A Dynamic Heuristic Optimization for Condition-based Maintenance Planning

EasyChair Preprint 2576

Abstract

For mission critical infrastructures (e.g., nuclear, railways and defense), an important issue is how to reduce maintenance costs and manage business risks while increasing the reliability, availability and safety of the assets. For this purpose, Prognostics and Health Management (PHM) methodologies are increasingly used to improve the maintainability of the operating systems and preparedness of the maintenance organization. PHM provides useful probabilistic indicators such as asset performance/degradation trend, health state severity, and Remaining Useful Life (RUL) of the systems and the overall system/asset performance state. With these PHM indicators, organizations can get better idea about overall performance of their systems to make better decisions on planning maintenance operations. However, for any maintenance planning to succeed on time, the planning itself needs to be efficient, keeping into account the constraints of the deadlines (e.g., RUL) and available resources (e.g., logistics, human resources, spares, weather conditions etc.). This translates the problem into the well-known Resource-Constrained Project Scheduling Problem (RCPSP). In this paper, we propose a novel priority-rule based approach for solving the RCPSP of maintenance planning.

Keyphrases: Condition-based Maintenance (CBM), Maintenance planning, Prognostics and Health Management (PHM), Resource Constrained Project Scheduling Problem (RCPSP), Scheduling Algorithms

@booklet{EasyChair:2576,
  author    = {Ajit Rai and Vepa Atamuradov and Sylvanus Mahe and Hamza Deroui and Ahmed Allali and Arthur Aumont and Jean-Gabriel Wacyk and Robert Plana},
  title     = {A Dynamic Heuristic Optimization for Condition-based Maintenance Planning},
  howpublished = {EasyChair Preprint 2576},
  year      = {EasyChair, 2020}}