Local Search for Integrated Predictive Maintenance and Scheduling in Flow-Shop

被引：0

作者：

Ecoretti, Andrea ^{[1
]}

Ceschia, Sara ^{[1
]}

Schaerf, Andrea ^{[1
]}

机构：

[1] Univ Udine, DPIA, Via Sci 206, I-33100 Udine, Italy

来源：

METAHEURISTICS, MIC 2022 | 2023年 / 13838卷

关键词：

Predictive maintenance; Local search; Flow-shop; Scheduling; FUZZY-LOGIC; OPTIMIZATION; JOBS; ALGORITHM; HYBRID;

D O I：

10.1007/978-3-031-26504-4_19

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We address the Permutation Flow-Shop Scheduling Problem with Predictive Maintenance presented by Varnier and Zerhouni (2012), that consists in finding the integrated schedule for production and maintenance tasks such that the total production time and the advance of maintenance services are minimized. Predictive maintenance services are scheduled based on a prognostics system that is able to provide the remaining useful life of a machine. To solve this problem, we propose a local search method with neighborhoods specifically tailored for maintenance interventions. Computational experiments performed on generated benchmarks demonstrate the effectiveness and scalability of our method with respect to an exact technique based on the mathematical model proposed by Varnier and Zerhouni (2012).

引用

页码：260 / 273

页数：14

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