Evidence map›Paper›PMID 37869458›Full record

ArticlePeerJ. Computer science2023

Novel variable neighborhood search heuristics for truck management in distribution warehouses problem.

Akram Y Sarhan, Loai Kayed B Melhim, Mahdi Jemmali, Faycel El Ayeb, Hadeel Alharbi, Ameen Banjar

Abstract read
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Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Akram Y SarhanDepartment of Information Technology, College of Computing and Information Technology at Khulis, University of Jeddah, Jeddah, Saudi Arabia.ORCID 0000-0002-1522-562X
Loai Kayed B MelhimDepartment of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia.
Mahdi JemmaliMARS Laboratory, University of Sousse, Sousse, Tunisia.ORCID 0000-0003-3462-8739
Faycel El AyebUnit of Scientific Research, Applied College, Qassim University, Saudi Arabia.
Hadeel AlharbiDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Hail, Saudi Arabia.
Ameen BanjarDepartment of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.ORCID 0000-0002-0871-5153

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Logistics and sourcing management are core in any supply chain operation and are among the critical challenges facing any economy. The specialists classify transport operations and warehouse management as two of the biggest and costliest challenges in logistics and supply chain operations. Therefore, an effective warehouse management system is a legend to the success of timely delivery of products and the reduction of operational costs. The proposed scheme aims to discuss truck unloading operations problems. It focuses on cases where the number of warehouses is limited, and the number of trucks and the truck unloading time need to be manageable or unknown. The contribution of this article is to present a solution that: (i) enhances the efficiency of the supply chain process by reducing the overall time for the truck unloading problem; (ii) presents an intelligent metaheuristic warehouse management solution that uses dispatching rules, randomization, permutation, and iteration methods; (iii) proposes four heuristics to deal with the proposed problem; and (iv) measures the performance of the proposed solution using two uniform distribution classes with 480 trucks' unloading times instances. Our result shows that the best algorithm is

Indexed as

AlgorithmsDistribution warehousesHeuristicsTruck managementVariable neighborhood search

Identifiers

PMID37869458
PMCPMC10588704

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LicenceCC BY
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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.