Scenario Cluster Lagrangian Decomposition in two stage stochastic mixed 0-1 optimization

In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem.

Scenario planning workshop plan

Planning a scenario planning workshop

Scenario sets

The outputs from the pilot work with CIBT to develop scenario guide based on existing work across European business, adding an education and more specifically IT perspective to generic scenarios.

New climate scenario framework implementation in the GCAM integrated assessment model

48 p.

Preliminary evaluation of a nuclear scenario involving innovative gas cooled reactors

In order to guarantee a sustainable supply of future energy demand without compromising the environment, some actions for a substantial reduction of CO 2 emissions are nowadays deeply analysed. One of them is the improvement of the nuclear energy use. In this framework, innovative gas-cooled reactors (both thermal and fast) seem to be very attractive from the electricity production point of view and for the potential industrial use along the high temperature processes (e.g., H 2 production by steam reforming or I-S process). This work focuses on a preliminary (and conservative) evaluation of possible advantages that a symbiotic cycle (EPR-PBMR-GCFR) could entail, with special regard to the reduction of the HLW inventory and the optimization of the exploitation of the fuel resources. The comparison between the symbiotic cycle chosen and the reference one (once-through scenario, i.e., EPR-SNF directly disposed) shows a reduction of the time needed to reach a fixed reference level from ∼170000 years to ∼1550 years (comparable with typical human times and for this reason more acceptable by the public opinion). In addition, this cycle enables to have a more efficient use of resources involved: the total electric energy produced becomes equal to ∼630 TWh/year (instead of only ∼530 TWh/year using only EPR) without consuming additional raw materials. © 2009 Barbara Vezzoni et al.

System Dynamics and Scenario Planning: Implementation Challenges

In this paper we discuss key implementation challenges of a systems approach that combines System Dynamics, Scenario Planning and Qualitative Data Analysis methods in tackling a complex problem. We present the methods and the underlying framework. We then detail the main difficulties encountered in designing and planning the Scenario Planning workshop and how they were overcome, such as finding and involving the stakeholders and customising the process to fit within timing constraints. After presenting the results from this application, we argue that the consultants or system analysts need to engage with the stakeholders as process facilitators and not as system experts in order to gain commitment, trust and to improve information sharing. They also need be ready to adapt their tools and processes as well as their own thinking for more effective complex problem solving.