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Record W3018789859 · doi:10.1287/opre.2019.1906

Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements

2020· article· en· W3018789859 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOperations Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAdvanced Queuing Theory Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsReservationRevenue managementDynamic pricingComputer scienceOperations researchService (business)HeuristicRevenueYield managementMathematical optimizationDuration (music)Demand managementTotal revenueMicroeconomicsBusinessEconomicsMarketingComputer networkMathematics

Abstract

fetched live from OpenAlex

In “Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements,” Lei and Jasin consider a fundamental dynamic pricing problem when resources are reusable. In this problem, demand arrives according to a price-sensitive nonstationary rate, requesting a service that uses a combination of different types of resources for a deterministic duration of time. The resources are reusable in the sense that they can be immediately used to serve a new customer on the completion of the previous service. Moreover, different customers may have different service time requirement and may book the service in advance. The objective is to construct a dynamic pricing control that maximizes expected total revenues. They develop real-time heuristic controls based on the solution of the deterministic relaxation of the original stochastic problem and show that the proposed controls are near optimal in the regime of large demand and large resource capacity.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.326
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it