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Record W7014241079

Obtaining optimal and approximate solutions to the problem of scheduling inbound and outbound trucks in cross docking operations

2009· article· en· W7014241079 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBorås Academic Digital Archive (University of Borås) · 2009
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsnot available
FundersWilfrid Laurier University
KeywordsTruckHeuristicScheduling (production processes)Job shop schedulingMathematical modelInteger programmingVehicle routing problem
DOInot available

Abstract

fetched live from OpenAlex

The thesis focuses on optimization of inbound and outbound truck scheduling with the\ngoal of minimizing total operation time of cross docking. A model of cross docking is\ndeveloped; two different methods are applied on the model in order to find an optimal\ndocking sequence for receiving and shipping trucks and their assignment to receiving and\nshipping docks, and product routing from receiving to shipping trucks.\nThe two methods used were mathematical modeling and heuristic algorithm. For the first\nmethod, a mixed integer programming model was developed to minimize total operation\ntime; AMPL modeling language is used for the mathematical modeling for small sized\nproblems. For the second method, a heuristic algorithm was developed to find near\noptimal solutions fast and was used for problems of larger size. In order to examine the\nperformance of heuristic algorithm, small problems were solved by both mathematical\nmodel and the heuristic algorithm.\nThe results from the mathematical model and the heuristic algorithm are very close with\nslight differences in receiving and shipping truck docking sequence, and in product\nrouting between these two methods. In addition, the heuristic algorithm also calculates\nnumber of products transferring from receiving trucks to the temporary storage as well as\nthe number of products transferring from the temporary storage to shipping truck in\ncontrary to the mathematical model. Total number of units of products passing through\nthe temporary storage calculated by heuristic algorithm is presented and it can be seen\nthat the heuristic algorithm transfers to the temporary storage as few products as possible.\nFurthermore, in cases that receiving and shipping trucks are divided into groups or\nclusters in the cross docking operation, heuristic algorithm can be used to calculate\noptimal number of receiving and shipping docks based on preferences of total operation\ntime or total number of products passing through the temporary storage.\nAnother issue which is focused on is the problem of dock door assignment. Close\nshipping docks to each receiving dock are determined and the percentage of products\ntransferred from a receiving dock to its close shipping docks is calculated as a method to\nmeasure the performance of the dock assignment solution.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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