This paper develops a novel hybrid framework that integrates clustering-enhanced Particle Swarm Optimization (PSO) with stretching techniques to solve Markowitz's quadratic portfolio optimization problem. The proposed approach avoids local optima traps that plague traditional optimization...

This study proposes a hierarchical signal-to-policy learning framework for risk-aware portfolio optimization that integrates model-based return forecasting, explainable machine learning, and deep reinforcement learning (DRL) within a unified architecture. In the first stage, next-period returns...

Purpose - We propose a method to build confidence sets for the solutions of stochastic discrete optimization problems solved through the sample average approximation method. Design/methodology/approach - By combining the concept of Model Confidence Set (MCS) with shrinkage estimation of large...

Background: Inbound logistics (IL) is a critical subsystem of the supply chain (SC) that supports production destined for the end consumer. Its effectiveness is reduced by uncertainty, which generates inaccuracies in production planning, disruptions, bottlenecks, and waste. Methods: This article...