In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

Automated guided vehicles (AGVs) form a large and important part of the logistic transport
systems in today's industry. They are used on a large scale, especially in Europe, for over a …

Handling constrained multiobjective optimization problems (CMOPs) is extremely
challenging, since multiple conflicting objectives subject to various constraints require to be …

Job shop scheduling (JSS) is a process of optimizing the use of limited resources to improve
the production efficiency. JSS has a wide range of applications, such as order picking in the …

A bilevel programming model represents the relationship in a specific decision process that
involves decisions within a hierarchical structure of two levels. The upper-level problem is …

The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial
optimisation problems for which numerous models and algorithms have been proposed. To …

Portfolio optimization concerns with periodically allocating the limited funds to invest in a
variety of potential assets in order to satisfy investors' appetites for risk and return goals …

In nowadays production, fluctuations in demand, shortening product life-cycles, and highly
configurable products require an adaptive and robust control approach to maintain …

Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-
heuristics to solve challenging optimization problems. They differ from traditional (meta) …

Literature is prolific with metaheuristics for solving continuous optimisation problems. But, in
practice, it is difficult to choose one appropriately for several reasons. First and …