Finite-Time Horizon Logistics Decision Making Problems: Consideration of a Wider Set of Factors
Bibliographic record
Abstract
The newsvendor's problem (NVP) formulation is applied to many logistics problems in which the principal decision is the level of inventory which should be ordered to meet stochastic demand during a finite time-horizon. This type of decision makes demand the central variable to be examined and since the time horizon is finite, there is variable risk throughout the period. While the NVP formulation is applicable to many areas (e.g. retail business, service booking, investment in health-insurance, humanitarian aid, defence inventory for operations), modelling and research into the factors affecting demand and its uncertainty has been conducted mainly where the goal is to increase demand (e.g. price, rebate, substitutability). This paper describes ongoing work on modelling demand within the NVP framework where little prior specific demand information exists and uncertainty plays a crucial role. The suggested approach is to model demand and its uncertainty using other causally related, casespecific factors by applying Bayesian inference. Initial work in progress on a case study is outlined. In future the approach will be tested in several case studies and will adopt the innovative approach of Sherbrooke (2004) and Cohen et al (1990) for its validation, through which the model's outputs along with the real life demand data are provided as inputs to a simulation and the results compared. Thus the simulation's final output is the evaluation measure. The future expected benefit from this work is to offer decision makers an intuitive demand modelling tool within an NVP framework where modelling uncertainty is of great importance and past demand data are scarce.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".