The Olivia Framework:Concepts for Use in Finely-Grained, Integrated Social Policy Analysis (Working Paper 45)

The Olivia framework is a set of concepts and measures that, when mature, will allow users to describe, in a consistent and integrated manner, everything about individuals and institutions that is of potential interest to social policy. The present paper summarizes the current stage of development in achieving this highly ambitious goal. The current version of the framework supports analysis of social trends and policy responses from many perspectives: • The point-in-time, resource-flow perspectives that underlie most traditional, economics-based policy analysis. • Life-course perspectives, including both transitions/trajectories analysis and asset-based analysis. • Spatial perspectives that anchor people in space and history and that provide a link to macro-analysis. • The perspective of the purposes/goals of individuals and institutions, including the objectives of different types of government programming. The concepts of the framework, which are all potentially measurable, provide a language that can support integrated analysis in all these areas at a much finer level of description than is customary. It provides a language that is especially well suited for analysis of the incremental policy changes that are typical of a mature welfare state. It supports both qualitative and quantitative analysis, enabling some integration between the two. It supports citizen-centric as well as a government-centric view of social policy. In its current version, the concepts are most highly developed as they related to social policies as they related to labour markets, equality and social integration, care-giving, immigration, income security, sustainability, and social and economic well-being more generally. However the paper points to likely extensions in the areas of health, justice and safety.

Robust scenario formulations for strategic supply chain optimization under uncertainty

Strategic supply chain optimization (SCO) problems are often modelled as a two-stage optimization problem, in which the first-stage variables represent decisions on the development of the supply chain and the second-stage variables represent decisions on the operations of the supply chain. When uncertainty is explicitly considered, the problem becomes an intractable infinite-dimensional optimization problem, which is usually solved approximately via a scenario or a robust approach. This paper proposes a novel synergy of the scenario and robust approaches for strategic SCO under uncertainty. Two formulations are developed, namely, naïve robust scenario formulation and affinely adjustable robust scenario formulation. It is shown that both formulations can be reformulated into tractable deterministic optimization problems if the uncertainty is bounded with the infinity-norm, and the uncertain equality constraints can be reformulated into deterministic constraints without assumption of the uncertainty region. Case studies of a classical farm planning problem and an energy and bioproduct SCO problem demonstrate the advantages of the proposed formulations over the classical scenario formulation. The proposed formulations not only can generate solutions with guaranteed feasibility or indicate infeasibility of a problem, but also can achieve optimal expected economic performance with smaller numbers of scenarios.