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Record W2907244310 · doi:10.1080/24725854.2018.1552820

Integrated order allocation and order routing problem for e-order fulfillment

2019· article· en· W2907244310 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

VenueIISE Transactions · 2019
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsUniversity of Windsor
FundersShanghai Education Development FoundationShanghai Municipal Education CommissionNational Natural Science Foundation of China
KeywordsOrder (exchange)SolverComputer scienceOrder fulfillmentMathematical optimizationRouting (electronic design automation)Integer programmingHeuristicBenchmarkingQuality (philosophy)Operations researchSupply chainEngineeringMathematicsEconomicsBusinessMarketingAlgorithmArtificial intelligenceComputer network

Abstract

fetched live from OpenAlex

In this article, we study the order fulfillment problem, which integrates order allocation and order routing decisions of an online retailer. Our problem is to find the best way to fulfill each customer’s order to minimize the transportation cost. We first present a mixed-integer programming formulation to help online retailers optimally fulfill customers’ order. We then introduce an adaptive large neighborhood search-based approach for this problem. With extensive computational experiments, we demonstrate the effectiveness of the proposed approach, by benchmarking its performance against a leading commercial solver and a greedy heuristic. Our approach can produce high-quality solutions in short computing times. We also experimentally show that products overlap among different fulfillment centers does affect the operation expense of e-tailers.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.475
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.248
Teacher spread0.235 · 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