求解分布式柔性作业车间调度问题的改进自适应混合算法

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-02-06 DOI:10.1016/j.swevo.2025.101873

Cuiyu Wang , Mengxi Wei , Qihao Liu , Xinjian Zhang , Xinyu Li

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引用次数: 0

摘要

随着经济全球化，企业之间的协作显著增加。复杂的产品现在经常在多个车间生产，要么在一个公司内，要么在几个车间生产。这种转变导致了分布式制造的兴起，这是一种现代的、迅速扩张的生产方式。针对分布式柔性作业车间问题，提出一种改进的自适应混合算法（IAHA）。根据分布式制造的特点，建立了DFJSP的数学模型。提出了一种混合解码规则，使用双层编码方法来表示工厂和作业。初始化、交叉和变异操作符的设计是为了有效地解决跨分布式工厂的作业分配挑战。在局部搜索阶段，采用自适应变量邻域搜索方法，重点关注关键工厂。在包含2、3和4个工厂的DFJSP实例基准集上进行的数值实验证明了IAHA的有效性，它打破了几个实例的记录，并为其他实例获得了最佳结果。与其他算法的比较表明，该算法在求解DFJSP问题上具有优越的性能。

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An improved adaptive hybrid algorithm for solving distributed flexible job shop scheduling problem

With economic globalization, collaboration between enterprises has increased significantly. Complex products are now often produced in multiple workshops, either within a single company or across several. This shift has led to the rise of distributed manufacturing, a modern and rapidly expanding production method. This paper puts forward an Improved Adaptive Hybrid Algorithm (IAHA) to address the Distributed Flexible Job Shop Problem (DFJSP). A mathematical model of DFJSP is established based on the characteristics of distributed manufacturing. A hybrid decoding rule is proposed, using a dual-layer encoding approach to represent both factories and jobs. The initialization, crossover, and mutation operators are designed to efficiently tackle the job allocation challenge across distributed factories. In the local search phase, an adaptive variable neighborhood search method focuses on critical factories. Numerical experiments on a benchmark set of DFJSP instances with 2, 3, and 4 factories demonstrate the effectiveness of IAHA, breaking records for several instances and achieving optimal results for others. Comparisons with other algorithms show the IAHA's superior performance in solving the DFJSP.

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

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.