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Record W6949083032 · doi:10.5281/zenodo.10260963

Evaluation Files: Multilayer Graph Partitioning for Enabling a Decentralized Path Planning for Large and Heterogeneous AGV Fleets

2023· article· en· W6949083032 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsnot available
Fundersnot available
KeywordsNetwork topologyMetric (unit)Intersection (aeronautics)Bounding overwatchGraphPath (computing)Topology (electrical circuits)

Abstract

fetched live from OpenAlex

Project Description This repository contains the evaluation files from “Multilayer Graph Partitioning for Enabling a Decentralized Path Planning for Large and Heterogeneous AGV Fleets”. First, an overview of the folder structure is given. Followed by an Overview of the used parameter values from MAGPart. Afterward, additional background information for each topology is presented. Folder Structure Each evaluated topology is contained in a separate folder (e.g. /Topo-1, /Topo-1-2-3, ...). The topologies Topo-1, Topo-2, Topo-3 and Topo-4 are single-layer topologies, whereby the remaining topologies are multilayer topologies and are constructed by combinations of the single-layer topologies. For example, the topology Topo-1-2-3-4 results from combining the topologies Topo-1, Topo-2, Topo-3 and Topo-4. Each folder of one topology contains (1) the boxplots for each evaluation metric, (2) the results from applying different Graph Partitioning (GP) algorithms and (3) an overview of the frequencies and workloads of edges. The “deviation” boxplots correspond to DEV metric and the “routes_frequencies” boxplots correspond to the RF metric mentioned in our paper. Additionally, the boxplots from an overlap metric O ∈ [0,1] are listed for each topology. The overlap metric measures the intersection area between overlapping Bounding Boxes (BBs) divided by the intersection area between overlapping BBs from different domains. Thus, the metric is zero if no BBs from different domains overlap and one if all overlapping BBs correspond to different domains. A lower overlap value is desirable, to reduce the communication between domains. Since the metrics RF and DEV strive to maximize their value, we invert the value from the overlap metric to achieve a uniform representation. Note that the boxplots result from the averaged values from ten executions of our pipeline. For each applied GP algorithm (Girvan-Newman, Infomap, MAGPart, Metis, Modularity-Maximization and Spectral-Clustering) in our pipeline, the results are contained in corresponding folders. Each folder contains a PDF file, where the topology and the resulting domains are visualized. The domains are visualized in different colors. Additionally, the value of the quality function and the number of domains k is given in the filename. The number of domains is only given if the GP algorithm (i.e. Girvan-Newman, Metis, Spectral-Clustering) requires the number of domains as parameter. To achieve a comparable result, the values of the parameters max_v and max_d from MAGPart are set to infinity, since the sizes of the domains and the number of domains cannot be set for all GP algorithms. The values and description of all utilized parameters by MAGPart are given in the table below. MAGPart Parameters This table highlights all parameters used by MAGPart, while providing the used value, the value range and a description. Parameter Used Value Value Range Description Matching Algorithm Maximum Weight Matching Any matching algorithm The matching algorithm which is used in the coarsening phase to contract vertices. γ 0,6 R The resolution for computing the modularity in the quality function. λ 1,0 [0,1] The workload scaling factor to scale the workload in the pre-processing phase. θ 0,35 [0,1/2] The ratio of removed vertices before and after an iteration in the coarsening phase. max_d INF N The maximal number of allowed domains to be created. max_v INF N The maximal number of nodes to be added to a domain. Background of the topologies Topo-1: This topology and the routes of AGVs were created manually to represent the possible layout from a warehouse with three spatially separated areas. Topo-2: This topology models the area of a building used for testing purposes with AGVs. The routes of the AGVs were distributed manually to result in three Hotspots, which are not spatially separated. Topo-3: This topology models another possible layout from a warehouse. At the top left goods are received and transported between production, Storage and Shipping. Outgoing goods are transported to the bottom left. The routes of AGVs were created manually, such that AGVs mainly traverse in the upper or in the lower half of the topology. Few AGVs traverse between the top and lower half. Topo-4: This topology was modeled based on the layout from a warehouse. The topology's center contains a storage area, whereas the outer sections are used for loading ingoing and outgoing goods. Routes of the AGVs were sampled randomly with a bias to mainly pass through the top or the lower half of the topology. Topology Topo-1 Topo-2 Topo-3 Topo-4 Topo-2-3 Topo-2-4 Topo-1-2-3 Topo-1-2-3-4 Vertices 304 213 475 1845 688 2058 992 2837 Edges 515 431 1040 3976 1921 5555 2278 7882

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.313
Teacher spread0.241 · 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