Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art
In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …
techniques into meta-heuristics for solving combinatorial optimization problems. This …
Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem
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 …
and many models and algorithms have been proposed to solve the VRP and its variants …
[HTML][HTML] Metaheuristics “in the large”
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 …
stories of optimization research. However, in order for research in metaheuristics to avoid …
Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs
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 …
learning methods and then introduces the concept of learnheuristics, a novel type of hybrid …
A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems
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 …
AMAM. In this proposal, each agent acts independently in the search space of a …
Biased randomization of heuristics using skewed probability distributions: A survey and some applications
Randomized heuristics are widely used to solve large scale combinatorial optimization
problems. Among the plethora of randomized heuristics, this paper reviews those that …
problems. Among the plethora of randomized heuristics, this paper reviews those that …
Agile optimization of a two‐echelon vehicle routing problem with pickup and delivery
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 …
vehicles, initially located at a depot, has to satisfy customers' demands in a two‐echelon …
A KNN quantum cuckoo search algorithm applied to the multidimensional knapsack problem
J García, C Maureira - Applied Soft Computing, 2021 - Elsevier
Optimization algorithms and particularly metaheuristics are constantly improved with the
goal of reducing execution times, increasing the quality of solutions, and addressing larger …
goal of reducing execution times, increasing the quality of solutions, and addressing larger …
A multi-agent based cooperative approach to decentralized multi-project scheduling and resource allocation
F Li, Z Xu, H Li - Computers & Industrial Engineering, 2021 - Elsevier
While project crashing is a prominent practice in project management and can be
approached by the well-known time–cost tradeoff problem (TCTP), the existing …
approached by the well-known time–cost tradeoff problem (TCTP), the existing …