Batch scheduling problems are NP-hard and often coupled with logistics optimization
problems in industrial manufacturing scenarios, further increasing the challenge of decision …

Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant
attention in the upcoming Industry 5.0 era because of its computational complexity and its …

The integration problem of production and transportation (IPPT) is one of the most important
decision issues in real-life manufacturing and flow industries. To illustrate its potential for …

In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging
avenue within the Evolutionary Computation (EC) community, which incorporates Meta …

Constrained multi-objective optimization problems (CMOPs) are widespread in practical
applications such as engineering design, resource allocation, and scheduling optimization …

Feature selection (FS) is a crucial step in machine learning and data mining projects. It aims
to remove redundant and uncorrelated features, thus improving the accuracy of models …

While new production areas (greenfields) have state-of-the-art technologies for
implementing digitalization, existing production areas (brownfields) and devices must first be …

Evolutionary multitasking (EMT) has been attracting much attention over the past years. It
aims to handle multiple optimization tasks simultaneously within limited computing …

In job-shop production environments, machine breakdowns are a significant factor in
reducing productivity. Existing approaches seldom consider algorithm improvement and …

Artificial neural networks (ANNs) are widely used machine learning techniques with
applications in various fields. Heuristic search optimization methods are typically used to …