The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new meta-heuristic algorithm called GLNSA (Global-local neighborhood search algorithm), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called "smart_cells" generates and shares information that helps to optimize instances of FJSP. The GLNSA algorithm is complemented with a tabu search that implements a simplified version of the Nopt1 neighborhood defined in [1] to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms and widely cited in the specialized bibliography, using 86 test problems, improving the optimal result reported in previous works in two of them.
A History-Guided Regional Partitioning Evolutionary Optimization for Solving the Flexible Job Shop Problem with Limited Multi-load Automated Guided Vehicles
Online Operator Design in Evolutionary Optimization for Flexible Job Shop Scheduling via Large Language Models
School of Computer Science, Guangdong University of Technology（广东工业大学计算机科学学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Discrete Differential Evolution Particle Swarm Optimization Algorithm for Energy Saving Flexible Job Shop Scheduling Problem Considering Machine Multi States
Business School, Shandong Normal University（山东师范大学商学院）；School of Mathematics, Yunnan Normal University（云南师范大学数学学院）；School of Physics and Electronics, Shandong Normal University（山东师范大学物理与电子科学学院）
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning
The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳））；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；National University of Singapore（新加坡国立大学）
Evaluation of bioinspired algorithms for the solution of the job scheduling problem
Universidad Industrial de Santander（桑坦德工业大学）；Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis (I3S)（索菲亚-安蒂波利斯信息、信号与系统实验室）；Université Nice Sophia Antipolis（尼斯索菲亚-安蒂波利斯大学）；Universidad Politécnica de Valencia（瓦伦西亚理工大学）；Ministerio de Tecnologias de la Información y las Comunicaciones MINTIC（信息与通信技术部）；Universidad de Puerto Rico（波多黎各大学）；Universidad Nacional Abierta y a Distancia – UNAD（国立开放与远程教育大学）
