MILP of multitask scheduling of geographically distributed maintenance tasks
Bibliographic record
Abstract
Due to its proactive impact on the serviceability of components in a system, preventive maintenance plays an important role particularly in systems of geographically spread infrastructure such as utilities networks in commercial buildings. What makes such systems differ from the classical schemes is the routing and technicians' travel times. Besides, maintenance in commercial buildings is characterized by its short tasks’ durations and spatial distribution within and between different buildings, a class of problems that has not been suitably investigated. Although it is not trivial to assign particular duties solely to multi-skilled teams under limited time and capacity constraints, the problem becomes more challenging when travel routes, durations and service levels are considered during the execution of the daily maintenance tasks. To address this problem, we propose a Mixed Integer Linear Programming Model that considers the above settings. The model exact solution recommends collaborative choices that include the number of maintenance teams, the selected tasks, routes, tasks schedules, all detailed to days and teams. The model will reduce the cost of labor, replacement parts, penalties on service levels and travel time. The optimization model has been tested using different maintenance scenarios taken from a real maintenance provider in the UAE. Using CPLEX solver, the findings demonstrate an inspiring time utilization, schedules of minimal routing and high service levels using a minimum number of teams. Different travel speeds of diverse assortment of tasks, durations and cost settings have been tested for further sensitivity analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".