Métodos Heurísticos e Meta-Heurísticos para a Resolução do Problema de Sequenciamento de Ordens de Manutenção Preventiva de Longo Prazo / Heuristic and Meta-Heuristic Methods to Resolve the Long-Term Preventive Maintenance Scheduling Problem
Resumo
O sucesso de uma empresa requer o bom funcionamento e a confiabilidade de seus sistemas com máquinas e equipamentos em bom estado. Para isso, é essencial um bom plano de manutenção preventiva, que tende a ficar mais complexo com o aumento do número de equipamentos e o horizonte de planejamento. O objetivo deste estudo é desenvolver algoritmos meta-heurísticos eficientes para tratar o Problema de Planejamento de Ordens de Manutenção Preventiva de Longo Prazo (PPOMPLP). O trabalho se inicia com o desenvolvimento de uma heurística construtiva e de alocação, seguido do desenvolvimento de algoritmos de busca local e meta-heurísticos baseados em Greedy Randomized Adaptive Search Procedure (GRASP), Simulated Annealing (SA) e Iterated Local Search (ILS). O desempenho dos algoritmos desenvolvidos foi comparado entre eles e com os da literatura. Para a calibragem e validação dos algoritmos meta-heurísticos, foram resolvidas instâncias fictícias pequenas. Após a calibragem, os algoritmos meta-heurísticos foram aplicados à resolução de instâncias maiores e à real. Os experimentos mostraram que o ILS foi o algoritmo de melhor desempenho e seu resultado na instância real foi 40,5%, melhor que o apresentado na literatura.
Palavras Chave. Planejamento de Manutenção de Longo Prazo. Grasp. Simulated Annealing. Iterated Local Search. Meta-Heurísticas.
ABSTRACT
The success of a company requires the proper functioning and reliability of its systems with machines and equipment in good condition. For this, a good preventive maintenance plan is essential, which tends to become more complex as the number of equipment and the planning horizon increases. The present work aims to develop efficient meta-heuristic algorithms for the Long-Term Preventive Maintenance Scheduling Problem (PPOMPLP). The work begins with the development of a constructive and allocation heuristic, followed by the development of local search and meta-heuristic algorithms based on Greedy Randomized Adaptive Search Procedure (GRASP), Simulated Annealing (SA), and Iterated Local Search (ILS). The performance of the proposed algorithms was compared among themselves and with those of other studies in the literature. Small fictitious instances were used to calibrate and validate the meta-heuristic algorithms. After calibration, they were applied to solve larger and real instances. The experiments showed that the ILS was the best-performing algorithm, and its result for the real instance was 40.5% better than that presented in the literature.
Keywords: Long-Term Maintenance Scheduling. Grasp. Simulated Annealing. Iterated Local Search. Meta-Heuristics.
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DOI: http://dx.doi.org/10.12819/2023.20.10.9
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