We investigate a class of fractional distributionally robust optimization problems with uncertain probabilities. They consist in the maximization of ambiguous fractional functions representing reward-risk ratios and have a semi-infinite programming epigraphic formulation. We derive a new fully parameterized closed-form to compute a new bound on the size of the Wasserstein ambiguity ball. We design a data-driven reformulation and solution framework. The reformulation phase involves the derivation of the support function of the ambiguity set and the concave conjugate of the ratio function. We design modular bisection algorithms which enjoy the finite convergence property. This class of problems has wide applicability in finance, and we specify new ambiguous portfolio optimization models for the Sharpe and Omega ratios. The computational study shows the applicability and scalability of the framework to solve quickly large, industry-relevant-size problems, which cannot be solved in one day with state-of-the-art mixed-integer nonlinear programming (MINLP) solvers.

This study investigates the mediating effect of brand experience on the relationship between customer motivation and engagement behavior and conceptualizes customer motivation from the multiple dimensions of information seeking, entertainment, and social interaction. Based on 565 valid questionnaires, it analyzes the impact of customer motivation on brand experience and customer engagement behavior using SPSS and AMOS. First, customer motivation with information seeking, entertainment, and social interaction significantly impacts customer brand experience. Second, customer brand experience significantly impacts customer engagement behavior of reuse, feedback, and WOM intentions. Finally, this study explores the mediating role of customer brand experience between customer motivation and customer engagement behavior and its impact on social media. It provides a reference for social media literature research. These findings will provide insights on motivating customers to participate in social media.

We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by machine learning models. We employ Gini’s Mean Difference (GMD) to specify the risk of a portfolio and use a set of technical indicators generated from a market index (e.g., S&P 500 index) to feed the machine learning models to predict market movements. Using a rolling-horizon approach, we conduct a series of computational tests with real financial data to evaluate the performance of the machine learning integrated portfolio rebalance framework. The empirical results show that the XGBoost model provides the best prediction of market movement, while the proposed portfolio rebalance strategy generates portfolios with superior out-of-sample performances in terms of average returns, time-series cumulative returns, and annualized returns compared to the benchmarks.

In recent decades, the explosion of the waste generation rate and corresponding environmental impacts worldwide have turned waste management into one of the most vital services in urban areas to alleviate the waste-related issues. In this study, a novel integrated model is developed to improve the municipal solid waste system by considering the facility location, shift scheduling, and vehicle routing decisions. The problem is formulated as a tri-objective mixed-integer linear programming model, striving to optimize the sustainable development goals in the waste system. These objectives encompass the total profit, air pollution emissions, citizen satisfaction, and social risk factors. The findings from this study illustrate that the proposed integrated framework empowers decision makers to maintain the resilience of the municipal solid waste system by concurrently addressing three critical sustainability aspects.