An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely
used to solve flow-shop scheduling problems (FSPs), an important branch of production …

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

A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic
methods applicable to a wide set of optimization problems for which exact/analytical …

Simulated Annealing (SA) is one of the oldest metaheuristics and has been adapted to solve
many combinatorial optimization problems. Over the years, many authors have proposed …

Lot streaming is the most widely used technique to facilitate overlap** of successive
operations. Inspired by real-world scenarios, this paper studies a multi-objective hybrid …

The particle swarm optimization (PSO) algorithm has been the object of many studies and
modifications for more than 25 years. Ranging from small refinements to the incorporation of …

This study addresses a reconfigurable distributed flowshop group scheduling problem
(RDFGSP), the characteristics of which lie in the reconfigurability of the flowlines, and the …

The design and development of metaheuristic algorithms can be time-consuming and
difficult for a number of reasons including the complexity of the problems being tackled, the …

Metaheuristic algorithms have become one of the preferred approaches for solving
optimization problems. Finding the best metaheuristic for a given problem is often difficult …

This paper defines a new combinatorial optimization problem, namely General
Combinatorial Optimization Problem (GCOP), whose decision variables are a set of …