Mitigation of Health and Safety Risks to Employees at Remote Locations Through Shift Scheduling
Bibliographic record
Abstract
The fly-in fly-out (FIFO) system is typical in remote mineral, oil, and energy operations, where employees work on-site for 2-3 weeks followed by an equal period at home. This schedule, involving shifts of 8, 10, or 12 hours, often leads to mental health, economic, and social impacts. This paper introduces an optimization model to address the atypical scheduling problem of two 12-hour shifts with unequal numbers of employees. Formulated as a mixed-integer non-linear model, the objective is to minimize the relative risks associated with night shifts. The model ensures an even distribution of night shifts among the team throughout the year, reducing night shifts to 6 hours per day with at least 6 hours of rest before and after a given nighttime work shift, implementing serial sleeping, and limiting cumulative night shift hours to less than 56 per month. Two case studies were carried out. The first case is a one-shot and solved by AMPL. The second case is a consecutive description and solved by an intelligent algorithm due to the size of the problem. Compared to the current schedule in the practice of working 14x12h=168h/m night hours, as well as to the benchmark/threshold 8x8h=64h/m night hours, the new schedule presents less risk to employees and by extension, to the operation by limiting the night-time working hours. This model improves workforce efficiency, safety, work quality, and can lead to financial savings for companies by distributing night shifts evenly among team members and avoiding permanent or block assignments.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".