{"title":"Dynamic flexible job shop scheduling algorithm for multi-task collaborative optimization","authors":"Zeyin Guo , Lixin Wei , Xin Li , Rui Fan","doi":"10.1016/j.swevo.2025.102114","DOIUrl":null,"url":null,"abstract":"<div><div>Due to customer demand or market changes, production orders in intelligent manufacturing workshops become uncertain. Based on the above issues, an order detection framework is constructed to detect different types of order changes. Different dynamic response mechanisms are designed for different types of order changes. The scheduling of jobs in discrete manufacturing has composability, resulting in a huge search space. Previous research methods that used a single population to solve scheduling schemes could not fully explore the search space. Considering the characteristics of multitasking in job shop scheduling, this study designs an auxiliary task collaborative optimization algorithm (ATCOA) to solve the optimal rescheduling schemes. To escape from the situation of optimizing local optima in the main task, a knowledge transfer probability model based on the main task is adopted to determine population communication between tasks. A multitask knowledge transfer strategy is proposed for exchanging individual information between tasks to improve the diversity distribution ability of optimization algorithm. To evaluate the effectiveness of the ATCOA algorithm, it is compared with other algorithms on the constructed dynamic order test cases. In the case of order cancellation and insertion, ATCOA obtained 10 minimum inverted generation distance and maximum spread metric values and 9 hypervolume values, respectively. ATCOA has improved completion efficiency by 8.9% compared to scheduling rules. In engineering simulation cases, the ATCOA algorithm improved workload deviation by 41.9% compared to other algorithms. The experimental results show that the ATCOA algorithm is more efficient and stable than other algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102114"},"PeriodicalIF":8.5000,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221065022500272X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Due to customer demand or market changes, production orders in intelligent manufacturing workshops become uncertain. Based on the above issues, an order detection framework is constructed to detect different types of order changes. Different dynamic response mechanisms are designed for different types of order changes. The scheduling of jobs in discrete manufacturing has composability, resulting in a huge search space. Previous research methods that used a single population to solve scheduling schemes could not fully explore the search space. Considering the characteristics of multitasking in job shop scheduling, this study designs an auxiliary task collaborative optimization algorithm (ATCOA) to solve the optimal rescheduling schemes. To escape from the situation of optimizing local optima in the main task, a knowledge transfer probability model based on the main task is adopted to determine population communication between tasks. A multitask knowledge transfer strategy is proposed for exchanging individual information between tasks to improve the diversity distribution ability of optimization algorithm. To evaluate the effectiveness of the ATCOA algorithm, it is compared with other algorithms on the constructed dynamic order test cases. In the case of order cancellation and insertion, ATCOA obtained 10 minimum inverted generation distance and maximum spread metric values and 9 hypervolume values, respectively. ATCOA has improved completion efficiency by 8.9% compared to scheduling rules. In engineering simulation cases, the ATCOA algorithm improved workload deviation by 41.9% compared to other algorithms. The experimental results show that the ATCOA algorithm is more efficient and stable than other algorithms.
期刊介绍:
Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.