A Hybrid Memetic and Set Partitioning Optimization Framework for Decision Support in Industrial Transportation: A Case Study of Employee Shuttle Routing
Bibliographic record
Abstract
Designing cost-effective shuttle services for large-scale industrial companies presents a significant challenge in the transportation industry.This challenge arises from the need to balance high-quality service with cost-effectiveness while considering various practical constraints.In this context, we introduce a novel approach to help decision-makers address Employee Shuttle Bus Routing Problems (ESBRP).Our method combines the Memetic Algorithm (MA), a metaheuristic, with the Set Partitioning Problem (SPP) model, an exact algorithm.The proposed framework consists of two phases: (1) generating routes that adhere to the real-world constraints of the ESBRP using the MA, and (2) allocating these routes to a heterogeneous fleet of vehicles by optimally solving the SPP Model.A unique feature of our approach is the extension of the framework to enable the transition from addressing the single-load scenario of the ESBRP problem to solving the mixed-load scenario.This transition is achieved by implementing the Single to Mixed Loads Heuristic (SMH).This paper presents the results of thorough computational tests conducted on multiple data instances of varying sizes.Additionally, we develop a mixed-integer programming (MIP) model for the ESBRP to compare and evaluate the results of the proposed framework.By assessing solution quality and execution times on small and moderate-sized data instances, the experiments demonstrate that the proposed approach is efficient and often generates near-optimal solutions.
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 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 | 0.001 |
| 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".