Models for maintenance planning and scheduling – a citation-based literature review and content analysis
Bibliographic record
Abstract
Purpose The findings of this study have lightened the focal research areas in maintenance planning and scheduling. These also served as effective guidelines for future studies in this area. This research, therefore, contributes in fulfilling the gap by carrying out an SLR of contemporary research studies in the area of models for maintenance planning and scheduling. At present, SLR rooted in BA has not been carried focusing on a survey over models for maintenance planning and scheduling. SLR uses advanced scientific methodologies from BA tools to unveil thematic structures. Design/methodology/approach We have systematically reviewed over 1,021 peer-reviewed journal articles. Advanced contemporary tools from Bibliometric Analysis (BA) are used to perform a Systematic Literature Review (SLR). First, exploratory analysis is presented, highlighting the influential authors, sources and region amongst other key indices. Second, the large bibliographical data is visualized using co-citation network analyses, and research clusters (themes) are identified. The co-citation network is extended into a dynamic co-citation network and unveils the evolution of the research clusters. Last, cluster-based content analysis and historiographical analysis is carried out to predict the prospect of future research studies. Findings BA tools first outlined an exploratory analysis that noted influential authors, production countries, top-cited papers and frequent keywords. Later, the bibliometric data of over 1,021 documents is visualized using co-citation network analyses. Later, a dynamic co-citation analysis identified the evolution of research clusters over time. A historiographical direct citation analysis also unveils potential research directions. We have clearly observed that there are two main streams of maintenance planning and scheduling applications. The first has focused on joint maintenance and operations on machines. The second focused on integrated production and maintenance models in an echelon setting for unrealizable production facilities. Originality/value There are many literature review-based research studies that have contributed to maintenance scheduling research surveys. However, most studies have adopted traditional approaches, which often fall short in handling large bibliometric data and therefore suffer from selection biases from the authors. As a result, in this area, the existing reviews could be non-comprehensive. This study bridges the research gap by conducting an SLR of maintenance models, which to the best of our knowledge, has not been carried out before this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.062 | 0.049 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".