Σχεδιασμός και αξιολόγηση ενός συστήματος διαχείρισης στόλου οχημάτων σε πραγματικό χρόνο για την αντιμετώπιση δυναμικών γεγονότων κατά την εκτέλεση αστικών διανομών προϊόντων
Bibliographic record
Abstract
In urban freight distribution, the use of an initial distribution plan, although necessary, is by no means sufficient to address unexpected events such as traffic congestion, adverse weather conditions and mechanical failures that are likely to occur during delivery execution and may have adverse effects on system performance.These events cause deviations between the actual and desired state during the execution of the schedule.Recent advances in mobile and positioning technologies allowed the development of fleet management systems that enable freight carriers to dynamically monitor their fleet and improve relevant delivery performance by intervening when such problems occur.Although the use of such technologies supports better utilization of the vehicles' fleet, the systems based on these technologies are not typically designed to address unforeseen events in a systemic fashion.As a result, interventions are often performed manually and the resulting decisions are local with limited effectiveness.The aim of this thesis is to enhance urban delivery execution by modelling the process of dynamic incident handling through the design and implementation of a real-time fleet management system.The latter has three main functionalities: a) it monitors delivery vehicles using mobile and positioning technologies, b) it detects deviations from the distribution plan, and c) it adjusts the schedule accordingly, by suggesting rerouting strategies.The research methodology that was followed combines three basic steps: a) literature review and interviews for requirements elicitation and system design, b) theoretical system testing and evaluation via simulation and c) confirmatory study of the theoretical results through field experiments in two freight operators.(SCMIS 2005), 6-8 July, Thessaloniki, Greece Zeimpekis, V., Giaglis, G. M., Minis I., (2005) "A dynamic real-time fleet management system for incident handling in city logistics" In the proceedings of 61 st Management and Information Systems
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".