We present an approximation method for the hybrid flow shop scheduling problem based on relaxation and machine learning techniques. Our model combines a suitable relaxation of the objective function to a continuous solution space with a self-supervised learning method for neural networks, which...

Decentral production control plays a crucial role within the paradigm of Industry 4.0. Due to the fast and flexible decisions on allocation and sequencing required by this type of control, there is no baseline production schedule in advance. This creates a dilemma for efficient staff deployment...

This study uses a two-level programming model to present a Stackelberg game. The two-level programming problems consist of two levels of decision-making, each level having its objective function. This model's first player (leader) includes the supplier and manufacturer, while the second player...

In this study, we propose a mathematical model of a 4D clustered traveling salesman problem (CTSP) to address the cost-effective security and risk-related difficulties associated with the TSP. We used a multiparent-based memetic genetic algorithm to optimize paths between all clusters and...

Surface Electromyography (sEMG) is a technique for measuring muscle activity by recording electrical signals from the surface of the body. It is widely used in fields such as medical diagnosis, human-computer interaction, and sports injury rehabilitation. The detection of the onset and offset of...

With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multi objective technique to solve large-scale portfolio optimisation problems with both long-term listed...