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

Ecoretti, Andrea; Ceschia, Sara; Schaerf, Andrea

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

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…Moreover, the defined MILP optimally solves small instances but it is not able to compute the optimal solution for instances with an important number of jobs and machines. To deal with large instances of the same problem, the study in [40] proposes a local search method that is proven to be effective and scalable compared to the exact approach (MILP). Authors in [41] designed an improved genetic algorithm (GA) to solve the PFSP with predictive maintenance, where several maintenance interventions are planned for each machine based on degradation information provided by PHM module.…”

Section: Related Workmentioning

confidence: 99%

An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops

Ladj,

Benbouzid-Si Tayeb,

Dahamni

et al. 2024

Technologies

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This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times.

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Section: Related Workmentioning

confidence: 99%