In this paper we consider the resource-constrained project scheduling problem with a flexible project structure and continuous activity durations. A mathematical model based on the resource-flow formulation is developed to tackle this problem. Due to the NP-hard nature of the problem, this...

The capacitated vehicle routing problem with dynamic demand and traffic conditions presents significant challenges in logistics and supply chain optimization. Traditional methods often fail to adapt to real-time uncertainties in customer demand and traffic patterns or scale to large problem...

Experimental design and robust design (RD) methodologies have received attention from researchers to improve the performance of many different quality characteristics and solve problems at low costs. However, there is room for improvement to simultaneously solve interdisciplinary optimization...

Effective management of inventory is essential for achieving high service levels, minimizing costs, and maintaining the overall resilience of retail supply chains-particularly in complex, real-world environments. Conventional strategies often prove inadequate because they rely on rigid...

Timely delivery is a critical performance metric in supply chain management, yet achieving consistent on-time delivery has become increasingly challenging in the face of global uncertainties and complex logistics networks. Recent disruptions, such as pandemics, extreme weather events, and...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in nowcasting across simulation and six country cases, traditional econometric models tend to outperform...

This study proposes a novel approach to improve the reliability of trading signals for gold market prediction by integrating technical analysis indicators, Moving Averages (MAs), MACD, and Ichimoku Cloud, with a Particle Swarm-Optimized Extreme Learning Machine (PSO-ELM). Traditional time-series...