Handling of Uncertainty in Life Cycle Inventory by Correlated Multivariate Lognormal Distributions: Application to the Design of Supply Chain Networks

Poster presented in the 24th European Symposium on Computer Aided Process Engineering (ESCAPE 24), Budapest, Hungary, June 15-18, 2014.

Reflections on the role and design of online dictionaries for specialised translation

This article is an updated and modified version of a Spanish article published in MonTi 6 (cf. Tarp 2014a). It deals with specialised translation dictionaries. Based on the principles of the function theory, it analyses the different phases and sub-phases of the translation process from a lexicographical perspective and shows that a translation dictionary should be much more than a mere bilingual dictionary if it really pretends to meet its users’ complex needs. Thereafter, it presents a global concept of a translation dictionary which includes various mono- and bilingual components in both language directions. Finally, the article discusses, by means of two concrete online projects, how this concept can be applied on the Internet in order to develop high-quality translation dictionaries with quick access to data that are still more adapted to the needs of each translator.

Handling of Uncertainty in Life Cycle Inventory by Correlated Multivariate Lognormal Distributions: Application to the Design of Supply Chain Networks

In this work, we analyze the effect of incorporating life cycle inventory (LCI) uncertainty on the multi-objective optimization of chemical supply chains (SC) considering simultaneously their economic and environmental performance. To this end, we present a stochastic multi-scenario mixed-integer linear programming (MILP) coupled with a two-step transformation scenario generation algorithm with the unique feature of providing scenarios where the LCI random variables are correlated and each one of them has the desired lognormal marginal distribution. The environmental performance is quantified following life cycle assessment (LCA) principles, which are represented in the model formulation through standard algebraic equations. The capabilities of our approach are illustrated through a case study of a petrochemical supply chain. We show that the stochastic solution improves the economic performance of the SC in comparison with the deterministic one at any level of the environmental impact, and moreover the correlation among environmental burdens provides more realistic scenarios for the decision making process.