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The travelling maintainer problem: integration of condition-based maintenance with the travelling salesman problem

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  • Fatih Camci

    (Antalya International University, Döşemealtı/ANTALYA, Turkey)

Abstract

The Travelling Salesman Problem (TSP) is one of the most studied problems in the literature due to its applicability to a large number of real cases. Most variants of the TSP consider total distance travelled. This paper presents a new generalised formulation of the TSP that aims to minimise the sum of functions of latencies to cities, rather than total distance travelled. Then, a new problem that uses a special function using the latency as input is presented, called the Travelling Maintainer Problem (TMP). The TMP integrates the output of prognostics in Condition-based Maintenance (CBM) with the TSP. CBM aims to minimise the failure and maintenance cost by identifying and predicting upcoming failures through the analysis of sensory information collected in real-time. Maintenance scheduling is performed using the predicted failure information obtained from the CBM. When the systems to be maintained are geographically distributed, maintenance scheduling requires integrated analysis of travel times and their effects on the failure progression in systems. This paper also presents Genetic Algorithm and Particle Swarm Optimisation-based solutions and their comparisons for the TMP on a case study.

Suggested Citation

  • Fatih Camci, 2014. "The travelling maintainer problem: integration of condition-based maintenance with the travelling salesman problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(9), pages 1423-1436, September.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:9:p:1423-1436
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    Cited by:

    1. da Costa, Paulo & Verleijsdonk, Peter & Voorberg, Simon & Akcay, Alp & Kapodistria, Stella & van Jaarsveld, Willem & Zhang, Yingqian, 2023. "Policies for the dynamic traveling maintainer problem with alerts," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1141-1152.
    2. Eric Lucas dos Santos Cabral & Mario Orestes Aguirre Gonzalez & Priscila da Cunha Jacome Vidal & Joao Florencio da Costa Junior & Rafael Monteiro de Vasconcelos & David Cassimiro de Melo & Ruan Lucas , 2024. "Optimization Models for Operations and Maintenance of Offshore Wind Turbines Based on Artificial Intelligence and Operations Research: A Systematic Literature Review," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(3), pages 1-1, June.
    3. Verleijsdonk, Peter & van Jaarsveld, Willem & Kapodistria, Stella, 2024. "Scalable policies for the dynamic traveling multi-maintainer problem with alerts," European Journal of Operational Research, Elsevier, vol. 319(1), pages 121-134.
    4. Havinga, Maik J.A. & de Jonge, Bram, 2020. "Condition-based maintenance in the cyclic patrolling repairman problem," International Journal of Production Economics, Elsevier, vol. 222(C).
    5. Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
    6. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    7. Dilaver, Halit Metehan & Akçay, Alp & van Houtum, Geert-Jan, 2023. "Integrated planning of asset-use and dry-docking for a fleet of maritime assets," International Journal of Production Economics, Elsevier, vol. 256(C).

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