A two-step post-optimality approach for a multi-objective railway maintenance planning problem
Issued Date
2024-06-01
Resource Type
ISSN
03608352
Scopus ID
2-s2.0-85193575560
Journal Title
Computers and Industrial Engineering
Volume
192
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computers and Industrial Engineering Vol.192 (2024)
Suggested Citation
Petchrompo S., Modhara S., Kirwan A., Parlikad A.K., Wattanapongsakorn N. A two-step post-optimality approach for a multi-objective railway maintenance planning problem. Computers and Industrial Engineering Vol.192 (2024). doi:10.1016/j.cie.2024.110207 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/98489
Title
A two-step post-optimality approach for a multi-objective railway maintenance planning problem
Corresponding Author(s)
Other Contributor(s)
Abstract
Maintenance planning for railway networks is an exacting task, impacting numerous stakeholders with diverse objectives. While a multi-objective optimization model addresses the problem, it often results in numerous solutions on a Pareto front. Past researchers have introduced various Pareto pruning methods to autonomously create a shortlist of promising solutions. Nonetheless, solely relying on data-driven approaches to finalize solutions can lead to undesirable outcomes for the decision maker (DM). To strike a balance between a human-involved and a data-centric decision, this article proposes a two-step approach. In the first step, Latent Profile Analysis (LPA) is utilized to group similar solutions on the Pareto front together, and a representative solution from each group is presented to the DM. After the DM identifies a focus region, Data Envelopment Analysis (DEA), including CCR and BCC models, is employed in the second step to rank the solutions in a selected group by their relative efficiency. Applying the proposed approach to a real-world case study demonstrates its capability in the railway asset management context. Key advantages of LPA lie in its ability to provide the appropriate number of solutions in the pruned set and its flexibility in grouping solutions. DEA enhances decision-making by ranking solutions within the focus region. Ultimately, by integrating automated algorithms with human insight, the two-step approach successfully identifies an efficient solution and mitigates the risk of obtaining undesirable outcomes on the Pareto front.