Evidence map›Paper›PMID 39669453›Full record

ArticlePeerJ. Computer science2024

A novel parameterized neutrosophic score function and its application in genetic algorithm.

Yi Zhao, Fangwei Zhang, Bing Han, Jun Ye, Jingyuan Li

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Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yi ZhaoCollege of Automotive and Traffic Engineering, Nanjing Forestry University, Nanjing, Jiangsu, China.
Fangwei ZhangInternational Business School, Shandong Jiaotong University, Weihai, Shandong, China.
Bing HanWarwick Manufacturing Group, University of Warwick, Coventry, United Kingdom.
Jun YeSchool of Civil and Environmental Engineering, Ningbo University, Ningbo, China.
Jingyuan LiInternational Business School, Shandong Jiaotong University, Weihai, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Efficiency, safety and cost are three major evaluation indexes of warehouse operation. However, the uncertainty of efficiency, safety and cost factors will lead to economic losses and waste of resources. The purpose of this study is to propose a novel parameterized neutrosophic objective-proportionate genetic algorithm model (PNO-PGA) to optimize the above three objectives. There are three main contributions of this study. Firstly, a novel score function of neutrosophic sets (NSs) is proposed to effectively integrate the fuzziness of efficiency, safety and cost to avoid the evaluation result being too idealized. Secondly, a novel proportionate genetic algorithm is applied to adaptively realize the iteration and inheritance processes. Finally, two parameters are proposed to make the algorithm model flexibly adapt to different types of environments and problems. Then, an example is used to compare the new method with genetic algorithm (GA). The result shows that PNO-PGA has better problem-solving ability in warehouse operation than GA.

Indexed as

Aggregating operatorGenetic algorithmMulti-objective optimizationNeutrosophic fuzzy setWarehouse operation

Identifiers

PMID39669453
PMCPMC11637011

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.