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

With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …

The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem,
and many models and algorithms have been proposed to solve the VRP and its variants …

Following decades of sustained improvement, metaheuristics are one of the great success
stories of optimization research. However, in order for research in metaheuristics to avoid …

This article presents a multi-agent framework for optimization using metaheuristics, called
AMAM. In this proposal, each agent acts independently in the search space of a …

This paper reviews the existing literature on the combination of metaheuristics with machine
learning methods and then introduces the concept of learnheuristics, a novel type of hybrid …

Randomized heuristics are widely used to solve large scale combinatorial optimization
problems. Among the plethora of randomized heuristics, this paper reviews those that …

In this paper, we consider a vehicle routing problem in which a fleet of homogeneous
vehicles, initially located at a depot, has to satisfy customers' demands in a two‐echelon …

Vehicle routing is a well-known optimization research topic with significant practical
importance. Among different approaches to solving vehicle routing, heuristics can produce a …

This paper presents the first results of an agent-based model aimed at solving a Capacitated
Vehicle Routing Problem (CVRP) for inbound logistics using a novel Ant Colony …