Work

A Didactic Review On Genetic Algorithms For Industrial Planning And Scheduling Problems*

Year: 2022

Type: article

Abstract: Most industrial planning and scheduling problems are NP-hard, stochastic, and subject to multi-objective. A wide variety of heuristic methods have been designed or adapted to solve them. However, the Genetic Algorithms (GA) family is both the most used and one of the most efficient for several well-known problems. This paper reviews GAs proposed in the literature, focusing on the techniques to overcome scheduling challenges (cycle avoidance and feasibility). This paper also has a didactic purpose and details modern approaches to reach high-quality solutions: self-adaptation, learning process, diversity-maintenance, parallel computation, multi-objective, and hybridization. These mechanisms are also essential to integrate the method in current IT systems.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2022.10.100

Authors: Anas Neumann, Adnene Hajji, Monia Rekik, Robert Pellerin

Institutions: Université Laval, Centre interuniversitaire de recherche sur les réseaux d'entreprise, la logistique et le transport, Polytechnique Montréal

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4312371765

FWCI: 0.964

Cites: 56

Cited by: 12

Related to: 10