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

Distributed three-stage assembly scheduling problem extensively exists in the real-life
assembly production process and is seldom considered. The integration of reinforcement …

This review discusses and analyses three main contributions championed by Professor
Mladenović. These include variable neighborhood search (VNS), variable formulation space …

This paper aims at integrating machine learning techniques into meta-heuristics for solving
combinatorial optimization problems. Specifically, our study develops a novel efficient …

Two-stage hybrid flow shop scheduling has been extensively considered in single-factory
settings. However, the distributed two-stage hybrid flow shop scheduling problem (DTHFSP) …

The urgent mission for carbon peak and carbon neutrality is demanding greater industrial
sustainability. Energy-efficient hybrid flow shop scheduling problem (EEHFSP) has been …

Combinatorial optimization problems (COPs) are the most important class of optimization
problems, with great practical significance. This class is concerned with identifying the best …

The no-idle permutation flowshop scheduling problem (NIPFSP) extends the well-known
permutation flowshop scheduling problem, where idle time is not allowed on the machines …

In this study, the Q‐learning algorithm is integrated into the binary water strider algorithm to
adaptively control the search operators and repair strategies. The proposed algorithm …

Global energy consumption is a pressing issue and is predicted to continue increasing
between 2010 and 2040. Among the various sectors, the industrial sector, particularly …