Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty

A scenario-based two-stage stochastic programming model for gas production network planning under uncertainty is usually a large-scale nonconvex mixed-integer nonlinear programme (MINLP), which can be efficiently solved to global optimality with nonconvex generalized Benders decomposition (NGBD). This paper is concerned with the parallelization of NGBD to exploit multiple available computing resources. Three parallelization strategies are proposed, namely, naive scenario parallelization, adaptive scenario parallelization, and adaptive scenario and bounding parallelization. Case study of two industrial natural gas production network planning problems shows that, while the NGBD without parallelization is already faster than a state-of-the-art global optimization solver by an order of magnitude, the parallelization can improve the efficiency by several times on computers with multicore processors. The adaptive scenario and bounding parallelization achieves the best overall performance among the three proposed parallelization strategies.

The Impact on Canada of the Rise of the Chinese Economy: Good, Bad or Indifferent? (Working Paper 50)

From humble beginnings less than twenty years ago, the economic relationship between Canada and the Chinese has flourished so that China is now Canada’s second most important trading partner after the United States. The Chinese demand for Canada’s exports, in particular in the natural resource sectors, has been a clear win for Canada, especially during the recent Great Recession where demand from other countries dropped sharply. But other aspects of the relationship have led to suggestions of costs. Canada’s large trade deficit with China at least looks superficially as a drag on the Canadian economy. It has been suggested the greater exposure to a low cost producer has displaced Canadian production and jobs and lowered wages. We find that each argument for costs to the relationship ignores important factors. On balance we conclude the relationship has been good for Canada, and it could be even better in the future.

Estimating Dynamic Treatment Effects from Project STAR (Working Paper 36)

This paper considers the analysis of data from randomized trials which offer a sequence of interventions and suffer from a variety of problems in implementation. In experiments that provide treatment in multiple periods (T>1), subjects have up to 2^{T}-1 counterfactual outcomes to be estimated to determine the full sequence of causal effects from the study. Traditional program evaluation and non-experimental estimators are unable to recover parameters of interest to policy makers in this setting, particularly if there is non-ignorable attrition. We examine these issues in the context of Tennessee's highly influential randomized class size study, Project STAR. We demonstrate how a researcher can estimate the full sequence of dynamic treatment effects using a sequential difference in difference strategy that accounts for attrition due to observables using inverse probability weighting M-estimators. These estimates allow us to recover the structural parameters of the small class effects in the underlying education production function and construct dynamic average treatment effects. We present a complete and different picture of the effectiveness of reduced class size and find that accounting for both attrition due to observables and selection due to unobservable is crucial and necessary with data from Project STAR

Reform and Redesign: A Review of the Changing Role of the Public Purposes in R&D and Technology Institutes (Working Paper 17)

As the globalization of knowledge has taken hold over the past decade, and as governments around the world review their new roles in support of the production of knowledge, several factors have shaped the context in governments’ approach to public research. Arguably, none has been more affected by these pressures for reform than government scientific and technology laboratories or institutes. Often ignored in the re-shaping of national systems of innovation, these organizations play an important role in advancing national economic and social objectives. This paper, by reviewing examples of reforms underway in several countries, including Canada, France, Germany, the UK, Japan, USA and Latin America, will argue that government research and technology institutes — often historically surrogates for industrial research — are gradually re-defining their mandates to meet the new pressures of globalization as well as satisfying growing public demands for increased relevance and efficiency in responding to citizens’ and industry needs.