A balance-oriented iterative greedy algorithm for the distributed heterogeneous hybrid flow-shop scheduling problem with blocking constraints

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

Swarm and Evolutionary Computation Pub Date : 2025-05-29 DOI:10.1016/j.swevo.2025.102015

Xiuli Wu, Yang Zhao

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引用次数: 0

Abstract

With the globalization of economy, production tasks usually need to be allocated among multiple factories to achieve a more efficient delivery. This paper studies the distributed heterogeneous hybrid flow-shop scheduling problem with blocking constraints (DHHFSPB) and proposes a balance-oriented iterative greedy algorithm(BOIG). The sigmoid-based adaptive(SA) decoding method is proposed to dynamically explore the solution space. Considering the characteristics of the problem, four initialization methods are developed to generate the initial solutions. Various operators are presented to balance the loads among factories. Some production tasks in the high-load factories are reassigned to the low-load factories by the perturbation operator. The structure of the solution is reorganized by the destruction and construction operators in a load-oriented manner. The local search operator balances the exploration and exploitation and a new neighborhood structure for the distributed problem is proposed. Additionally, an improved metropolis criterion is adopted to accept solutions. The results of experiments show that the BOIG algorithm can effectively solve the DHHFSPB.

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针对具有阻塞约束的分布式异构混合流车间调度问题，提出了一种面向平衡的迭代贪心算法

随着经济的全球化，生产任务通常需要在多个工厂之间进行分配，以实现更高效的交付。研究了具有阻塞约束的分布式异构混合流车间调度问题，提出了一种面向平衡的迭代贪心算法。提出了一种基于sigmoid的自适应解码方法来动态探索解空间。针对问题的特点，提出了四种初始化方法来生成初始解。提出了各种操作来平衡工厂之间的负荷。通过扰动算子将高负荷工厂的部分生产任务重新分配到低负荷工厂。解决方案的结构由破坏者和建设者以负荷为导向的方式重新组织。局部搜索算子平衡了分布式问题的探索和开发，提出了一种新的邻域结构。此外，采用了改进的大都市标准来接受解决方案。实验结果表明，BOIG算法可以有效地解决DHHFSPB问题。

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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.