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Scheduling multi-component maintenance with a greedy heuristic local search algorithm

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Abstract

As many large-scale systems age, and due to budgetary and performance efficiency concerns, there is a need to improve the decision-making process for system sustainment, including maintenance, repair, and overhaul (MRO) operations and the acquisition of MRO parts. To help address the link between sustainment policies and acquisition, this work develops a greedy heuristic-based local search algorithm (GHLSA) to provide a system maintenance schedule for multi-component systems, coordinating recommended component maintenance times to reduce system downtime costs, thereby enabling effective acquisition. The proposed iterative algorithm aims to minimize the sum of downtime, earliness and tardiness costs of scheduling, which contains three phases: (1) the construction phase, which uses a heuristic to construct an initial partial solution, (2) an improvement phase, which aims to improve the partial solution generated in the construction phase, and finally, (3) a local search phase, which performs a local search technique to the partial solution found in the improvement phase. The proposed algorithm makes a trade-off between exploration and exploitation of solutions. The experimental results for small (10 jobs) and large size (50 jobs) problems indicate that GHLSA outperforms both genetic algorithm and simulated annealing approaches in terms of solution quality and is similar in terms of efficiency.

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Abbreviations

DoD:

Department of Defense

GA:

Genetic algorithm

GAO:

Government accountability office

GHBI:

Greedy heuristic-based improvement

GHLSA:

Greedy heuristic local search algorithm

GRASP:

Greedy randomized adaptive search

LSD:

Least significant difference

MRO:

Maintenance, repair and overhaul

MTBF:

Mean time between failure

OEM:

Original equipment manufacturer

PMSP:

Preventive maintenance scheduling problem

RCM:

Reliability-centered maintenance

RPD:

Relative proportion deviation

SA:

Simulated annealing

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Acknowledgements

This publication results from research supported by the Naval Postgraduate School Assistance Grant/Agreement No. N00244-12-1-0069 awarded by the NAVSUP Fleet Logistics Center San Diego. The views expressed in written materials or publications do not necessarily reflect the official policies of the Naval Postgraduate School nor do mention of trade names, commercial practices, or organizations imply endorsement by the US Government.

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Authors and Affiliations

  1. Industrial Engineering Technology, University of Southern Mississippi, Long Beach, USA

    Seyedmohsen Hosseini

  2. School of Industrial and Systems Engineering, University of Oklahoma, Norman, USA

    Sifat Kalam & Kash Barker

  3. School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, USA

    Jose E. Ramirez-Marquez

Authors
  1. Seyedmohsen Hosseini
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  2. Sifat Kalam
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  3. Kash Barker
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  4. Jose E. Ramirez-Marquez
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Correspondence to Seyedmohsen Hosseini.

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Hosseini, S., Kalam, S., Barker, K. et al. Scheduling multi-component maintenance with a greedy heuristic local search algorithm. Soft Comput 24, 351–366 (2020). https://doi.org/10.1007/s00500-019-03914-7

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