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

In this paper, a Mixed-Integer Linear Programming (MILP) model to simultaneously schedule
jobs and transporters in a flexible flow shop system is suggested. Wherein multiple jobs …

A greedy randomized adaptive search procedure (GRASP) is a multi-start metaheuristic for
combinatorial optimization problems, in which each iteration consists basically of two …

Abstract Greedy Randomised Adaptive Search Procedure (GRASP) is one of the best-
known metaheuristics to solve complex combinatorial optimisation problems (COPs). This …

This paper investigates a multi-objective optimization distributed no-wait permutation flow
shop scheduling problem under the constraint of sequence dependent setup time. Our …

The hybrid flow shop scheduling problem is one of the most relevant optimization problem in
manufacturing industry. In this paper, we investigate the blocking hybrid flow shop …

In this paper, we study the resolution of a permutation flow shop problem with sequence‐
independent setup time. The objective is to minimize the maximum of job completion time …

Scheduling jobs in a permutation flow shop (PFS) environment is one of the most studied
problems in scheduling theory and practice. In this paper, we propose a new efficient …

Production scheduling is one of the most critical activities in manufacturing. Under the
context of Industry 4.0 paradigm, shop scheduling becomes even more complex …

This study introduces, HHABS, a new hyper-heuristic for permutation-based problems. It is a
high-level local search that generates tailored genetic algorithms for considered problem …