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

被引：3
作者：
Neumann, Anas [1 ,2 ]
Hajji, Adnene [1 ,2 ]
Rekik, Monia [1 ,2 ]
Pellerin, Robert [1 ,3 ]
机构：
[1] CIRRELT, Quebec City, PQ, Canada
[2] Laval Univ, Dept Operat & Decis Syst, Quebec City, PQ, Canada
[3] Polytech Montreal, Dept Math & Ind Engn, Montreal, PQ, Canada
来源：
IFAC PAPERSONLINE | 2022年 / 55卷 / 10期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Genetic Algorithm; Planning; Scheduling; Hybrid Metaheuristic; Self-Adaptive; Parallel Computation; Multi-Objective; OPTIMIZATION; TAXONOMY; DESIGN;
D O I：
10.1016/j.ifacol.2022.10.100
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. Copyright (C) 2022 The Authors.
引用
收藏
页码：2593 / 2598
页数：6
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