Hotel Capacity Planning Using Queuing Systems and Meta-Heuristic Algorithms
Bibliographic record
Abstract
Deciding on optimal hotel capacity is strategically important and even very sensitive for investors in the hospitality industry.This article is an attempt to determine optimal hotel capacity with a novel approach, and then present a mathematical optimization model based on queuing theory.In that respect, upon simulating the hotel check-in system via the models of queuing and making use a limited two-dimensional backpack pattern, the optimal capacity and the hotel rooms number are acquired.Given the fact that the suggested model has high complexity in large scales, a meta-innovative approach is utilized to solve the problem of optimal hotel capacity determination.Contrary to previous models and approaches, merely applied to a specific hypothetical situation, the queuing theory, thanks to the existence of various models and the power to generate new patterns utilizing Markov chains, makes it possible to adapt the proposed model to different real conditions.There exist several queuing models, which can be implemented based on different conditions.Such models are progressively increasing and being expanded according to various requirements for modeling real environments.It seems necessary and innovative to expand the model proposed in the present study, employing non-Markov queuing models along with the general distribution functions.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".