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Record W4281676987 · doi:10.1002/mma.8459

Optimal replenishment policy for non‐instantaneous deteriorating items with stochastic demand under advance sales discount and available capacity

2022· article· en· W4281676987 on OpenAlexaff
Kuo‐Lung Hou, H. M. Srivastava, Li‐Chiao Lin, Bhaba R. Sarker, Shih‐Fang Lee

Bibliographic record

VenueMathematical Methods in the Applied Sciences · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCommitEconomic shortageEconomic order quantityProfit (economics)Order (exchange)Operations researchWarehouseScheduleMathematical optimizationStockoutHolding costMicroeconomicsComputer scienceEconomicsBusinessMathematicsSupply chainMarketingFinance

Abstract

fetched live from OpenAlex

This paper presents a two‐warehouse inventory model for non‐instantaneous deteriorating items with a price‐dependent stochastic demand under advance sales discount and available capacity. In this model, the retailer offers a price discount to customers if they can commit their orders prior to the sales period. It encourages the retailer to order a large quantity that can be stored in an owned warehouse and then in a rented warehouse if there are excess items. Shortages are allowed and partially backlogged. The objective of this paper is to simultaneously determine the optimal price discount, the optimal order quantity, and the optimal replenishment schedule in such a way that the expected total profit is maximized. Two theorems are proved in order to verify that the expected total profit function is concave and to identify that an optimal replenishment decision exists. We further propose an algorithm to find the optimal replenishment policy by the retailer. Finally, we use numerical examples to illustrate the results presented herein. Moreover, based upon the sensitivity analysis, some important managerial implications are also discussed.

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 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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.493
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.326
Teacher spread0.266 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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