{"title":"Two-stage double deep Q-network algorithm considering external non-dominant set for multi-objective dynamic flexible job shop scheduling problems","authors":"","doi":"10.1016/j.swevo.2024.101660","DOIUrl":null,"url":null,"abstract":"<div><p>With the continuous advancement of the manufacturing industry, many random disturbances gradually appear in the job shop production process, such as the insertion of new jobs or random machine failures. This type of disruption often creates production chaos and scheduling problems. This paper takes new job insertion and random machine failure as dynamic events. It establishes an optimization model for dynamic flexible job shop scheduling (DFJSP) problems to cope with this issue. A two-stage double deep Q-network, TS-DDQN algorithm is proposed based on an improved deep reinforcement learning algorithm to solve the complex multi-objective DFJSP optimization problem by adopting two-stage decision-making. In the TS-DDQN, an external non-dominated set is used to enhance the training speed of the network and the quality of the solution, which can store the non-dominated solutions. Moreover, the solutions on the Pareto front are used to train the network parameters. Extensive experimentation is conducted on benchmark datasets to evaluate the performance of the proposed algorithm against the existing scheduling methods. The outcomes underscore the superior efficacy of the proposed algorithm concerning solution quality, convergence speed, and adaptability within dynamic environments. This research contributes to advancing the methods in solving multi-objective DFJSP problems and highlights the potential of deep reinforcement learning to yield managerial advantages in manufacturing industries.</p></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2024-07-18","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/S2210650224001986","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
With the continuous advancement of the manufacturing industry, many random disturbances gradually appear in the job shop production process, such as the insertion of new jobs or random machine failures. This type of disruption often creates production chaos and scheduling problems. This paper takes new job insertion and random machine failure as dynamic events. It establishes an optimization model for dynamic flexible job shop scheduling (DFJSP) problems to cope with this issue. A two-stage double deep Q-network, TS-DDQN algorithm is proposed based on an improved deep reinforcement learning algorithm to solve the complex multi-objective DFJSP optimization problem by adopting two-stage decision-making. In the TS-DDQN, an external non-dominated set is used to enhance the training speed of the network and the quality of the solution, which can store the non-dominated solutions. Moreover, the solutions on the Pareto front are used to train the network parameters. Extensive experimentation is conducted on benchmark datasets to evaluate the performance of the proposed algorithm against the existing scheduling methods. The outcomes underscore the superior efficacy of the proposed algorithm concerning solution quality, convergence speed, and adaptability within dynamic environments. This research contributes to advancing the methods in solving multi-objective DFJSP problems and highlights the potential of deep reinforcement learning to yield managerial advantages in manufacturing industries.
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.