Two metaheuristic algorithms for the technician routing and scheduling problem with time windows and balanced workloads
Bibliographic record
Abstract
Purpose This paper aims to address the technician routing and scheduling problem (TRSP), a daily operational challenge faced by telecommunication service providers. The study is motivated by a real-world application in Saskatchewan, Canada, and aims to develop an effective and scalable model for technician assignment and routing under practical constraints. The design of the problem is unique because of the vast working areas in Saskatchewan. Design/methodology/approach A mixed-integer programming model is formulated to model the TRSP, capturing realistic constraints such as soft time windows, variable working hours, lunch breaks (LBs) and overnight shifts. Because of the NP-hard nature of the problem, the authors propose two metaheuristic algorithms – simulated annealing (SA) and genetic algorithm (GA) – to solve large-scale instances. Computational experiments are conducted using real data, and the metaheuristics’ performance is benchmarked against a commercial exact solver. Findings Results indicate that both SA and GA produce high-quality solutions within significantly reduced computation times compared to the exact solver. The GA consistently outperforms SA in terms of optimality gaps and solution robustness. The findings highlight the practical viability of using metaheuristics in large-scale technician scheduling problems. Practical implications The proposed approach offers telecom service providers a flexible and scalable solution for managing technician assignments efficiently while accommodating operational constraints. The metaheuristic algorithms can be integrated into decision-support systems to improve customer service and reduce scheduling inefficiencies. Originality/value This research makes two main contributions. From a modeling perspective, it incorporates various available technician working hours as well as LBs into the overnight TRSPTW model. From a solution methodology perspective, it develops two metaheuristic algorithms – an SA and a GA – to solve the overnight TRSPTW with the LB model. The effectiveness of these two metaheuristics is analyzed via computational experiments using real-world scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".