English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many real-world decision-making problems involve multiple decision-makingstages and various objectives. Besides, most of the decisions need to be madebefore having complete knowledge about all aspects of the problem leaves somesort of uncertainty. Deep uncertainty happens when the degree of uncertainty isso high that the probability distributions are not confidently knowable. Inthis situation, using wrong probability distributions leads to failure.Scenarios, instead, should be used to evaluate the consequences of anydecisions in different plausible futures and find a robust solution. In thisstudy, we proposed a novel multi-stage multi-scenario multi-objectiveoptimisation framework for adaptive robust decision-making under deepuncertainty. Two approaches, named multi-stage multi-scenario multi-objectiveand two-stage moving horizon, have been proposed and compared. Finally, theproposed approaches are applied in a case study of sequential portfolioselection under deep uncertainty, and the robustness of their solutions isdiscussed.

A novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty