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.

Ecological forecasting: new tools for coastal and marine ecosystem management

Policy makers, natural resource managers, regulators, and the public often call on scientists to estimate the potential ecological changes caused by both natural and human-induced stresses, and to determine how those changes will impact people and the environment. To develop accurate forecasts of ecological changes we need to: 1) increase understanding of ecosystem composition, structure, and functioning, 2) expand ecosystem monitoring and apply advanced scientific information to make these complex data widely available, and 3) develop and improve forecast and interpretative tools that use a scientific basis to assess the results of management and science policy actions. (PDF contains 120 pages)

Harmful algal research and response: A human dimensions strategy

Harmful Algal Research and Response: A Human Dimensions Strategy (HARR-HD) justifies and guides a coordinated national commitment to human dimensions research critical to prevent and respond to impacts of harmful algal blooms (HABs). Beyond HABs, it serves as a framework for developing hu-man dimensions research as a cross-cutting priority of ecosystem science supporting coastal and ocean management, including hazard research and mitigation planning. Measuring and promoting commu-nity resilience to hazards require human dimensions research outcomes such as effective risk commu-nication strategies; assessment of community vulnerability; identification of susceptible populations; comprehensive assessment of environmental, sociocultural, and economic impacts; development of effective decision support tools; and improved coordination among agencies and stakeholders. HARR-HD charts a course for human dimensions research to achieve these and other priorities through co-ordinated implementation by the Joint Subcommittee on Ocean Science and Technology (JSOST) In-teragency Working Group on HABs, Hypoxia and Human Health (IWG-4H); national HAB funding programs; national research and response programs; and state research and monitoring programs. (PDF contains 72 pages)