Hotel Capacity Planning Using Queuing Systems and Meta-Heuristic Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it