基于教-学的多服务器加工模型的紧急休假和报废机器保留优化

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-06-25 DOI:10.1016/j.swevo.2025.102036

C.K. Anjali, Sreekanth Kolledath

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引用次数: 0

摘要

紧急假期是指维修人员为应对不可预见的紧急危机（如自然灾害、突发卫生事件或COVID-19大流行等重大中断）而立即和计划外的休假。在这些假期，维修设施停止服务的故障单元等待完成。本研究的重点是建立具有紧急休假的多服务器加工模型。该系统由K台操作机、S台备用机和R台修理工组成。它还通过实施保留策略解决了失败单元在等待服务时食言的挑战。利用矩阵分析法对模型进行稳态评价，得到了各种性能指标。对获得的度量进行图形分析，以确定可以优化这些度量的因素。通过TLBO实现了模型的成本优化，并利用粒子群算法和遗传算法对模型的优化结果进行了比较。

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Teaching–learning based optimization of a multi-server machining model with emergency vacation and retention of reneged machines

Emergency vacations refers to the immediate and unplanned leave of absence that repairmen take in response to unforeseen and urgent crises, such as natural calamities, health emergencies, or significant disruptions like the COVID-19 pandemic. During these vacations, the repair facility halts the service of failed units waiting before completion. This study focuses on developing a multi-server machining model with emergency vacation. The system comprises of K operating machines, S standbys and R repairmen. It also resolves the challenge of failed units reneging while waiting for service by implementing retention strategies. The steady state evaluation of the model is conducted utilizing the matrix analytic method, and various performance metrics are obtained. A graphical analysis of the obtained metrics is conducted to identify factors that can optimize these measures. The cost optimization of the model is implemented through TLBO and the results obtained through it is compared using PSO and GA techniques.

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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.