in engineer-to-order project planning

A major driver of planning complexity in engineer-to-order (ETO) projects is design uncertainty far into the engineering and production processes. This leads to uncertainty in technical information and will typically lead to a revision of parts of the project network itself. Hence, this uncertainty is different from standard task completion uncertainty. We build a stochastic program to draw attention to, and analyse, the engineering-design planning problem, and in particular, to understand what role design flexibility plays in hedging against such uncertainty. The purpose is not to devise a general stochastic dynamic model to be used in practice, but to demonstrate by the use of small model instances how design flexibility actually adds value to a project and what, exactly, it is that produces this value. This will help us understand better where and when to develop flexibility and buffers, even when not actually solving stochastic models.acceptedVersio

Sensitivity Analysis in Stochastic Second Order Cone Programming for Mobile Ad Hoc Networks

AbstractIn this paper sensitivity analysis is adopted in order to understand the randomness of a stochastic second order cone program for mobile ad hoc networks [3]. The algorithm looks for a destination node and sets up a route by means of the expected zone, the region where the sender node expects to find the destination node and the requested zone defined by the sender node for spreading the route request to the destination node. Sensitivity analysis is performed by considering different costs of flooding and latency penalty. Evaluation of EVPI and VSS [2]-[4] allows us to find the range of values in which it is safe to save time by using a deterministic approach instead of a stochastic one

Refining bounds for stochastic linear programs with linearly transformed independent random variables

A linear stochastic program where the right-hand side elements are linear transformations of independent stochastic variables is considered. We show how bounds on the recouse (second-stage) problem can be found by working directly on the independent stochastic variables instead of the right-hand side elements.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26111/1/0000187.pd

Problem-driven scenario generation:an analytical approach for stochastic programs with tail risk measure

Scenario generation is the construction of a discrete random vector to represent parameters of uncertain values in a stochastic program. Most approaches to scenario generation are distribution-driven, that is, they attempt to construct a random vector which captures well in a probabilistic sense the uncertainty. On the other hand, a problem-driven approach may be able to exploit the structure of a problem to provide a more concise representation of the uncertainty. In this paper we propose an analytic approach to problem-driven scenario generation. This approach applies to stochastic programs where a tail risk measure, such as conditional value-at-risk, is applied to a loss function. Since tail risk measures only depend on the upper tail of a distribution, standard methods of scenario generation, which typically spread their scenarios evenly across the support of the random vector, struggle to adequately represent tail risk. Our scenario generation approach works by targeting the construction of scenarios in areas of the distribution corresponding to the tails of the loss distributions. We provide conditions under which our approach is consistent with sampling, and as proof-of-concept demonstrate how our approach could be applied to two classes of problem, namely network design and portfolio selection. Numerical tests on the portfolio selection problem demonstrate that our approach yields better and more stable solutions compared to standard Monte Carlo sampling

On Zurek's derivation of the Born rule

Recently, W. H. Zurek presented a novel derivation of the Born rule based on a mechanism termed environment-assisted invariance, or "envariance" [W. H. Zurek, Phys. Rev. Lett. 90(2), 120404 (2003)]. We review this approach and identify fundamental assumptions that have implicitly entered into it, emphasizing issues that any such derivation is likely to face.Comment: 8 pages; v2: minor clarifications added; v3: reference to Zurek's quant-ph/0405161 added. To appear in Foundations of Physics (Cushing Volume

Stochastic service network design with rerouting

Service network design under uncertainty is fundamentally crucial for all freight transportation companies. The main challenge is to strike a balance between two conflicting objectives: low network setup costs and low expected operational costs. Together these have a significant impact on the quality of freight services. Increasing redundancy at crucial network links is a common way to improve network flexibility. However, in a highly uncertain environment, a single predefined network is unlikely to suit all possible future scenarios, unless it is prohibitively costly. Hence, rescheduling is often an effective alternative. In this paper, we proposed a new stochastic freight service network design model with vehicle rerouting options. The pro- posed model explicitly introduces a set of integer variables for vehicle rerouting in the second stage of the stochastic program. Although computationally more expensive, the resultant model provides more options (i.e. rerouting) and flexibility for planners to deal with uncertainties more effectively. The new model was tested on a set of instances adapted from the literature and its performance and characteristics are studied through both comparative studies and detailed analyses at the solution structure level. Implications for practical applications are discussed and further research directions are also provided