Data-driven Approximation of Distributionally Robust Chance Constraints using Bayesian Credible Intervals

The non-convexity and intractability of distributionally robust chance constraints make them challenging to cope with. From a data-driven perspective, we propose formulating it as a robust optimization problem to ensure that the distributionally robust chance constraint is satisfied with high probability. To incorporate available data and prior distribution knowledge, we construct ambiguity sets for the distributionally robust chance constraint using Bayesian credible intervals. We establish the congruent relationship between the ambiguity set in Bayesian distributionally robust chance constraints and the uncertainty set in a specific robust optimization. In contrast to most existent uncertainty set construction methods which are only applicable for particular settings, our approach provides a unified framework for constructing uncertainty sets under different marginal distribution assumptions, thus making it more flexible and widely applicable. Additionally, under the concavity assumption, our method provides strong finite sample probability guarantees for optimal solutions. The practicality and effectiveness of our approach are illustrated with numerical experiments on portfolio management and queuing system problems. Overall, our approach offers a promising solution to distributionally robust chance constrained problems and has potential applications in other fields

A branch-and-price algorithm for solving the cutting strips problem

After giving a suitable model for the cutting strips problem, we present a branch-and-price algorithm for it by combining the column generation technique and the branch-and-bound method with LP relaxations. Some theoretical issues and implementation details about the algorithm are discussed, including the solution of the pricing subproblem, the quality of LP relaxations, the branching scheme as well as the column management. Finally, preliminary computational experience is reported

A Study of the Research Hot Topics and Visualization Analysis of Cross-border Ecommerce in China

With many incentive policies recently released, cross-border e-commerce has been highly concerned by all sectors of society. As an emerging research field, it has great research value. Applying SATI to the keywords in CSSCI papers relevant to CBEC from the CNKI periodical database, we undergo bibliometric and visualization study in terms of word frequency analysis. The visualization analysis reveals that: (1) hot topics in CBEC research fall into 4 areas: e-commerce and international business, government policy and supervision, cross-border logistics and cross-border e-commerce finance; (2) prospective research will focus on talent training, synergy, big data, import , customs supervision, etc