An improved genetic programming hyper-heuristic for the dynamic flexible job shop scheduling problem with reconfigurable manufacturing cells

Created by W.Langdon from gp-bibliography.bib Revision:1.7686

@Article{GUO:2024:jmsy,

• author = "Haoxin Guo and Jianhua Liu and Yue Wang and Cunbo Zhuang",
• title = "An improved genetic programming hyper-heuristic for the dynamic flexible job shop scheduling problem with reconfigurable manufacturing cells",
• journal = "Journal of Manufacturing Systems",
• volume = "74",
• pages = "252--263",
• year = "2024",
• ISSN = "0278-6125",
• DOI = "doi:10.1016/j.jmsy.2024.03.009",
• URL = "https://www.sciencedirect.com/science/article/pii/S027861252400058X",
• keywords = "genetic algorithms, genetic programming, Dynamic flexible job shop scheduling, Reconfigurable manufacturing cell, Hyper-heuristics",
• abstract = "The Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is a classical and important research direction. However, current research usually considers the case where each manufacturing cell has a fixed and constant process capability. There are often situations in non-machining shops where each manufacturing cell is capable of capability reconfiguration and performs many different types of process operations, such as assembly shops and test shops. The variability of the manufacturing cell's capabilities increases the complexity of the problem compared to traditional FJSP. In this paper, we study the dynamic flexible job shop scheduling problem considering reconfigurable manufacturing cells (DFJSP-RMCs) with completion time, delay time and reconfiguration time as optimization objectives, and propose an improved Genetic Programming Hyper-Heuristic (GPHH) method to solve it. The method weighs the solution efficiency and the effectiveness of the results. In addition, an individual simplification policy (ISP) is proposed to reduce the evaluation time of the heuristic. Finally, random instances were generated under three production conditions and 10 independent runs were performed for each. Experiments show that the proposed method significantly reduces the time consumption while ensuring the quality of the results",

}

Genetic Programming entries for Haoxin Guo Jianhua Liu Yue Wang Cunbo Zhuang

Citations